(PDF) White Paper IoT and Crisis Preparedness - AIOTI FG Buildings and Communities

White Paper IoT and Crisis Preparedness

Release 1.0

AIOTI FG Buildings and Communities

June 2023

2

Executive Summary

The main objective of this vision paper is to identify the steps to be followed to develop societal

resilience towards short-term and long-term risks. The purpose is to showcase the best practices

and insights needed to (1) SENSE, (2) RESPOND, (3) DEFEND, and (4) RECOVER within the IoT

domain by involving all key stakeholders of this value chain.

According to the World Health Organization

1

, the emergency cycle is divided into four different

phases: (1) Prevent; (2) Prepare; (3) Respond, and (4) Recover.

Hence, this whitepaper aims to cover the four phases of emergency management as an

integrated overview of four different fields of threats:

1. Pandemics and other Health-related threats,

2. Natural Disasters,

3. Cyber Attacks, and

4. Attacks on Public Spaces.

1

https://www.euro.who.int/en/health-topics/health-emergencies/pages/about-health-emergencies-in-the-european-

region/emergency-cycle

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© AIOTI. All rights reserved. 
Table of Contents 
2.1 Overview of the Current State of Play ................................................................................................................................. 9 
2.2 Research Challenges and Objectives .............................................................................................................................. 11 
2.3 Vision Ideal Scenarios Innovation and Technology ........................................................................................................ 12 
2.3.1 Adaptation of Smartphones within Europe ............................................................................................................ 12 
2.3.2 Contact Tracing Apps during the Pandemic ......................................................................................................... 13 
2.4 Risk Analysis ........................................................................................................................................................................ 13 
2.5 Behavioural and Cultural Challenges ............................................................................................................................... 15 
2.6 Overview of Data Analysis and Value-Based Healthcare .............................................................................................. 16 
2.7 Scenarios and Best Cases .................................................................................................................................................. 17 
TRIALOG (Personalised Decision support system + IAMHappy Smart Health IoT-based Recommender System [well-being 
recommendation system]). ......................................................................................................................................................... 18 
Bi-rex: Big Data Innovation and Research Excellence | HomeEmo - Home dialysis ............................................................. 19 
INFN Laboratory for Technology Transfer (National Institute of Nuclear Physics-Bologna) ................................................... 21 
Disaster Management and IoT .................................................................................................................................................... 24 
3.1 Overview of the current State of Play ............................................................................................................................... 24 
3.2 Research Challenges and Objectives .............................................................................................................................. 26 
3.3 Vision Ideal Scenarios Innovation and Tech .................................................................................................................... 27 
3.4 Risk Analysis ........................................................................................................................................................................ 28 
3.5 Behavioural and Cultural Challenges ............................................................................................................................... 28 
3.6 Overview of Data Analysis ................................................................................................................................................. 30 
3.7 Scenarios and Best Cases .................................................................................................................................................. 31 
Cross-domain Emergency Managing and Planning about Hazard Crisis data integration using ontologies ..................... 31 
SPADE: multi-purpoSe Physical-cyber Agri-forest Drones Ecosystem for governance and environmental observation ... 31 
SEP: Smart Event  Processor - a framework that  combines real-time data from different  sources, correlating  them and 
concluding on the existence of an event (alarm), activating a rapid response from the reaction mechanisms. ............ 32 
4.1 Overview of the current State of Play ............................................................................................................................... 34 
4.2 Research Challenges and Objectives .............................................................................................................................. 35 
4.3 Vision Ideal Scenarios Innovation and Tech .................................................................................................................... 36 
4.4 Risk Analysis ........................................................................................................................................................................ 37 
4.5 Behavioural and Cultural Challenges ............................................................................................................................... 38 
4.6 Overview of Data Analysis ................................................................................................................................................. 39 
4.7 Scenarios and Best Cases .................................................................................................................................................. 41 
Cyber Crisis Management: A Decision-Support Framework for Disclosing Security Incident Information ........................... 41 
Crisis Management in a Federation – Cybernetic Lessons from a Pandemic ........................................................................ 41 
Cyber Crisis Management Roles – A Municipality Responsibility Case Study ......................................................................... 41 
 


© AIOTI. All rights reserved. 
1 Overview of the current State of Play ............................................................................................................................... 42 
2 Research Challenges and Objectives .............................................................................................................................. 43 
3 Vision Ideal Scenarios Innovation and Technology ........................................................................................................ 45 
4 Risk Analysis ........................................................................................................................................................................ 46 
5 Behavioural and Cultural Challenges ............................................................................................................................... 47 
6 Overview of Data Analysis ................................................................................................................................................. 48 
7 Scenarios and Best Cases .......................................................................................................................................................... 49 
SemAttack: Natural Textual Attacks via Different Semantic Spaces ...................................................................................... 49 
1 Research Recommendations ............................................................................................................................................ 50 
2 Standardisation and Interoperability Recommendations ............................................................................................... 51 
3 Business-driven Recommendations .................................................................................................................................. 53 
4 Policy-driven Recommendations ...................................................................................................................................... 55 
 
 
  

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Table of Figures

Figure 1: Three system states of resilience for the short-, medium and long-term perspectives (Chelleri et al., 2015) 7

Figure 2: Covid-19 cases in the world. (9.04.2020) Source: Center for Systems Science and Engineering (CSSE) at JHU 10

Figure 3: Floods reported in Europe 23

Figure 4: SEP: Smart Event Processor overview 32

Figure 5: "sudo" stands for "superuser do", to execute commands with superuser privileges in Linux bash terminal (WSL). 34

Figure 6: Grafana, a multi-platform open-source analytics and interactive visualisation web application that provides charts,

graphs, and alerts for the web when connected to supported data sources. 40

Figure 7: High police presence in Lyon, France, during the 25th weekend of the yellow vests movement (2019) 42

6

List of Tables

Table 1: Digital health tools implementation for addressing COVID-19’s spread 10

Table 2: Risk Analysis 13

Table 3: Cloud computing advantages and limitations (prevention of public spaces attacks) 37

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1. Introduction

According to several scientific reports

2

, hurricanes, cyclones, earthquakes, mudslides, floods,

wildfires, volcanic eruptions and weather events like extreme droughts and monsoons are

increasing worldwide due to climate change. In 2020, according to NOAA

3

, all months except

December were the warmest ever recorded. Moreover, the 2020 Annual Climate Report

4

states

that combined land and ocean temperature has increased at an average rate of 0.08ºC per

decade since 1880. Lastly, many reports have shown that these climate changes have led to

the appearance of Natural Disasters.

On the other hand, ecosystems and societies have also struggled with adverse events from non-

natural causes, such as terrorist attacks and worldwide pandemics. Terrorism can take several

forms (including technology, biological agents, or other methods)

5

to coerce societies and

governments, and it can happen anytime. When faced with a worldwide pandemic,

communities and ecosystems face an invisible threat capable of spreading across international

borders. From natural to non-natural causes, disasters harm public safety and must be

addressed as an integrated and complete scenario.

Resilience is the capacity of social, economic and environmental systems to cope with a

hazardous event or trend or disturbance, responding or reorganising in ways that maintain their

essential function, identity and structure while also maintaining the capacity for adaptation,

learning and transformation (IPCC, 20141).

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Resilience also refers to ‘the degree to which the

system minimises the level of service failure magnitude and duration over its design life when

subject to exceptional conditions’ (Butler et al., 2014, p. 3). Resilience becomes relevant when

the probability of avoiding failure decreases due to shocks or stresses (Figure 1.)

Figure 1: Three system states of resilience for the short-, medium and long-term perspectives (Chelleri et al., 2015)

2

https://sciencing.com/negative-effects-natural-disasters-8292806.html

3

https://www.climate.gov/news-features/understanding-climate/climate-change-global-temperature

4

https://www.ncdc.noaa.gov/sotc/global/202013

5

https://www.readynh.gov/disasters/terrorism.htm

6

https://archive.ipcc.ch/pdf/assessment-report/ar5/wg2/WGIIAR5-AnnexII_FINAL.pdf

8

Planning for recovery requires replacing and repairing affected infrastructure and investing in

the capacity to reduce risk, and therefore begins before the occurrence of a disaster

(UNDP/BCPR, 2011). Planning for recovery means creating conditions that allow an appropriate,

effective and quick response to a disaster or disruption threat. Policies must be based on

understanding the risks and vulnerabilities, capacity, exposure of people and assets, the

environment and the characteristics of the disaster.

This approach can help prevent and mitigate the risk and prepare and respond effectively

(UNISDR, 2015). Planning to reduce disaster risk is essential to enhance resilience in all aspects of

the system since it increases the capacity of the system to absorb shocks and prevent or reduce

losses (UNISDR, 2015; Chelleri et al., 2015). Effective recovery planning requires preparation for

not only risks but also the anticipation of the consequences. Capacities must be in place to

effectively respond to recovery, rehabilitation and reconstruction after a disaster. Good

planning may provide an opportunity to rebuild the physical, social, economic or environmental

system to prepare it for future shocks better.

This strategy, called ‘Build Back Better’, is an ideal reconstruction and recovery process that

delivers resilient, sustainable, and efficient recovery solutions to disaster-affected communities.

The ‘Build Back Better’ (BBB) concept motivates communities to become stronger and more

resilient after a disaster (Mannakkara et al., 2008). BBB was defined as a way to utilise the

reconstruction process to improve a community’s physical, social, environmental, and

economic conditions to create a more resilient community, where resilience is defined as “the

capacity to recover or ‘bounce back’ after an event” (Twigg, 2007). The approach includes

focusing on clean energy, such as investing in renewable energy sources like wind and solar

power and transitioning away from fossil fuels but also includes investments in transportation

infrastructure, such as electric vehicle charging stations, and improvements to public

transportation systems. In addition, the BBB approach includes investments in education and job

training programs, focusing on creating opportunities for underserved communities and helping

people transition to new industries.

Overall, the Build Back Better approach is about creating a more equitable, sustainable, and

prosperous future by investing in the areas that will help us overcome the present challenges

and build a better tomorrow. Therefore, the concept of BBB proposes a broad, holistic approach

to post-disaster reconstruction to address the wide range of prevalent issues, including those

mentioned above and ensure that affected communities are regenerated in a resilient manner

for the future. It has been adopted by several countries, including the United States, Canada,

and Japan, to address the challenges presented by the COVID-19 pandemic and to create a

more resilient and sustainable future. Investing in infrastructure, education, healthcare, and

other areas will help create a more equitable and sustainable society. Its goal is to recover from

the pandemic and build a more resilient and prosperous future.

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2. An Era of Pandemics

‘It’s an era of pandemics we are entering. If you look at what has been happening over the

past few years, I mean from HIV to Ebola to MERS to SARS, these were all epidemics which could

be contained, but we should not think it is all over when we’ve overcome Covid-19. The risk is

still there. The EU must prepare for an era of Pandemics’

President von der Leyen, European Commission

Digital health technologies hold the potential to bring about major improvements in the

efficiency of health systems, both in terms of care provision and the administration of the system

as a whole. For many years, there have been efforts across European health systems to facilitate

and promote their use. However, realising the potential of digital tools in health care has proved

to be a long, arduous and complex endeavour with mixed results.

The unique challenges generated by COVID-19 have nevertheless created different needs and

pushed a new momentum for digital health technologies adoption, resulting in their use

accelerating during the pandemic. Digital health tools have become integral to pandemic

responses across the region in supporting communication and monitoring, the continued

provision of health services, and transitions from pandemic-related restrictions.

Our society needs to leverage the momentum and consolidate evidence from digital health

during the pandemic to provide an evidence base for making the best use of digital health tools

in the medium and longer term.

2.1 Overview of the Current State of Play

Before the COVID-19 pandemic, Europe had a wide discrepancy regarding policy and strategy

development towards digital health by area and country. According to the 2015 World Health

Organization (WHO) global survey on eHealth, 70% of reporting countries in the WHO Europe

region had an eHealth policy or strategy in place. Still, only 27% had one for telehealth

7

, with a

greater propensity in the northern EU countries compared to the Central and Southern countries.

According to the WHO (EU), 43% of reporting countries had policies or legislation defining

medical jurisdiction, liability or reimbursement of eHealth services. 53% had no legislation

allowing individuals to access their electronic health records, and only 13% had policies on

regulating the use of big data in the health sector. In addition, only 11 reporting countries had

a national authority responsible for managing this.

The pandemic prompted a rapid uptake and concrete implementation of digital health tools.

Despite this, various countries were positioned differently at the pandemic's start. Northern EU

countries were relatively well set, with digital health already integrated into their health systems.

Other countries were advanced in some ways but not in others, e.g. having a well-developed

technical infrastructure but relatively restrictive regulations. In contrast, others had not yet

integrated digital health tools into their wider health systems. This limitation caused countries to

start from different points when the pandemic struck.

7

N. Fahy, G.A. Williams, COVID-19Health System, Response Monitor Network; “Use of Digital Health Tools in Europe, before, during

and after COVID-19”, WHO Regional Office for Europe, 2021.

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Figure 2: Covid-19 cases in the world. (9.04.2020) Source: Center for Systems Science and Engineering (CSSE) at JHU

Overall, there are four main areas (Table 1) where digital health tools are being used in response

to COVID-19: first, communication and information; second, monitoring and surveillance; third,

supporting provision of health services; and fourth, vaccination, immunity and

pharmacovigilance.

Table 1: Digital health tools implementation for addressing COVID-19’s spread

Areas

Main actions

Communication and

Information

- Communicating information on COVID-19 with the public

- Combating misinformation on COVID-19

Monitoring and

Surveillance

- Adapting existing tools to support monitoring surveillance and contact tracing

- Using mobility data to model diffusion of COVID-19

- Using genomic data to detect and track new variants

- Public databases and social media data to support monitoring and surveillance

- Using mobile apps to support contact tracing

- Mobile and web-based apps to support symptom tracking and self-diagnosis

- Using mobile apps to support or enforce self-isolation and quarantine

Supporting Provision of

Health Services

- Using remote consultations to support the provision of essential care

- Using digital tools to manage hospital capacity

- Using AI to identify infections and potential treatments

Vaccination, Immunity

and

Pharmacovigilance

- Identifying individuals eligible for vaccination

- Combating vaccine hesitancy

- Monitoring of adverse reactions

- Using immunity certificates to support the reopening of economies

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On top of this, on 31 March 2021, the Commission launched an online public consultation on the

Health Emergency Preparedness and Response Authority (HERA)

8

. Part of the EU's answer to the

COVID-19 pandemic and a strong European Health Union, HERA aims to improve Europe's

capacity and readiness to respond to cross-border health threats and emergencies.

HERA is part of the European Health Union, as announced by President von der Leyen in her

September 2020 State of the Union address. It will provide a dedicated structure to support the

development, manufacturing and deployment of medical countermeasures during a health

crisis of natural or deliberate origin. HERA could use foresight, surveillance and market

intelligence to plan and coordinate medical countermeasures. The exact remit of HERA will be

set out in a legislative proposal later this year. The Commission will consider the feedback

received before finalising the legislative proposal.

Coordinate EU-wide risk assessments for emerging or unknown cross-border health threats based

on the available epidemic intelligence knowledge at the relevant EU bodies, including the

ECDC and the Union Civil Protection Knowledge Network, to identify medical countermeasures.

Further, cooperation will be sought with the World Health Organization (WHO).

2.2 Research Challenges and Objectives

Due to COVID-19’s spread, the European healthcare systems’ capacity has been put under

unprecedented pressure. The crisis highlighted the strengths and weaknesses of dealing with an

event of these proportions. This pandemic has made explicit that we should take the opportunity

to implement an ambitious reform agenda for European health systems through the direct

involvement of all stakeholders and policymakers at the regional, national and EU levels.

In particular, the following challenges should be considered as the key ones to be addressed:

▪ New investment in the healthcare and societal sector. In particular, the pandemic has

made clear how local primary services have suffered a reduction of investments over the

last decades, limiting the territory's capacity to address the challenges coming from

COVID-19 adequately. Additionally, the crisis has clarified the consequences of confusing

short-term cost savings with efficiency gains through improved care delivery design. For

example, the reduction of the workforce and implicit attrition in service delivery have left

some countries ill-equipped to deal with the spike in demand for healthcare.

▪ Developing an integrated budget framework for health investments to limit and address

the fragmentation of the EU's health systems.

▪ Improved investments in the EU's health data infrastructure and digital health. Despite the

pandemic manifesting the potentiality of digital solutions to free up resources and make

health systems more resilient, the deployment of digital health tools and infrastructure is

still fragmented across the EU. On top of this, the ability to rapidly access real-time and

comparable data on how patients and populations are affected by the disease and the

effectiveness of different public health measures and treatments is crucial for high-quality

analysis and support to the decision-making process. Additionally, in this scenario, it

became relevant to invest in the improvement of the EU standardisation process of health

data quality, collection and interoperability and to improve and accelerate the creation

of a European Health Data Space with a clear governance framework for access to data

including for secondary use for research.

According to the above-reported main points, this chapter aims to shed light on scenarios and

best practices implemented by the AIOTI members and the need to implement a value-based

approach to drive innovation and the EC and European countries' policy-making process.

8

Health Emergency Preparedness and Response Authority

(HERA)https://ec.europa.eu/commission/presscorner/detail/en/IP_21_1522

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2.3 Vision Ideal Scenarios Innovation and Technology

Overall, the COVID-19 pandemic has demonstrated the importance of innovation and

technology in responding to public health emergencies. IoT solutions can help mitigate the

impact of pandemics by improving public health surveillance, enhancing healthcare delivery,

and supporting remote work and education, with some scenarios detailed below.

▪ Telemedicine and remote health monitoring: One of the biggest challenges of

pandemics is the rapid spread of the disease, which can quickly overwhelm healthcare

systems. Telemedicine and remote health monitoring technologies can help mitigate this

by enabling doctors and nurses to remotely monitor patients' health, reducing the need

for in-person visits and freeing up hospital beds for more severe cases.

▪ Contact tracing is a critical tool for controlling the spread of infectious diseases. IoT

technologies, such as wearables and location-tracking devices, can help automate and

improve the accuracy of contact tracing efforts, making it easier to identify and isolate

potential sources of infection.

▪ Smart buildings and infrastructure technologies can help reduce the risk of transmission

by automatically adjusting ventilation systems, monitoring air quality, and controlling

access to public spaces. IoT-enabled infrastructure can also improve the efficiency and

safety of transportation systems, reducing the risk of transmission for essential workers who

need to travel.

▪ Remote education and e-learning: Pandemics can disrupt traditional education systems,

but e-learning and remote education technologies can help ensure continuity of

education. IoT-enabled platforms can facilitate remote learning, allowing students to

access digital resources, collaborate with peers and teachers, and engage in virtual

classrooms.

▪ Data analytics and forecasting based on the large amounts of data collected from IoT

systems on the spread of infectious diseases, which can be analysed and used to

develop predictive models and forecasts. These capabilities can help governments and

healthcare systems better prepare for future outbreaks, allocate resources more

effectively, and develop targeted interventions.

2.3.1 Adaptation of Smartphones within Europe

In 2020, 81% of people aged 16-74 in the EU used a smartphone for private purposes in the three

months before the survey. However, 18% responded that when using or installing an app on the

smartphone, they never restricted or refused access to personal data such as their location or

contacts. In 6% of the cases, they did not know if it was possible to restrict or refuse access to

their data when using or installing an app. The share of people who never restricted or refused

such access was highest among the youngest (20% for 16-24 years old) and lowest among the

eldest (14% for 65-74 years old).

9

https://ec.europa.eu/eurostat/web/products-eurostat-news/-/ddn-20210506-2

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2.3.2 Contact Tracing Apps during the Pandemic

Coronavirus contact-tracing apps were meant to play a significant role in how some countries

dealt with the spread of the disease. However, so far, they have had a limited impact. Many IT

companies have released tracking applications based on Bluetooth low-energy technology

10

.

These programs, however, have several limits; first of all, they must be downloaded by a large

percentage of users to be fully useful. Furthermore, they must be made functional: Bluetooth

connection must always be on, and the device needs to be connected to the Internet at least

once a day to transmit keys to the central server, which can constitute a problem for people

over 65 who have poor access to and understanding these procedures. On top of this, there

are fundamental issues in centralised or decentralised systems, the influence of powerful Tech

firms such as Google and Apple, and privacy and confidentiality concerns/issues; there has also

been a behavioural challenge that not much emphasis has been put on.

2.4 Risk Analysis

A risk analysis systematically evaluates the risks and threats associated with implementing and

using technology, such as software applications, hardware devices, and network infrastructure.

Its main goal is to identify and assess the likelihood and impact of various risks, such as security

breaches, system failures, and compliance violations, and develop strategies to mitigate or

manage them. A thorough risk analysis typically involves several steps, including asset

identification (hardware, software and network components that need to be evaluated), threat

identification (such as cyberattacks, data breaches or natural disasters), risk assessment,

mitigation and monitoring strategies.

Risk assessment aims at evaluating the likelihood and impact of the identified threats and

determining the level of risk associated with each one. Risk mitigation develops and implements

strategies to mitigate or manage the identified risks, such as security controls, backup and

recovery procedures, or disaster response plans. Finally, risk monitoring and review handle the

continuous monitoring of the technology environment for new risks and update the risk

management strategies as required.

A technological risk analysis is an essential component of any comprehensive risk management

plan, as it helps organisations identify and manage potential risks to their technology assets and

ensure the continued availability, integrity, and confidentiality of those assets.

10

Zastrow M. Coronavirus contact-tracing apps: can they slow the spread of COVID-19? Nature 2020; 10.1038/d41586-020-01514-2.

14

Table 2: Risk Analysis

Risk Category

Potential Risks

Mitigation Strategies

Privacy and

Security

Breach of sensitive data such as health

records

Use of encryption and secure protocols for

data transmission and storage,

implementation of access controls and user

authentication

Unauthorised access to IoT devices and

networks

Implementation of strong password policies,

regular software updates and patch

management, regular security assessments

and testing

Malware and ransomware attacks on IoT

devices and networks

Use of security software and firewalls,

regular backup and recovery procedures,

incident response and disaster recovery

plans

Technical

Hardware and software failures

Regular maintenance and monitoring, use

of redundant systems and failover

mechanisms

Integration challenges with legacy

systems and infrastructure

Use of standard protocols and APIs for

interoperability, implementation of testing

and validation procedures

Connectivity issues and network

congestion

Use of load balancing and traffic

management techniques, implementation

of Quality of Service (QoS) policies, use of

edge computing and distributed

architectures

Ethical

Bias and discrimination in data collection

and analysis

Regular review and monitoring of data

models and algorithms, implementation of

fairness and transparency principles

Inequitable access to IoT technologies

and resources

Promotion of universal access and inclusion,

implementation of community engagement

and participation strategies

Misuse of data for surveillance or control

purposes

Implementation of clear and transparent

data governance policies, adherence to

ethical principles and human rights

frameworks

15

2.5 Behavioural and Cultural Challenges

The Behavioural and Cultural Challenges faced by Europe in an era of pandemics can be quite

complex and varied, such as:

▪ Non-Compliance with Guidelines may be due to a lack of understanding or trust in the

guidelines or cultural or social factors that make it difficult for people to comply.

▪ Stigma and Discrimination against certain groups, such as people of specific nationalities

or ethnicities, frontline workers, or people who have contracted the disease, can lead to

social isolation and exclusion and make it difficult to contain the spread of the disease.

▪ Misinformation and Rumours can be a significant challenge during a pandemic, leading

to confusion and mistrust of public health authorities, making it more difficult to control

the disease's spread, and leading to non-compliance with guidelines.

▪ Lack of Preparedness and response planning may be due to a lack of funding or

resources or due to cultural attitudes that downplay the severity of the threat.

▪ Socioeconomic Factors can often make the impact of a pandemic felt more severely by

certain socioeconomic groups, such as those with lower incomes, in precarious

employment, or living in crowded conditions, which can lead to more significant

inequalities in health outcomes and exacerbate existing social and economic disparities.

Research indicates why key healthcare stakeholders need to be faster to adopt health IT and

leverage opportunities afforded by digital transformation (DT). In particular, the following must

be considered for this chapter's purposes.

1) The adoption of health IT is usually resisted by powerful actors in healthcare delivery

11

2) The resistance stems from various factors such as professional norms [physicians regard

tasks aside from patient treatment as administrative nuisances]

12

3) Adverse influence [powerful, tech-averse physicians affect other’s use of health IT]

13

4) Threats to professional autonomy (physicians aim to maintain the status and refuse new

technology)

14

, and

5) Privacy concerns [due to hacked medical devices

15

and absent and opaque app

privacy policies

16

].

11

Bhattacherjee, Anol, and Neset Hikmet. 2007. Physicians’ resistance toward healthcare information technology: a theoretical model

and empirical test. European Journal of Information Systems 16 (6): 725–737.

12

Fichman, Robert G., Rajiv Kohli, and Ranjani Krishnan. 2011. Editorial overview—the role of information systems in healthcare: current

research and future trends. Information Systems Research 22 (3): 419–428.

13

Venkatesh, Viswanath, Xiaojun Zhang, and Tracy A. Sykes. 2011. ‘‘Doctors do too little technology’’: A longitudinal field study of an

electronic healthcare system implementation. Information Systems Research 22 (3): 523–546.

14

Walter, Zhiping, and Melissa Succi Lopez. 2008. Physician acceptance of information technologies: Role of perceived threat to

professional autonomy. Decision Support Systems 46 (1): 206–215.

15

Meskó, Bertalan, Zsófia Drobni, Éva Bényei, Bence Gergely, and Zsuzsanna Gy}orffy. 2017. Digital health is a cultural transformation

of traditional healthcare. Health 3 (38).

16

Sunyaev, Ali, Tobias Dehling, Patrick L. Taylor, and Kenneth D. Mandl. 2015. Availability and quality of mobile health app privacy

policies. Journal of the American Medical Informatics Association 22 (e1): e28–e33.

16

Additional barriers for organisations to adopt healthcare IT solutions include initial and ongoing

costs. Shifting towards the organisational level, the providers need more incentives to implement

health IT systems or share their data due to competitive concerns

17

. Such proprietary

strategising, however, not only impedes the digital transformation of healthcare means that

healthcare providers cannot leverage the total patient data available across systems

18

.

Response to public health emergencies requires a change in regular behavioural patterns;

therefore, coordination and understanding of communities within different cultures is a

prerequisite. Understanding the information ecosystem and communicating with communities

will help create sustainable behavioural change since epidemics centre around human

behaviour.

2.6 Overview of Data Analysis and Value-Based Healthcare

The global Internet of Things (IoT) in the healthcare market size is expected to experience

significant growth by reaching USD 446.52 billion by 2028

19

. This is attributed to the rising focus on

active patient-centric care and patient engagement, the rise in high-speed technologies for IoT

connectivity, and the rising need for implementing cost-control actions within the healthcare

sector. On top of this, the increasing awareness regarding fitness and health is leading to the

demand for self-health management techniques, which has surged the demand for several

medical wearable devices globally as people can easily access and monitor their health

anytime. Moreover, several manufacturers are focusing on introducing advanced monitoring

devices to cater to the growing consumer demand. Additionally, the COVID-19 pandemic has

caused a change in providers’ willingness to implement IoT solutions, which helped diagnose

the virus using IoT.

Despite this potentially growing role of AHA-IoT services in the healthcare market, a systemic

dimension of innovation still needs to be reached. A value-driven innovation can support

decision-makers choices and promote innovation sustainability.

We are increasingly experimenting, both at local and global dimensions, that EVIDENCE of the

AHA-IoT services value is needed to help decision-makers adopt innovation to support social

and healthcare services sustainability. Limited available resources, the increasing number of

older people and the level of demand for chronic diseases management services push

decision-makers to choose between the resources they would need to fund all potentially useful

interventions (e.g., social or healthcare programmes) and those that are actually in their

budgets. It is a fact that, in limited resource settings, the available budget must be allocated as

efficiently as possible, and decision-makers are called to make comparisons across alternative

uses of the same amount of resources. The achievement of this objective and evaluation of the

services’ effectiveness level can be supported by a structured process to generate objective

EVIDENCE of services’ costs-effectiveness.

It is also essential to consider that the healthcare sector's IoT market is highly fragmented. A

comprehensive picture of the AHA-IoT services' value and sustainability is needed at the EU level

to promote the quality and equity of AHA services around Europe and strengthen the European

Digital Market. That is why a shared EVIDENCE generation process is perceived as a critical

objective to be pursued, leading to replication and scaling up processes of innovative services.

17

Ozdemir, Zafer, Jack Barron, and Subhajyoti Bandyopadhyay. 2011. An analysis of the adoption of digital health records under

switching costs. Information Systems Research 22 (3): 491–503.

18

Romanow, Darryl, Sunyoung Cho, and Detmar Straub. 2012. Editor’s comments: riding the wave: past trends and future directions for

health IT research. MIS Quarterly iii-x.

19

Internet of Things (IoT) in Healthcare Market Size, Share and COVID-19 Impact Analysis, By Component (Devices, Software, and

Services), By Application (Telemedicine, Patient Monitoring, Operations and Workflow Management, Remote Scanning, Sample

Management, and Others), By End-User (Laboratory Research, Hospitals, Clinics, and Others), and Regional Forecast, 2021-2028

17

2.7 Scenarios and Best Cases

The outbreak of COVID-19 has shown that healthcare resources must be prioritised to tackle the

pandemic properly and continue carrying out the most urgent interventions from common

pathologies. In this line, new medical services should be developed to optimise scarce

healthcare resources. Patients' remote assistance and remote monitoring are two services that

can be implemented with that objective. In this regard, the AIOTI members represent many

different IoT/ICT services.

In particular, this paragraph provides possible IoT and edge computing business-driven

scenarios, examples and use cases that can be applied to address the IoT and edge computing

high-level challenges and objectives in the health domain; a snapshot of the most relevant is

reported here.

18

TRIALOG (Personalised Decision support system + IAMHappy Smart Health IoT-based

Recommender System [well-being recommendation system]).

Use Case: Personalised Health Knowledge Graph

Trialog's current health applications do not adequately consider contextual and personalised

knowledge about patients. To design “Personalised Coach for Healthcare” applications to

manage chronic diseases, there is a need to create a Personalised Healthcare Knowledge

Graph (PHKG) that takes into consideration a patient’s health condition (personalised

knowledge) and enriches that with contextualised knowledge from environmental sensors and

Web of Data (e.g., symptoms and treatments for diseases). To develop PHKG, aggregating

knowledge from various heterogeneous sources such as the Internet of Things (IoT) devices,

clinical notes, and Electronic Medical Records (EMRs) is necessary. This paper explains the

challenges of collecting, managing, analysing, and integrating patients’ health data from

various sources to synthesise and deduce meaningful information embodying the vision of the

Data, Information, Knowledge, and Wisdom (DIKW) pyramid. Furthermore, it promotes a solution

that combines: 1) IoT data analytics and 2) explicit knowledge, illustrated using three chronic

disease use cases on users – asthma, obesity, and Parkinson’s

20

- for more accurate diagnostics,

preventive health and better doctor assistance. The stakeholders were patients and doctors

(paediatric specialists in both the pulmonary and sleep departments), the main facilitators

encountered in the service's implementation. In contrast, the main barriers to the service’s

implementation were data accessibility and interlinking datasets from heterogeneous sources.

Use Case: IAMHappy

Smart Health IoT-based Recommender System. Healthy lifestyles, fitness, and diet habits have

become central applications in our daily lives. Positive psychology, such as well-being and

happiness, is the ultimate dream of everyday people's feelings (even without awareness).

Wearable devices are being increasingly employed to support well-being and fitness. Those

devices produce physiological signals that machines analyse to understand emotions and

physical states. The Internet of Things (IoT) technology connects (wearable) devices to the

Internet to easily access and process data, even using Web technologies (aka Web of Things).

We design IAMHAPPY, an innovative IoT-based well-being recommendation system, to

encourage everyday people's happiness. The system helps people deal with day-to-day

discomforts (e.g., minor symptoms such as headache and fever) by using home remedies and

related alternative medicines (e.g., naturopathy, aromatherapy), activities to reduce stress,

among others.

A web-based knowledge repository for emotion was developed to focus on happiness and well-

being by helping analyse data produced by IoT devices to understand users' emotions and

health. The semantics-based knowledge repository is integrated with a rule-based engine to

suggest recommendations to achieve happiness in the quotidian. The naturopathy application

scenario supports recommendation system

21

on users for preventive health and improved well-

being. The stakeholders involved were users willing to boost their immune systems during the

COVID-19 pandemic; the main barriers encountered in the service’s implementation have been

accessibility to data, interlinking datasets from heterogeneous sources, and retrieving and citing

scientific knowledge from unstructured knowledge (scientific publications, websites, books,

among others) to prove facts.

20

Personalized Health Knowledge Graph [Gyrard et al. 2018]

https://scholarcommons.sc.edu/cgi/viewcontent.cgi?article=1005andcontext=aii_fac_pubImpact

21

IAMHAPPY: Towards an IoT knowledge-based cross-domain well-being recommendation system for everyday happiness [Gyrard et

al. 2021 Elsevier Smart Health Journal]

https://corescholar.libraries.wright.edu/cgi/viewcontent.cgi?article=2541andcontext=knoesisImpact

19

Bi-rex: Big Data Innovation and Research Excellence | HomeEmo - Home dialysis

Partners: MISTER Smart Innovation, Tecnoideal, MEDICA SpA | Experience’s duration: 2019-2022

|Experience’s funding source/s – procurer: Emilia-Romagna Region local funding through “LR

14/2014” – 2019 Call

Home dialysis, and therefore relocated from hospital centres, is for all companies in the

Healthcare sector an essential line of research. Its advantages are many:

▪ Greater comfort for patients: they are often older people who suffer from home/hospital

travel.

▪ According to many studies, home dialysis, possibly being carried out over a long time,

has greater effectiveness.

▪ Being more effective, patients treated with home dialysis need less medication.

▪ The healthcare company saves both the costs of transporting the patient and the

number of drugs administered to him.

Nowadays, technical difficulties remain, thus strongly limiting the spread of this kind of method.

They can be summarised in three categories:

1. Disinfection management. Traditional hospital machines have internal hydraulic circuits

that must be disinfected after each treatment to avoid bacterial proliferation, which is

dangerous for the patient. The cleaning and disinfection operation is difficult to manage

at home, both for problems related to the supply of disinfectants and the operation itself.

2. Patient safety. As no nursing specialised staff is present, the machine must be able to

identify more potentially critical situations than the hospital ones, directly analysing fluids

(blood and dialysate) with in-line sensors.

3. Treatment control. The caregiver cannot properly conduct treatment verification and

machine supervision at home. To avoid that, specialised personnel have to move

periodically to check the machine; it is necessary not only to have a direct remote

connection with it, independent of the patient's home, but also that the machine can

report in advance any technical or clinical criticalities self-learning thanks to its

integrated sensors.

HomeEmo intends to solve the mentioned problems. Regarding the problem of disinfection, it is

intended to radically change course developing the components and sensors necessary to

carry out home dialysis with a new generation of disposable hydraulic circuits; regarding patient

safety, we will develop a series of optical sensors based on spectrometric analysis, able to

monitor different parameters of both blood and dialysate. In particular: haematocrit, saturation,

and pH in the blood; haemoglobin (for identifying any haemolysis) and urea in the dialysate.

Finally, to remote control the machine, we will develop a Machine Learning technology that,

working on the large amount of data acquired, can provide accurate evaluations of therapies

progress according to the treatment parameters.

This functionality will be beneficial to manage the Post Marketing Clinical Follow Up, i.e.

monitoring and evaluation activities of medical devices' clinical performance that will become

fundamental with the adoption of the new European regulation MDR 2017/745 in force since

the end of May 2020.

20

- Outcomes of the experience: The optical sensors developed within the project will be applied

in the home dialysis machine and also on many other devices already produced by Tecnoideal

for the Medica group. The ability to obtain clinically relevant information from the large amount

of data saved by a machine can also be used on different devices already produced by

Tecnoideal, i.e., those that treat chronic patients, for whom it is important to analyse the progress

of treatments over time. Finally, the home dialysis machine would constitute a unique device on

the market. Tecnoideal and the Medica group also intend to enhance this new asset through

an industrial partnership to perform the necessary clinical validations preparatory to marketing.

- Experience’s implemented exploitation and eventual next steps: With the new sensors on

board, it is expected to gain significant market share for devices for CRRT and Apheresis, for

which it is expected to double the current turnover, of c.ca 3 M€, within a couple of years.

The introduction of the urea sensor and the dual hematocrit sensor on chronic dialysis machines

should allow the Devices of the Medical Group to enter a market otherwise saturated and well-

manned by multinationals. With the unique characteristics mentioned above, it is expected to

produce a thousand machines annually within three years. As mentioned, the home dialysis

machine will need a short clinical verification before the market accepts it. It will take about two

years after the project's end to start selling the first machines.

21

INFN Laboratory for Technology Transfer (National Institute of Nuclear Physics-Bologna)

1. A secure cloud-edges computing architecture for metagenomics analysis

Partners: INFN, CNR, Genova University | Experience’s duration: 2 years| Experience’s funding

source/s – procurer: Local funding

2. Smart city Active ageing

Partners: INFN TTLab. Unibo DEI, BiRex Pilot Plant |Experience’s duration: 1 year | Experience’s

funding source/s – procurer: Local funding

3. Healthcare: Smart and remote control

Partners: INFN TTLab. Unibo DEI, BiRex Pilot Plant |Experience’s duration: 1 year | Experience’s

funding source/s – procurer: Local funding

1. A secure cloud-edge computing architecture for metagenomics analysis

Portable sequencing machines, such as the Oxford Nanopore MinION, are making genome

sequencing ubiquitous. Consequently, metagenomic studies are becoming increasingly

popular, yielding important insights into microbial communities covering diverse environments,

from terrestrial to aquatic ecosystems. Furthermore, the adoption of low-power IoT computing

devices represents a feasible way of distributing and managing those machines on the field.

However, a key issue is represented by the huge amount of data produced during operations,

whose management is challenging considering the resources required for efficient data transfer

and processing.

This experience proposes a novel architecture combining Edge and Cloud computing

paradigms to address such challenges. The focus of the experience is the Edge layer,

responsible for the dynamic management of the full analysis pipeline of IoT devices producing

large datasets like the MinION ones while adopting proper security mechanisms that handle the

authentication of on-field devices and the confidentiality of the transmitted data. The key

aspect is that the Edge computing environment allows splitting the analysis workflow between

the data source and the Cloud infrastructure. Once the sequencing information is pre-

processed at the edge of the infrastructure, a Cloud-based IoT platform, such as the one

provided by Thingsboard, can collate data, trigger alarms or identify and maintain set points

through machine learning techniques.

In this experience, the team designed the general Edge-Cloud architecture and a prototype

implementation integrated with the INFN Cloud infrastructure, specifically focusing on access

security. Specifically, we carried out some preliminary experiments that confirm that the

approach is promising both from the performance and the security point of view.

A key result is that the proposed architecture has broad relevance because it represents a

composable, open-source and reusable solution addressing the problem of managing networks

of IoT devices through custom-built services producing large datasets in different operative

conditions and for heterogeneous sets of use cases.

22

2. Smart City Active ageing

This experience produced a full data collection workflow from a laser sensor connected to a

mobile application, working as an edge device, which transmits the data to a Cloud platform

for analysing and processing data.

The app was developed to receive data from electronic boards controlling IoT sensors via the

Bluetooth Low Energy protocol, sending it to an IoT Cloud endpoint of the INFN Cloud

infrastructure based on the Thingsboard solution via the MQTT(S) communication protocol.

Several functions are implemented in this application, such as BLE device scanning, secure

connections, multiple authentication methods, and data transfer via MQTT.

The IoT Cloud endpoint is fully customisable for multiple use cases, connecting to other services

of the INFN Cloud infrastructure, such as solutions for stream processing based on Kafka, Big

Data analytics processing based on Spark, and visualisation and selection based on Grafana

and Elasticsearch.

3. Healthcare: Smart and remote control

This experience analysed Edge controlled/assisted systems with Multi-access Edge Computing

based approach O-RAN (Transforming the Radio Access Networks Industry Towards Open,

Intelligent, Virtualized and Fully Interoperable RAN).

The main outcome of the experience is an intelligent edge-pervasive service through which

users can get real-time, personalised assistance and experiences. The service fills the need for

automatic real-time optimisation of heterogeneous resources and fast configurations, optimally

selecting network functions and AI techniques.

Regarding the exploitation and further development, processing a large amount of data can

benefit from using MEC (multi-access edge computing) instead of uploading data to the cloud,

which could cause additional round-trip delays. Low latency is a key requirement in this

experience.

The proposed use cases consisted of implementing these three services in a controlled but real

pilot to validate the obtained improvements and measure the better use of healthcare

resources. The two services, patient remote assistance and remote monitoring, enable remote

homecare and monitoring channels to communicate between patients and doctors. Remote

monitoring and assistance services allow data collection from connected medical devices at

home (scale, tensiometer pulse oximeter, thermometer, activity wristband, among others),

register the data in a Health Platform, and the doctors consult the patient’s information and

contact them if necessary.

23

3. Natural Disasters

Disasters are a mixture of threats, threat factors and inadequate capability or risk-reduction

measures. Disasters, primarily caused by natural hazards, are not the greatest threat to

humankind. A hazard becomes a disaster when it coincides with a vulnerable situation when

societies or communities cannot cope with their resources and capacities. A hazard is a physical

event, phenomenon or human activity that can cause loss of life or injury, damage to property,

social and economic disruption or degradation of the environment. Hazards have various

origins: natural (geological, hydro, meteorological, and biological) or human (environmental or

technological) actions. Risk is the probability of adverse effects or predicted losses (deaths,

accidents, properties, livelihoods, disrupted economic activity or damaged environment)

arising from interactions between natural or manufactured hazards and vulnerable populations.

Climate change will create new hazards, such as the melting of glaciers, sea level rise, and

extreme weather in proportions never seen before. A natural disaster is a natural event that

overwhelms local resources and threatens the community's function and safety. Disasters are

hard to plan and anticipate since they are innately different from common emergencies.

22

Figure 3: Floods reported in Europe

The United Nations defines a disaster as “the occurrence of sudden or major misfortune which

disrupts the basic fabric and normal functioning of the society or community”.

22

Study and Exposure to Natural’s Disaster for World Cities (IJSRD, 11.2021). Retrieved on 30 November 2021 from: IJSRD - International

Journal for Scientific Research and Development| Vol. 8, Issue 11, 2021 | ISSN (online): 2321-0613

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Disaster Management and IoT

The first 72 hours of a disaster’s aftermath are crucial. Disaster management is coping with

natural and human-caused disasters and preventing them. It involves preparation, response

and recovery to minimise the impact of disasters. IoT helps to improve response, minimises risks,

and can transform disaster management from a reactive approach to a proactive one thanks

to the data generated by these devices. IoT enables our prediction and early warning systems

to improve our response and preparedness systems. According to the Global Disaster

Preparedness Center

23

, emergency management has four phases: Mitigation, Preparedness,

Response, and Recovery. In summary, the four stages with the combinatorial value in which IoT

devices (can) play a crucial role:

Mitigation: Minimising the effects of disaster, such as implementing building zoning and codes,

public education and vulnerability analyses. IoT devices and sensors can collect near-real-time

data on barometric readings, water levels, and volcanic activity to detect cloudbursts,

earthquakes, volcanic activities, tornadoes, and wildfires and send early warnings. Critical

infrastructure can be protected through predictive maintenance. Sensors to monitor pollutants

and contaminants, including radioactive situations, enable hazard mitigation.

Preparedness: Preparing response, including emergency exercises, early warning systems,

training and preparedness plans. Real-time data from sensors, cameras and other connected

devices can be embedded into infrastructure, allowing city managers to prioritise repairs and

employ preventive maintenance. Connected devices deployed in bridges, roads, and

buildings and other infrastructure can also provide alerts and enhance communications. IoT

devices can monitor the strategic reserves of water, food, clothing, medical equipment and

other vital supplies to ensure acceptable levels.

Response: Minimise hazards created by disasters, such as emergency relief and search and

rescue. IoT can facilitate response planning and actions through sensors to monitor key

personnel's movement, sensors, and smart clothing. First responders can also be equipped with

audio and video sensors supported by autonomous drones and vehicles, allowing dangerous

situations to be monitored and assessed from a safe distance. In the meantime, IoT can help

people know where to find a safe location or life-saving supplies. Connected digital signs, such

as bus stops on roadways, can also spread critical information quickly. Battery-powered IoT

devices can enable limited communications services such as emergency micro-messaging.

Recovery: Return the community to normal through medical care, temporary housing,

restoration of communication, and disbursement of assistance in cash or kind. IoT devices can

help rescue and search operations and monitor post-disaster conditions, with vital infrastructure

and continuous flow of information and disseminate information to the public. At the same time,

normal communications are still being repaired.

Disaster management can be described as coordinating and managing resources and

responsibilities to deal with all humanitarian aspects of emergencies, particularly preparedness,

response and recovery to mitigate the impact of disasters. A disaster is an event or series of

events resulting in casualties and destruction or loss of property, facilities, climate, critical services

or livelihoods on such a scale beyond the usual capacity of the community concerned. Such

disasters include floods, hurricanes, earthquakes and volcanic eruptions, which can have

immediate effects on human health, as well as secondary impacts causing more death and

destruction from floods causing landslides, earthquakes resulting in explosions, tsunamis causing

severe flooding and typhoons sinking ferries. Such emergencies involve technology or

manufacturing, typically involving hazardous material, which occurs where these materials are

made, used or transported.

3.1 Overview of the current State of Play

23

Global Disaster Preparedness Center. Home - PrepareCenter

25

Nowadays, critical assets face numerous threats that can compromise their safety and lead to

unforeseen disasters with significant impacts. The proactive management of natural disasters

and extreme climatic conditions are considered to be the resilience of critical infrastructure and

societal functions

24

. Europe is highly and particularly vulnerable to natural disasters due to its

geographical location, climate, and topography, whose current state of play can be described

as follows:

▪ Increasing frequency and intensity of natural disasters such as floods, wildfires, droughts,

and storms are becoming more frequent and intense in Europe due to climate change,

resulting in an increased risk of loss of life, property damage, and economic losses. This

increase amplifies the risk for critical assets, which is also evident by recent projections

that the extreme flooding events in Europe are expected to increase substantially by

2050

25

, with a major impact on deteriorating structures.

▪ Ageing infrastructure that has exceeded its design lifespan and faces major deterioration

issues

26

. The outdated design of these critical assets constructed in a different era

indicates that infrastructure is not expected to withstand natural disasters and extreme

environmental conditions.

▪ Varied impact across Europe, with some regions being more vulnerable than others. For

example, Southern Europe is more vulnerable to wildfires, while Northern Europe is more

vulnerable to flooding. Cascading effects and systemic risks can trigger even minor

incidents, causing significant disruption to other assets and human and financial losses.

▪ Improved disaster management: European countries have significantly improved their

capabilities in recent years, enhancing early warning systems, developing disaster risk

reduction plans, and improving emergency response and recovery.

▪ Cooperation and solidarity: EU has established a framework for cooperation and

solidarity among member states to manage natural disasters better. The Civil Protection

Mechanism allows member states to request and provide assistance during emergencies,

such as natural disasters.

▪ Continued challenges still need to be addressed, despite the improvements in disaster

management. Some examples are the improvement of the resilience of critical

infrastructure, addressing socioeconomic inequalities that contribute to vulnerability, and

developing effective long-term strategies for climate adaptation.

Even though many devices exist to monitor hazard evolution and impact on critical assets, the

capability to connect the majority of sensors and actuators to provide a large-scale assessment

based on the computation of data at every level and asset has not been satisfactorily exploited.

24

Koursari, E., Wallace, S., Valyrakis M., and Michalis P. (2019). The need for real time and robust sensing of infrastructure risk due to

extreme hydrologic events. 2019 UK/China Emerging Technologies (UCET), Glasgow, United Kingdom, 2019, 1–3. doi:

10.1109/UCET.2019.8881865

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Jongman, B.; Hochrainer-Stigler, S.; Feyen, L.; Aerts, J.C.J.H.; Mechler, R.; Botzen, W.J.W.; Bouwer, L.M.; Pflug, G.; Rojas, R.; Ward, P.J.

(2014). Increasing stress on disaster risk finance due to large floods. Nat. Clim. Chang., 4, 264–268.

26

Michalis, P., Sentenac, P. (2021) Subsurface condition assessment of critical dam infrastructure with non-invasive geophysical sensing.

Environ Earth Sci 80, 556 (2021). https://doi.org/10.1007/s12665-021-09841-x

26

3.2 Research Challenges and Objectives

The sudden nature of many natural hazards indicates that, in most cases, they can proactively

compromise the ability to respond to such threats with disastrous consequences. Reliable

methods to evaluate natural hazards and deteriorating factors are important for the efficient

and proactive management of critical assets

27

. Despite the recent advances in developing and

applying IoT systems, critical infrastructure is still considered to be managed traditionally. For

example, this process involves on-site engineers making decisions based on their skills and

experience, mostly using paper-based analytics. At the same time, the existing sensing devices

do not exploit the interoperability aspects to deliver better services that optimise the operation

and maintenance of critical systems. The European Union (EU) has identified research

challenges and objectives for natural hazards to improve its disaster risk management and

resilience:

▪ Improve the EU’s understanding of natural hazards, including their frequency, intensity,

and impact, as well as their interactions with human and built environments.

▪ The EU aims to improve its early warning systems to provide accurate and timely

information to decision-makers and the public to reduce the risk of disasters.

▪ Develop better risk assessment and mapping tools to identify and prioritise areas most

vulnerable to natural hazards.

▪ The EU aims to improve its disaster response and recovery capabilities by enhancing

stakeholder communication, coordination, and cooperation.

▪ Develop effective strategies to adapt to the impacts of climate change, which are

expected to exacerbate the frequency and intensity of natural hazards.

▪ The EU aims to promote the development of innovative technologies and solutions that

can improve disaster risk management and resilience, such as remote sensing, artificial

intelligence, and blockchain.

▪ Finally, socioeconomic factors contributing to vulnerability to natural hazards, such as

poverty, inequality, and inadequate infrastructure, are targets to address.

One of the main challenges is that there are still several uncertainties about the real-time

evolution of natural and climatic hazards. This challenge is because most IoT applications

provide limited and scarce information, mainly focused on assessing the safety of single assets.

The numerous existing sensing solutions also do not have interoperability characteristics.

Therefore asset owners do not take full advantage of existing technological developments with

advanced prediction capabilities and the potential to incorporate all critical information into

one management platform. The latter is expected to enhance decision-making in the different

disaster management phases, considering the dynamics derived from the infrastructure system

perspective.

27

Pytharouli, S.; Michalis, P.; Raftopoulos, S. (2019). From Theory to Field Evidence: Observations on the Evolution of the Settlements of

an Earthfill Dam, over Long Time Scales. Infrastructures, 4, 65. https://doi.org/10.3390/infrastructures4040065

27

3.3 Vision Ideal Scenarios Innovation and Tech

Earthquakes, storms, heat waves and floods have been leading causes of death from natural

disasters during the past year, affecting all regions worldwide. The most recent figures show that

natural disasters caused losses of $131.7 billion in 2018

28

and affected millions worldwide. While

Mother Nature is unpredictable, several technologies promise to help with forecasting and

prevention and allow responders to act sooner rather than later:

1. The development of smart and resilient infrastructure can help to minimise the impact of

natural hazards by incorporating advanced monitoring systems, remote sensing, and

other technologies that can detect and respond to hazards in real-time.

2. Next generation forecasting and early warning systems can help to predict and prevent

the impact of natural hazards, which can be achieved by integrating data from multiple

sources, including remote sensing, social media, and citizen science.

3. Using data analytics and modelling can help improve risk assessment and mapping,

allowing decision-makers to understand the impact of natural hazards and develop

effective disaster management strategies.

4. The development of climate-resilient agriculture can help address the impact of natural

hazards on food security through precision agriculture technologies, such as sensors,

drones, and artificial intelligence.

5. Innovative risk financing solutions can help mitigate natural hazards' economic impact,

achieved through insurance products, catastrophe bonds, and other financial

instruments that can provide rapid and flexible funding in the event of a disaster.

6. Developing citizen engagement and social innovation can help build community

resilience through social media, crowdsourcing, and other technologies that facilitate

communication, collaboration, and community-driven solutions.

Overall, these ideal scenarios for innovation and technology can help to address the challenges

of natural hazards in Europe by enhancing disaster risk management and resilience and

promoting sustainable and climate-resilient development. With technological advancements

such as Artificial Intelligence and Machine Learning making it easier for scientists to process and

interpret a large amount of data, weather prediction and its impact on populations can now

be better addressed. Different technological solutions may apply when analysing each natural

disaster as a single one.

▪ Landslides and Earthquakes: Predictive solutions for earthquakes now use artificial

intelligence and machine learning, technologies capable of processing and interpreting

large data. A recent solution developed by experts at Cornell University

29

can better

forecast so-called “slow-slip earthquakes,” a type of tectonic motion of much lower

intensity that can last hours or days. While prediction is a tricky word for those studying

seismology, advancements in artificial intelligence may help scientists find patterns

otherwise impossible to track.

28

https://www.usnews.com/news/best-countries/slideshows/technology-can-save-the-world-from-natural-disasters

29

https://arxiv.org/abs/1909.06787

28

▪ Storms: Weather prediction currently does not rely on new technology -- if anything, it

uses rather old technology, such as radars that were first used in World War II. Despite the

rather conservative methods of collecting data, innovation may improve forecasting.

Advances in artificial intelligence promise to allow for a larger quantity of weather data

to be analysed faster, more accurately and in greater detail, making weather predicting

outcomes more accurate. At the same time, only sophisticated algorithms such as

efficient and intelligent signal and image processing, quality control mechanisms, pattern

recognition, data fusion (combining diverse streams of observations), data assimilation,

or mapping will soon be able to handle in-depth analyses of data from multiple domains,

such as geophysics, the atmosphere, the ocean, and the biosphere.

▪ Extreme Temperatures: Forecasting models use complex algorithms to assess upcoming

heat waves' onset, duration and demise. According to a study conducted in India

30

,

predicting heat waves is becoming increasingly important worldwide, as their frequency

is growing and the losses they bring are overarching. Heat waves cause the loss of human

lives but also cause health issues, increase economic spending, and affect agricultural

production, energy and infrastructure.

▪ Floods: Floods caused by natural causes, such as increased rainfall or storms, can also be

predicted with sophisticated technological models. Artificial intelligence can help

technologists better interpret a growing amount of data and create forecasting models

that automatically alert authorities.

▪ Epidemics: Several methods are currently used to forecast the spread of particular

emerging epidemics. Forecasting models can identify hot spots of emerging diseases

and predict trends regarding where the problem will most likely expand.

3.4 Risk Analysis

A business impact analysis (BIA) determines the potential impacts of interrupting time-sensitive

or critical business processes. There are numerous hazards; depending on their timing,

magnitude and location, many possible scenarios could unfold for each hazard. Therefore, BIA

is presented as a qualitative or quantitative approach to determine the nature and extent of

disaster risk by analysing potential hazards and evaluating existing conditions of exposure and

vulnerability that together could harm people, property, services, livelihoods and the

environment on which they depend.

There are many “assets” at risk from hazards. Hazard scenarios that cause significant injuries

should be highlighted to ensure appropriate emergency plans are in place. The potential for

environmental impact and an incident's impact on customer relationships, community, and

other stakeholders should also be considered.

As the risk assessment is conducted, the focus is on weaknesses that make an asset more

susceptible to damage from a hazard. Vulnerabilities include deficiencies in building

construction, process systems, security, protection systems and loss prevention programs. They

contribute to the severity of damage when an incident occurs.

For example, a building without a fire sprinkler system could burn to the ground, while a properly

designed, installed and maintained fire sprinkler system would suffer minor fire damage. Finally,

the impacts of hazards can be reduced by investing in mitigation. Creating a mitigation strategy

should be a high priority if there is a potential for significant impacts.

3.5 Behavioural and Cultural Challenges

30

https://www.nature.com/articles/s41598-019-45430-6

29

Climate change and environmental degradation contribute to the frequency and intensity of

natural hazards, posing new challenges for disaster risk management and resilience. Addressing

these issues requires collective action and behavioural change at the societal level. Many

European individuals and communities lack awareness of the risks and impacts of natural

hazards. They are not adequately prepared to respond to disasters, leading to a lack of

readiness, inadequate response, and increased vulnerability.

Effective risk communication is crucial to help individuals and communities understand the risks

associated with natural hazards and to encourage appropriate behaviours. However, risk

perception can be influenced by factors such as culture, beliefs, and values, making it difficult

to communicate risk effectively.

Social inequalities and vulnerabilities, such as poverty, lack of access to healthcare, and

inadequate infrastructure, can exacerbate the impact of natural hazards on certain

populations, particularly those already marginalised or disadvantaged. Furthermore, trust in

authorities and institutions is important for effective disaster risk management, as it can affect

the willingness of individuals and communities to follow guidelines and take action. However,

trust can be eroded by past experiences, political factors, and misinformation.

In response to the UN Environment Programme (UNEP), the Adaptation Gap Report 2021

urgently outlines the need to step up efforts to adapt to climate change. It is encouraging to

see the rise of stakeholder engagement during the COVID-19 pandemic in the development

and adaptation plans/measures, which is critical for the change required to tackle climate risks

and increase resilience effectively. Nevertheless, whereas Technology Transfer is being

mentioned in the report, the emphasis on Behavioural Change has not, although the latest is

crucial and central to adaptation.

30

3.6 Overview of Data Analysis

An overview analysis of data and IoT solutions can play a crucial role in helping Europe tackle

the challenges of natural hazards:

1. The use of data analytics and modelling can help decision-makers better understand the

risks and impact of natural hazards, allowing for more informed and effective decision-

making, which can include risk assessment, early warning systems, and disaster response

planning.

2. Real-time monitoring and response IoT solutions, such as sensors and remote sensing

technologies, can provide real-time data on natural hazards, allowing for quick and

effective responses. These include monitoring floods, landslides, and earthquakes and

tracking the movement of natural hazards such as hurricanes.

3. IoT solutions can help to improve communication and coordination among different

stakeholders involved in disaster response, including emergency responders, government

agencies, and citizens. This solution can include using social media and other

communication technologies to disseminate information and coordinate response

efforts.

4. Infrastructure can become more resilient with IoT to withstand the impact of natural

hazards better by including sensors to monitor the structural integrity of buildings, bridges,

and other infrastructure and using advanced materials and construction techniques.

5. Data and IoT solutions can also help Europe adapt to the impacts of climate change,

which are likely to increase the frequency and severity of natural hazards, via monitoring

changes in weather patterns and sea levels, as well as developing new technologies to

mitigate the impact of climate change.

Overall, data and IoT solutions can help Europe to build resilience to natural hazards by

improving decision-making, response, and communication, as well as developing resilient

infrastructure and adapting to the impacts of climate change. However, it is important to note

that effectively using these solutions requires addressing the behavioural and cultural challenges

that can affect their adoption and implementation.

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3.7 Scenarios and Best Cases

Cross-domain Emergency Managing and Planning about Hazard Crisis data integration using

ontologies

Partners: Manas Gaur, Saeedeh Shekarpour, Amelie Gyrard, Amit Sheth. empathi: An ontology for

Emergency Managing and Planning about Hazard Crisis. International Conference on Semantic

Computing Conference (ICSC) 2019.

In the domain of emergency management during hazard crises, having sufficient situational

awareness information is critical. It requires capturing and integrating information from sources

such as satellite images, local sensors and social media content generated by local people. A

bold obstacle to capturing, representing and integrating such heterogeneous and diverse

information is the lack of a formal ontology which properly conceptualises this domain and

aggregates and unifies datasets. Thus, in this paper, we introduce the empathy ontology, which

conceptualises the core concepts concerning the domain of emergency management and

planning of hazard crises. Although empathy has a coarse-grained view, it considers the

concepts and relations essential in this domain.

SPADE: multi-purpoSe Physical-cyber Agri-forest Drones Ecosystem for governance and

environmental observation

Partners: Trialog, AnySolution. More information: https://ec.europa.eu/info/funding-

tenders/opportunities/portal/screen/how-to-participate/org-

details/999999999/project/101060778/program/43108390/details

The strategic objective of SPADE is to develop an Intelligent Ecosystem to address the multiple

purposes concept in the light of deploying UAVs to promote sustainable digital services for the

benefit of a large scope of various end users in the sectors of agriculture, forestry, and livestock,

including individual UAV usability, UAV type applicability (e.g., swarm, collaborative,

autonomous, tethered), UAV governance models availability and trustworthiness. Multi-

purposes will be further determined in the sensing dataspace reusability based on trained AI/ML

models. These will enable sustainability and resilience of the overall life cycle of developing,

setting up, offering, providing, testing, validating, and refining, as well as enhancing digital

transformations and ‘innovation building’ services in Forestry, Cropping and Livestock Farming.

Pilot prototypes will contribute towards greater challenges such as deforestation, precision

cropping and animal welfare. First, SPADE will create a digital platform that can realise the

potential benefits of using drones. This platform is making drone operations better accessible

and controllable and providing a service channel for value-added services enabled by drones.

Second, SPADE demonstrates three innovative use cases of drones using the digital platform.

While demonstrating the use cases, the benefits coming from the use of drones are analysed

and quantified on a detailed stakeholder-level basis. The project use cases will demonstrate the

new business opportunities, and the demonstrations/pilots will also serve as an analysis platform

to investigate the regulatory framework at an international and national level.

32

SEP: Smart Event Processor - a framework that combines real-time data from different sources,

correlating them and concluding on the existence of an event (alarm), activating a rapid response

from the reaction mechanisms.

Partners: Ubiwhere

Cities need strong and resilient critical infrastructure to adapt to natural and human-made

catastrophes, as these are dynamic and evolving realities that require systematic monitoring.

Municipalities have many sensors capable of signalling the existence of an irregularity: air quality

stations, CCTV cameras, and fire-fighting systems, among others. However, without

interoperability between these systems and the emergency systems, the necessary responses

will not be activated to inform about the urgency of moving firefighting teams to put out the fire

and paramedics to rescue the victims and take them to the hospital. The Smart Event Processor

provides a rule-based alarms’ layer to monitor data from IoT systems in a city ecosystem. It allows

the combination of data in a given event to activate the response mechanisms in an agile and

reliable way. It is of key value in scenarios where the provision of rapid response is fundamental,

particularly in Security and Civil Protection.

The Smart Event Processor is deployed on the network edge close to where the raw data is

generated. This component processes large amounts of raw data, generating complex events

and transferring them to a third-party control application running in the cloud. The amount of

data transferred to the cloud/core of the network is highly reduced, improving the real-time

processing performance of the whole system.

Figure 4: SEP: Smart Event Processor overview

Raw data processor

First, the sensing data provided by the sensing devices are pre-processed, filtered and stored in

a database. Some types of data are time-sensitive, e.g. data from accelerometers capable of

detecting earthquakes. Other data types that are more delay tolerant are processed by the

data aggregation submodule and later dispatched when a configurable amount of data or

elapsed time is reached.

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Complex event generator

Is implemented based on a Complex Event Processing (CEP) rule engine. The data received

from the raw data processor will be matched with rules in the rule database. If the matching is

successful, complex events will be inferred and generated to send to the upper application. In

some cases, the CEP engine also analyses previously generated complex events (history) to infer

certain events more accurately.

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4. Cybersecurity take on Crisis Management

Cybercriminals, who are becoming increasingly sophisticated, could take advantage of natural

disasters such as hurricanes, wildfires and tornadoes to wreak havoc on critical infrastructures,

experts say, including transportation, emergency response, water and sewer systems and

hospitals.

4.1 Overview of the current State of Play

Natural disasters can strike anytime and often without warning. For small- and medium-sized

businesses (SMBs), the aftermath of a natural disaster can be devastating regarding property

damage and data loss. Most businesses face unique cybersecurity challenges when hit by

natural disasters. With damaged or destroyed infrastructure, power outages, and limited

resources, all businesses, especially SMBs, must be prepared to protect their data and systems

from cyberattacks. By taking proactive measures to bolster their cybersecurity posture, these

businesses try to ensure that they can weather the storm - literally and figuratively.

Figure 5: "sudo" stands for "superuser do", to execute commands with superuser privileges in Linux bash terminal (WSL).

After a natural disaster strikes, organisations often scramble to restore critical business functions.

This can leave them vulnerable to cyberattacks from opportunistic criminals who seek to take

advantage of the chaos. Some of the most common cybersecurity threats businesses face in

the aftermath of a natural disaster include:

Phishing attacks: Phishing attacks are one of the most common types of cyberattacks, and they

can be particularly devastating in the wake of a natural disaster. With employees working

remotely and using personal devices to access corporate data, companies must have stringent

anti-phishing measures.

Ransomware attacks: Ransomware is malware that encrypts a victim’s files and demands a

ransom be paid for the files to be decrypted. Ransomware attacks can be particularly

damaging to an organisation, as they may not have the resources or backup systems to recover

from an attack.

Data breaches: In the aftermath of a natural disaster, many organisations experience severe

breaches due to damaged or destroyed infrastructure, power outages, or employee errors. To

protect against data breaches, implement comprehensive security solutions such as firewalls,

intrusion detection/prevention systems, and data loss prevention solutions. Companies should

also have strict policies and procedures for managing data backups and recovery.

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4.2 Research Challenges and Objectives

There are several steps that a business can take to mitigate the cybersecurity threats they face

in the aftermath of a natural disaster. These steps include:

1. A business continuity plan:

Creating a business continuity plan is a significant first step to protecting your company data.

As seen before, comprehensive business continuity plans that include security solutions such as

firewalls, intrusion detection/prevention systems, encryption and data loss prevention solutions

are essential for protecting against data breaches. This plan should outline how the business will

continue to operate in the event of a natural disaster. It should include information on backup

locations for critical data, alternative means of communication, and steps for rebuilding the

physical infrastructure.

Firewalls: A firewall is a software or hardware-based system that filters traffic between two or

more networks. It can block incoming or outgoing traffic from unauthorised sources to

unauthorised destinations. Firewalls can be either network-based or host-based. Network-based

firewalls are typically used to protect an entire network, while host-based firewalls are installed

on individual computers or servers.

Intrusion Detection/Prevention Systems: An intrusion detection system (IDS) monitors network

traffic for signs of suspicious activity or attempts to access unauthorised resources. An intrusion

prevention system (IPS) goes one step further by blocking traffic that meets certain criteria.

IDS/IPS systems can be either network-based or host-based.

Encryption: Encryption is a process of transforming readable data into an unreadable format

using a key. This prevents unauthorised individuals from accessing the data. Encryption can be

used for both storage and communication purposes. When encrypting data at rest (i.e., stored

on a server), it is important to use a strong encryption algorithm such as AES256. For data in transit

(i.e., being sent over a network), SSL/TLS encryption should be used.

2. A robust backup solution:

A robust backup solution should be one of the most important components of a business

continuity plan. A robust backup and recovery plan is crucial for recovering from a ransomware

attack or other types of data loss. This solution should be able to quickly and easily restore the

data if it is lost or corrupted. Cloud-based backup solutions are often more secure than on-

premise solutions because they are housed off-site and away from potential threats.

3. Training:

For cybersecurity measures to be effective, educating employees on cybersecurity best

practices is one of the most effective ways to prevent phishing attacks and other human error-

based incidents. Employees should be trained on proper security protocols such as password

management, handling sensitive information, and recognising phishing emails/websites.

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4.3 Vision Ideal Scenarios Innovation and Tech

Several times, cybersecurity is applied to Critical Infrastructures such as energy. This technology

addresses several solutions, such as platforms that allow the isolation of the network, public

access to critical or industrial infrastructure, the isolation of communities of renewable energy,

and island-to-island service recovery. This is commonly based on three methods: (1) validation

of the feasibility of island operation, (2) protection and monitoring against the digital intrusion of

the operation's IT systems electricity on an island, and (3) validation of the absence of computer

risk allows reconnection to public networks in electrical and information security.

Secure communication channels are essential for coordinating response efforts and sharing

critical information in a crisis. Innovation and technology can help to develop secure

communication solutions, including encrypted messaging platforms and secure video

conferencing tools. Effective cybersecurity in crisis management requires up-to-date

information about the latest cyber threats and vulnerabilities. Innovation and technology can

help to develop cyber threat intelligence platforms that provide real-time information about

emerging threats and vulnerabilities, allowing organisations to take proactive measures to

protect against cyber attacks.

In the event of a cyber-attack during a crisis, time is of the essence. Innovation and technology

can help to develop incident response automation tools that allow organisations to detect and

respond to cyber-attacks quickly, which includes automated threat detection and response

systems that use machine learning and artificial intelligence to identify and respond to cyber

threats.

Effective cybersecurity in crisis management requires a culture of cybersecurity awareness and

training among all stakeholders. ICT can help to develop cybersecurity training and awareness

programs that use gamification and other interactive techniques to engage and educate

stakeholders about cyber risks and best practices. Finally, cyber insurance can help

organisations to mitigate the financial impact of a cyber attack during a crisis. Innovation and

technology can help develop new cyber insurance models that consider the unique risks and

challenges associated with crisis management.

Innovation and technology can be crucial in enhancing cybersecurity in crisis management by

providing secure communication channels, cyber threat intelligence, incident response

automation, cybersecurity training and awareness, and cyber insurance solutions. However, it is

essential to note that effective cybersecurity in crisis management requires a holistic approach

considering the people, processes, and technology involved.

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4.4 Risk Analysis

Cybersecurity risk assessments help organisations understand, control, and mitigate all forms of

cyber risk. It is a critical component of risk management strategy and data protection efforts. As

organisations rely more on information technology and information systems to do business, the

digital risk threat landscape expands, exposing ecosystems to new critical vulnerabilities. Risk

analysis for cybersecurity on crisis management has identified the following:

▪ Cybersecurity threats to crisis management can come from various sources, including

cybercriminals, hacktivists, and nation-state actors. These threats can be ransomware

attacks, denial-of-service attacks, data breaches or others.

▪ Cyber attackers can exploit vulnerabilities in crisis management systems and networks.

These vulnerabilities can arise from outdated software, weak passwords, unpatched

systems, or misconfigured settings.

▪ The consequences of a cybersecurity breach during a crisis can be severe, from

disruption of critical infrastructure, loss of confidential data, and reputational damage,

up to financial losses.

▪ The likelihood of a cybersecurity breach during a crisis depends on various factors,

including the level of preparedness and security measures in place, the complexity of the

crisis, and the sophistication of the attackers.

▪ The impact of a cybersecurity breach during a crisis can be significant. It can disrupt

communication channels, compromise critical systems and infrastructure, and impede

the response efforts of crisis management teams.

Implementing cybersecurity measures to address the above risks is essential, including:

1. Regular risk assessments to identify vulnerabilities and threats.

2. Regular software updates and patches to address known vulnerabilities.

3. Strong password policies and two-factor authentication mechanisms.

4. Regular cybersecurity training and awareness programs for all stakeholders.

5. Incident response plans that outline steps to take in the event of a cybersecurity breach.

6. Regular testing and simulations of incidents to ensure the readiness of all stakeholders.

7. Regular backups and data recovery plan to ensure that critical data can be restored

during a cyber attack.

By implementing these measures, organisations can improve their cybersecurity posture and

reduce the risk of a cybersecurity breach during a crisis.

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4.5 Behavioural and Cultural Challenges

It is important to develop a comprehensive cybersecurity culture across all stakeholders involved

in crisis management, which includes awareness campaigns, regular training and education,

and the development of a shared understanding of the importance of cybersecurity.

Additionally, it may be necessary to implement governance frameworks that foster

collaboration and communication between different teams and stakeholders. Finally, ensuring

adequate resources are available to invest in cybersecurity measures, including personnel and

funding, is important. Some behavioural and cultural challenges for cybersecurity in crisis

management are present and need to be tackled:

Lack of awareness: Many stakeholders involved in crisis management may not be fully aware of

the potential cyber threats and the importance of cybersecurity measures, which can lead to

a lack of preparedness and a failure to implement necessary security measures.

Resistance to change: Crisis management teams may be resistant to change or reluctant to

adopt new technologies or processes, which can impede the implementation of effective

cybersecurity measures.

Complacency: In some cases, crisis management teams may become complacent about

cybersecurity measures, assuming their current systems are secure enough, leading to a false

sense of security and an increased risk of cyber attacks.

Siloed approach: Crisis management teams may operate in silos, with limited communication

and collaboration between different teams and stakeholders. Implementing a coordinated and

holistic approach to cybersecurity can make it challenging.

Limited resources: Crisis management teams may have limited resources both in terms of

funding and personnel, making it difficult to invest in necessary cybersecurity measures and

ensure sufficient cybersecurity experts on staff.

Cultural attitudes: Cultural attitudes towards cybersecurity may vary widely across different

regions and countries. Some cultures may view cybersecurity as a lower priority than other issues,

impacting the willingness to invest in cybersecurity measures.

By investing in cybersecurity measures, crisis management teams can better protect critical

infrastructure and confidential data and ensure the continuity of operations during a crisis.

Ultimately, it is essential to recognise that cybersecurity is a shared responsibility and requires a

coordinated effort across all stakeholders involved in crisis management.

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4.6 Overview of Data Analysis

Data analysis is critical to cybersecurity in crisis management, enabling stakeholders to make

data-driven decisions and develop effective strategies to mitigate cyber risks. Data analysis for

cybersecurity on crisis management involves collecting, processing, and analysing large

amounts of data to identify and mitigate cyber risks, which should be collected from various

sources, such as security logs, threat intelligence feeds, network traffic, and user behaviour data.

Here are some concrete examples of data analysis processes used in cybersecurity:

▪ Intrusion detection systems (IDS) collect large amounts of network traffic data to identify

and analyse potential attacks. Machine learning is used to train IDS to identify patterns

of normal and abnormal behaviour and flag suspicious activity for further investigation.

▪ Vulnerability scanners collect data on the software and systems running on a network

and compare them against known vulnerabilities. These vulnerabilities are then prioritised

based on severity and potential impacts, allowing organisations to focus on the most

critical issues.

▪ User behaviour analytics (UBA) uses data from network logs and other sources to identify

user behaviour patterns that may indicate security risks. Clustering and anomaly

detection can identify unusual user activity and flag potential threats.

▪ Threat intelligence feeds collected data on known threats and vulnerabilities from various

sources. This data is then analysed to identify patterns and connections between threats,

helping organisations better understand the threat landscape and develop more

effective defence strategies.

▪ During a security incident, data analysis can identify the source of the attack quickly,

determine the scope of the breach, and prioritise response efforts. Data visualisation tools

such as dashboards and heatmaps can be used to communicate the incident's status

and help teams coordinate their response efforts.

Data analysis involves several steps to achieve these different scenarios, including data

collection, cleaning, pre-processing, analysis, and visualisation. During data cleaning, irrelevant

or erroneous data is removed, and missing values are imputed. Data pre-processing involves

transforming and normalising the data to ensure it is suitable for analysis.

Data analysis techniques, such as machine learning, statistical analysis, and data mining, can

be used to identify patterns and anomalies in the data. These techniques can help to identify

potential cyber threats and vulnerabilities, enabling crisis management teams to address them

proactively.

Data visualisation tools, such as dashboards and graphs, can communicate the insights and

findings from the data analysis to stakeholders, helping to understand cybersecurity better and

inform decision-making processes.

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Figure 6: Grafana, a multi-platform open-source analytics and interactive visualisation web application that provides charts,

graphs, and alerts for the web when connected to supported data sources.

Overall, data analysis is an essential tool for cybersecurity in crisis management and should be

integrated into crisis management plans and strategies.

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4.7 Scenarios and Best Cases

Cyber Crisis Management: A Decision-Support Framework for Disclosing Security Incident

Information

Authors: O. Kulikova, R. Heil, J. van den Berg and W. Pieters, "Cyber Crisis Management: A Decision-

Support Framework for Disclosing Security Incident Information," 2012 International Conference on

Cyber Security, Alexandria, VA, USA, 2012, pp. 103-112, doi: 10.1109/CyberSecurity.2012.20.

The growing sophistication and frequency of cyber attacks force modern companies to be

prepared beforehand for potential cybersecurity incidents and data leaks. A proper incident

disclosure strategy can significantly improve the timeliness and effectiveness of incident

response activities, reduce legal fines, and restore the confidence and trust of a company's key

stakeholders. This scenario introduces four factors that shape organisational preferences

regarding incident information disclosure. Together, they create challenges for a company

when deciding whom, when, what, and how to share cyber security incident information. We

propose a decision-support framework that provides step-by-step guidance for organisations to

address these challenges and develop an appropriate incident disclosure strategy.

Crisis Management in a Federation – Cybernetic Lessons from a Pandemic

Author: Markus Schwaninger, University of St. Gallen, St. Gallen, Switzerland. Part of the Lecture Notes

in Computer Science book series (LNCS,volume 13789). First Online: 10 February 2023. DOI:

10.1007/978-3-031-25312-6_75

This scenario aims to contribute to improving the management of pandemic crises. Its focus is

on federal systems, which are particularly powerful in dealing with environmental complexity.

We study crisis management in the Swiss Federation through five waves of the pandemic,

spanning a year and a half. This research aims to learn how to deal with crises of the same type

in the future. We apply the Viable System Model (VSM) as a framework for our inquiry,

elaborating a diagnosis and a design for managing epidemic or pandemic crises. The VSM is a

conceptual tool that is particularly strong for analysing federal systems. Hence substantial

insights have surfaced to orientate future crisis management.

Cyber Crisis Management Roles – A Municipality Responsibility Case Study

Authors: Information Technology in Disaster Risk Reduction, 2020, Volume 575. ISBN : 978-3-030-48938-

0. Grethe Østby, Basel Katt. DOI: 10.1007/978-3-030-48939-7_15

In this paper, the authors propose a role model that can be applied in societal cyber crisis

management to build safety and standard procedures during cyber security crises. The authors

define a societal cyber crisis as a cyber crisis which affects the society in which disaster is or

might be the consequence. The process of creating our model started by analysing regulations

and responsibilities in Norwegian municipalities, and we used steps of a design science research

(DSR) research approach to create our suggested artefact. A combination of conventional crisis

management and cyber crisis management is proposed to identify the interrelationships among

diverse stakeholders when managing the preparation for and reaction to a cyber crisis incident.

We present a cyber incident handling role model (CIHRM) usable for visualising cyber crises in

diverse organisations. After our model has been reviewed by the cyber security research

community, we plan to implement the model when analysing crisis management in various

organisations to prepare for instructions, training and exercises at our training environment - The

Norwegian Cyber Range.

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5. Attacks on Public Spaces

5.1 Overview of the current State of Play

Public spaces such as commercial centres, recreational venues, open crowded gathering areas and

events constitute “targets” for attacks (individuals or mass) that strongly impact the safety of citizens.

In this context, a smart city uses digital technology not only to provide preparedness and early

detection of threats to first responders and city authorities but also to connect, protect, and enhance

the lives of citizens. The threat of attacks on public spaces in Europe has become a major concern

in recent years, with numerous high-profile incidents occurring across the continent.

One example of this was the November 2015 Paris attacks, in which terrorists used encrypted

messaging apps to communicate with each other and coordinate their actions. This scenario

highlighted the need for law enforcement agencies to have the capability to monitor and decrypt

such communications in order to prevent similar attacks in the future. Another example is the use of

drones in attacks on public spaces, such as the attempted drone attack on Gatwick Airport in the

UK in December 2018. This incident demonstrated the need for law enforcement agencies to have

effective counter-drone technology to detect and disable rogue drones that threaten public safety.

Figure 7: High police presence in Lyon, France, during the 25th weekend of the yellow vests movement (2019)

First responders have learned many lessons from these attacks, including the need for better

communication and coordination among different agencies involved in responding to such

incidents. There is also a growing recognition of the importance of using technology, such as artificial

intelligence and machine learning, to analyse large amounts of data and identify potential threats

in real time. In response to these threats, many European countries have increased their investments

in security technology and implemented new measures to protect public spaces.

43

For example, many cities have installed CCTV cameras and other surveillance systems to monitor

public spaces and detect potential threats. Using advanced video analytics technology, CCTV can

detect abandoned objects, track individuals, and identify potential threats, helping security

personnel respond quickly and effectively to incidents. In addition, many public spaces now have

enhanced security measures, such as metal detectors and bag checks, to deter potential attackers.

In addition, many European cities have implemented mobile applications that allow members of the

public to report suspicious behaviour or incidents in real time. These apps often include location-

based reporting features, enabling users to pinpoint the exact location of the incident or threat.

Another area where ICT is being used to prevent attacks on public spaces is the development of

early warning systems. These systems use a combination of sensors, cameras, and other data sources

to detect potential threats, such as suspicious vehicles or individuals, and alert security personnel in

real-time to respond and prevent potential attacks before they occur quickly. Finally, social media

monitoring and analysis are also used to prevent public space attacks. Law enforcement agencies

are increasingly using social media to monitor and identify potential threats and to track the activity

of individuals who may pose a risk to public safety. This technology enables law enforcement

agencies to detect potential threats and take action before an attack can occur.

Overall, while attacks on public spaces in Europe continue to pose a significant threat, advances in

technology and increased investments in security measures are helping to improve the ability of first

responders to prevent and respond to such incidents.

5.2 Research Challenges and Objectives

The generation, processing and sharing of large quantities of data in smart cities make urban systems

and services potentially more responsive and able to act upon real-time data. Such data are

extracted from CCTV camera systems, systems for crowd transitions and people density, systems for

abnormal detection and situational awareness, and several sensors (sound, drones, fire/smoke,

chemical precursors for explosives) that are part of an IoT network.

For such data that are continuously generated, strict latency-aware computational processing

capabilities, as well as a homogeneous approach for data processing and generation of associated

event information, are demanded. Also, novel Artificial Intelligence (AI) paradigms, Next Generation

emergency call technologies (NG112), and new IoT applications, including Augmented Reality (AR),

Virtual Reality (VR), Digital Twins (DT), virtual simulations, real-time searching engines, real-time sensing

and measurements, and discovery services bring new challenges.

Expensive computing hardware with sufficient storage capacities can address the challenges of

massive storage and scale computing. Cloud computing has already been used in previous years to

eliminate the need for dedicated, expensive computing hardware. Cloud computing can be

considered an efficient technology that enhances the Quality of Experience (QoE). Also, it provides

on-demand storage and processing capabilities, mainly a cost-effective approach. The following

table presents the advantages and limitations of cloud computing.

Overall, while cloud computing can provide significant benefits in terms of scalability, flexibility, and

cost-effectiveness, which has made its adoption possible in several applications, it also has several

limitations when it comes to preventing and responding to attacks on public spaces. As such,

organisations must carefully consider these limitations when implementing cloud-based security

solutions and ensure that appropriate measures are taken to mitigate these risks.

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Table 3: Cloud computing advantages and limitations (prevention of public spaces attacks)

Advantages of cloud computing

Limitations of cloud computing

Scalability

Network latency causing delays

Flexibility

Data security breaches

Elasticity

Lack of reliable and secure network connectivity on public spaces

Multitenancy

Lack of skills and expert staff

Storage capacity

Computational time for data processing

Resource pooling

Cost-effectiveness

The recent edge computing technology is considered a flexible and viable solution that can

overcome the limitations of cloud computing. Thus, edge computing can be used for real-time smart

city environments enabling: i) context awareness, ii) geo-distributed capabilities, iii) low latency, iv)

migration of computing resources from the remote cloud to the network edge, and v) data

aggregation, analysis and management at the edge. According to those above, an IoT-based smart

city that exploits connected smart sensors and IoT devices and adopts an edge computing scheme

aligns with recent technology to improve citizens’ safety and quality of life and provide them with

security measures from first responders against several types of attacks.

Transforming a city into a smart city requires collaborative efforts between all stakeholders (e.g.,

government, industry, practitioners, residents and researchers) from many disciplines, such as

computer science, survey/geoinformatics and civil engineering, systems engineering, electrical and

computer engineering, among others. Bringing together stakeholders with different backgrounds to

support smart city initiatives produces several challenges. Some of these challenges involve the

differences in expert vocabulary, differences in disciplinary cultures, identification of available and

appropriate resources, and integration of heterogeneous data and knowledge. A smart city mainly

refers to six characteristics: 1) Smart economy, 2) Smart mobility, 3) Smart environment, 4) Smart

people, 5) Smart living, and 6) Smart governance. However, other subcategories of those above

include Smart health, Smart energy, Smart water and Smart waste, and Smart safety of public spaces,

just to name a few. In such a context, a smart city should consider several objectives and research

challenges:

1. Data from different IoT sources/devices should be available to be easily aggregated;

2. Data should be easily visualised and securely accessible, respecting privacy;

3. Detailed, measurable, real-time knowledge should be available at every level;

4. Analytics and decision-making systems should be used;

5. The city should incorporate state-of-the-art technologies for automation and further relevant

extensibility;

6. The city should have a network of collaborative spaces;

7. Moreover, the decision-making processes should be much more open and inclusive.

One of the leading research challenges is developing reliable and effective IoT-based security

systems that can detect potential threats and prevent attacks, which involves designing, improving

and integrating sensors and other IoT devices that can accurately detect suspicious activity and

provide real-time alerts to security personnel.

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Secondly, another research objective is to improve the interoperability and compatibility of IoT

devices and systems, which is critical in public spaces where different organisations or stakeholders

may use multiple IoT devices and systems. Standardisation efforts are needed to ensure these systems

can work seamlessly and effectively.

Thirdly, there is a need to develop advanced analytics and machine learning algorithms to process

and analyse the large volumes of data generated by IoT devices, from the edge to the cloud, which

involves developing algorithms that can detect patterns and anomalies in the data and provide

actionable insights to security personnel.

Fourthly, another research objective is to ensure the security and privacy of the data generated by

the devices, which involves integrating secure communication protocols, encryption techniques,

and other security measures to protect sensitive data from cyber threats and unauthorised access.

Finally, there is a need to develop practical training and education programs for security personnel

and other stakeholders to ensure they can effectively use and manage IoT-based security systems,

which involves developing training programs that provide hands-on experience with IoT devices and

systems and teach personnel how to respond to security threats in real-time.

Overall, preventing attacks on public spaces using IoT technologies is a complex and challenging

area of research. However, with continued investment and innovation, it is possible to develop

effective IoT-based security systems that can help prevent attacks and ensure the safety of public

spaces.

5.3 Vision Ideal Scenarios Innovation and Technology

Innovation is crucial in achieving this vision of preventing attacks on public spaces using IoT

technologies. It involves developing advanced and reliable systems that can accurately detect

potential threats, provide real-time alerts, and prevent attacks before they occur. There are

several areas where innovation is needed:

1. Development of advanced sensors and other IoT devices that can accurately detect

suspicious activity in real-time, which must be designed to be highly sensitive and

responsive to potential threats while also being robust and reliable enough to operate in

a range of environments.

2. Another area of innovation is the development of machine learning algorithms and

advanced analytics tools that can process and analyse large volumes of data

generated by IoT devices to detect patterns and anomalies in the data and provide

actionable insights to security personnel, enabling them to respond quickly and

effectively to potential threats.

3. Furthermore, innovation is needed in communication protocols and encryption

techniques to ensure that the data generated by IoT devices are secure and protected

from cyber threats and unauthorised access, which involves developing new techniques

and protocols that are robust and reliable enough to withstand potential attacks and

provide secure communication channels for IoT devices.

4. Innovation is also needed in training and education to ensure security personnel and

other stakeholders are adequately trained and prepared to respond to potential threats

by developing comprehensive training programs that provide hands-on experience with

IoT devices and systems and teach personnel how to respond to security threats in real

time.

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In summary, the vision and ideal scenarios for innovation and technology in preventing attacks

on public spaces using IoT technologies involve developing advanced and reliable systems that

can accurately detect potential threats, provide real-time alerts, and prevent attacks before

they occur. This vision requires continued investment and innovation in sensor development,

analytics, communication protocols, encryption, and training and education.

5.4 Risk Analysis

It is essential for identifying potential risks and threats associated with IoT-based security systems

for public spaces, as described below:

▪ Cybersecurity risks: These threats include hacking, malware, and denial of service

attacks, resulting in unauthorised access to sensitive data, system malfunction, and

system shutdown. The risk level for this threat is high, as cyber-attacks are becoming

increasingly sophisticated and common.

▪ False alarms can cause unnecessary panic and disruption and waste valuable time and

resources. The risk level for this threat is moderate, as false alarms can be mitigated

through advanced analytics and machine learning algorithms.

▪ Technical failures such as system malfunction or power outages can render IoT-based

security systems ineffective. The risk level for this threat is moderate, as it can be mitigated

through regular maintenance, system backups, and redundant systems.

▪ Privacy concerns: Using IoT-based security systems for public spaces raises concerns

about privacy violations. The risk level for this threat is moderate, as it can be mitigated

through GDPR-compliant and privacy-preserving technologies such as encryption and

access controls.

▪ Physical damage to IoT devices due to vandalism, natural disasters, or accidents can

render security systems ineffective. The risk level for this threat is low, as it can be mitigated

through proper installation and maintenance.

▪ False sense of security: Using IoT-based security systems can create a false sense of

security among the public, resulting in complacency and reduced vigilance. The risk level

for this threat is low, as it can be mitigated through proper education and training of

security personnel and the public.

In conclusion, IoT-based security systems for public spaces offer several benefits but pose risks

and threats; therefore, detailed risk analysis and mitigation strategies are required to ensure the

public's security and safety.

47

5.5 Behavioural and Cultural Challenges

Behavioural and cultural challenges are important factors that can impact the effectiveness of

security measures against attacks on public spaces in Europe, including:

Cultural diversity: Europe is a diverse continent with different cultures, languages, and religions,

making it challenging to develop security measures that are sensitive to the needs of different

groups while still being effective.

Public perception of security measures, such as CCTV cameras, metal detectors, and bag

searches, may be seen as intrusive or even oppressive by some public members, creating

resistance and making it difficult to implement them effectively.

Fear and panic can be caused among the public by attacks on public spaces, making it difficult

to respond effectively. For example, people may rush to exit a building or area, potentially

causing further injuries or fatalities.

Lack of awareness and preparedness: Many people may not be aware of the risks associated

with attacks on public spaces or know what to do in the event of an attack, making it difficult

to implement effective prevention and response measures.

Limited resources: Local governments and law enforcement agencies may need more

resources to prevent and respond to attacks on public spaces, making it challenging to

implement adequate security measures and respond to incidents quickly and effectively.

Addressing these behavioural and cultural challenges requires a multi-faceted approach that

involves educating the public about the risks associated with attacks on public spaces,

developing culturally sensitive security measures, and engaging with local communities to build

trust and cooperation.

48

5.6 Overview of Data Analysis

By collecting and analysing data from various sources, law enforcement agencies and security

professionals can identify patterns and trends that may indicate a potential threat and take

proactive measures to prevent an attack, thus playing an essential role in preventing and

responding to attacks on public spaces in Europe. Some examples include:

1. Social media platforms can be valuable sources of information about potential threats

to public spaces. Monitoring social media activity and analysing keywords and hashtags

allow law enforcement agencies to identify potential threats and take proactive

measures to prevent attacks.

2. CCTV cameras are widely used in public spaces in Europe, and video analytics can help

to identify potential threats and suspicious activity. For example, facial recognition

technology can identify individuals flagged as potential threats or previously involved in

criminal activity.

3. Predictive analytics involves using machine learning algorithms to analyse data and

identify patterns and trends that may indicate a potential threat. For example, predictive

analytics may be used to analyse data from social media, CCTV cameras, and other

sources to identify potential threats and take proactive measures to prevent attacks.

4. Geographic information systems (GIS) technology can map and analyse data about

potential threats and vulnerabilities in public spaces. For example, GIS can identify areas

at higher risk of an attack based on population density, proximity to potential targets,

and previous incidents.

Effective data analysis requires a multi-disciplinary approach that involves not only technology

and data science experts but also security professionals and law enforcement agencies. By

working together to collect and analyse data from various sources, they can develop a more

comprehensive understanding of the risks associated with attacks on public spaces and take

proactive measures to prevent them.

49

5.7 Scenarios and Best Cases

SemAttack: Natural Textual Attacks via Different Semantic Spaces

Authors: Boxin Wang, Chejian Xu, Xiangyu Liu, Yu Cheng, Bo Li. Published at Findings of NAACL 2022

Recent studies show pre-trained language models (LMs) are vulnerable to textual adversarial

attacks. However, existing attack methods either suffer from low attack success rates or fail to

search efficiently in the exponentially large perturbation space. The authors propose an efficient

and effective framework SemAttack to generate natural adversarial text by constructing

different semantic perturbation functions. In particular, SemAttack optimises the generated

perturbations constrained on generic semantic spaces, including typo space, knowledge

space (e.g., WordNet), contextualised semantic space (e.g., the embedding space of BERT

clusterings), or the combination of these spaces. Thus, the generated adversarial texts are

semantically close to the original inputs. Extensive experiments reveal that state-of-the-art

(SOTA) large-scale LMs (e.g., DeBERTa-v2) and defence strategies (e.g., FreeLB) are still

vulnerable to SemAttack. The authors further demonstrate that SemAttack is general and able

to generate natural adversarial texts for different languages (e.g., English and Chinese) with high

attack success rates. Human evaluations also confirm that our generated adversarial texts are

natural and barely affect human performance.

50

6. Recommendations

6.1 Research Recommendations

Research plays a key role in evolving technologies for Crisis Preparedness and Management,

therefore, some recommendations should be taken into consideration, such as:

▪ Fostering innovative tools, technologies and processes aimed at enabling and improving

operations and its efficiency, as well as data sharing between stakeholders and different

organisations

▪ Assurance of interoperable and secure incident management and better integration of

IoT for early warning systems and simultaneous data collection for analysis, learning,

decision-making and efficient communication with organisations and stakeholders;

▪ Resilience improvement of critical infrastructures, such as transport, energy and

communication

▪ Ensure the creation of relations between topics such as Innovation, Testbeds and

Standardisation to accelerate the RandD and enable early testing and validation of

ideas and concepts

▪ Design and leverage methodologies already available to guide the initiation and the

realisation of testbeds for IoT and Crisis Preparedness, where the AIOTI testbed

methodology is one of them.

51

6.2 Standardisation and Interoperability Recommendations

While standards like ISO 22320, ETSI TS 103 463, and NG-112 have made significant contributions

to improving crisis preparedness and IoT-based security systems, there are still gaps and

challenges that require further research and efforts. Some of these gaps and challenges

include:

▪ Interoperability: Achieving seamless interoperability between different IoT devices,

communication networks, and emergency response systems remains a challenge. There

is a need for further research and standardisation efforts to deliver protocols and

frameworks that enable effective data exchange and communication among diverse

systems

▪ Scalability: As IoT deployments and data volumes continue to grow, there is a need to

address the scalability of IoT-based security systems for public spaces. Research efforts

should focus on developing scalable architectures, data management approaches, and

analytics techniques that can handle the increasing volume and velocity of data

generated by IoT devices

▪ Privacy and data protection: The collection and analysis of data in IoT-based security

systems raise concerns about privacy and data protection. Research is needed to

develop robust mechanisms for ensuring privacy compliance, secure data storage and

transmission, and informed consent from individuals whose data is being collected

▪ Human factors: The successful implementation of IoT-based security systems relies not only

on technology but also on human factors. Research efforts should explore human-

centred design principles, user interfaces, and training programs that promote effective

utilisation of IoT systems by first responders and other relevant stakeholders.

While standards such as ISO 22320

31

, ETSI TS 103 463

32

, and NG-112

33

exist, there may still be gaps

and inconsistencies in terms of coverage and implementation. Research and collaborative

efforts are needed to address these gaps, ensure comprehensive coverage, and foster

harmonisation between different standards and guidelines.

The rapid evolution of IoT technologies poses challenges for existing legal and regulatory

frameworks. Research efforts should focus on developing flexible and adaptive frameworks that

address the unique characteristics and challenges of IoT-based security systems, while also

ensuring compliance with data protection, cybersecurity, and privacy regulations.

31

ISO 22320:2018 - Societal security - Emergency management - Requirements for incident response: this international standard by ISO provides

requirements for establishing, implementing, operating, monitoring, reviewing, maintaining, and improving incident response capabilities. It focuses on the

coordination and interoperability of organisations involved in emergency management to effectively respond to incidents. ISO 22320 was published in

2018 and is currently in use worldwide, providing a framework for organisations involved in crisis management to establish effective incident response

capabilities. It promotes a systematic and coordinated approach to managing crises, improving interoperability and coordination between different

entities involved in emergency response.

32

ETSI TS 103 463 - Cybersecurity for Consumer Internet of Things: this technical specification, developed by ETSI, provides guidelines and best practices for

ensuring cybersecurity in consumer IoT devices. It addresses the unique security challenges posed by IoT devices and offers recommendations for

protecting against cyber threats. ETSI TS 103 463 was published in 2017 and has undergone subsequent revisions and updates, to ensure cybersecurity in

IoT devices for maintaining the resilience of critical infrastructure and systems during crises. ETSI TS 103 463 provides guidance to device manufacturers,

service providers, and consumers to enhance the security of IoT devices, reducing the risk of cyber attacks that could disrupt emergency response systems.

33

NG-112, also known as Next Generation 112, is an initiative focused on improving emergency communications and response systems, specifically for

public safety answering points (PSAPs) or emergency call centres. NG-112 aims to enhance the capabilities of emergency services by leveraging advanced

technologies and communication networks, including IoT. NG-112 is being developed by various standardisation organisations and industry alliances,

which includes stakeholders from the public safety and telecommunications sectors. These specifications include the use of IP-based networks, multimedia

capabilities, and the integration of various communication channels (voice, video, text, and data) to enable more efficient and effective emergency

response, to provide enhanced situational awareness and improved communication capabilities to emergency services, allowing them to respond more

effectively to incidents and emergencies.

52

IoT-based security systems need to be resilient and robust to withstand various threats and

disruptions. Research is needed to identify vulnerabilities, develop countermeasures, and

conduct comprehensive risk assessments to enhance the resilience of IoT systems against natural

disasters, cyber attacks, and other disruptions.

Addressing these gaps and challenges requires collaboration among researchers, industry

experts, policymakers, and practitioners. Continued research and concerted efforts are

essential to drive innovation, improve standards, and develop best practices that enhance the

effectiveness, reliability, and security of IoT-based security systems for crisis management and

public safety.

These collaborations within the standardisation and innovation ecosystem, which can also be

incorporated into funded projects or associations such as AIOTI, are expected to accelerate

the standardisation and technology deployment processes and support the provision of

validated solutions to standards development process allowing for a faster specification of

standards with no decrease of quality.

53

6.3 Business-driven Recommendations

In the context of crisis preparedness, businesses can play a critical role in leveraging IoT

technologies to enhance response coordination, resource deployment, monitoring, and

surveillance.

Response Coordination and Resource Deployment:

To facilitate better response coordination and deployment of resources, businesses should:

▪ Implement IoT-enabled platforms that enable real-time communication and

collaboration between different response agencies and stakeholders. These platforms

streamline information sharing, resource allocation, and coordination efforts, ensuring a

synchronised response to crisis situations.

▪ Develop automated resource management systems that leverage IoT technologies to

track and manage the availability and deployment of resources. By monitoring resource

usage in real-time, businesses can ensure efficient allocation based on evolving

demands and optimise their response capabilities.

▪ Integrate IoT sensors and data analytics to monitor resource usage, identify bottlenecks,

and optimise the allocation of resources. Real-time insights derived from IoT data enable

businesses to make data-driven decisions, ensuring effective resource utilisation during

crisis situations.

Monitoring and Surveillance Systems/Strategies:

To enhance monitoring and surveillance capabilities, businesses should:

▪ Deploy IoT sensors and monitoring systems in critical infrastructure and public spaces to

gather real-time data on environmental conditions, security threats, and potential risks.

This data can be analysed to detect anomalies, identify patterns, and trigger early

warnings, enabling timely response and mitigation efforts.

▪ Enhance existing video surveillance systems with IoT technologies, such as intelligent

video analytics and behaviour recognition, to improve situational awareness. IoT-

enabled video surveillance can detect and identify suspicious activities, track individuals

of interest, and provide valuable insights for effective crisis management.

▪ Develop predictive analytics models that leverage IoT data and machine learning

algorithms to identify patterns and trends related to crisis events. By analysing historical

data, businesses can anticipate potential threats, optimise resource allocation, and

implement proactive measures to mitigate risks.

The following recommendations focus on improving the effectiveness and efficiency of crisis

management across the four phases of the process: mitigation, preparedness, response, and

recovery.

54

Mitigation Phase:

Mitigation involves measures taken to minimise the impact of potential crises. During this phase,

businesses should focus on identifying vulnerabilities, assessing risks, and implementing strategies

to prevent or reduce the severity of crises. By utilising IoT-enabled risk assessment tools and

predictive models, businesses can identify potential hazards and vulnerabilities. This data-driven

approach enables them to develop targeted mitigation strategies and allocate resources

efficiently.

Preparedness Phase:

Preparedness is centred around planning, readiness, and capacity building to effectively

respond to crises. Businesses should focus on developing comprehensive emergency response

plans and procedures that outline roles, responsibilities, and communication protocols for all

stakeholders involved. IoT technologies can support preparedness efforts by facilitating real-

time communication and enabling seamless information sharing among response teams.

Additionally, conducting drills and simulations using IoT technologies allows businesses to test

response plans, identify gaps, and refine strategies. IoT sensors and monitoring systems provide

real-time data during simulations, allowing for evaluation and improvement of preparedness

efforts.

Response Phase:

The response phase involves the immediate actions taken to address a crisis event. Businesses

should deploy IoT-enabled situational awareness platforms that integrate data from various

sources, such as sensors, social media, closed-circuit television (CCTV), and other relevant

sources. By leveraging IoT data analytics, businesses can gain real-time insights into the evolving

situation, enabling informed decision-making and effective response coordination. Furthermore,

utilising IoT devices and wearables can enhance responder safety by monitoring their well-being

and providing timely assistance during response operations.

Recovery Phase:

The recovery phase focuses on restoring normalcy and facilitating the recovery of affected

communities and infrastructure. Businesses can leverage IoT technologies for post-crisis data

collection and analysis to assess damages, evaluate response effectiveness, and inform

recovery strategies. IoT-based monitoring systems can track the progress of recovery operations,

monitor infrastructure rehabilitation, and ensure the safe return of affected communities.

By implementing these business-driven recommendations across the four phases of the crisis

management process, organisations can effectively utilise IoT technologies to enhance

response coordination, improve monitoring and surveillance capabilities, and build resilience

against natural and man-made hazards.

55

6.4 Policy-driven Recommendations

The role of policymakers in crisis preparedness is crucial for ensuring effective response

coordination, resource allocation, and the use of IoT technologies to enhance monitoring and

surveillance. The following recommendations focus on policy initiatives that can strengthen crisis

management efforts across all phases: mitigation, preparedness, response, and recovery.

Mitigation Phase:

Invest in research and development initiatives focused on leveraging IoT technologies for risk

assessment, hazard mapping, and early warning systems, support collaborations between

research institutions, industry partners, and government agencies to develop innovative IoT-

based solutions for hazard mitigation and prevention and encourage the use of IoT-enabled

predictive analytics models and machine learning algorithms to identify potential risks, optimise

resource allocation, and inform mitigation strategies. By defining strong requirements

beforehand, one can ensure that any strategic planning previously designed and defined is

carried out without any predicament or additional difficulty.

Besides fostering Research and Development, promote Public Awareness and Education by

implementing public awareness campaigns to educate citizens about the benefits of IoT

technologies in crisis preparedness and mitigation, develop educational programs and training

initiatives to enhance digital literacy and promote the responsible use of IoT devices and

technologies during emergencies, and, finally, foster partnerships with educational institutions,

community organisations, and the private sector to disseminate information, best practices, and

guidelines related to IoT and crisis management.

Preparedness Phase:

For the preparedness phase the overall recommendations are to develop national IoT

preparedness strategies and foster Public-Private Partnerships. One approach is to formulate

national strategies that emphasise the integration of IoT technologies into crisis preparedness

plans, including provisions for IoT infrastructure, data governance, and interoperability

standards, and collaborate with relevant stakeholders, including government agencies, industry

experts, and academia, to establish guidelines for the implementation and adoption of IoT

technologies in crisis management.

Another is to allocate resources for capacity building, training, and awareness programs to

enhance the understanding and utilisation of IoT technologies among emergency responders

and relevant personnel, and encourage partnerships between government entities and private

sector organisations to promote the development and deployment of IoT solutions for crisis

preparedness. Deploy secure communication systems for real-time, enabling decision makers

with all the tools necessary for informed and structured decision making as well as strategic

thinking and planning.

Facilitate information sharing and collaboration among stakeholders to promote innovation,

exchange best practices, and leverage expertise in the field of IoT and crisis management and

establish mechanisms to incentivize private sector investments in IoT technologies for crisis

preparedness through grants, tax incentives, or research funding.

56

Response Phase:

Ensure Interoperability and Data Sharing by implementing policies that promote interoperability

among IoT devices, systems, and platforms used by various response agencies to enable

seamless data sharing and collaboration during crisis events. Furthermore, establish data

governance frameworks and protocols that ensure privacy, security, and ethical use of IoT-

generated data during response operations, and encourage the development of common

data standards and formats to enable effective integration and analysis of IoT data across

different response systems.

Invest in Communication Infrastructure to enhance it, including high-speed networks and

resilient connectivity, to support real-time data transmission and communication during crisis

situations, while promoting the use of IoT-based communication systems, such as satellite

communication and mesh networks, to overcome communication challenges in remote or

disrupted areas. Finally, develop policies that prioritise the continuity and reliability of

communication networks during emergencies, ensuring seamless information exchange among

responders and affected communities.

Recovery Phase:

Encourage the use of IoT-enabled data analytics and visualisation tools to assess damages,

evaluate the effectiveness of response efforts, and inform recovery strategies. This data-driven

recovery strategy should facilitate the integration of IoT data with existing recovery databases

and systems to streamline data collection, analysis, and decision-making processes. Moreover,

establish guidelines for the ethical and responsible use of IoT-generated data in recovery

planning, respecting privacy rights and data protection regulations.

With the purpose of supporting smart infrastructure rehabilitation, promote policies that

incentivise the integration of IoT technologies in infrastructure rehabilitation efforts to enhance

resilience and ensure future preparedness, while encouraging the adoption of smart building

technologies, sensor networks, and IoT-enabled monitoring systems in the reconstruction and

retrofitting of critical infrastructure.

Last but not least, provide financial support and grants to encourage the deployment of IoT

solutions that enhance the safety, efficiency, and sustainability of recovered infrastructure.

By implementing these policy-driven recommendations, policymakers can create an enabling

environment that supports the integration of IoT technologies in crisis preparedness and

facilitates effective response coordination, recovery efforts, and mitigation strategies.

57

7. Conclusions

In this paper, we have explored various aspects of crisis management and the role of IoT

technologies in addressing challenges related to pandemics, natural disasters, cybersecurity,

and attacks on public spaces. Through an analysis of the current state of play, research

challenges, ideal scenarios, risk analysis, behavioural and cultural challenges, and data analysis,

we have identified key insights and best cases in each domain. These findings provide valuable

recommendations for research, standardisation, business strategies, and policy initiatives.

For the era of pandemics, the adaptation of smartphones within Europe and the use of contact

tracing apps have demonstrated the potential of IoT technologies in enhancing public health

surveillance and response. However, challenges such as privacy concerns and behavioural

acceptance need to be addressed for wider adoption and effectiveness. In the context of

natural disasters, the integration of IoT in disaster management has shown promising results in

improving early warning systems, response coordination, and post-disaster recovery.

Nevertheless, there are still challenges to overcome, including interoperability issues and the

need for robust data analysis frameworks to leverage the full potential of IoT-generated data.

The cybersecurity aspect of crisis management highlights the importance of protecting critical

infrastructure and ensuring the resilience of communication networks. IoT-based solutions offer

innovative approaches to enhance cyber crisis management, but risks such as data breaches

and sophisticated cyber-attacks require continuous research and proactive measures to

mitigate. The threats posed by attacks on public spaces require innovative IoT technologies to

enhance surveillance, threat detection, and emergency response. While there have been

advancements in this area, challenges related to privacy, ethical considerations, and data

analysis methods need to be addressed to ensure effective and responsible use of IoT

technologies in securing public spaces.

Based on our analysis, we put forward a set of recommendations. Research recommendations

call for further exploration of emerging technologies, such as AI, to enhance crisis management

capabilities. Standardisation and interoperability recommendations emphasise the need for

harmonised frameworks to facilitate seamless integration and communication among diverse

IoT systems. Business-driven recommendations underscore the importance of better response

coordination, resource deployment, and data-driven decision-making. Finally, policy-driven

recommendations highlight the role of policymakers in creating an enabling environment that

supports the adoption and responsible use of IoT technologies in crisis preparedness.

In conclusion, the integration of IoT technologies in crisis management holds great promise in

addressing the complex challenges posed by pandemics, natural disasters, cybersecurity

threats, and attacks on public spaces. By embracing the recommended strategies and

approaches, stakeholders can unlock the full potential of IoT and foster a resilient and adaptive

society better equipped to handle future crises.

58

8. References

(Butler et al., 2014, p. 3) Butler, D., Farmani, R., Fu, G., Ward, S., Diao, K., and Astaraie-Imani, M. (2014). A new

approach to urban water management: Safe and sure.

(Chelleri et al., 2015) Butler, Ruth and Shibaz, Limor. (2013). Striving to connect and striving to learn: Influences of

relational and mastery goals for teaching on teacher behaviors and student interest and help seeking.

International Journal of Educational Research. 65. 10.1016/j.ijer.2013.09.006.

(Mannakkara et al., 2008) “Build Back Better” Principles for Reconstruction. Sandeeka Mannakkara*, Suzanne

Wilkinson and Tinu Rose Francis. Department of Civil and Environmental Engineering, The University of Auckland,

Auckland, New Zealand

(Twigg J ,2007) Characteristics of a disaster-resilient community – a guidance note. DFID Disaster Risk Reduction

Interagency Coordination Group, London

59

Contributors

The document was written by several participants of the AIOTI FG Buildings and Communities.

Editor:

● Ricardo Vitorino, Ubiwhere

Reviewer:

● Damir Filipovic, AIOTI Secretary General

Contributors:

Name

Company/Organisation

Ana Pereira

Ubiwhere

Rita Santiago

Ubiwhere

Patricia Jimenez

NTT Data

Pietro Dionisio

Medea

Lazaros Karagiannis

ICCS

Evangelos Maltezos

ICCS

Panagiotis Michalis

ICCS

Dimitris Diagourtas

Satways

George Suciu

BEIA Consult

Monica Florea

SIMAVI

Asbjorn Hovsto

Hafenstrom

Flemming Sven

Hafenstrom

Tonny Velin

CENTIC

Dolores Ordonez

AnySolution

Karoline Krenn

Fraunhofer Fokus

Romain Vanhee

Yncrea ISEN

Rute Sofia

fortiss

Roumen Nikolov

Virtech

Amelie Gyrard

Trialog

Gabriel Petrescu

BEIA Consult

Mario Drobics

AIT

Editors and Contributions made by previous AIOTI members:

Name

Company/Organisation

Celine Prins (Co-Editor)

Institute for Future of Living

Fa Somers

Arthur's Legal

Arthur van der Wees

Arthur's Legal

Erik van der Wijk

DeWaarde Fabriek

60

Acknowledgements

document is provided ‘as-is’ and for general information purposes only; it does not constitute

strategic or any other professional advice. The content or parts thereof may not be complete,

accurate or up to date. Notwithstanding anything contained in this document, AIOTI disclaims

responsibility (including where AIOTI or any of its officers, members or contractors have been

negligent) for any direct or indirect loss, damage, claim, or liability any person, company,

organisation or other entity or body may incur as a result, this to the maximum extent permitted

by law.

61

About AIOTI

AIOTI is the multi-stakeholder platform for stimulating IoT and Edge Computing Innovation in

Europe, bringing together small and large companies, academia, policy makers and end-users

and representatives of society in an end-to-end approach. We work with partners in a global

context. We strive to leverage, share and promote best practices in the IoT and Edge

Computing ecosystems, be a one-stop point of information on all relevant aspects of IoT

Innovation to its members while proactively addressing key issues and roadblocks for economic

growth, acceptance and adoption of IoT and Edge Computing Innovation in society. AIOTI’s

contribution goes beyond technology and addresses horizontal elements across application

domains, such as matchmaking and stimulating cooperation in IoT and Edge Computing

ecosystems, creating joint research roadmaps, driving convergence of standards and

interoperability and defining policies.

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