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Designing a FIWARE-Based Smart Campus with IoT Edge-Enabled Intelligence


Abstract and Figures

Higher education institutions are passing through a fast digital transformation process that has the potential to enable frictionless, touchless, and more intuitive experiences in academia. Moreover, students are now digital natives and demand from higher education institutions new digital services for all academic purposes. In this article, we introduce the design methodology used for the architecture specification of the IPVC Smart & Sustainable Campus (IPVC-S2C), a FIWARE-based platform with edge-enabled intelligence. The current research also surveys and characterizes low-cost IoT edge hardware capable of performing distributed machine learning. Lastly, a proof of concept focus on Indoor Air Quality monitoring on the campus is presented and the forthcoming research is outlined.
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Designing a FIWARE-Based Smart
Campus with IoT Edge-Enabled
Pedro Martins1,SérgioI.Lopes
1ADiT - Instituto Politécnico de Viana do Castelo,
4900-348 Viana do Castelo, Portugal
2IT - Instituto de Telecomunicações, Campus Universitário de Santiago,
3810-193 Aveiro, Portugal
3Prometheus - Instituto Politécnico de Viana do Castelo,
4900-348 Viana do Castelo, Portugal
Abstract. Higher education institutions are passing through a fast dig-
ital transformation process that has the potential to enable frictionless,
touchless, and more intuitive experiences in academia. Moreover, stu-
dents are now digital natives and demand from higher education insti-
tutions new digital services for all academic purposes. In this article, we
introduce the design methodology used for the architecture specification
of the IPVC Smart & Sustainable Campus (IPVC-S2C), a FIWARE-
based platform with edge-enabled intelligence. The current research also
surveys and characterizes low-cost IoT edge hardware capable of per-
forming distributed machine learning. Lastly, a proof of concept focus
on Indoor Air Quality monitoring on the campus is presented and the
forthcoming research is outlined.
Keywords: Smart campus ·IoT ·Context-driven ·Edge-intelligence
1 Introduction
The higher education landscape is passing through a continuous and fast digi-
tal transformation process that is enabling frictionless, touchless, and intuitive
experiences in academia. Students who are nowadays digital natives push the
higher education institutions into the digital transformation and at the same
time foster new digital services that will turn into reality a fully and connected
digital campus experience. A digital campus also referred to as a smart campus,
takes advantage of existing Information and Communication Technologies (ICT)
and state-of-the-art Internet of Things (IoT) technologies to provide automated
and intelligent services on the campus.
In this article, we introduce the core elements and the ICT infrastructure
needed to support the IPVC Smart & Sustainable Campus (IPVC-S2C) imple-
mentation and put forward a FIWARE-based architecture that enables full inte-
gration with other legacy systems that higher education institutions still have
The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Á. Rocha et al. (Eds.): WorldCIST 2021, AISC 1367, pp. 557–569, 2021.
558 P. Martins et al.
in use, by taking a case study as an example, cf. Sect. 5. Moreover, a set of core
criteria for the selection of IoT edge hardware capable of performing distributed
machine learning is also included for future edge intelligence integration.
The remainder of this document is organized as follows: Sect. 2presents
existent frameworks/platforms used in the development of smart solutions in the
context of a smart campus; Sect. 3introduces the IPVC-S2C ICT infrastructure;
Sect. 5introduces the IPVC-S2C architecture and describes in detail its core
elements; lastly, in Sect. 7the main conclusions of this work are introduced and
future work guidelines presented.
2 Related Works
SmartCAMPUS UWB project develops the concept of a reduced scale model
of a city that enables the creation of a testbed for smart and IoT technologies.
Currently, it involves 9 faculties equipped with LoRaWAN and Sigfox communi-
cation infrastructures. Several applications are already in use inside the campus,
such as KETCube, which is a prototype and demo platform; Environment, on
which it is possible to detect fluctuations in temperature or humidity; Cloud
which collects and evaluates information from the implemented systems; Park-
ing which manages car parking with IoT sensors; IoT Lab which is a lab devoted
to students developing IoT projects and oers a Data Warehouse with open data
captured by several IoT Devices [1].
WiseTown, “Web Information Streams Enhancer for Your Town” is an appli-
cation that uses FIWARE and collects information from dierent data sources,
making data easy to manage and the information organized, supporting the Pub-
lic Administration by improving urban planning, modernizing public services,
and streamlining city management [2].
SmartMetropolis is a project developed in the Digital Metropole Institute
(IMD) in the Federal University of Rio Grande do Sul. The main goal of this
project is the development of methods and techniques to support the implemen-
tation of services to be integrated by smart cities by creating applications to
support strategic areas, such as security, tourism, public transportation, edu-
cation, big data, cloud computing, and IoT. IMD current projects include the
monitoring of Water and Energy, using sensors that communicate via GPRS;
Smart Place, which manages air conditioners and the lightning inside buildings,
allowing the management which reduces the average cost of the electricity bill;
SIGNatal, an open data application that shares geographical data from Natal
city with users; and finally the ROTA-Viatura, a system of dispatching police
vehicles and policemen for a faster attending on police emergency occurrences [3].
Bettair is a platform that, as described in [4], works as a service and allows
the mapping of air pollution in cities with the help of the Bettair Static Nodes, an
autonomous device that is installed in streetlights. With the information recov-
ered from the sensors, urban planners can take action to improve air quality.
Before using FIWARE, the platform was developed with a monolithic archi-
tecture, which was unable to be scaled and modified as requested by using a
Designing a FIWARE-Based Smart Campus with Edge-Enabled Intelligence 559
FIWARE-based architecture, the Context Broker manages air pollution infor-
mation that is delivered by the sensors, accelerating the development of a Smart
Solution, oering a possibility to connect with other existing FIWARE platforms
that the city might already have.
In [5], the authors present the architecture of the University of Málaga Smart
Campus that has been designed to transform its university campuses into a small
smart city that can support ecient management of their area as well as inno-
vative educatio nal a nd research activities. The actions of t his i nitiative follow
six main application categories: 1) Emissions, Energy and Water, 2) Nature
and Environment, 3) Health and Well-Being, 4) Mobility, 5) ICTs, 6) Research,
Teach i n g , a n d I n n ova t i on. The authors prop o s e a n a r chitecture that uses IoT
technologies and several existent telecommunications resources to deliver a uni-
fied infrastructure that is used in several application domains that can be inte-
grated into learning activities.
3 IPVC Smart and Sustainable Campus
The Polytechnic Institute of Viana do Castelo (IPVC) is a higher education insti-
tution created in 1980, serving the Northwest region of Portugal. The IPVC has
six campuses spread through four places in Alto-Minho region (Viana do Castelo,
Ponte de Lima, Valença, and Melgaço). To reinforce the links between all IPVC
campuses and engage the IPVC community towards sustainable development, it
is strategic to put forward the IPVC Smart & Sustainable Campus, also referred
to as IPVC-S2C, which will allow not only the engagement of all IPVC commu-
nity and sta(students, professors, researchers, and employees) in all process
but will also secure the commitment and participation of the managerial ocers
as a whole.
The general ICT infrastructure of the IPVC-S2C is shown in Fig. 1,inwhich
the overall digital ecosystem with all included ICT infrastructure elements and
their use is properly categorized. Moreover, the relations of the dierent ICT
infrastructure elements and their interactions are also identified.
The foundation of the IPVC-S2C ICT infrastructure is based on the IoT
Edge Devices, such as sensors and actuators that are being used in three main
application categories: 1) Smart Metering, which includes emissions, water dis-
tribution, and renewable energy production management, 2) Smart Mobility,
including the context information management of a network of electric bicy-
cles (BiRa Project), and 3) Smart Building, which includes Indoor Air Quality
assessment in the classroom and access/attendance control. These devices will
communicate the acquired data via WiFi, Cellular 3G/4G, or LoRaWAN, which
will reach the IoT Agent that creates a standardized interface to all IoT inter-
actions, allowing the data to be managed by the FIWARE Context Broker.
After context managing and if some predefined events have been detected,
authenticated users, such as Sta, Professors, and/or Students can be notified
to take actions, or requests can be made (HTTP, MQTT, etc.) to the IPVC-S2C
560 P. Martins et al.
Radio Access
Administration &
Management (OAM)
IoT Edge Devices
(Sensors, Actuators)
(IoT-ED) Smart Metering
IoT Agents IoT Agents
Research &
Learning (RL)
Staff StudentsProfessors
OAM APIs (REST, etc)
Cellular 3G/4G
IPVC Smart Campus
Application Server Other OAM
Smart Building
Smart Mobility
Context Broker
Identity Manager
Ethernet, Optical Fiber
Ethernet, Optical Fiber
Fig. 1. IPVC-S2C ICT infrastructure.
Application Server or other OAM Servers, so the applications change the behav-
ior (automatically), triggering smart decisions within the context of a Smart and
Sustainable Campus.
Before using FIWARE, several platforms had been developed by using a
monolithic architecture, allowing services to be deployed as a single solution.
Given the fact that these are small applications, their development was easier to
be achieved. However, being scalability a priority in the current days and since
monolithic applications are dicult to modify, a new approach was required.
Based on the related works and having in mind the application requirements
and the methodology, the proposed architecture is conceptually split into two
major building blocks:
a) IoT Devices with Edge-enabled Intelligence;
b) IPVC-S2C Digital Ecosystem
1. FIWARE Generic Enablers;
2. Short-Term Historic and Real-Time Data;
3. Application Server.
The data will be collected and transmitted by several IoT Edge devices that are
being used in several monolithic projects inside the campus. Communications
using WiFi or LoRaWAN networks are used for Smart Metering and Smart
Building applications, and LoRaWAN or Cellular 3G/4G for Smart Mobility
applications, as shown in Fig. 1.Thefollowingtwosections(Sect.4and 5)will
be used for a detailed description of the two main building blocks previously
Designing a FIWARE-Based Smart Campus with Edge-Enabled Intelligence 561
4 IoT Devices with Edge-Enabled Intelligence
Edge devices can sense, measure, interpret, and transmit data up to the cloud
through an internet gateway. In cloud-centered architectures, the raw data is
pushed to a centralized server by the end device without any type of processing.
However, IoT Edge devices are becoming more ecient, aordable, and power-
ful, which enables low-latency real-time processing and the distribution of the
computational cost between the IoT Edge devices.
Recently, the fusion of Artificial Intelligence (AI) and edge computing is
becoming a reality. On one hand, AI intends to implement intelligent human
behavior in devices/machines by extracting knowledge and learning from data.
On the other hand, edge computing aims at coordinating a multitude of collabo-
rative edge devices and servers to process the generated data in proximity to the
source of data. Another relevant requirement that must be taken into account
when considering edge-enabled Intelligence is the low-latency, which can be guar-
anteed by one of the three relevant architectures relies on Deep Neural Networks
(DNNs) that are executed at the end device. Alternatively, Edge server–based
computation relies on data that is sent by the end devices to edge servers for
computation. Lastly, Joint computation includes the possibility of having cloud
However, bringing AI to the edge is a challenge due to the limited resources
available in common hardware used to design IoT edge devices. One approach
that has been implemented with relative success is to reduce the model’s infer-
ence time. To run an AI model in an embedded IoT device, the hardware needs
to be properly selected to fit the model design and compression [6]. Edge AI is
normally based on models with a reduced number of parameters in the Deep
Neural Network (DNN) model, which considerably reduces memory needs and
execution latency while preserving high accuracy.
Model compression, i.e. reduction of the model size, can be achieved using
quantization and pruning techniques, individually or working together. Post-
training quantization reduces computing power demand and energy consumption
at the expense of a slight loss in accuracy, allowing to run the model on tiny
devices. On the other hand, pruning eliminates non-essential connections for the
Neural Network (NN) and consequently reduces (1) the number of computations
and (2) the demand for memory space for the NN [6].
Hardware selection must be based on the analysis of 4 relevant metrics: 1)
Accuracy; 2) Energy eciency; 3) Throughput; 4) Cost. The accuracy of the
machine learning algorithms must be quantified using large data sets to guar-
antee that the obtained results are valid. Energy eciency is a metric directly
related to the adaptation of the model to the context change, i.e., the model
adapts its weights as the scenario changes, which involves recurrent memory
access for reading/writing weight values, resulting in increased energy consump-
tion. The throughput metric represents the number of operations required per
unit of time and the cost is directly related to the amount of memory required
to host the model, because memory is still the critical building block in com-
putational systems and although model compression can be applied, the model
562 P. Martins et al.
size can still be in the order of tens or hundreds of megabytes, heavily impacting
the overall edge device cost. Table 1presents a selection of hardware commonly
used in the design of IoT devices that can perform edge computing and specifi-
cally run Machine Learning (ML) Libraries, such as Tensorflow Lite. Tensorflow
Lite is an example of an ML library specifically implemented to be used in
microcontroller-based constrained edge devices [7].
Table 1. IoT hardware compatible with TensorFlow Lite. Adapted from [6].
Board Processor Power Connectivity Cost
Arduino Nano 33
BLE Sense [8]
ARM Cortex-M4
32-bit@64 MHz
52 µA/MHz BLE 27
Edge [9]
ARM Cortex-M4F
32-bit@48/96 MHz
6µA/MHz BLE 5 15
EdgeBadge [10]
32-bit@120 MHz
65 µA/MHz BLE/WiFi 35
DevKitC [11]
Xtensa dual-core
32-bit@160/240 MHz
2mA/MHz BLE/WiFi 10
Figure 2depicts the generic architecture of the IoT Edge device with its core
elements identified—sensors and actuators, microprocessor, and communication
radios—which have been in use in several application domains, cf. [1822], within
the IPVC S2C Digital Ecosystem.
Power Management
Hardware Security
and Actuatos
HTTPs Secure
AES Encrypted
Signal Processing
and Analytics Communications
Fig. 2. IoT Edge generic architecture.
Designing a FIWARE-Based Smart Campus with Edge-Enabled Intelligence 563
5 IPVC-S2C Digital Ecosystem
The blocks that are the base of the IPVC-S2C Digital Ecosystem are illustrated
in Fig. 3.Thisapplication-orientedarchitecturewasdesignedusingasetof
FIWARE Generic Enablers (GEs) that interact with the IoT Edge devices and
other third-party systems through a context broker. Short-Term historic and
Real-Time pre-processed data will be stored and aggregated in a time-series
database (TSDB). TSDB can handle large amounts of data while delivering
fast response due to its native optimization for storing and querying time series
data, enhancing the data compression rate and the data manipulation speed
when real-time analytics over large sets of timestamped data is required [12].
Client APP
IPVC S2C Digital Ecosystem
IoT Edge 0
IoT Edge N
. . .
External APIs
IoT Edge 1
Short-Term Historic
& Real-Time Data
Complex Event
IoT Edge Devices
FIWARE App Server
IPVC S2C App Server
Application Data
Time Series Data
Fig. 3. Overall System Architecture including the FIWARE Generic Enablers and other
third party services and modules.
In the proposed architecture, four FIWARE GEs were used. The Orion Con-
text Broker, allows the register, update, and query operations of the context data
and working with publish/subscribe communication patterns, via notifications to
the responsible organization. The Keyrock Identity Management module enables
identity management and authentication/authorization security to the services
and applications. The IoT Agent allows data transfer between the sensors and
the Context Broker. The Complex Event Manager (CEP) analyses event data
in real-time and enables instant responses to change conditions, such as notifi-
cations, emails, tweets, and messages [13].
564 P. Martins et al.
The TSDB directly connects to the Context Broker and the IoT agents and
can serve data directly to the client App through Grafana as-a-service, enabling
the on-the-fly generation of rich and interactive dashboards. Since external ser-
vices can be deployed independently, and since other already existent database
legacy systems are in use on the campus they will also be integrated through
external APIs and Webservices, such as RESTful APIs. In Subsects. 5.1 to 5.3,
each building block of the IPVC-S2C Digital Ecosystem will be introduced and
explained in detail.
5.1 FIWARE App Server
The main core (and only needed) of any FIWARE-based application is the
Orion Context Broker. According to [14], Orion Context Broker is the Pub-
lish/Subscribe implementation, decoupling consumers data, functioning based
on an Open API Next Generation Service Interface (NGSI), on two versions,
NGSI-9 and NGSI-10, which defines the data model, context data interface, and
availability [15], that allow the registration, updates, queries and notifications of
context data.
To analyze and pro cess data in real - t i m e , a C o m p l e x E vent Processing (CEP),
also referred to as event stream analysis, or real-time event correlation, will
be installed, allowing immediate response to changing conditions, like sending
emails, SMS messages, HTTP requests, tweets, etc. The CEP API allows the
management of rules, exposing CRUD operations, being triggered by feeding
them with NGSI10 notifications [13].
For security m e a s u r e s, the Keyro ck Identity M a n a g e r w i l l b e u s e d , w h i ch will
be responsible for authentication and providing access to information, oering a
Graphic User Interface based (GUI-based) or API-based interaction to admin-
istrative users, roles, and permissions enabling the addition of OAuth2-based of
users and devices, user profile management, privacy-preserving of personal data,
Single Sign-On and Identity Federation across multiple domains, enabling the
register of OAuth 2.0 consumers as Service Providers [16]. It is used to create a
secure FIWARE application and contains data of Users - any human actor inter-
acting with a FIWARE application and Organizations - An association allowing
certain users to administer all rights. The Identity Manager ensures that only
the right individuals get access to resources, such as usernames, passwords, and
roles, and the access control is the selective restriction of access to resources, with
Authorization and Authentication. The Identity Manager is key in the architec-
ture since it reduces the work on account creation and management, using the
user profile storage as a Software as a Service, supporting the usage of policies
and procedures. The administrators can easily configure access to services and
the handling of error notifications. Since it re-uses attribute data, it allows easy
and convenient management of profile information [13].
An IoT Agent is the GE that makes it possible for a group of devices to
send their data to be managed from a Context Broker. It translates an IoT
specific protocol into an NGSI v2, overcoming common problems in the IoT
domain, such as mapping the data received in a meaningful manner, abstracting
Designing a FIWARE-Based Smart Campus with Edge-Enabled Intelligence 565
communications so users can remain unaware of the device-specific protocols,
bringing a standard interface to all IoT interactions at the context information
level. The IoT Agent supports a single message format and can be configured to
use transports such as HTTP, MQTT, and AMQP [13].
5.2 Short-Term Historical and Real-Time Data
The Short-Term Historic and Real-Time Data block of the Smart Campus archi-
tecture is composed of a single time-series database, InfluxDB, an open-source
database designed to handle high write and query loads, which will be used to
store the data sent by the IoT Edge Devices. Since the Smart Campus will use
multiple sensors, InfluxDB was chosen to store multiple data collected by the
sensors, because it is fast and scalable, supporting millions of writes per second,
having the ability to handle specific functions to accelerate data processing [17].
Historical context data can be persisted to the InfluxDB, resulting in a series
of data points, which are meaningless on their own but combined can be trans-
formed into meaningful statistics, which can be displayed, with an easy user
interface and enhanced visual analytics to the User as Dashboards or metrics
and KPIs for distinct periods: Real-Time (last hour), Short-Term (last 7 days),
using Grafana-as-a-service in the Front End.
5.3 IPVC-S2C App Server
The IPVC-S2C App Server is conceptually a block composed of two main cores
that can be described as the external services and the application server, which
can be considered as a Back End to the IPVC Management Front End and the
AppDB. The App Server will be used as an external API, allowing the connection
to the External API Services for the existing Core Applications, such as ICT’s,
Nature and Environment, Emissions, Water and Energy, Mobility, Health, and
Research and Innovation. The AppDB is a MongoDB open-source database,
which is dynamic and object-oriented, having high performance, availability, and
automatic scaling, and will be used to the Back End data that already exists
from the monolithic approach.
The proof of concept used to test the IPVC S2C digital ecosystem was set up
using an IoT Edge device designed for Indoor Air Quality monitoring in the
schools, cf. [22]. The device was designed to monitor Indoor Air Quality and
collect parameters such as particle matter (PM1.0, PM2.5, and PM10), total
volatile organic compounds (TVOC), CO2concentration, and air temperature
and relative humidity.
Figure 4depicts the IAQ4Classroom client application, a cartography based
web-application platform centered around a map, using Leaflet and Geoserver,
two geographic information system (GIS) tools to enhance visual data analytics
566 P. Martins et al.
Fig. 4. Map-centered front end with related IoT Edge device dashboard.
that allows high-level building management using native and geo-referenced hier-
archies between entities, i.e., school > floor > classroom > sensor, hierarchies
defined and obtained using the FIWARE reference data context model (NGSI
v2), to perform spatial queries, by entity or entity aggregate. These entities are
implemented as GeoJSON vectors and are assigned a specific color that changes
in real-time, related to a risk indicator or a legal limit. For an easier user data
visualization, Grafana will be used to display Dashboards, KPIs, and Metrics
according to the collected data in three time periods: Very Short-Term for peri-
ods of 24 h, Short-Term for periods of 1week to three months, and Long-Term
for periods of 3 months plus.
With the implementation of FIWARE Orion Context Broker, operations
related to the entity state, such as create, query and update are basic for syn-
chronous context producer and consumer applications. However, Orion Context
Broker has a strong advantage of letting the user know the information as soon
as it arrives since it allows the ability to subscribe to context information so
the application gets an asynchronous notification when “something” happens,
enabling a faster and better deployment since it removes polling. In addition to
the Broker, the CEP analyses data in real-time to generate an instant response,
manual or automatic, to changing conditions, such as notifications to the end-
user. The IoT Agent is automatically connected to the IAQ4Classroom sensor
and the corresponding data with specific content in the Orion Context Broker,
storing the devices’ configuration in the AppDB. In our use case scenario, each
time the measurement is collected, the data is updated by the Orion Context
7 Conclusions and Future Work
The current research shows the design methodology, which serves as the ground
for the specification of a FIWARE-based Smart & Sustainable Campus with
Designing a FIWARE-Based Smart Campus with Edge-Enabled Intelligence 567
Edge-enabled Intelligence. At the time of writing, the project is being tested
using a LoRa-based IoT Edge device to measure the Indoor Air Quality, within
the Smart Building category, as presented in Fig. 1.FIWAREalsoallowsthe
inclusion of several modules that are not displayed in the proposed architecture,
cf. Fig. 3, that may be added if necessary, such as the CKAN extension, an open
data publication module that allows the publication of data-sets and assignment
of terms, policies, pricing and pay-per-use schemes to data-sets [13]. The use
of FIWARE technologies enables simplifies the design of new applications and
streamlines its integration with other legacy systems that are already in use
on the campus. Moreover, the use of a context broker centralizes and provides
context to data that comes from dierent IoT Edge devices. In this approach,
external entities can collect, process, and display information without needing
to directly interact with data sources.
Future wo r k w i l l i n c l u d e t he integration of other existing m o n o l i t h i c a p p l i -
cations that are still in use on the campus, mainly concerning the project
Refill_H20, which proposes to reduce the sale of plastic water bottles in Poly-
technic Institute of Viana do Castelo (IPVC), a higher education institution,
to promote the circular economy, by reducing plastic materials use and waste.
By promoting the reduction in disposable waste production, project Refill_H20
will help to reduce energy consumption and greenhouse gases emission. The
ICT infrastructure for the IPVC-S2C is still under development, lacking bench-
mark tests to the IoT Edge devices for the Smart Metering and the Smart
Mobility application categories, however, the application to Project Refill_H20
will help to boost its implementation. Context data notifications will be also
implemented to advise the responsible user or organization or to automatically
trigger actions in response to certain physical or environmental conditions. New
FIWARE Generic Enablers will also be implemented according to the needs of
the community. Usability tests should be developed and deployed for the Front
End application to evaluate the application flow and improve its user interface.
The goal of these tests is the simplification of the user interaction and the per-
ceptibility of the data acquired by the sensors.
Acknowledgments. The authors wish to thank especially the Program Environment,
Climate Change and Low Carbon Economy, created following the establishment of
a Memorandum of Understanding between Portugal and Iceland, Liechtenstein and
Norway, under the EEA and Norway Grants 2014–2021, for the program areas of
Environment and Ecosystems (PA11), and Climate Change Mitigation and Adaptation
(PA13), for financing the project 10_SGS#1_REFILL_H20.
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national Conference on Intelligent Edge Processing in the IoT Era, 2–4 December
2020. N/A, Cyberspace (2020)
... The concept of sustainability has been widely discussed in recent decades in all areas of society and has become an increasingly constant presence in our daily lives [1,2]. HEIs play a decisive role not only in the training of future generations of decision-makers and professionals, providing them with the specific knowledge necessary to understand the interactions between human beings and the environment [6][7][8][9][10][11] but also by promoting a smarter and more sustainable campus designed to favoring wellbeing, health and safety, waste reduction, moderating water and energy consumption, promoting local and regional community participation, and developing new curricular environmental activities. ...
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Higher education institutions (HEIs) are favored environments for the implementation of technological solutions that accelerate the generation of smart campi, given the dynamic ecosystem they create based on the involvement of inspired and motivated human resources (students, professors, and researchers), moving around in an atmosphere of advanced digital infrastructures and services. Moreover, HEIs have, in their mission, not only the creation of integrated knowledge through Research and Development (R&D) activities but also solving societal problems that address the academic community expectations concerning environmental issues, contributing, therefore, towards a greener society embodied within the United Nations (UN) Sustainable Development Goals (SDGs). This article addresses the design and implementation of a Smartbottle Ecosystem in which an interactive and reusable water bottle communicates with an intelligent water refill station, both integrated by the Internet of Things (IoT) and Information and Communications Technologies (ICT), to eliminate the use of single-use plastic water bottles in the premises of the Polytechnical Institute of Viana do Castelo (IPVC), an HEI with nearly 6000 students. Three main contributions were identified in this research: (i) the proposal of a novel methodology based on the association of Design Thinking and Participatory Design as the basis for Sustainable Design; (ii) the design and development of an IoT-enabled smartbottle prototype; and (iii) the usability evaluation of the proposed prototype. The adopted methodology is rooted in Design Thinking and mixes it with a Participatory Design approach, including the end-user opinion throughout the Smartbottle Ecosystem design process, not only for the product design requirements but also for its specification. By promoting a participatory solution tailored to the IPVC academic community, recycled plastic has been identified as the preferential material and a marine mammal was selected for the smartbottle shape, in the process of developing a solution to replace the single-use plastic bottles.
... 1) IoT and Communication: includes the BIRA U-Bikes and LoRaWAN communication protocols; 2) FIWARE App Server: application server that handles all the data coming from the IoT devices throughout the Orion context broker; 3) IPVC Authentication Server: includes the IPVC authentication databases and external services; 4) Web Application: includes the front-end application and its features, which are available to the end-user. The usage of the IPVC Smart & Sustainable Campus (IPVC S2C) platform is essential since it allows a product-ready application that standardizes the adoption of a common interface for IoT and Big Data analytics, allowing better management of the usage and maintenance of the BIRA bicycles [12]. ...
... The Smartbottle Ecosystem was designed to achieve the main goal of the Refill_H2O, an EEA Grants Portugal environmental project [6], that aims to eliminate the use of plastic water bottles on the IPVC Campus, through the design and development of an interactive Smartbottle that 'communicates' with a Smart Water Refill Station to foster more eco-friendly attitudes among local users such as students, teachers and employees, thus contributing to the reduction of plastic consumption in bars, canteens and halls of residence in the IPVC campus. Figure The proposed ecosystem includes five main components: 1) the Smartbottle (interactive artifact); 2) the deployed IoT Edge Devices (Smart Water Refill Station) that communicates with the Smartbottle using RFID technology and the Student ID Card for user authentication; 3) the IPVC Wide Area Network, i.e. the ICT infrastructure that will perform backhaul communications; 4) the IPVC Authentication Server (that can be accessed in a "as-a-service" approach); 5) the FIWARE Application Server handles all communication between the IoT edge devices, data storage, and the client app through a context broker [7], whose architecture is described in detail in [8]. ...
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Public higher education institutions have a particular moral responsibility in increasing the awareness, knowledge, skills and values required to create a fair and sustainable future. Through sustainable design, the Project Refill_H20 aims to eliminate the use of plastic water bottles in the 6 schools of the Polytechnic Institute of Viana do Castelo (IPVC), respective bars, canteens and halls of residence A survey of the academic community will identify the set of physical, aesthetic and functional features to create the product specifications for the Smartbottle and Water Refill Station. ICT and IoT technologies will encourage autonomy, pedagogically helping users to acknowledge, identify and reduce their environmental footprint. Applying the principles of circular economy, this academic project promotes the reduction of plastic consumption, production and waste. Contributing towards a paradigm shift, sustainable design canvasses conditions to reduce plastic in the oceans, improving the environment and the quality of life on Earth.
In this work, an open source platform, based on the FIWARE software framework and other open source components, is used to perform experimental cloud control on two use cases from the smart building and smart grid domains. All communication between the platform and the field layer is realized via the public internet and therefore encryption, authentication, and authorization measures are installed. In the first use case, the supply temperature of a conventional heating circuit is controlled as it is a common task in building energy systems. In the second use case, the power balance of a simulated microgrid is monitored by real phasor measurement units and a controller is used to maintain grid stabilty. The suitability of the platform is validated using requirements derived from literature. The platform is applicable to both use cases. Though, limitations and prospective areas for improvement are identified.
Open-source technologies enable communication channels between web platforms and innovative architectures to provide reliable data distribution, in which healthcare applications can particularly benefit from them. This work presents a communication channel design to improve the user experience about telemedicine apps, especially when patients are in remote locations while assuring their information using an innovative approach. The general purpose is to avoid users having to physically go to medical facilities by the correct data management related to their appointments and medical history. By preventing the attendance to healthcare facilities, patients do not expose themselves unnecessarily to viruses and bacteria. Therefore, this research includes a data communications model based on the FIWARE platform and cloud technologies for reliable user medical information distribution. The prototype is developed based on open-source technologies and registered the evaluation of different performance metrics that included cases scenarios in which administrators of healthcare centers configured options according to the availability of assets and informatics resources. The results show the effectiveness of the communication model under realistic conditions for encouraging the acceptance of telemedicine alternatives, especially when patients and medical staff present limitations regarding mobility.
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Due to its pervasive nature, the Internet of Things (IoT) is demanding for Low Power Wide Area Networks (LPWAN) since wirelessly connected devices need battery-efficient and long-range communications. Due to its low-cost and high availability (regional/city level scale), this type of network has been widely used in several IoT applications, such as Smart Metering, Smart Grids, Smart Buildings, Intelligent Transportation Systems (ITS), SCADA Systems. By using LPWAN technologies, the IoT devices are less dependent on common and existing infrastructure, can operate using small, inexpensive, and long-lasting batteries (up to 10 years), and can be easily deployed within wide areas, typically above 2 km in urban zones. The starting point of this work was an overview of the security vulnerabilities that exist in LPWANs, followed by a literature review with the main goal of substantiating an attack vector analysis specifically designed for the IoT ecosystem. This methodological approach resulted in three main contributions: (i) a systematic review regarding cybersecurity in LPWANs with a focus on vulnerabilities, threats, and typical defense strategies; (ii) a state-of-the-art review on the most prominent results that have been found in the systematic review, with focus on the last three years; (iii) a security analysis on the recent attack vectors regarding IoT applications using LPWANs. Results have shown that LPWANs communication technologies contain security vulnerabilities that can lead to irreversible harm in critical and non-critical IoT application domains. Also, the conception and implementation of up-to-date defenses are relevant to protect systems, networks, and data.
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Indoor Air Quality (IAQ) is an essential requirement for improving building sustainability. In fact, indoor pollution creates serious problems for human health and occupants’ well-being. Considering that Europeans spend on average 90% of their time inside buildings, IAQ plays a decisive role in human health, especially for the most vulnerable groups such as the elderly and children. Concerning children and youth, due to the presence for long periods of time in school classrooms, they tend to be more susceptible to developing chronic diseases such as asthma, allergies, and respiratory problems or make these problems more increased. In these circumstances, to prevent the occurrence of these specific illnesses, it is essential to improve the school environment, namely, classroom \(^\prime \) indoor air quality. This research aims to specify both the design and development processes of a LoRa-based IoT Edge device for classroom IAQ monitoring, by using low-cost commercial off-the-shelf components, capable of measuring relevant IAQ parameters specifically selected for a specific case-study analysis, namely the following: carbon dioxide (CO\(_{2}\)), particle matter, and volatile organic compounds (VOC). At last, the prototype is delivered and assessed under controlled conditions. It is also worth highlighting that the prototype’s overall cost is approximately 150€.
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Radon is a naturally occurring radioactive gas that can easily accumulate in indoor environments. According to the World Health Organization (WHO), radon gas is the second largest risk factor associated with lung cancer, after tobacco smoking. People spend at least half their life inside buildings, which are becoming increasingly more hermetic due to the pursuit of high energy efficiency – an increase in ventilation rates tends to increase heat losses. In this context, energy efficiency and Indoor Air Quality (IAQ) concepts, if not studied in a balanced way, can move in opposite directions. The introduction of Internet of Things (IoT) technologies for continuous assessment of the IAQ can help to achieve an optimally integrated balance between them. This article focus on the specification and design of the RnProbe, an IoT Edge Device developed under the scope of the RnMonitor R&D project whose main objective was the specification and development of a Cyber-Physical System (CPS) for integrated Radon Risk Management in public buildings, such as schools, kindergartens, offices, and hospitals, that are restricted to regular occupancy schedules, so that policymakers and building managers can reduce public health risks associated with the exposure to this pollutant. The device collects, aggregates, and transmits up to the cloud, several indoor environmental parameters. When combined these measurements are used to perform specific mitigation actions in the building, to improve IAQ.
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In a few years, the world will be populated by billions of connected devices that will be placed in our homes, cities, vehicles, and industries. Devices with limited resources will interact with the surrounding environment and users. Many of these devices will be based on machine learning models to decode meaning and behavior behind sensors’ data, to implement accurate predictions and make decisions. The bottleneck will be the high level of connected things that could congest the network. Hence, the need to incorporate intelligence on end devices using machine learning algorithms. Deploying machine learning on such edge devices improves the network congestion by allowing computations to be performed close to the data sources. The aim of this work is to provide a review of the main techniques that guarantee the execution of machine learning models on hardware with low performances in the Internet of Things paradigm, paving the way to the Internet of Conscious Things. In this work, a detailed review on models, architecture, and requirements on solutions that implement edge machine learning on Internet of Things devices is presented, with the main goal to define the state of the art and envisioning development requirements. Furthermore, an example of edge machine learning implementation on a microcontroller will be provided, commonly regarded as the machine learning “Hello World”.
Conference Paper
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Depending on the architectural typology and function of a specific building or compartment, Building Occupancy Estimation (BOE) is a critical factor for effective green building management. By estimating the building occupancy over time we can reduce the overall energy consumption, and therefore, improve the building energy efficiency. In a public building context, for example, such as an administration office, a school or a kindergarten, building occupancy is normally restricted to a regular work schedule and its effective occupation can considerably vary in this period, in terms of number of occupants per compartment or in the overall building, and therefore considerable impact the building energy consumption for heating, HVAC, lightning, etc. This paper presents the design and implementation of a cost-effective IoT edge device for BOE. The proposed device collects and transmits up to the cloud, several networking and indoor environmental parameters, that combined, will be later used to specify a multi-parameter metric for effective BOE.
Conference Paper
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IoT-based monitoring (i.e. smart monitoring) technologies have been recently used for on-line monitoring in many application fields, such as home, environmental and industrial process monitoring. People spend at least half of their life inside buildings, therefore, Indoor Air Quality (IAQ) plays an important role both on human health and on buildings’ sustainability. Radon gas is one of the most important parameters regarding IAQ assessment, being considered by the World Health Organization (WHO) as the second-largest risk factor associated with lung cancer. This paper aims to present RnMonitor, a WebGIS- based platform developed for effective Radon Risk Management and expedite in situ deployment of IoT-based sensors. Given the fact that the spatial context is key for visual and data analytics, the proposed platform takes advantage of a hierarchy of spatially related entities (buildings/rooms/devices) that are natively georeferenced in the system, and thus providing spatial context to acquired data, and other relevant metrics, by means of a simple, responsive and intuitive web-based application.
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Secure electronic identification (eID) is one of the key enablers of data protection, privacy and the prevention of online fraud. However, until now, the lack of common legal basis prevented European Member States from recognizing and accepting eIDs issued in other Member States. The Electronic Identification and Trust Services (eIDAS) Regulation provides a solution to these issues by ensuring the cross-border mutual recognition of eIDs. FIWARE is an European initiative that provides a rather simple yet powerful set of APIs (Application Programming Interfaces) that ease the development of Smart Applications in multiple vertical sectors and oriented to the Future Internet. In this paper we propose a model that enables the connection of FIWARE OAuth 2.0-based services with the eID authentication provided by eIDAS reference. Thanks to this model, services already connected with an OAuth 2.0 identity provider can be automatically connected with eIDAS nodes for providing eID authentication to European citizens. For validating the proposed model we have deployed an instance of the FIWARE Identity Manager connected to the Spanish eIDAS node. Then, we have registered two services (a private videoconferencing system and a public smart city deployment) and extended their functionalities for enriching the user experience leveraging the eID authentication. We have evaluated the integration of both services in the eIDAS network with real users from seven different countries. We conclude that the proposed model facilitates the integration of generic and FIWARE-based OAuth 2.0 services to the eIDAS infrastructure, making the connection transparent for developers.
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For the past few years, the concept of the Internet of Things (IoT) has been a recurrent view of the technological environment where nearly every object is expected to be connected to the network. This infrastructure will progressively allow one to monitor and efficiently manage the environment. Until recent years, the IoT applications have been constrained by the limited computational capacity and especially by efficient communications, but the emergence of new communication technologies allows us to overcome most of these issues. This situation paves the way for the fulfillment of the Smart-City concept, where the cities become a fully efficient, monitored, and managed environment able to sustain the increasing needs of its citizens and achieve environmental goals and challenges. However, many Smart-City approaches still require testing and study for their full development and adoption. To facilitate this, the university of Málaga made the commitment to investigate and innovate the concept of Smart-Campus. The goal is to transform university campuses into “small” smart cities able to support efficient management of their area as well as innovative educational and research activities, which would be key factors to the proper development of the smart-cities of the future. This paper presents the University of Málaga long-term commitment to the development of its Smart-Campus in the fields of its infrastructure, management, research support, and learning activities. In this way, the adopted IoT and telecommunication architecture is presented, detailing the schemes and initiatives defined for its use in learning activities. This approach is then assessed, establishing the principles for its general application.
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The use of smart devices in buildings is many times compromised by its form and size. Smart devices are composed of several components including sensors, boards, batteries, processing units, and antennas. However, the form and size of the smart devices are usually limited due to antenna restrictions. In this paper, we propose the architecture of a compact low-cost LoRa smart device designed for easy deployment in smart building applications. The proposed device architecture features a reduced size embedded antenna and an ultra-low-power microcontroller to interface several sensors and actuators. The results obtained have shown that the proposed design can be used for communication, between two compact LoRa devices, in line-of-sight for up to 4.2 km, in urban environments for up to 1.2 km and also for in-building communications for up to 152 m, without compromising the low-power features that LoRa supports.
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As an extension to the concept of the Internet of Things (IoT), Web of Things (WoT) represents a step towards connecting smart things to the existing web environment while considering issues such as heterogeneity, scalability, and usability. This paper is dedicated to current opportunities as well as challenges for development in the concept of WoT. The theoretical foundations of the Internet of Things concept, such as architecture, protocols, services, and things themselves, which form the basis of both concepts, are described in the paper. The paper deals with the necessary preconditions for developing the concept of Web of Things. The main contribution of the paper is a proposal of architecture based on the FIWARE platform as the basis for the development of Web of Things.The demonstration of the proposed architecture is described by a real case scenario.
As the Internet of Things (IoT) becomes a reality, millions of devices will be connected to IoT platforms in smart cities. These devices will cater to several areas within a smart city such as healthcare, logistics, and transportation. These devices are expected to generate significant amounts of data requests at high data rates, therefore, necessitating the performance benchmarking of IoT platforms to ascertain whether they can efficiently handle such devices. In this article, we present our results gathered from extensive performance evaluation of the cloud-based IoT platform, FIWARE. In particular, to study FIWARE’s performance, we developed a testbed and generated CoAP and MQTT data to emulate large-scale IoT deployments, crucial for future smart cities. We performed extensive tests and studied FIWARE’s performance regarding vertical and horizontal scalability. We present bottlenecks and limitations regarding FIWARE components and their cloud deployment. Finally, we discuss cost-efficient FIWARE deployment strategies that can be extremely beneficial to stakeholders aiming to deploy FIWARE as an IoT platform for smart cities.