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Smart Interconnected Infrastructure for Security and Safety in Public Places

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Smart Interconnected Infrastructure for Security and
Safety in Public Places
Angelos Chatzimichail
Information Technologies Institute
Centre for Research and Technology
Hellas, Thessaloniki, Greece
angechat@iti.gr
Christina Karaberi
Department of Research and
Communication
e-Trikala S.A.
Trikala, Greece
xkarabaeri@e-trikala.gr
Dimitrios G. Kogias
Technical Department
iTrack Services Ltd
Piraeus, Greece
dimikog@itrack.gr
Stefanos Vrochidis
Information Technologies Institute
Centre for Research and Technology
Hellas, Thessaloniki, Greece
stefanos@iti.gr
Christos Chatzigeorgiou
Department of Electrical and
Electronics Engineering
University of West Attica
Egaleo, Greece
chrihatz@uniwa.gr
Georgios Meditskos
Information Technologies Institute
Centre for Research and Technology
Hellas, Thessaloniki, Greece
gmeditsk@iti.gr
Georgios Gorgogetas
EU projects Department
e-Trikala S.A,
Trikala, Greece
ggorgogetas@e-trikala.gr
Charalampos Patrikakis
Department of Electrical and
Electronics Engineering
University of West Attica
Egaleo, Greece
bpatr@uniwa.gr
Fotis Andritsopoulos
Technical Department
iTrack Services Ltd
Piraeus, Greece
fotis@itrack.gr
Panagiotis Kasnesis
Department of Electrical and
Electronics Engineering
University of West Attica
Egaleo, Greece
pkasnesis@uniwa.gr
Athina Tsanousa
Information Technologies Institute
Centre for Research and Technology
Hellas, Thessaloniki, Greece
atsan@iti.gr
Ioannis Kompatsiaris
Information Technologies Institute
Centre for Research and Technology
Hellas, Thessaloniki, Greece
ikom@iti.gr
Abstract In this paper, we present work in progress on the
development of an intelligent interconnected infrastructure for
public security and protection domain. In contrast to other
Internet of Things (IoT) frameworks, the proposed system aims
to effectively combine device and human awareness to achieve
situational awareness, so as to provide a protection and security
environment for citizens. The emphasis is placed on tourists, by
creating the appropriate infrastructure to address a set of
urgent situations, such as health-related problems and missing
children in overcrowded environments, supporting smart links
between humans and entities on the basis of goals, and adapting
device operation to comply with human objectives, profiles and
privacy. The framework effectively combines state-of-the-art
technologies on IoT data collection and analytics, knowledge
representation and interoperability, crowdsourcing, data fusion
and decision-making.
KeywordsInternet of Things, sensors, ontologies,
crowdsourcing, Security
I. INTRODUCTION
Internet of Things (IoT) platforms have received a
significant amount of attention due to the simplicity and
efficiency they bring in creating business value, linking the
IoT endpoints to applications and analytics. They are
essentially the linchpin in a holistic IoT solution because they
enable data generated at endpoints to be collected and
analysed, spawning the growth of big data analytics and
applications. Based on generic middleware, Open APIs and
tools, they provide standard-based, secure infrastructures and
interfaces to build IoT applications, manage connected
devices and the data those endpoints generate, and streamline
common features that would otherwise require considerable
additional time, effort and expense.
The emphasis in the current IoT landscape, however, is
mainly placed on the device-to-device interaction. Machines
and sensors are already being combined and passively
gathering, transmitting and sharing data from which we can
derive useful insights. Humans have a rather passive role in
this hype, acting as data providers, e.g., through wearable
sensors, or high-end decision makers. However, people are
evolving as an integral part of the IoT ecosystem, interacting
with processes, data and things driving the evolution toward a
ubiquitously connected world with immense possibilities. In
this new realm, novel concepts and methods are needed to
infuse and transform human awareness into situation
awareness, support smart links between humans and entities
on the basis of goals, and to adapt device operation to comply
with human objectives, profiles and privacy.
In parallel, there are numerous use-cases being explored
around IoT-enabled sectors, such as maritime [1], agriculture
[2], smart factories [3] and cities [4]. Specifically in smart
cities, IoT technologies are used from collecting and
interrogating city-center parking metrics, to the use of so-
called ‘smart’ street lighting to generate efficiencies. One of
the most compelling, however, use case is the technology’s
use in a public safety context. In this context, the challenge is
to use both humans and devices interchangeably to achieve
operational goals and respond to emergency situations, such
as natural disasters, health-related incidents, vandalisms,
missing people in overcrowded places.
In this paper, we describe the key technologies that
underpin the development of DESMOS, a novel framework
for the intelligent interconnection of smart infrastructures,
mobile and wearable devices and apps for the provision of a
secure environment for citizens, especially for visitors and
tourists. The platform aims to promote the collaboration
between people and devices for protecting tourists, supporting
timely reporting of incidents, adaptation of the interconnected
environments in case of emergency and the provision of
assistance by empowering local authorities and volunteers.
More specifically, the framework aims to support: a) fast,
timely and accurate notifications in case of emergencies (e.g.
medical incidents), sending at the same time all contextual
information needed to help authorities coordinate and assist
people, protecting the privacy of the monitored people, b)
anonymous reporting of incidents using crowdsourcing, with
a special focus on incidents involving tourists, e.g. thefts, and
c) adaptability of services, devices and people to respond to
incidents and protect citizens/tourists.
In order to realize the abovementioned goals, the platform
follows a systematic approach for interconnecting people,
services and devices using: a) applications in mobile and
wearable devices that will be used by volunteers, citizens and
local authorities, b) smart spots able to listen for reports and
requests for help and further propagate them in the local
intelligent network, c) fusion and interpretation of
heterogeneous events and information through semantic
reasoning and decision making.
The contribution of the presented framework lies in the
effective combination of five multidisciplinary research
fields. More specifically:
Multi sensor data analytics and events, developing
intelligent machine learning and rule-based
algorithms for context-aware data fusion for event
recognition, device localization, etc.
Semantic representation of information, reasoning
and interoperability, based on Semantic Web
technologies.
Crowdsourcing techniques for the collaborative
reporting and analysis of situations, where humans
will act as sensors and use devices to prevent or even
mitigate critical situations.
Design and development of mobile apps and human-
computer interfaces for collecting and transmitting
data, allowing users to be informed about and timely
report critical situations.
Security and privacy, addressing the requirements of
the new General Data Protection Regulation
(GDPR).
The platform will be evaluated in pilot trials that will take
place in the smart city of Trikala (Greece) that has a strong
commitment towards enhancing the feeling of safety of
people. More specifically, the pilots will take place a) in
Christmas Theme Park that is reported to have more than one
million visitors yearly, and b) in the central square and
sidewalk of the city.
The rest of the paper is organized as follows. In Section II,
we describe relevant work. In Section III the architecture of
the proposed system is presented. Section IV describes the
primary components of the architecture. The semantic
representation and analysis can be found in Section V. In
Section VI, different use cases scenarios are presented.
Section VII concludes the paper.
II. RELATED WORK
IoT deployments are not based on a single technology but
rather on the integration of multiple technologies. The
research process of designing an interconnected infrastructure
for smart cities, comprises various components. The first
components include the sensors interacting with the physical
world, the communication between server platforms and data
protocols. The second category components include data
representation and analytics. The third stage components are,
wherever applicable, the data privacy techniques and the last
one the crowdsourcing mechanisms that will be considered in
order to gather the data from humans.
The continuous advancement in electronics allowed many
IoT devices e.g. smart phones and smart watches to be
equipped with dozens of sensors. These devices through the
incorporated sensors can measure different physical
parameters and are deployed in different IoT applications. The
measured physical parameters can be from motion, position
sensors to optical sensors and etc. Data generated from all
these sensors on the devices have to be sent in a central system
in order to be processed. Data communication is done through
a network which, according to its network topology, can be
either local (e.g., Local Area Network LAN) or wide (e.g.,
Wide Area Network WAN). The most known protocol for
data communication in a LAN is the Wi-Fi (or IEEE 802.11).
However, due to its high-power consumption, Low Power Wi-
Fi is used instead in IoT devices. A new communication
protocol is the Bluetooth Low Energy (BLE), which allows
data rates of about 1.3 Mbps [5]. In our project we are going
to deploy those technologies for better energy efficiency.
Ontologies and technologies of Semantic Web are being
widely used for the representation of the data and the
ontologies of the IoT. Ontologies provide the means for a
structured description of an object or concept. They are used
to semantically enhance resources, through facilitating the
understanding of the meaning of the metadata that are
associated with sensor objects [6]. The addition of semantics
to sensor networks leads to the so-called Semantic Sensor
Web (SSW). In 2012, W3C suggested an innovative ontology,
the Semantic Sensor Network (SSN), as a human and
machine-readable specification that covers networks of
sensors and their deployment on top of sensors and
observations [7]. The target of this project was to face the
problems arisen from the heterogeneous data from different
devices. Although, there are limited ontologies that annotate
the time space correlation between the sensor data and the
resources. Also, SSN has only one class for all the sensors,
make it difficult to annotate the different parameters of each
sensor. In order to overcome that, [8] deployed the Sensor,
Observation, Sample, and Actuator (SOSA) ontology
providing a formal but lightweight general-purpose
specification for modelling the interaction between the entities
involved in the acts of observation, actuation, and sampling.
A few other research projects on IoT semantic framework
are: (i) Open-IoT project relies on a blueprint cloud-based IoT
architecture, which leverages the W3C SSN ontology for
modeling sensors [9]. (ii) The IoT- Lite ontology [10] an
instantiation of the semantic sensor network (SSN) ontology
to describe key IoT concepts allowing interoperability,
discovery of sensory data in heterogeneous IoT platforms by
a lightweight semantics. This project was deployed to address
the concern that semantic techniques increase the complexity
and the processing time. (iii) The FIESTA-IoT [11] proposed
a holistic and a light-weight ontology that aimed to achieve
semantic interoperability among various fragmented testbeds,
reusing core concepts from various popular ontologies and
taxonomies (SSN, IoT-Lite).
Event [12] was one core ontology for event annotation.
This ontology was centered around the notion of event, seen
here as the way by which cognitive agents classify arbitrary
time/space regions. The agent class was derived from the
FOAF (Friend of a Friend) ontology, a core ontology for the
social relationships [13]. In [14] presented an ontology for
situation awareness the SAW. These ontologies annotated
events with general situations and could be expanded with
supplementary ontologies. In [15] researchers developed a
platform to assist citizens in reporting security threats together
with their severity and location. The threats were classified
using a general top-level ontology, with domain ontologies
supporting the detailed specification of threats. The
information about the threats were stored in a knowledge base
of the system which allowed for lightweight reasoning with
the gathered facts. In [16] an ontology was developed based
on dangerous events on the road traffic for the people safety.
In the ontology, the general categories of threats were stored,
whereas in the database, the actual data about selected areas
in particular time were located. In TRILLION H2020 project
2016, a modular ontology was developed for public security.
Through this ontology citizens could report an event through
mobile devices. On the framework of this proposed project
new ontologies have to be deployed that are not incorporated
in the above-mentioned ontologies. These ontologies must
annotate every urgent event related with the public security.
Tracking and localization of human objects is a task of
increasing interest and of great relativity to DESMOS project.
Human localization is quite complex and its difficulty is
growing when the task is performed in indoor spaces, due to
the existence of crowd and obstacles [17]. Fusion of multiple
sensor data in object localization studies is usually performed
by Kalman filtering. Its simplicity creates the advantage of
speed but the underlying distribution assumptions cause
limitations [18]. In [17] a Kalman filter data fusion technique
is proposed, that combines sensors embedded in a wearable
platform, namely accelerometer, gyroscope and
magnetometer, for indoor localization and position tracking.
Kalman based fusion is also applied in [18] in order to
combine audio and visual data from heterogeneous sensors for
object tracking. A localization algorithm based on an
extension of Kalman filtering is presented in [19] where data
from both vision sensors and smartphone-based acoustic
ranging are fused for real-time dynamic position estimation
and tracking.
Data generated from sensors can be accompanied with
sensitive and/or personal data. In order to protect the privacy
of a user, solutions have been given under the anonymity
frame One solution for protecting users’ privacy is given with
encryption which protects data from being eavesdropped. The
modern cryptosystems are divided in two categories: a)
symmetric key cryptosystems and b) public key
cryptosystems. In symmetric key cryptosystems, encryption
and decryption are done using a shared private key. In public
key cryptosystems, encryption is done using a public key
while decryption is done with a private key that only the data
receiver knows. According to Howe [20], crowdsourcing
refers to the undertaking of a process traditionally performed
by an authorized representative (usually an employee) and
outsourcing to an indeterminate, generally large group of
people in the form of an open call for expressions of interest.
Common characteristics in crowdsourcing is the use of human
intelligence for solving problems that a machine could not
solve or would consume much more time and energy than a
human. Crowdsourcing is applied in many sections ranging
from cultural [21] to autonomous driving [22].
III. OVERVIEW OF SYSTEM ARCHITECTURE
The proposed system architecture is presented in Fig. 1. It
consists of two basic layers, each containing modules that
determine its functionality: i) the Middleware layer, that
includes both the edge components (e.g., sensors, actuators)
and a middleware module, responsible for managing the
communication between the two basic layers of DESMOS, ii)
the DESMOS platform, a cloud based layer that is used for
storing, processing of the collected data and generation of
reports that will be sent back to the Middleware.
Figure 1: DESMOS proposed System Architecture
In more details, the modules that will be part of the
Middleware layer include, mainly, mobile devices (e.g.,
smartphones and wearables) which will incorporate
embedded sensors to generate data and “fuse” the system with
them. Actuators will be also used (e.g., defibrillators), as part
of the system’s smart infrastructure, in order to increase the
performance of the system responding to certain, critical
events (e.g., health cardiac issues of visitors). In DESMOS
two mobile applications will be developed. The first one
would be used by the visitors before entering certain areas of
the city of Trikala, in order to enable them to enter, register
and use the smart capabilities provided by DESMOS system,
when those are needed. The second mobile application will be
used by the responsible personnel to gain access to detailed
information regarding on-going events that might need their
action. Wearables and button-like sensors will also serve this
layer mainly through the collection of data (e.g., user’s
location) that will provide to the system. A middleware
module will, also, be considered in this section, responsible to
handle all the heterogeneous edge devices (e.g., smartphones,
wearables) and manage the communication of the data
between the two basic aforementioned architecture
components, achieving bi-directional communication. The
design and development of the needed APIs will facilitate and
serve the needed inter-section communication. Special care
for data integrity and privacy will be applied for this
communication.
The DESMOS platform layer will be mainly considered as
a cloud implementation where the storing and processing of
data will take place. Specifically, this involves a semantic
repository (triple-store) for storing sensor observations, user
profiles, as well as ontology reasoning modules to implement
a set of data fusion and aggregation services. All in all, the
purpose of this layer is to semantically analyze the combined
available information in order to derive higher level events
and adapt the environment, taking into account both
contextual information and business policies. Example
adaptations include the generation of appropriate alerts to
inform local authorities and volunteers, as well as to prepare
certain devices to respond to incidents and protect
citizens/tourists, such as defibrillators. The specifics of each
layer are discussed in the following sections.
IV. MONITORING AND DATA COLLECTION
A. Sensor Types
The infrastructure takes advantage of two common sensor
types found in almost every mobile device: position and
optical sensors. Position sensors are based on radio navigation
systems and, more specific, on GPS (Global Positioning
System) which is the system used in every modern mobile
device and in IoT applications. Mobile applications will
acquire the location of the personnel and send the data to the
cloud. If an event happens, the system will be able to know
who is the closest person to that event and notify him/her.
Moreover, in case a citizen needs help, his/her location will be
able to be acquired using the GPS and notify the personnel.
Complementary metal oxide semiconductor (CMOS) camera
will be the choice for the optical sensor. Using the camera in
their mobile device, citizens and tourists will be able to send
photographs of critical parts were an event happens or a
vandalism has happened.
Communication between the cloud platform and the
mobile devices is achieved using both Wi-Fi and cellular
networks (i.e. LTE). Mobile devices are connected to the
internet using the existing Wi-Fi infrastructure in the testing
environment. However, if the connectivity is lost, the devices
can use the cellular networks to access the internet and
exchange data with the platform.
In addition, BLE technology is used. Children and other
special treatment people visiting the testing environment will
wear a bracelet equipped with BLE which broadcasts its
address in regular time intervals. The address is received by
the mobile devices and, together with the received signal
strength indication (RSSI), is sent to the platform. When the
person wearing the bracelet goes out of range from the mobile
devices, the system notifies the personnel about the event.
Furthermore, if a child goes far away from his/her parents, the
system is able to find the child using trilateration. Lastly,
buttons equipped with BLE technology will be given to
citizens who may need help. The citizen will press the button
and the device will broadcast its address. In the same way as
with bracelets, the address and the RSSI will be uploaded to
the platform where, using trilateration, the button’s location
will be calculated and a notification will be sent to the closest
personnel person.
B. Data Collection Infrastructure
A cloud infrastructure based on OpenStack [23] is used to
host the necessary services of the platform. Using the MQTT
protocol, the cloud infrastructure receives data from the
mobile devices. Each service that processes data, save data in
the cloud and informs the other services about the availability
of new data in order to trigger additional processing tasks.
Depending on the type of data, communication is performed
either using MQTT or REST APIs. When real-time data
propagation and analysis is needed, MQTT is used as the
means to communicate with other modules. Otherwise, REST
APIs are used for pushing data to the cloud and sending
respective output to other modules.
C. Crowdsourcing Techniques
The project will use groups of people in order to gather
data in two ways. In both ways, people will use an application
in a mobile device which will send the data to the cloud
infrastructure. In the first way, the users don’t have to do
anything other than installing and setting up the application
which runs in the background. It listens for broadcasted
addresses from BLE devices and sends the data in the cloud
infrastructure along with the device’s location. In the second
way, the users have to interact with the application to gather
and send data. The users will be able to send information about
an event to the cloud infrastructure using their mobile devices.
The information will include the location of the event, a short
description and an image. The event can be anything abnormal
in the surrounding area including a theft, a vandalism or
violence.
D. Data Protection and privacy
Taking into consideration the ever-increasing concerns
people have about their data, one of the most important
project’s target is to protect the users’ data privacy. The first
step in accomplishing this, is by protecting the
communications between the services and the mobile devices
using authentication, authorization and encryption
techniques. In that way, only the intended services have
access to data they need. Anonymization techniques are used,
such as the Privacy Protection Communication Gateway
(PPCG) [20]. Furthermore, in order to be compliant with
Europe’s strict directive regarding users’ data which started
in 2018 under the name “General Data Protection
Regulation” (GDPR) [24], only the necessary data for the
system’s functionality is gathered from the users. Finally, the
data will be used only for purposes related to the project and
will not be disclosed to third parties.
V. SEMANTIC REPRESENTATION AND ANALYSIS
A. Ontologies
In order to enable the use of deductive reasoning over the
collected IoT data to detect hazardous events, we designed the
DESMOS ontology. It is an ontology that leverage concepts
from SSN and SOSA [8], IoT-lite [10], Geo (i.e., WGS84) and
FOAF [13], an ontology that links people and information
using the Web. The entities of DESMOS ontology combined
with the aforementioned ontologies, covers most of the
concepts that are needed for detecting events and selecting the
appropriate actions.
Fig. 2 illustrates an abstract representation of these
ontologies and how they are linked with DESMOS ontology.
The main entities/concepts that we use for representing data
in DESMOS platform are the following:
SOSA: Sensor represents the devices or even agents
(including humans when it comes to crowdsourcing)
that make observations.
IoT-Lite: Coverage represents the network coverage of
a sensor.
SOSA: Observation represents the data that are
produced by the Sensor class.
DESMOS: Event represents the events that are
generated through the data processing (i.e.,
observations).
DESMOS: Category maps an event into a category of
interest, based on the defined use cases.
DESMOS: Action represents the appropriate actions
that are required when a specific event occurs.
FOAF: Agent represents the human beings or
organizations, while DESMOS: Role denotes their role
(e.g., public servants, volunteers etc.).
Geo: Point is used for representing the coordinates of
a sensor or an event.
Fig. 2. Abstract representation of the combination of existing ontologies with
DESMOS ontology.
B. Intepretation Layer
The interpretation layer provides a reasoning framework
over the ontology representation layer described earlier for
achieving context awareness and recognizing critical
situations. This is achieved through the combination of the
OWL 2 reasoning paradigm and the execution of SPARQL
rules [26] in terms of CONSTRUCT query patterns over RDF
graphs.
Although SPARQL is mostly known as a query language
for RDF, by using the CONSTRUCT graph pattern, it is able
to define SPARQL rules that can create new RDF data,
combining existing RDF graphs into larger ones. Such rules
are defined in the interpretation layer in terms of a
CONSTRUCT and a WHERE clause: the former defines the
graph patterns, i.e. the set of triple patterns that should be
added to the underlying RDF graph upon the successful
pattern matching of the graphs in the WHERE clause. As an
example, we present the following rule that is used to notify
the volunteers and to trigger the preparation of the defibrillator
that are closer to an incident reported as a heart attack.
CONSTRUCT {
[] a :Notification;
:to ?v;
:location ?l .
[] a :DevicePreparation;
:to ?d .
}
WHERE {
?e a :HeartAttack
:location ?l.
?v a : Volunteer ;
:near ?l .
?d a :Defibrillator ;
:near ?l .
}
More specifically, the rule matches heart attack events ?e
reported by people (either through the mobile app or using the
button-like sensors), retrieving at the same time the location ?l
of the incident. It then retrieves the volunteer(s) ?v and
defibrillator ?d that are closer to the incident. As described in
section IV, we use the GPS of the mobile apps to determine
the location of the volunteers, while the location of the
defibrillator is known in advance. When the graph patterns in
the WHERE clause is successfully matched, then the KB is
further populated with a notification and device preparation
instance. The former triggers the notification mechanisms of
the platform in order to inform the volunteer(s) about the type
of the incident and the location. The latter notification
activates the defibrillator actuator, i.e. the light above the
defibrillator is turned on.
VI. USE CASES
All of the use case scenarios were the result of two focus
groups with representatives from the theme park organizing
team, the city’s Municipal Authorities, as well as
representatives from the theme park security and health
personnel. During these meetings the participants were asked
to report, analyze and prioritize the most frequent security and
protection issues occurring both in the theme park and in the
city during the Christmas period. The use case scenarios that
came out from the focus groups were then presented, further
analyzed and finalized during project partners meeting. The
final three scenarios to be implemented are the following:
1) MylosKarpa & CityKarpa: "Certified KARPA users &
Defibrillators, Chronic Health Problems such as Diabetes,
Heart Diseases". This use case scenario is all about
accomplishing immediate and on time treatment of a health
incident by the appointed health personnel of the theme park
and the certified users of KARPA both in the area of the park
and in the city center.
2) MylosKidFinder: "Alarm and Locate children that have
been lost in the Theme Park". The second use case scenario
concerns applications that will enable the early identification
of children that have gone missing inside the Theme Park by
the staff and their parents.
3) MylosSense & CitySense: "Record & Real-time
reporting of natural disasters or vandalism of buildings -
monuments - points of interest.” The third use case scenario is
about real-time reporting of criminal acts, incidents of
violence, theft, etc. for the timely handling of dangerous
incidents and the recording of frequency as a preventive
criterion in the future in both the theme park and the city
center.
A. Description of the testing environment
The three use case scenarios will be implemented and
evaluated in two different places:
1) The Christmas Theme Park "Mill of the Elves" which
operates in the city of Trikala for 40 days and has a large
number of visitors (approximately 1.200.000 visitors per year
during 40 days of operation). In this case all use case scenarios
will be tested.
2) The city-center of Trikala. The protection and security
challenges the time period of the Christmas Theme Park are
increased. In this place, the first and the third use case scenario
will be evaluated.
B. Use case scenario MylosSense & City Sense
The service implementation of the MylosSense &
CitySense use cases concerns the early intervention by the
theme parks appointed personnel in events involving crime
and natural disasters through the direct recording and
reporting of the event on the platform. The staff (employees
and volunteers) will have an alert button application that will
enable them to immediately record a dangerous event and
categorize it so that security personnel can decode the incident
and intervene accordingly. Each event will be recorded on the
platform mentioning the exact location. This service will
enable staff to intervene in a timely manner on dangerous
incidents and to deal with them effectively or even prevent
them. At the same time, the platform allows the parks
management team to gather information about when and
where most dangerous incidents take place. This information
will be useful when re-designing the theme park the following
years. Additionally, this service will be tested in the city of
Trikala as every citizen or visitor will be able to download the
application and send alerts of dangerous incidents taking place
at the city center.
VII. CONCLUSION AND FUTURE WORK
In this paper we presented the DESMOS platform, an
intelligent interconnected infrastructure for public security
and protection domain. Its architecture modules facilitate the
direct and fast response for detecting urgent events and
selecting the appropriate actions. The platform requirements
derived from the Themes Park personnel in order to achieve
on the highest level the public needs. The technologies that are
being developed will be validated in two pilots in two different
public places which protection and security challenges are
higher.
ACKNOWLEDGMENT
This research has been co-financed by the European Union
and Greek national funds through the Operational Program
Competitiveness, Entrepreneurship and Innovation, under the
call RESEARCH-CREATE-INNOVATE (project code:
T1EDK-03487).
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... The DESMOS architecture (originally published in [23], Figure 6) consists of three basic layers: (a) Hardware (mobile devices, sensors, actuators); (b) Middleware (interaction between edge components, i.e., sensors, actuators, as well as the interconnection between edge layers of DESMOS); and (c) Cloud (data storage, processing, and reporting). The cloud includes two databases: (i) For data processing; and (ii) for localization. ...
... DESMOS architecture. Adapted with permission from ref.[23]. Copyright 2019, IEEE. ...
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