The European Integrated Border Management (IBM) Strategy addresses the aspect of the “four-tier access control model” in order to develop and implement IBM at national and European Union (EU) level: (i) measures undertaken with third countries or service providers; (ii) cooperation with neighboring countries; (iii) border control and counter-smuggling measures and (iv) control measures within the area of free movement. Within this challenging objective lies the need for a well defined framework and technical platform supporting cross-agency and cross-country collaboration and information exchange. In this paper we present research results in the context of an innovative risk-based border management paradigm shift and exchange of information related to risks and alerts perceived and reported across all involved stakeholders (Border Guards, Custom Authorities and Border Control Point (BCP) Operators), both within EU Member States and Schengen area, as well as collaboration with third countries. The presented framework includes an integrated platform implementation demonstrated in a relevant operational environment of air, land and sea BCP.
Dempster Shafer theory (DST) of evidence is an effective approach for decision analysis, especially in cases of high uncertainty. It is an evidence based probabilistic reasoning technique which differs from the traditional decision support methods by introducing belief functions not only on sets of propositions but also on all corresponding subsets which provides the capability of distinguishing beliefs on propositions from potential uncertainties among them. However,a significant factor that determines the reliability of reasoning systems is the Fairness that characterizes the processes and the outcomes of the systems. In this paper, it is proposed a modified fairness – by – design Dempster – Shafer reasoning system that quantitative fairness metrics are taken into consideration within the algorithmic procedure. For each evidence provided, a dedicated fairness estimation function determines whether the evidence is compliant with the pre – defined Ethics/Legal regulations of a given Fairness framework. Each fairness estimation function acts as a doubt factor for the evidence and reduces the belief value of the corresponding hypothesis and increases the related to the hypothesis uncertainty. This way unfairness limits the trustfulness of the corresponding evidence and as a result weakens its contribution. The proposed solution is tested against a simulated queue surveillance use case scenario, where 2 CCTV cameras input are used for inferring malicious behavior of people in the queue. For proof of concept, the one of the two cameras, introduces discrimination bias which violates the pre-defined Fairness regulation. Results show that the modified DST systems tolerates unfairness effectively while retaining algorithmic accuracy to a satisfying level.
KEYWORDS: Information security, Computer security, Control systems, System integration, C2I, Sensors, Computer simulations, Visualization, Data fusion, Evolutionary algorithms
Increased passenger flows at airports and the need for enhanced security measures from ever increasing and more complex threats lead to long security lines, increased waiting times, as well as often intrusive and disproportionate security measures that result in passenger dissatisfaction and escalating costs. As expressed by the International Air Transport Association (IATA), the Airports Council International, (ACI) and the respective industry, todays airport security model is not sustainable in the long term. The vision for a seamless and continuous journey throughout the airport and efficient security resources allocation based on intelligent risk analysis, set the challenging objectives for the Smart Security of the airport of the future. FLYSEC, a research and innovation project funded by the European Commission under the Horizon 2020 Framework Programme, developed and demonstrated an innovative integrated and risk-based end-to-end airport security process for passengers, while enabling a guided and streamlined procedure from landside to airside and into the boarding gates, offering for the first time an operationally validated innovative concept for end-to-end aviation security. With a consortium of eleven highly specialised partners, coordinated by the National Center for Scientific Research “Demokritos,” FLYSEC developed and tested an integrated risk-based security system with a POC (Proof Of Concept) validation field trial at the Schönhagen Airport in Berlin, and a final pilot demonstration under operational conditions at the Luxembourg International Airport.
Maritime “awareness” is currently a top priority for Europe in regards with the marine environment and climate change, as well as the maritime security, border control against irregular immigration and safety. MARINE-EO is the first European Earth Observation (EO) Pre-Commercial Procurement (PCP) project and aims at the following objectives: (i) Develop, test and validate two sets of demand-driven EO-based services, adopted on open standards, bringing incremental or radical innovations in the field of maritime awareness and leveraging on the existing Copernicus Services and other products from the Copernicus portfolio, (ii) Propose a set of “support” / “envelop” services which will better integrate the EO-based services to the operational logic and code of conduct, (iii) Strengthen transnational collaboration in maritime awareness sector by facilitating knowledge transfer and optimization of resources for the public authorities participating in the buyers group.
In this paper the architecture of an autonomous human behavior detection system is presented. The proposed system architecture is intended for Security and Safety surveillance systems that aim to identify adverse events or behaviors which endanger the safety of people or their well-being. Applications include monitoring systems for crowded places (Malls, Mass transport systems, other), critical infrastructures, or border crossing points. The proposed architecture consists of three modules: (a) the event detection module combined with a data fusion component responsible for the fusion of the sensor inputs along with relevant high level metadata, which are pre-defined features that are correlated with a suspicious event, (b) an adaptive learning module which takes inputs from official personnel or healthcare personnel about the correctness of the detected events, and uses it in order to properly parameterise the event detection algorithm, and (c) a statistical and stochastic analysis component which is responsible for specifying the appropriate features to be used by the event detection module. Statistical analysis estimates the correlations between the features employed in the study, while stochastic analysis is used for the estimation of dependencies between the features and the achieved system performance.
KEYWORDS: Reliability, Analytics, Detection and tracking algorithms, Telecommunications, Data modeling, Human-machine interfaces, Flame detectors, Visualization, Information fusion, Sensors, Web 2.0 technologies, Information operations
In this paper a solution is presented aiming to assist the early detection and localization of a fire incident by exploiting
crowdsourcing and unofficial civilian online reports. It consists of two components: (a) the potential fire incident
detection and (b) the visualization component. The first component comprises two modules that run in parallel and aim
to collect reports posted on public platforms and conclude to potential fire incident locations. It collects the public
reports, distinguishes reports that refer to a potential fire incident and store the corresponding information in a structured
way. The second module aggregates all these stored reports and conclude to a probable fire location, based on the
amount of reports per area, the time and location of these reports. In further the result is entered to a fusion module
which combines it with information collected by sensors if available in order to provide a more accurate fire event
detection capability. The visualization component is a fully – operational public information channel which provides
accurate and up-to-date information about active and past fires, raises awareness about forest fires and the relevant
hazards among citizens. The channel has visualization capabilities for presenting in an efficient way information
regarding detected fire incidents fire expansion areas, and relevant information such as detecting sensors and reporting
origin. The paper concludes with insight to current CONOPS end user with regards to the inclusion of the proposed
solution to the current CONOPS of fire detection.
KEYWORDS: Humidity, Monte Carlo methods, Infrared sensors, Temperature metrology, Sensors, Flame detectors, Data fusion, Web 2.0 technologies, Control systems, Probability theory, Reliability, Unmanned aerial vehicles, Infrared cameras, Environmental sensing
The aim of this paper is to present the sensor monitoring and decision level fusion scheme for early fire detection which
has been developed in the context of the AF3 Advanced Forest Fire Fighting European FP7 research project, adopted
specifically in the OCULUS-Fire control and command system and tested during a firefighting field test in Greece with
prescribed real fire, generating early-warning detection alerts and notifications. For this purpose and in order to improve
the reliability of the fire detection system, a two-level fusion scheme is developed exploiting a variety of observation
solutions from air e.g. UAV infrared cameras, ground e.g. meteorological and atmospheric sensors and ancillary sources
e.g. public information channels, citizens smartphone applications and social media. In the first level, a change point
detection technique is applied to detect changes in the mean value of each measured parameter by the ground sensors
such as temperature, humidity and CO2 and then the Rate-of-Rise of each changed parameter is calculated. In the second
level the fire event Basic Probability Assignment (BPA) function is determined for each ground sensor using Fuzzy-logic
theory and then the corresponding mass values are combined in a decision level fusion process using Evidential
Reasoning theory to estimate the final fire event probability.
Location-based and navigation services are really needed to help visitors and audience of big events, complex buildings, shopping malls, airports and large companies. However, the lack of GPS and proper mapping indoors usually renders location-based applications and services useless or simply not applicable in such environments. SYNAISTHISI introduces a mobile application for smartphones which offers navigation capabilities outside and inside buildings and through multiple floor levels. The application comes together with a suite of helpful services, including personalized recommendations, visit/event management and a helpful search functionality in order to navigate to a specific location, event or person. As the user finds his way towards his destination, NFC-enabled checkpoints and bluetooth beacons assist him, while offering re-routing, check-in/out capabilities and useful information about ongoing meetings and nearby events. The application is supported by a back-end GIS system which can provide a broad and clear view to event organizers, campus managers and field personnel for purposes of event logistics, safety and security. SYNAISTHISI system comes with plenty competitive advantages including (a) Seamless Navigation as users move between outdoor and indoor areas and different floor levels by using innovative routing algorithms, (b) connection to and powered by IoT platform, for localization and real-time information feedback, (c) dynamic personalized recommendations based on user profile, location and real-time information provided by the IoT platform and (d) Indoor localization without the need for expensive infrastructure and installations.
KEYWORDS: Security technologies, Video surveillance, Information security, In vitro testing, System integration, Control systems, Information science, Telecommunications, In vivo imaging
Complementing the ACI/IATA efforts, the FLYSEC European H2020 Research and Innovation project (http://www.fly-sec.eu/) aims to develop and demonstrate an innovative, integrated and end-to-end airport security process for passengers, enabling a guided and streamlined procedure from the landside to airside and into the boarding gates, and offering for an operationally validated innovative concept for end-to-end aviation security. FLYSEC ambition turns through a well-structured work plan into: (i) innovative processes facilitating risk-based screening; (ii) deployment and integration of new technologies and repurposing existing solutions towards a risk-based Security paradigm shift; (iii) improvement of passenger facilitation and customer service, bringing security as a real service in the airport of tomorrow;(iv) achievement of measurable throughput improvement and a whole new level of Quality of Service; and (v) validation of the results through advanced “in-vitro” simulation and “in-vivo” pilots. On the technical side, FLYSEC achieves its ambitious goals by integrating new technologies on video surveillance, intelligent remote image processing and biometrics combined with big data analysis, open-source intelligence and crowdsourcing. Repurposing existing technologies is also in the FLYSEC objectives, such as mobile application technologies for improved passenger experience and positive boarding applications (i.e. services to facilitate boarding and landside/airside way finding) as well as RFID for carry-on luggage tracking and quick unattended luggage handling. In this paper, the authors will describe the risk based airport security management system which powers FLYSEC intelligence and serves as the backend on top of which FLYSEC’s front end technologies reside for security services management, behaviour and risk analysis.
KEYWORDS: Control systems, Web 2.0 technologies, Geographic information systems, Visualization, Information visualization, Sensors, Ions, Telecommunications, Reliability, Flame detectors
AF3 (Advanced Forest Fire Fighting2) is a European FP7 research project that intends to improve the efficiency of current fire-fighting operations and the protection of human lives, the environment and property by developing innovative technologies to ensure the integration between existing and new systems. To reach this objective, the AF3 project focuses on innovative active and passive countermeasures, early detection and monitoring, integrated crisis management and advanced public information channels. OCULUS Fire is the innovative control and command system developed within AF3 as a monitoring, GIS and Knowledge Extraction System and Visualization Tool. OCULUS Fire includes (a) an interface for real-time updating and reconstructing of maps to enable rerouting based on estimated hazards and risks, (b) processing of GIS dynamic re-construction and mission re-routing, based on the fusion of airborne, satellite, ground and ancillary geolocation data, (c) visualization components for the C2 monitoring system, displaying and managing information arriving from a variety of sources and (d) mission and situational awareness module for OCULUS Fire ground monitoring system being part of an Integrated Crisis Management Information System for ground and ancillary sensors. OCULUS Fire will also process and visualise information from public information channels, social media and also mobile applications by helpful citizens and volunteers. Social networking, community building and crowdsourcing features will enable a higher reliability and less false alarm rates when using such data in the context of safety and security applications.
Market analysis studies of recent years have shown a steady and significant increase in the usage of RFID technology. Key factors for this growth were the decreased costs of passive RFIDs and their improved performance compared to the other identification technologies. Besides the benefits of RFID technologies into the supply chains, warehousing, traditional inventory and asset management applications, RFID has proven itself worth exploiting on experimental, as well as on commercial level in other sectors, such as healthcare, transport and security. In security sector, airport security is one of the biggest challenges. Airports are extremely busy public places and thus prime targets for terrorism, with aircraft, passengers, crew and airport infrastructure all subject to terrorist attacks. Inside this labyrinth of security challenges, the long range detection capability of the UHF passive RFID technology can be turned into a very important tracking tool that may outperform all the limitations of the barcode tracking inside the current airport security control chain. The Integrated Systems Lab of NCSR Demokritos has developed an RFID based Luggage and Passenger tracking system within the TASS (FP7-SEC-2010-241905) EU research project. This paper describes application scenarios of the system categorized according to the structured nature of the environment, the system architecture and presents evaluation results extracted from measurements with a group of different massive production GEN2 UHF RFID tags that are widely available in the world market.
KEYWORDS: Antennas, Multiplexers, Computer architecture, Multiplexing, Received signal strength, Control systems, Statistical analysis, Algorithm development, System identification, Electromagnetism
Radio frequency identification (RFID) systems based on passive tags are used successfully in a wide range of object
identification applications. However, the increasing needs to meet new demands on applications of localization and
tracking create a new field for evolution of the RFID technology. This paper presents the design, implementation, and
evaluation of a cost-effective localization system for in-building usage that is able to localize objects that carry passive
RFID tags. The RFID reading is performed by a single Reader and an array of directional antennas through multiplexing.
Evaluation and experimental results from three localization algorithms based on RSSI are presented.
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