Avgoustinos Filippoupolitis

Distributed Building Evacuation Simulator for Smart Emergency Management

We describe a distributed simulation tool which addresses the unique needs for the simulation of emergency response scenarios. The simulation tool adopts the multi-agent paradigm, so as to facilitate the modelling of diverse and autonomous agents, and it provides mechanisms for the interaction of the entities that are being simulated. It operates in a distributed fashion to reduce the simulation time required for such large-scale systems. The simulation tool represents the individuals that need to be evacuated, the resources that contribute to the evacuation including human rescuers, and other active resources and entities which may include robots and which can autonomously interact with the environment and with each other and take individual or collaborative decisions. We illustrate the tool with an application and compare the results for both centralized and distributed execution. Our results also show the significant reduction in execution time that is achieved for different degrees of distribution of the simulator on multiple servers.

A distributed decision support system for Building Evacuation

The evacuation of a building is a challenging problem, since the evacuees most of the times do not know or do not follow the optimal evacuation route. Especially during an ongoing hazard present in the building, finding the best evacuation route becomes harder as the conditions along the paths change in the course of the evacuation procedure. In this paper we propose a distributed system that will compute the best evacuation routes in real-time, while a hazard is spreading inside the building. The system is composed of a network of decision nodes and sensor nodes, positioned in specific locations inside the building. The recommendations of the decision nodes are computed in a distributed manner, at each of the decision nodes, which then communicate them to evacuees or rescue personnel located in their vicinity. We evaluate our proposed system in various emergency scenarios, using a multi-agent simulation platform for Building Evacuation. Our results indicate that the presence of the system improves the outcome of the evacuation with respect to the evacuation time and the injury level of the evacuees.

Intelligent Navigation Systems for Building Evacuation

Intelligent navigation systems can significantly improve the evacuation of civilians in urban emergencies by providing dynamic directions to the evacuees as the hazard spreads. In this paper, we propose two distributed and adaptive systems for building evacuation. The first system, called intelligent evacuation system (IES), is composed of a wireless network of static decision nodes (DNs) installed in the building which provide movement decision support to evacuees by directing them to exits via less hazardous routes. The second system, called opportunistic communications based evacuation system (OCES), is based on mobile communication nodes (CNs) carried by civilians, which form an opportunistic network to exchange information on the situation and provide directions to civilians. We evaluate our systems’ performance with simulation experiments of a three-floor office building. Our results indicate that both systems can greatly improve the outcome of an emergency.

Spatial Computers for Emergency Support

We present two spatially distributed computing systems that operate in a building and provide intelligent navigation services to people for evacuation purposes. These systems adapt to changing conditions by monitoring the building and using local communication and computation for determining the best evacuation paths. The first system, called distributed evacuation system (DES), comprises a network of decision nodes (DNs) positioned at specific locations inside the building. DNs provide people with directions regarding the best available exit. The second system, called opportunistic emergency support system (OESS), consists of mobile communication nodes (CNs) carried by people. CNs form an opportunistic network in order to exchange information regarding the hazard and to direct the evacuees towards the safest exit. BothDESandOESSemploy sensor nodes deployed at fixed locations for monitoring the hazard.We evaluate the spatial systems using simulation experiments with a purpose-built emergency simulator called DBES.We show how parameters such as the frequency of information exchange and communication range affect the system performance and evacuation outcome.

Physical indicators of cyber attacks against a rescue robot

Responding to an emergency situation is a challenging and time critical procedure. The primary goal is to save lives and this is directly related to the speed and efficiency at which help is provided to the victims. Rescue robots are able to benefit an emergency response procedure by searching for survivors, providing access to inaccessible areas and establishing an on-site communication network. This paper investigates how a cyber attack on a rescue robot can adversely affect its operation and impair an emergency response operation. The focus is on identifying physical indicators of an ongoing cyber attack, which can help to design more efficient detection and defense mechanisms. A number of experiments have been conducted on an Arduino based robot, under different cyber attack scenarios. The results show that the cyber attacks effects have physical features that can be used in order to improve the robots robustness against this type of threat.

An Emergency Response System for Intelligent Buildings

Finding the best evacuation path during an emergency situation inside a building is a challenging task, due to the dynamically changing conditions and the strict time constraints. Information systems can benefit the evacuation process by providing directions to the evacuees in an efficient and timely manner. In this paper we propose the use of such a system and evaluate it with a specialised software platform that we have developed for simulation of disasters in buildings. The system provides movement decision support to evacuees by directing them through the less hazardous routes to an exit. It is composed of a network of Decision Nodes and sensor nodes, positioned in specific locations inside the building. The recommendations of the Decision Nodes are computed in a distributed manner, at each of the Decision Nodes, which then communicate them to evacuees or rescue personnel located in their vicinity. The system computes the best evacuation routes in real-time, while a hazard is spreading inside the building. It also takes into account the spatial characteristics of hazard propagation inside a confined space. Our simulation results show that the outcome of the evacuation procedure is improved by the use of the decision support system.

An Adaptive System for Movement Decision Support in Building Evacuation

In this paper we propose the use of a system that provides movement decision support to evacuees. We first present a fully distributed system, which takes into account the spatial characteristics of hazard propagation. We also design and evaluate a system that is based on a decentralised architecture. We use a multi-agent simulation platform for building evacuation that we developed, in order to evaluate our proposed systems.

Autonomous navigation systems for emergency management in buildings

The evacuation of urban areas during an emergency is complex and challenging due to the dynamic conditions and ambiguity of information available to people in the affected area. Autonomous navigation systems can improve the outcome of such evacuations by providing up-to-date guidance and directions to people during the emergency. In this paper we present two distributed navigation systems deployed inside a confined space, such as a building, that use simple but effective communications to gather and disseminate information for the computation of evacuation paths. The first system is composed of a network of static decision nodes (DNs) positioned in the building, where DNs distributedly compute the best paths using local communication and computation, and each DN provides directions to people in its vicinity. The second system is composed of mobile communication nodes (CNs) carried by the people in the area. CNs form an opportunistic network in order to exchange information regarding the hazard and each CNs directs its user towards the safest/closest exit. Sensor nodes pre-deployed in the building monitor the environment and provide their measurements to both systems. We investigate the effect of failures of DNs on the evacuation outcome and study how the two systems can be used in conjunction to overcome such problems. A multi-agent simulation platform is used for the performance evaluation of our proposed systems in evacuation scenarios inside a three-floor building.

Occupancy detection for building emergency management using BLE beacons

Being able to reliable estimate the occupancy of areas inside a building can prove beneficial for managing an emergency situation, as it allows for more efficient allocation of resources such as emergency personnel. In indoor environments, however, occupancy detection can be a very challenging task. A solution to this can be provided by the use of Bluetooth Low Energy (BLE) beacons installed in the building. In this work we evaluate the performance of a BLE based occupancy detection system geared towards emergency situations that take place inside buildings. The system is composed of BLE beacons installed inside the building, a mobile application installed on occupants’ mobile phones and a remote control server. Our approach does not require any processing to take place on the occupants’ mobile phones, since the occupancy detection is based on a classifier installed on the remote server. Our real-world experiments indicated that the system can provide high classification accuracy for different numbers of installed beacons and occupant movement patterns.

Location-Enhanced Activity Recognition in Indoor Environments Using Off the Shelf Smart Watch Technology and BLE Beacons

Activity recognition in indoor spaces benefits context awareness and improves the efficiency of applications related to personalised health monitoring, building energy management, security and safety. The majority of activity recognition frameworks, however, employ a network of specialised building sensors or a network of body-worn sensors. As this approach suffers with respect to practicality, we propose the use of commercial off-the-shelf devices. In this work, we design and evaluate an activity recognition system composed of a smart watch, which is enhanced with location information coming from Bluetooth Low Energy (BLE) beacons. We evaluate the performance of this approach for a variety of activities performed in an indoor laboratory environment, using four supervised machine learning algorithms. Our experimental results indicate that our location-enhanced activity recognition system is able to reach a classification accuracy ranging from 92% to 100%, while without location information classification accuracy it can drop to as low as 50% in some cases, depending on the window size chosen for data segmentation.