Emergency Response Simulation Using Wireless Sensor Networks

Abstract

During emergency response situations, decisions have to be made in a timely manner. Multiple entities have to be optimally coordinated and numerous resources must be allocated efficiently, creating a very interesting and challenging technical problem. In this paper we present a simulation system that models the evacuation of a multi-storey building. Autonomous intelligent agents are used to represent various types of actors that interact inside a virtual physical world. We also model virtual hazards, such as fire, that spread inside the building evacuation simulator. A real wireless sensor network is used to monitor the spread of the hazards while an external event generator provides input to the sensors. We study the effect of different disaster scenarios and agent behaviours, such as human behaviour during an emergency, on the result of the evacuation procedure. Our initial results indicate that the safety of the evacuees and the evacuation time depend on local interactions between the participants and are affected by the actors' decisions. The integration with the wireless sensor network gives us the opportunity to investigate the effect of sensed information on resource allocation and allows us to study the impact of network issues on the decision making process.

Erol Gelenbe: A Career in Multi-Disciplinary Probability Models

“…His team studied fast decision algorithms based on learning a wide range of problem instances and their optimal rescuer and rescue vehicle allocations [38,158,159], and selecting in real-time the allocation that best matches the current observed emergency situation. They also studied low-cost, light-weight and disruption tolerant techniques that can offer robust communications in emergency environments [173], and many of these methods actually span both the military and the civilian domain [36,171,172,186,203].…”

The Random Neural Network Applied to an Intelligent Search Assistant

“…The RNN represents more closely how signals are transmitted in many biological neural networks where they actual travel as spikes or impulses, rather than as analogue signal levels. It has been used in different applications including network routing with cognitive packet networks [10], search for exit routes for evacuees in emergency situations [11,12], pattern based search for specific objects [13], video compression [14], and image texture learning and generation [15].…”

Problem Decomposition for Evacuation Simulation Using Network Flow

“…Sharma [4] described modeling of occupant personality as an agent behaviour strategy, featuring panic states. Filippoupolitis et al's [5] distributed simulator incorporated a simulated sensor network in a graph-model based simulation. Korhonen et al's [7] work combined a fluid-dynamics-based fire simulation with a field-based pedestrian model.…”

“…We make use of a 2D vector-space pedestrian simulator to simulate the individual movements of pedestrians in the building. This simulator provides a realistic model of pedestrian movement but is computationally intensive compared to simpler models such as Cellular Automata [2,4,10] or Graph-based simulations [5]. The usefulness of any prediction information is contingent on both the timeliness and the accuracy of the simulation that generated it.…”

“…Simulating the movement of evacuating pedestrians can provide useful information to building designers and to fire safety officials, including evacuation time estimates and locations at risk of traffic congestion or evacuee injury [1][2][3][4][5][6][7]. We make use of a 2D vector-space pedestrian simulator to simulate the individual movements of pedestrians in the building.…”