|
|
Imane Benkhelifa, Samira Moussaoui, & Nadia Nouali-Taboudjemat. (2013). Locating emergency responders using mobile wireless sensor networks. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 432–441). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Emergency response in disaster management using wireless sensor networks has recently become an interest of many researchers in the world. This interest comes from the growing number of disasters and crisis (natural or man-made) affecting millions of lives and the easy-use of new and cheap technologies. This paper details another application of WSN in the post disaster scenario and comes up with an algorithm for localization of sensors attached to mobile responders (firefighters, policemen, first aid agents, emergency nurses, etc) while assisted by a mobile vehicle (fire truck, police car, or aerial vehicle like helicopters) called mobile anchor, sent to supervise the rescue operation. This solution is very efficient and rapidly deployable since no pre-installed infrastructure is needed. Also, there is no need to equip each sensor with a GPS receiver which is very costly and may increase the sensor volume. The proposed technique is based on the prediction of the rescuers velocities and directions considering previous position estimations. The evaluation of our solution shows that our technique takes benefit from prediction in a more effective manner than previous solutions. The simulation results show that our algorithm outperforms conventional Monte Carlo localization schemes by decreasing estimation errors with more than 50%.
|
|
|
|
José Miguel Castillo, Starr Roxanne Hiltz, & Murray Turoff. (2012). Monte Carlo and decision making support in crisis management. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Simulation is an interdisciplinary science applicable to many branches of knowledge. One field in which simulation is relevant is decision making support (DMS), in which we use computers to run models of real or possible scenarios in order to evaluate alternative actions before carrying them out. We will obtain a useful simulation system only when the model (engine of the simulation process) has been made accurately to represent reality. Thus it is necessary to use a methodology that helps us to construct a simulation system. This paper presents some classifications of simulation systems and an introduction to the Monte Carlo method, with the objective of creating a framework of application of this method for the construction of simulation systems for decision making support in crisis management. One area of applicability is scenario-based simulations for training for cross-national teams to cooperate in large scale disasters. The final aim of this research will be the recommendation of standards and methodologies to build simulation systems in crisis management, specifically in decision making support. © 2012 ISCRAM.
|
|
|
|
Felix Wex, Guido Schryen, & Dirk Neumann. (2012). Operational emergency response under informational uncertainty: A fuzzy optimization model for scheduling and allocating rescue units. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Coordination deficiencies have been identified after the March 2011 earthquakes in Japan in terms of scheduling and allocation of resources, with time pressure, resource shortages, and especially informational uncertainty being main challenges. We suggest a decision support model that accounts for these challenges by drawing on fuzzy set theory and fuzzy optimization. Based on requirements from practice and the findings of our literature review, the decision model considers the following premises: incidents and rescue units are spatially distributed, rescue units possess specific capabilities, processing is non-preemptive, and informational uncertainty through linguistic assessments is predominant when on-site units vaguely report about incidents and their attributes, or system reports are not exact. We also suggest a Monte Carlo-based heuristic solution procedure and conduct a computational evaluation of different scenarios. We benchmark the results of our heuristic with results yielded through applying a greedy approach. The results indicate that using our Monte Carlo simulation to solve the decision support model inspired by fuzzy set theory can substantially reduce the overall harm. © 2012 ISCRAM.
|
|
