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%.
|
Olof Görnerup, Per Kreuger, & Daniel Gillblad. (2013). Autonomous accident monitoring using cellular network data. 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. 638–646). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Mobile communication networks constitute large-scale sensor networks that generate huge amounts of data that can be refined into collective mobility patterns. In this paper we propose a method for using these patterns to autonomously monitor and detect accidents and other critical events. The approach is to identify a measure that is approximately time-invariant on short time-scales under regular conditions, estimate the short and long-term dynamics of this measure using Bayesian inference, and identify sudden shifts in mobility patterns by monitoring the divergence between the short and long-term estimates. By estimating long-term dynamics, the method is also able to adapt to long-term trends in data. As a proof-of-concept, we apply this approach in a vehicular traffic scenario, where we demonstrate that the method can detect traffic accidents and distinguish these from regular events, such as traffic congestions.
|
Faisal Luqman, & Martin Griss. (2011). Leveraging mobile context for effective collaboration and task management. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Collaboration and task management is challenging in distributed, dynamically-formed teams, typical in large scale disaster response scenarios. Ineffective collaboration may potentially result in poor performance and loss of life. The increased adoption of sensor rich mobile devices allow for mobile context to be leveraged. In this paper, we present Overseer, an agent-based system that exploits context information from mobile devices to facilitate collaboration and task allocation. We describe how mobile context can be used to create dynamic role-based assignments to enhance collaboration and effective task management.
|
