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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.

Keywords: Bayesian networks; Carrier mobility; Inference engines; Information systems; Sensor networks; Traffic congestion; Anomaly detection; Bayesian inference; Cellular network; Crisis management; Emergency response; Large scale sensor network; Mobile communication networks; Vehicular traffic scenarios; Accidents

Track: Analytical Modelling and Simulation

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Abstract: In this paper we describe a disaster management system. It is assumed that each individual in the field is equipped with a PDA (Personal Digital Assistant) and that can communicate with other PDAs in the vicinity. Together the PDAs form an ad-hoc network. Users can enter their own observations to the PDA, like the position of victims, or a description of the current situation at particular location (e.g. smoke, emergency exits, traffic congestion). This information is entered in a special iconic language. Reversibly, the PDAs inform the users on the overall current situation of the crisis. In order to come to a shared view of the world, the knowledge that is present in the network has to be shared and fused. The proposed way to communicate is via a shared blackboard. This approach facilitates communication in a time and place independent way.

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	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. Keywords: Bayesian networks; Carrier mobility; Inference engines; Information systems; Sensor networks; Traffic congestion; Anomaly detection; Bayesian inference; Cellular network; Crisis management; Emergency response; Large scale sensor network; Mobile communication networks; Vehicular traffic scenarios; Accidents Track: Analytical Modelling and Simulation Share to FB \| Save citation: RTF PDF LaTeX \| Export record: Atom XML MODS XML OAI_DC XML ODF XML SRW_DC XML SRW_MODS XML
	Bogdan Tatomir, & Leon J.M. Rothkrantz. (2005). Crisis management using mobile ad-hoc wireless networks. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management (pp. 147–149). Brussels: Royal Flemish Academy of Belgium. Abstract: In this paper we describe a disaster management system. It is assumed that each individual in the field is equipped with a PDA (Personal Digital Assistant) and that can communicate with other PDAs in the vicinity. Together the PDAs form an ad-hoc network. Users can enter their own observations to the PDA, like the position of victims, or a description of the current situation at particular location (e.g. smoke, emergency exits, traffic congestion). This information is entered in a special iconic language. Reversibly, the PDAs inform the users on the overall current situation of the crisis. In order to come to a shared view of the world, the knowledge that is present in the network has to be shared and fused. The proposed way to communicate is via a shared blackboard. This approach facilitates communication in a time and place independent way. Keywords: Ad hoc networks; Disaster prevention; Information systems; Personal digital assistants; Traffic congestion; Wireless telecommunication systems; Crisis management; Current situation; Disaster management; Distributed blackboard; Icons; Mobile ad hoc wireless networks; Pda; Wireless communications; Management information systems Track: POSTER SESSION Share to FB \| Save citation: RTF PDF LaTeX \| Export record: Atom XML MODS XML OAI_DC XML ODF XML SRW_DC XML SRW_MODS XML