Mitchell L. Moss, & Anthony M. Townsend. (2006). Disaster forensics: Leveraging crisis information systems for social science. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 305–312). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: This paper contributes to the literature on information systems in crisis management by providing an overview of emerging technologies for sensing and recording sociological data about disasters. These technologies are transforming our capacity to gather data about what happens during disasters, and our ability to reconstruct the social dynamics of affected communities. Our approach takes a broad review of disaster research literature, current research efforts and new reports from recent disasters, especially Hurricane Katrina and the Indian Ocean Tsunami. We forecast that sensor networks will revolutionize conceptual and empiricial approaches to research in the social sciences, by providing unprecedented volumes of high-quality data on movements, communication and response activities by both formal and informal actors. We conclude with a set of recommendations to designers of crisis management information systems to design systems that can support social science research, and argue for the inclusion of post-disaster social research as a design consideration in such systems.
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Daniel Hahn. (2007). Non-restrictive linking in wireless sensor networks for industrial risk management. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 605–609). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The OSIRIS project addresses the disaster management workflow in the phases of risk monitoring and crisis management. Risk monitoring allows the continuous observation of endangered areas combined with sensor deployment strategies. The crisis management focuses on particular events and the support by sensor networks. Four complementary live demonstrations will validate the OSIRIS approach. These demonstrations include water contamination, air pollution, south European forest fire, and industrial risk monitoring. This paper focuses on the latter scenario: the industrial risk monitoring. This scenario offers the special opportunity to demonstrate the relevance of OSIRIS by covering all the aspects of monitoring, preparation and response phases of both environmental risk and crisis management. The approach focuses on non-restrictive linking in a wireless sensor network in order to facilitate the addition and removal of nodes providing open interaction primitives allowing the comfortable integration, exclusion, and modification. A management layer with an event-triggered and service-based middleware is proposed. A live lab with real fire is illustrated.
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Mike Botts, George Percivall, Carl Reed, & John Davidson. (2008). OGC® sensor web enablement: Overview and high level architecture. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 713–723). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: A precursor paper (also available as an OGC White Paper) provides a high-level overview of and architecture for the Open Geospatial Consortium (OGC) standards activities that focus on sensors, sensor networks, and a concept called the “Sensor Web”. This OGC focus area is known as Sensor Web Enablement (SWE). For readers interested in greater technical and architecture details, please download and read the OGC SWE Architecture Discussion Paper titled “The OGC Sensor Web Enablement Architecture” (OGC document 06-021r1).
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Lívia C. Degrossi, Guilherme G. Do Amaral, Eduardo S. M. De Vasconcelos, João Porto De Albuquerque, & Jo Ueyama. (2013). Using wireless sensor networks in the sensor web for flood monitoring in Brazil. 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. 458–462). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Flood is a critical problem that will increase as a result of climate changes. The problem of flooding is particularly challenging over the rainy season in tropical countries like Brazil. In this context, wireless sensor networks that are capable of sensing and reacting to water levels hold the potential of significantly reducing the damage, health-risks and financial impact of events. In this paper, we aim to outline our experiences with developing wireless sensor network for flood monitoring in Brazil. Our approach is based on Open Geospatial Consortium's (OGC) Sensor Web Enablement (SWE) standards, so as to enable the collected data to be shared in an interoperable and flexible manner. We describe the application of our approach in a real case study in the city of São Carlos/Brazil, emphasizing the challenges involved, the results achieved, and some lessons learned along the 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.
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