Abstract. We describe a prototype emergency and disaster information system designed and implemented using DDDAS concepts. The system is designed to use real-time cell phone calling data from a geographical region, including calling activity -who calls whom, call duration, services in use, and cell phone location information -to provide enhanced situational awareness for managers in emergency operations centers (EOCs) during disaster events. Powered-on cell phones maintain contact with one or more within-range cell towers so as to receive incoming calls. Thus, location data about all phones in an area are available, either directly from GPS equipped phones, or by cell tower, cell sector, distance from tower and triangulation methods. This permits the cell phones of a geographical region to serve as an ad hoc mobile sensor net, measuring the movement and calling patterns of the population. A prototype system, WIPER, serves as a test bed to research open DDDAS design issues, including dynamic validation of simulations, algorithms to interpret high volume data streams, ensembles of simulations, runtime execution, middleware services, and experimentation frameworks [1].
Cell phone networks produce a massive volume of service usage data which, when combined with location data, can be used to pinpoint emergency situations that cause changes in network usage. Such a change may be the results of an increased number of people trying to call friends or family to tell them what is happening or a decrease in network usage caused by people being unable to use the network. Such events are anomalies and managing emergencies effectively requires identifying anomalies quickly. This problem is difficult due to the rate at which very large volumes of data are produced. In this paper, we discuss the use of data stream clustering algorithms for anomaly detection.
By identifying potential composite states that occur in the Sel'kov-Gray-Scott (GS) model, we show that it can be considered as an effective theory at large spatiotemporal scales, arising from a more fundamental theory (which treats these composite states as fundamental chemical species obeying the diffusion equation) relevant at shorter spatiotemporal scales. When simulations in the latter model are performed as a function of a parameter M=λ-1, the generated spatial patterns evolve at late times into those of the GS model at large M, implying that the composites follow their own unique dynamics at short scales. This separation of scales is an example of dynamical decoupling in reaction diffusion systems.
In this chapter we consider a cell phone network as a set of automatically deployed sensors that records movement and interaction patterns of the population. We discuss methods for detecting anomalies in the streaming data produced by the cell phone network. We motivate this discussion by describing the Wireless Phone Based Emergency Response (WIPER) system, a proof-of-concept decision support system for emergency response managers. We also discuss some of the scientific work enabled by this type of sensor data and the related privacy issues. We describe scientific studies that use the cell phone data set and steps we have taken to ensure the security of the data. We describe the overall decision support system and discuss three methods of anomaly detection that we have applied to the data.
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