Fault Tolerant Localization and Tracking of Multiple Sources in WSNs using Binary Data
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A fault tolerant localization and tracking algorithm for multiple event sources in WSNs using only binary sensor observations. The proposed algorithm, FTLT (Fault Tolerant Localization and Tracking), has the following three main phases: (i) Identification, (ii) Localization, and (iii) Smoothing. In the Identification phase, all alarmed sensor nodes broadcast their status in their own neighborhood. We have chosen to focus on the simple and minimalistic setting of binary sensors because the cost and power consumption of sensor nodes is a severe constraint in large-scale deployments, and both can be significantly reduced by restricting the nodes to provide binary detection. Our focus in this paper is on the efficacy of collaborative tracking rather on the communication protocols used by the sensor nodes. Thus, we assume that all of the sensor readings are available at a centralized processor, which can then estimate the targets’ locations and trajectories. Distributed implementations of our algorithms, in which neighbors collaborate to estimate segments of trajectories, are possible, but are not considered here. We note that the binary sensing model has minimal communication requirements, hence this assumption of centralized processing is quite practical: a sensor need only convey the intervals at which it switches on” and off” (assuming that the readings are averaged so as to remain reasonably steady, this is far more efficient than sending a sample of the sensor’s readings at regular intervals). we develop a particle filtering algorithm based on a cost function that personalizes changes in velocity. An extensive set of simulations, as well as experiments with passive infrared sensors, are reported. While these combinatorial arguments bring out the difficulty of target counting based on sensor readings at a given time, they leave open the possibility of accurate counting and tracking by exploiting the evolution of the sensor readings across time.
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