Decentralized Source Localization Using Wireless Sensor Networks from Noisy Data
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In this paper, the source (event) localization problem is studied in decentralized wireless sensor networks under the fault model where the sensor nodes observe the source and report their decisions to the Fusion Center (FC) for estimating source location. Due to fault model, sensor nodes may provide false positive or false negative decisions to the FC. Event localizations have many applications such as localizing intruder, pollutant sources like biological and chemical weapons, enemies positions in combat monitoring, and faults in power systems. We propose two methods to estimate the source location under the fault model: hitting set approach and feature selection method, which utilize the noisy data set at the FC for estimation of the source location. We have shown that these methods are more fault tolerant in estimating the source location and are not complex as well. We also study the lower bound on the sample complexity requirement for hitting set method. These methods have also been extended for multiple sources localization. Finally, extensive simulations are carried out for different parameters (i.e., the number of sensor nodes and sample complexity) to validate our proposed methods, which show that the proposed methods achieve better performances under the fault model.
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