  This paper studies the source (event) localization problem in decentralized
wireless sensor networks (WSNs) under the fault model without knowing the
sensor parameters. Event localizations have many applications such as
localizing intruders, Wifi hotspots and users, and faults in power systems.
Previous studies assume the true knowledge (or good estimates) of sensor
parameters (e.g., fault model probability or Region of Influence (ROI) of the
source) for source localization. However, we propose two methods to estimate
the source location in this paper under the fault model: hitting set approach
and feature selection method, which only utilize the noisy data set at the
fusion center for estimation of the source location without knowing the sensor
parameters. The proposed methods have been shown to localize the source
effectively. We also study the lower bound on the sample complexity requirement
for hitting set method. These methods have also been extended for multiple
sources localizations. In addition, we modify the proposed feature selection
approach to use maximum likelihood. Finally, extensive simulations are carried
out for different settings (i.e., the number of sensor nodes and sample
complexity) to validate our proposed methods in comparison to centroid, maximum
likelihood, FTML, SNAP estimators.
