  In this work, we consider the task of target localization using quantized
data in Wireless Sensor Networks (WSNs). We propose an energy efficient
localization scheme by modeling it as an iterative classification problem. We
design coding based iterative approaches for target localization where at every
iteration, the Fusion Center (FC) solves an M-ary hypothesis testing problem
and decides the Region of Interest (ROI) for the next iteration. The coding
based iterative approach works well even in the presence of Byzantine
(malicious) sensors in the network. We further consider the effect of non-ideal
channels. We suggest the use of soft-decision decoding to compensate for the
loss due to the presence of fading channels between the local sensors and the
FC. We evaluate the performance of the proposed schemes in terms of the
Byzantine fault tolerance capability and probability of detection of the target
region. We also present performance bounds which help us in designing the
system. We provide asymptotic analysis of the proposed schemes and show that
the schemes achieve perfect region detection irrespective of the noise variance
when the number of sensors tends to infinity. Our numerical results show that
the proposed schemes provide a similar performance in terms of Mean Square
Error (MSE) as compared to the traditional Maximum Likelihood Estimation (MLE)
but are computationally much more efficient and are resilient to errors due to
Byzantines and non-ideal channels.
