This paper considers the problem of source localization using quantized observations in wireless sensor networks where, due to bandwidth constraint, each sensor’s observation is usually quantized into one bit of information. First, a channel-aware adaptive quantization scheme for target location estimation is proposed and local sensor nodes dynamically adjust their quantization thresholds according to the position-based information sequence. The novelty of the proposed approach comes from the fact that the scheme not only adopts the distributed adaptive quantization instead of the conventional fixed quantization, but also incorporates the statistics of imperfect wireless channels between sensors and the fusion center (binary symmetric channels). Furthermore, the appropriate maximum likelihood estimator (MLE), the performance metric Cramér-Rao lower bound (CRLB), and a sufficient condition for the Fisher information matrix being positive definite are derived, respectively. Simulation results are presented to show that the appropriated CRLB is less than the fixed quantization channel-aware CRLB and the proposed MLE will approach their CRLB when the number of sensors is large enough.
A Novel Data Fusion Strategy Based on Extreme Learning Machine Optimized by Bat Algorithm for Mobile Heterogeneous Wireless Sensor Networks
