Source Location with Quantized Sensor Data Corrupted by False Information

In this paper, we investigate the problem of source location estimation in wireless sensor networks (WSNs) based on quantized data in the presence of false information attacks. Using a Gaussian mixture to model the possible attacks, we develop a maximum likelihood estimator (MLE) to locate the source with sensor data corrupted by injected false information, and call the approach quantized received signal strength with a Gaussian mixture model (Q-RSS-GM). The Cramer-Rae lower bound (CRLB) for this estimation problem is also derived to evaluate the estimator's performance. Simulation results show that the proposed estimator is robust in various cases with different attack probabilities and parameter mismatch, and it significantly outperforms the approach that ignores the possible false information attacks.