Local Threshold Design for Target Localization Using Error Correcting Codes in Wireless Sensor Networks in the Presence of Byzantine Attacks

In this paper, we revisit the received signal strength (RSS)-based target localization technique presented in Vempaty et al., where a simple threshold quantizer was employed to quantize the RSS values prior to sending them to the fusion center. It was shown that the probability of misclassification of the distributed classification fusion using error correcting codes scheme vanishes as the number of sensors tends to infinity. This result was obtained based on an intuitive threshold design at the local sensors, and the question of how much a careful design of local thresholds can help improve the overall performance was not addressed. In this paper, we demonstrate the significance of threshold design for accurate and robust target localization in wireless sensor networks, particularly, when the number of sensors is finite. With this objective, we derive an upper bound on the probability of misclassification as a function of RSS thresholds by using the union inequality. The RSS thresholds that algorithmically minimize the derived misclassification error bound are then numerically obtained over a mirror-based homomorphic sensor deployment structure. Simulations over fading wireless links show that the scheme based on newly found optimized RSS thresholds considerably outperforms the previous scheme using the thresholds that are intuitively selected, especially in the presence of Byzantine attacks that severely impact information security.