IoUT Based Underwater Target Localization in the Presence of Time Synchronization Attacks
Xiang Pan, Yining Shen, Jiangfan Zhang
Abstract
Target localization by using an Internet of Underwater Things (IoUT) network in three dimensional shallow water is considered in the presence of time synchronization attacks (TSA) which introduce additional delays in the signals received at the attacked sensors. To mitigate the impact of TSAs, we consider the task of joint target localization and attack detection. We show that this task can be formulated as a mix-integer programming problem with the number of optimization variables proportional to the number of multipaths, and hence is formidable when the multipath effect is severe. We show that if the magnitude of the correlation between multipath signals is upper bounded by some constant, which can be easily satisfied in practice, then the mix-integer programming can be simplified, and the number of optimization variables can be reduced and does not depend on the number of multipaths anymore. Next, we employ two computationally efficient algorithms to solve the simplified problem. The numerical results show that as the signal-to-noise ratio increases, the attack detection error of our approaches decreases to zero rapidly, and the target localization performance of our approaches is very close to that of the clairvoyant algorithm which is assumed to know the set of attacked sensors.