Genetic Algorithm With Opposition-Based Learning and Redirection for Secure Localization Using ToA Measurements in Wireless Networks

Abstract

Accurate localization plays a crucial role in wireless networks, and addressing security threats, such as spoofing and jamming attacks on anchor nodes, is essential. In this article, a two-stage algorithm for localizing the target node while considering anchor node attacks is proposed. In the first stage, the problem of detecting corrupted nodes is optimally solved by employing a combination of the genetic algorithm (GA) with opposition-based learning (OBL) and redirection techniques. In the second stage, the noncorrupted nodes are utilized to localize the target node, which is transformed into a generalized trust region subproblem (GTRS) that is solved using a bisection procedure. Simulation results demonstrate that the proposed algorithm can achieve successful detection of corrupted nodes and superior localization accuracy compared to benchmark algorithms.