학술논문

Genetic Algorithm With Opposition-Based Learning and Redirection for Secure Localization Using ToA Measurements in Wireless Networks
Document Type
Periodical
Source
IEEE Internet of Things Journal IEEE Internet Things J. Internet of Things Journal, IEEE. 10(24):22294-22304 Dec, 2023
Subject
Computing and Processing
Communication, Networking and Broadcast Technologies
Location awareness
Genetic algorithms
Jamming
Distance measurement
Communication system security
Clustering algorithms
Wireless sensor networks
Detection algorithms
Time of arrival estimation
Attack detection
genetic algorithm (GA)
opposition-based learning (OBL)
redirection
secure localization
Time of Arrival (ToA)
Language
ISSN
2327-4662
2372-2541
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.