Accurate RSS-Based Localization Using an Opposition-Based Learning Simulated Annealing Algorithm

TL;DR

This paper introduces a novel RSS-based localization method combining opposition-based learning and simulated annealing to improve accuracy and avoid local optima in wireless sensor networks.

Contribution

It proposes a new hybrid algorithm that enhances localization accuracy by integrating opposition-based learning with simulated annealing, addressing issues of convergence and local optima.

Findings

Outperforms existing algorithms in localization accuracy

Reduces likelihood of trapping in local optima

Demonstrates robustness in simulated environments

Abstract

Wireless sensor networks require accurate target localization, often achieved through received signal strength (RSS) localization estimation based on maximum likelihood (ML). However, ML-based algorithms can suffer from issues such as low diversity, slow convergence, and local optima, which can significantly affect localization performance. In this paper, we propose a novel localization algorithm that combines opposition-based learning (OBL) and simulated annealing algorithm (SAA) to address these challenges. The algorithm begins by generating an initial solution randomly, which serves as the starting point for the SAA. Subsequently, OBL is employed to generate an opposing initial solution, effectively providing an alternative initial solution. The SAA is then executed independently on both the original and opposing initial solutions, optimizing each towards a potential optimal solution. The final solution is selected as the more effective of the two outcomes from the SAA, thereby reducing the likelihood of the algorithm becoming trapped in local optima. Simulation results indicate that the proposed algorithm consistently outperforms existing algorithms in terms of localization accuracy, demonstrating the effectiveness of our approach.