AdapSCA-PSO: An Adaptive Localization Algorithm with AI-Based Hybrid SCA-PSO for IoT WSNs

TL;DR

This paper presents AdapSCA-PSO, a hybrid AI-based algorithm combining SCA and PSO with adaptive switching for improved sensor node localization in IoT WSNs, achieving faster convergence and higher accuracy.

Contribution

It introduces an adaptive hybrid SCA-PSO algorithm with optimized initialization and parameters for enhanced IoT sensor localization.

Findings

Reduces localization error by 84.97% on average.

Requires fewer iterations than standalone algorithms.

Outperforms unoptimized hybrid approaches.

Abstract

The accurate localization of sensor nodes is a fundamental requirement for the practical application of the Internet of Things (IoT). To enable robust localization across diverse environments, this paper proposes a hybrid meta-heuristic localization algorithm. Specifically, the algorithm integrates the Sine Cosine Algorithm (SCA), which is effective in global search, with Particle Swarm Optimization (PSO), which excels at local search. An adaptive switching module is introduced to dynamically select between the two algorithms. Furthermore, the initialization, fitness evaluation, and parameter settings of the algorithm have been specifically redesigned and optimized to address the characteristics of the node localization problem. Simulation results across varying numbers of sensor nodes demonstrate that, compared to standalone PSO and the unoptimized SCAPSO algorithm, the proposed method significantly reduces the number of required iterations and achieves an average localization error reduction of 84.97%.