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

2025-07-29

AI Summary

To address the low localization accuracy and slow convergence of Internet of Things (IoT) wireless sensor networks (WSNs) in complex environments, this paper proposes an AI-driven adaptive hybrid metaheuristic localization algorithm, SCA-PSO. The method introduces a novel adaptive switching mechanism that dynamically integrates the global exploration capability of the Sine Cosine Algorithm (SCA) with the local exploitation strength of Particle Swarm Optimization (PSO). Furthermore, it tailors population initialization, the fitness function, and parameter adaptation strategies specifically for node localization tasks. Experimental results across multiple network scales demonstrate that, compared to standard PSO and the baseline SCA-PSO, the proposed algorithm reduces the number of iterations by over 60% and decreases the average localization error by 84.97%. Consequently, it significantly enhances localization accuracy, robustness, and convergence efficiency.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Local Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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%.

Problem

• Accurate sensor node localization in IoT WSNs
• Hybrid SCA-PSO algorithm for robust localization
• Reducing localization error and iteration count

Innovation

• Hybrid SCA-PSO algorithm for IoT localization
• Adaptive switching between global and local search
• Optimized initialization and parameter settings