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2000
Volume 15, Issue 4
  • ISSN: 2210-3279
  • E-ISSN: 2210-3287

Abstract

Background and Objective

Enhancing localization accuracy while minimizing development costs poses significant challenges in deploying and managing wireless sensor networks (WSNs). This paper presents an advanced algorithm for node localization in indoor environments, integrating sophisticated optimization techniques. The hybrid algorithm, HSPPSO-TLBO, combines Hierarchical Structure Poly Particle Swarm Optimization (HSPPSO) with Teaching–Learning-Based Optimization (TLBO).

Methods

The proposed algorithm HSPPSO-TLBO aims to minimize the mean squared range error (MSRE) resulted by calculating internal distances between nodes using Received Signal Strength Indicator (RSSI). TLBO, with its robust global search capabilities, complements HSPPSO’s local search, preventing convergence to inappropriate local optima. HSPPSO-TLBO offers easy implementation and leverages the cost-free feature of RSSI, making it an attractive choice for enhancing localization precision.

Results and Discussion

Simulation results demonstrate the superior performance of HSPPSO-TLBO compared to other algorithms using different meta-heuristic optimization techniques. The outstanding performance of HSPPSO-TLBO is evident across various evaluation metrics, including localization error, localization rate, and simulation runtime.

Conclusion

The proposed algorithm utilizing HSPPSO and TLBO is exceptionally effective in improving localization precision in indoor WSNs due to several key characteristics. These include the seamless integration and easy implementation of both HSPPSO and TLBO, along with the cost-free advantage of using the RSSI technique. This combination makes the algorithm a highly functional solution for improving localization accuracy.

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2024-09-27
2026-01-03

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/content/journals/swcc/10.2174/0122103279335199240912100619
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Enhancing Indoor Localization Accuracy in Wireless Sensor Networks: A Hybrid Approach Integrating Hierarchical Structure Poly Particle Swarm Optimization and Teaching–Learning-Based Optimization (HSPPSO-TLBO)
Int J Sensors Wirel Commun Control 15, 341 (2025); https://doi.org/10.2174/0122103279335199240912100619
/content/journals/swcc/10.2174/0122103279335199240912100619
/content/journals/swcc/10.2174/0122103279335199240912100619
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  • Article Type:     Research Article
Keyword(s): hierarchical structure poly particle swarm optimization; meta-heuristic optimization techniques; ranging model; RSSI; teaching–learning-based optimization; Wireless sensor networks

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