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GTIACO: energy efficient clustering algorithm based on game theory and improved ant colony optimization

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Abstract

Recently, wireless sensor networks have been widely used for environmental and structural safety monitoring. However, node batteries cannot be replaced or easily recharged in harsh environments. Maximizing network lifetime remains a challenging issue in designing WSN routing. This paper introduces GTIACO, a novel metaheuristic clustering protocol. It employs an optimal cluster head function to determine cluster number and utilizes game theory for selecting optimal cluster heads. To optimize inter-cluster routing, improved ant colony optimization (ACO) is introduced to construct gathering paths from clusters to the base station. Both blind pathways, pheromone concentration, and angle factors are considered to improve path exploration and transmission efficiency in ant colonies. To assess network performance, various scenarios involving different base station placements and network densities are examined. Experimental results demonstrate GTIACO's superiority over LEACH, ACO, SEP, and PRESPE protocols in network lifetime, stability, energy, and throughput. The proposed GTIACO shows an improvement of at least 4.3% in network lifetime and 32.8% in network throughout. It exhibits superior stability and transmission efficiency across diverse network densities.

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Funding

This paper was supported by the National Natural Science Foundation of China (Grant numbers 51977061 and 51407063) and the Research Startup Fund of Hubei University of Technology (Grant numbers XJ2021004901).

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Authors and Affiliations

  1. Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan, 430068, China

    Hang Wan, Zhizhuo Qiu & Rui Quan

  2. CRAN, CNRS UMR 7039, University of Lorraine, 54516, Vandœuvre-les-Nancy Cedex, France

    Michael David & William Derigent

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HW and ZQ wrote the main manuscript text and RQ, MD, WD prepared figures, tables and algorithms. All authors reviewed the manuscript.

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Correspondence to Rui Quan.

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Wan, H., Qiu, Z., Quan, R. et al. GTIACO: energy efficient clustering algorithm based on game theory and improved ant colony optimization. Telecommun Syst (2024). https://doi.org/10.1007/s11235-024-01132-7

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