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<p>This paper presents the development of an energy-efficient hybrid clustering and routing algorithm, the Mayfly Ant Colony Optimisation (MACO), for ZigBee-based Wireless Sensor Networks (WSNs) deployed in farm environments. Although WSNs have become an essential tool for real-time monitoring and decision-support systems in farming, their performance is limited by foliage-induced signal attenuation and uneven energy depletion across sensor nodes, which reduces network reliability and lifetime. This study aims to design a routing protocol that enhances energy efficiency, load balancing, and overall network sustainability under such field conditions. To ensure realistic performance assessment, an empirical radio energy model was integrated into MATLAB-based simulations to accurately estimate power consumption. The MACO algorithm combines Mayfly-based clustering, fuzzy logic for cluster-head selection, and multi-hop routing guided by Ant Colony Optimisation to achieve adaptive and energy-aware communication. Simulation results revealed that MACO significantly outperformed the Low Energy Adaptive Clustering Hierarchy (LEACH), Hybrid Energy-Efficient Distributed (HEED), and Fuzzy-LEACH protocols, achieving First Node Dead (FND), Half Node Dead (HND), and Last Node Dead (LND) values of 590, 720, and 830 rounds, respectively, in terms of network lifetime. Moreover, MACO maintained the lowest average energy consumption, highest residual energy, and greatest number of active nodes throughout the simulation, confirming its superior routing stability and energy efficiency. Hence, this research presents a novel hybrid optimisation framework that enhances the reliability and sustainability of WSNs for precision agriculture.</p>

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Hybrid Mayfly Ant colony optimization for energy-efficient clustering and routing in ZigBee wireless sensor networks for precision agriculture

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月7日

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  • Babatunde Ademola Iyaomolere,
  • Jide Julius Popoola,
  • Kayode Francis Akingbade

摘要

This paper presents the development of an energy-efficient hybrid clustering and routing algorithm, the Mayfly Ant Colony Optimisation (MACO), for ZigBee-based Wireless Sensor Networks (WSNs) deployed in farm environments. Although WSNs have become an essential tool for real-time monitoring and decision-support systems in farming, their performance is limited by foliage-induced signal attenuation and uneven energy depletion across sensor nodes, which reduces network reliability and lifetime. This study aims to design a routing protocol that enhances energy efficiency, load balancing, and overall network sustainability under such field conditions. To ensure realistic performance assessment, an empirical radio energy model was integrated into MATLAB-based simulations to accurately estimate power consumption. The MACO algorithm combines Mayfly-based clustering, fuzzy logic for cluster-head selection, and multi-hop routing guided by Ant Colony Optimisation to achieve adaptive and energy-aware communication. Simulation results revealed that MACO significantly outperformed the Low Energy Adaptive Clustering Hierarchy (LEACH), Hybrid Energy-Efficient Distributed (HEED), and Fuzzy-LEACH protocols, achieving First Node Dead (FND), Half Node Dead (HND), and Last Node Dead (LND) values of 590, 720, and 830 rounds, respectively, in terms of network lifetime. Moreover, MACO maintained the lowest average energy consumption, highest residual energy, and greatest number of active nodes throughout the simulation, confirming its superior routing stability and energy efficiency. Hence, this research presents a novel hybrid optimisation framework that enhances the reliability and sustainability of WSNs for precision agriculture.