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<p>Wireless Sensor Networks (WSNs) employ multi-hop routing to efficiently transmit data, but energy consumption remains a significant challenge in ensuring effective communication. Optimizing network interactions and reducing energy consumption are crucial for the long-term viability of WSNs. Despite the advantages of multi-hop routing, energy constraints limit the effectiveness of sensor nodes (SNs) in transmitting data across the network. The challenge lies in finding the optimal route to minimize energy expenditure while maintaining reliable data transmission. To improve the efficiency of multi-hop routing in WSNs, we proposed an optimal way for Cluster Head (CH) selection in WSN using an Improved Q learning based Artificial Bee Colony Algorithm (IQ-ABC). This study introduces an improved version of the ABC algorithm, incorporating Q-learning to enhance both the exploration and exploitation phases. A modified Q-learning mechanism enhances the IQ-ABC’s exploitative capabilities. In the proposed system, every SN transfers data to the CH using the most energy-efficient route determined by the IQ-ABC algorithm. Additionally, a multi-objective fitness function balances key factors, such as energy efficiency, latency, and trust, to optimize the CH selection with weight assignment using Fuzzy Logic. Simulation outcomes demonstrate that the IQ-ABC algorithm significantly reduces energy consumption and extends the lifespan of SN compared to traditional routing algorithms. In Case 1, where SNs are positioned centrally, IQ-ABC achieves the lowest energy consumption, with only 0.253&#xa0;units of energy used at 1200 rounds, outperforming Low-Energy Adaptive Clustering Hierarchy (LEACH) (0.38), Hybrid Energy-Efficient Distributed Clustering (HEED) (0.361), and Ant Colony Optimization (ACO) (0.6). Similarly, in Case 2 and Case 3, IQ-ABC continues to outperform, with energy usage of 0.30 and 0.33&#xa0;units, respectively, significantly lower than ACO’s 0.72.</p>

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Energy-aware and efficient cluster head selection and routing in wireless sensor networks using improved artificial bee Colony algorithm

  • 期刊论文

  • 发表日期：

    2025年1月11日

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  • Hatim Alsuwat,
  • Emad Alsuwat

摘要

Wireless Sensor Networks (WSNs) employ multi-hop routing to efficiently transmit data, but energy consumption remains a significant challenge in ensuring effective communication. Optimizing network interactions and reducing energy consumption are crucial for the long-term viability of WSNs. Despite the advantages of multi-hop routing, energy constraints limit the effectiveness of sensor nodes (SNs) in transmitting data across the network. The challenge lies in finding the optimal route to minimize energy expenditure while maintaining reliable data transmission. To improve the efficiency of multi-hop routing in WSNs, we proposed an optimal way for Cluster Head (CH) selection in WSN using an Improved Q learning based Artificial Bee Colony Algorithm (IQ-ABC). This study introduces an improved version of the ABC algorithm, incorporating Q-learning to enhance both the exploration and exploitation phases. A modified Q-learning mechanism enhances the IQ-ABC’s exploitative capabilities. In the proposed system, every SN transfers data to the CH using the most energy-efficient route determined by the IQ-ABC algorithm. Additionally, a multi-objective fitness function balances key factors, such as energy efficiency, latency, and trust, to optimize the CH selection with weight assignment using Fuzzy Logic. Simulation outcomes demonstrate that the IQ-ABC algorithm significantly reduces energy consumption and extends the lifespan of SN compared to traditional routing algorithms. In Case 1, where SNs are positioned centrally, IQ-ABC achieves the lowest energy consumption, with only 0.253 units of energy used at 1200 rounds, outperforming Low-Energy Adaptive Clustering Hierarchy (LEACH) (0.38), Hybrid Energy-Efficient Distributed Clustering (HEED) (0.361), and Ant Colony Optimization (ACO) (0.6). Similarly, in Case 2 and Case 3, IQ-ABC continues to outperform, with energy usage of 0.30 and 0.33 units, respectively, significantly lower than ACO’s 0.72.