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<p>Wireless sensor networks (WSNs) play an important role in fields such as environmental monitoring, and industrial automation. However, it is faced with several challenges due to limited energy resources. The existing clustering and routing methods are, therefore, not able to balance energy efficiency, the network’s life time, and the quality of service. This research presents a new optimization method derived from the blending of Multi-Objective Genetic Algorithm (MOGA) and Lightning Search Algorithm (LSA) for overcoming these drawbacks. MOGA minimizes the intra-cluster distances and energy consumption and selects an optimal CH. MOGA is combined with local search algorithm (LSA) which efficiently identifies globally efficient routes for data transmission. Compared to the current techniques, MOGAGSA, LEACH, and PSO, the proposed approach decreases energy consumption by 48%, it improves data delivery rates and extends the lifetime of the network. Performance evaluation of the simulation results shows efficient results in terms of various metrics such as energy consumption, packet lost per second and average end-to-end delay. To ensure that any limitations found in using a single metaheuristic algorithm are resolved, there’s a combination of several metaheuristic algorithms, thus creating a comprehensive solution for large scale WSNs. Its broad implications include improving sustainability and reliability for any IoT and smart environment applications.</p>

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Cluster based WSN routing with MOGA and LSA optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月19日

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  • Weiqin Tan,
  • Fei Wang

摘要

Wireless sensor networks (WSNs) play an important role in fields such as environmental monitoring, and industrial automation. However, it is faced with several challenges due to limited energy resources. The existing clustering and routing methods are, therefore, not able to balance energy efficiency, the network’s life time, and the quality of service. This research presents a new optimization method derived from the blending of Multi-Objective Genetic Algorithm (MOGA) and Lightning Search Algorithm (LSA) for overcoming these drawbacks. MOGA minimizes the intra-cluster distances and energy consumption and selects an optimal CH. MOGA is combined with local search algorithm (LSA) which efficiently identifies globally efficient routes for data transmission. Compared to the current techniques, MOGAGSA, LEACH, and PSO, the proposed approach decreases energy consumption by 48%, it improves data delivery rates and extends the lifetime of the network. Performance evaluation of the simulation results shows efficient results in terms of various metrics such as energy consumption, packet lost per second and average end-to-end delay. To ensure that any limitations found in using a single metaheuristic algorithm are resolved, there’s a combination of several metaheuristic algorithms, thus creating a comprehensive solution for large scale WSNs. Its broad implications include improving sustainability and reliability for any IoT and smart environment applications.