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The Wireless Sensor Networks (WSNs) are essential to vast Internet of Things (IoT) ecosystem, energy-efficient practices must be implemented in order to enable sensor networks to be seamlessly integrated into intelligent environments. As amount of sensor nodes increases, the challenges associated with Energy Consumption (EC) and weighted clustering become more pronounced. In this research, propose an Improved K-Means clustering-based and Directional Mutation Rule-based Cooperative Optimization Algorithm (DMRC CoatiOA) for clustering and routing selection in WSNs. The primary objective of the proposed DMRC CoatiOA approach is to optimize the routing procedure in WSNs, aiming for enhanced efficiency. The performance evaluation of the proposed method demonstrates superior results, achieving an energy consumption of 1.80, network lifetime of 6200, average delay of 0.15, and throughput of 290 at 50 nodes. This outperforms existing methods such as Reinforcement-Learning based Energy-Efficient (EE) Optimized Routing Protocol for WSN (RLER) and EE Learning Automata and Grouping-Based Routing Algorithm for Wireless Sensor Networks (EDILA).

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An Effective Energy Consumption and Weighted Clustering Using Improved K-Means Clustering

  • 会议论文

  • 发表日期：

    2025年

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  • Abbas Hameed Abdul Hussein,
  • Pushpa Mohan,
  • Smitha Kurian,
  • Shankar Nayak Bhukya,
  • M. Mohan

摘要

The Wireless Sensor Networks (WSNs) are essential to vast Internet of Things (IoT) ecosystem, energy-efficient practices must be implemented in order to enable sensor networks to be seamlessly integrated into intelligent environments. As amount of sensor nodes increases, the challenges associated with Energy Consumption (EC) and weighted clustering become more pronounced. In this research, propose an Improved K-Means clustering-based and Directional Mutation Rule-based Cooperative Optimization Algorithm (DMRC CoatiOA) for clustering and routing selection in WSNs. The primary objective of the proposed DMRC CoatiOA approach is to optimize the routing procedure in WSNs, aiming for enhanced efficiency. The performance evaluation of the proposed method demonstrates superior results, achieving an energy consumption of 1.80, network lifetime of 6200, average delay of 0.15, and throughput of 290 at 50 nodes. This outperforms existing methods such as Reinforcement-Learning based Energy-Efficient (EE) Optimized Routing Protocol for WSN (RLER) and EE Learning Automata and Grouping-Based Routing Algorithm for Wireless Sensor Networks (EDILA).