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<p>Wireless Sensor Networks (WSNs) are used in industrial automation, health care, as well as environmental monitoring applications. However, sensor nodes are constrained in energy, subject to minimal network scalability, and lifetime of networks. Hence, this paper proposed an energy-efficient routing protocol to improve the lifetime of the WSN environment. The proposed Modernized Harris Hawk Voting Deep Learning (MHHV-DL) is integrated with the optimization-based deep learning model for improved network lifetime. The MHHV-DL model utilizes Harris Hawk optimization for the energy optimized routing in WSN network. With the deep learning-based voting mechanism to select intelligent paths for the energy efficiency data transmission routing path. The proposed MHHV-DL model focused on energy harvesting and priority nodes depending on the current available residual energy and link stability. The simulation analysis for the varying (50–150 nodes), stated that the MHHV-DL achieves the highest network lifetime by 52.2%, with minimal energy utilization by 26.5%, and provides the highest 98.6% of the packet delivery ratio at 150 nodes. The comparative analysis with the existing routing algorithms, including LEACH, TEEN, and DL-LEACH, the suggested protocol sustains a higher amount of residual energy (51.7&#xa0;J compared to 34.5&#xa0;J, at 2000 rounds) and routing path stability (up to 94.1%). Also, the average delay is decreased to 105 ms, and the efficiency in energy harvesting is increased to 88.7%, which confirms the appropriateness of the protocol in sustainable and scalable WSN implementations.</p>

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Design and Implementation of Energy-Efficient Routing Protocols for Maximizing Lifetime in Wireless Sensor Networks

  • 期刊论文

  • 发表日期：

    2026年5月13日

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  • H. V. Adarsha Sagar,
  • V. A. Sheetal,
  • B. M. Vikranth

摘要

Wireless Sensor Networks (WSNs) are used in industrial automation, health care, as well as environmental monitoring applications. However, sensor nodes are constrained in energy, subject to minimal network scalability, and lifetime of networks. Hence, this paper proposed an energy-efficient routing protocol to improve the lifetime of the WSN environment. The proposed Modernized Harris Hawk Voting Deep Learning (MHHV-DL) is integrated with the optimization-based deep learning model for improved network lifetime. The MHHV-DL model utilizes Harris Hawk optimization for the energy optimized routing in WSN network. With the deep learning-based voting mechanism to select intelligent paths for the energy efficiency data transmission routing path. The proposed MHHV-DL model focused on energy harvesting and priority nodes depending on the current available residual energy and link stability. The simulation analysis for the varying (50–150 nodes), stated that the MHHV-DL achieves the highest network lifetime by 52.2%, with minimal energy utilization by 26.5%, and provides the highest 98.6% of the packet delivery ratio at 150 nodes. The comparative analysis with the existing routing algorithms, including LEACH, TEEN, and DL-LEACH, the suggested protocol sustains a higher amount of residual energy (51.7 J compared to 34.5 J, at 2000 rounds) and routing path stability (up to 94.1%). Also, the average delay is decreased to 105 ms, and the efficiency in energy harvesting is increased to 88.7%, which confirms the appropriateness of the protocol in sustainable and scalable WSN implementations.