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The proposed work discusses aggregation methods for deep reinforcement learning (DRL)-based wireless sensor networks. The aggregation algorithm plays a vital role in reducing overall energy consumption. The aggregation method aggregates the data samples, removes the redundant data, and reduces the number of overall packets, reducing overall energy consumption. We begin with the introduction, followed by the discussion on routing protocols with aggregation for MWSNs, which presents an overview of different routing protocols with aggregation. Optimized Link State Routing (OSLR) performance without aggregation is compared with the OLSR protocol with aggregation. Subsequently, routing protocols based on SOM concerning WSNs are elaborated. The SoM-OLSR with aggregation is reviewed and compared with the SoM-based routing protocol without aggregation for MWSNs. Similarly, the DRL with aggregation is reviewed and compared with the DRL-based routing protocol without aggregation for MWSNs. The result proves that DRL-OLSR ‘with aggregation’ saves 50% of energy with respect to ‘without aggregation.’ The results prove that the average energy saving is more than 30% for a high-density network. Thus, we can conclude that the proposed DRL-based aggregation-based method performs much better with respect to other existing methods.

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Deep Reinforcement Learning-Based Energy-Efficient Aggregation Model for Wireless Sensor Network

  • 会议论文

  • 发表日期：

    2025年

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  • Nada Alsalmi,
  • Keivan Navaie,
  • Hossein Rahmani

摘要

The proposed work discusses aggregation methods for deep reinforcement learning (DRL)-based wireless sensor networks. The aggregation algorithm plays a vital role in reducing overall energy consumption. The aggregation method aggregates the data samples, removes the redundant data, and reduces the number of overall packets, reducing overall energy consumption. We begin with the introduction, followed by the discussion on routing protocols with aggregation for MWSNs, which presents an overview of different routing protocols with aggregation. Optimized Link State Routing (OSLR) performance without aggregation is compared with the OLSR protocol with aggregation. Subsequently, routing protocols based on SOM concerning WSNs are elaborated. The SoM-OLSR with aggregation is reviewed and compared with the SoM-based routing protocol without aggregation for MWSNs. Similarly, the DRL with aggregation is reviewed and compared with the DRL-based routing protocol without aggregation for MWSNs. The result proves that DRL-OLSR ‘with aggregation’ saves 50% of energy with respect to ‘without aggregation.’ The results prove that the average energy saving is more than 30% for a high-density network. Thus, we can conclude that the proposed DRL-based aggregation-based method performs much better with respect to other existing methods.