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<p>In the realm of IoT, wireless sensor networks (WSNs) play a crucial role in efficient data collection and task execution. However, energy constraints, particularly in battery-powered WSNs, present significant challenges. Energy harvesting (EH) technologies extend battery life but introduce variability that can impact quality of service (QoS). This paper introduces QoSA, a reinforcement learning (RL) agent designed to optimize QoS while adhering to energy constraints in IoT gateways. QoSA employs both single-policy and multi-policy RL methods to address trade-offs between conflicting objectives. This study investigates the performance of these methods in identifying Pareto front solutions for optimal service activation. A comparative analysis highlights the strengths and weaknesses of each proposed algorithm. Experimental results show that multi-policy methods outperform their single-policy counterparts in balancing trade-offs, demonstrating their effectiveness in real-world IoT applications.</p>

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Investigating the performance of multi-objective reinforcement learning techniques in the context of IoT with harvesting energy

  • 期刊论文

  • 发表日期：

    2025年2月17日

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  • Bakhta Haouari,
  • Rania Mzid,
  • Olfa Mosbahi

摘要

In the realm of IoT, wireless sensor networks (WSNs) play a crucial role in efficient data collection and task execution. However, energy constraints, particularly in battery-powered WSNs, present significant challenges. Energy harvesting (EH) technologies extend battery life but introduce variability that can impact quality of service (QoS). This paper introduces QoSA, a reinforcement learning (RL) agent designed to optimize QoS while adhering to energy constraints in IoT gateways. QoSA employs both single-policy and multi-policy RL methods to address trade-offs between conflicting objectives. This study investigates the performance of these methods in identifying Pareto front solutions for optimal service activation. A comparative analysis highlights the strengths and weaknesses of each proposed algorithm. Experimental results show that multi-policy methods outperform their single-policy counterparts in balancing trade-offs, demonstrating their effectiveness in real-world IoT applications.