用决策理论加速强化学习，让智能农场在缺电和攻击下仍稳定监控。
Sustainable Smart Farm Networks: Enhancing Resilience and Efficiency with Decision Theory-Guided Deep Reinforcement Learning
- 结合决策理论与深度强化学习，优化能耗与监控质量。
- 训练时间减少47.5%，性能接近传统方法但更高效。
- 适合关注农业物联网安全与能效的系统设计者。
基于太阳能传感器的监测系统已成为农业创新的关键，通过融合传感技术、物联网及边缘与云计算，推动了农场管理与动物福利的提升。然而，这类系统在应对网络攻击以及适应动态且受限的能源供应方面的韧性仍缺乏深入研究。为此，本文提出一种可持续的智能农场网络，旨在各种网络威胁和能量波动条件下维持高质量的动物监测。方法采用深度强化学习（DRL）制定最优策略，以最大化监测效果与能源效率。为克服DRL固有的收敛慢问题，引入迁移学习（TL）与决策理论（DT）以加速学习过程。通过决策理论引导策略，优化监测质量与能源可持续性，在显著缩短训练时间的同时实现相当的性能回报。实验结果表明，决策理论引导的DRL相比仅使用迁移学习的DRL模型，系统性能更高，训练时间减少47.5%。
原文摘要 · Abstract (English)
Solar sensor-based monitoring systems have become a crucial agricultural innovation, advancing farm management and animal welfare through integrating sensor technology, Internet-of-Things, and edge and cloud computing. However, the resilience of these systems to cyber-attacks and their adaptability to dynamic and constrained energy supplies remain largely unexplored. To address these challenges, we propose a sustainable smart farm network designed to maintain high-quality animal monitoring under various cyber and adversarial threats, as well as fluctuating energy conditions. Our approach utilizes deep reinforcement learning (DRL) to devise optimal policies that maximize both monitoring effectiveness and energy efficiency. To overcome DRL's inherent challenge of slow convergence, we integrate transfer learning (TL) and decision theory (DT) to accelerate the learning process. By incorporating DT-guided strategies, we optimize monitoring quality and energy sustainability, significantly reducing training time while achieving comparable performance rewards. Our experimental results prove that DT-guided DRL outperforms TL-enhanced DRL models, improving system performance and reducing training runtime by 47.5%.
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