Sustainable Smart Farm Networks: Enhancing Resilience and Efficiency with Decision Theory-Guided Deep Reinforcement Learning

TL;DR

This paper presents a sustainable smart farm network that uses decision theory-guided deep reinforcement learning, combined with transfer learning, to improve resilience and energy efficiency in animal monitoring systems under cyber threats and energy constraints.

Contribution

It introduces a novel integration of decision theory with DRL and transfer learning to enhance learning speed and system robustness in smart farm networks.

Findings

DT-guided DRL outperforms TL-enhanced DRL in system performance

Training time reduced by 47.5% with DT-guided strategies

Enhanced resilience and energy efficiency in smart farm monitoring

Abstract

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%.