Sustainable Smart Farm Networks: A Decision Theory-Guided Deep Reinforcement Learning Approach

Abstract

Solar-powered sensor networks are reshaping agriculture by enabling continuous farm management and animal welfare monitoring through Internet-of-Things (IoT) devices, edge intelligence, and cloud analytics. Yet, their sustainability is threatened by two underexplored challenges: vulnerability to cyber-attacks and instability under fluctuating energy supplies. To address these issues, we propose a sustainable smart farm framework that ensures reliable monitoring under adversarial and resource-constrained conditions. Our approach employs deep reinforcement learning (DRL) to derive adaptive policies that jointly optimize monitoring fidelity and energy efficiency. To overcome DRL’s slow convergence and unstable adaptation in dynamic environments, we design a decision theory (DT)-guided transfer learning (TL) framework. Embedding DT principles accelerates policy learning while balancing trade-offs among monitoring quality, energy sustainability, and system resilience. Experimental evaluations on realistic farm scenarios show that DT-guided DRL reduces training runtime by 47.5% while outperforming TL-enhanced DRL in robustness and monitoring performance. This work makes three contributions: (1) unifying cyber resilience and energy adaptability in agricultural monitoring, (2) advancing methodological innovation with DT-guided TL for stable DRL convergence, and (3) providing design insights for sustainable agricultural cyber-physical systems. Collectively, these contributions extend sensor networks in agriculture by emphasizing resilience, sustainability, and intelligent adaptation.