This work designs a decision theory (DT)-guided transfer learning (TL) framework that unifying cyber resilience and energy adaptability in agricultural monitoring, advancing methodological innovation with DT-guided TL for stable DRL convergence, and providing design insights for sustainable agricultural cyber-physical systems.
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.
This study establishes a viable pathway toward integrating federated learning and green AI principles for the sustainable, intelligent operation of future 6G WSNs and introduces an adaptive client selection mechanism that prioritizes nodes with higher residual energy and better local model quality to participate in each training round, further enhancing sustainability.
Hongyuan Wang, Shi-Kai He, Yiping Jiang et al.· International Journal of Com...· 0 citations
The residential market has seen the rapid take-up of Internet of Things (IoT) devices, connected IIoT smart systems due to it. This situation gives rise to new issues and challenges for the energy management implemented in real time. The traditional centralized energy management mechanisms face great challenges like excessive latency, confidentiality vulnerability, scalability issues across heterogeneous smart home networks. The paper proposes a Federated Deep Reinforcement Learning (D-RLR) framework based on DQN agents and federated learning method. The framework allows for the decentralized training of DQN models in smart home nodes with the restriction that raw energy consumption data remain local. Each agent carries out the processing of grid price signal, usage data of appliances, observed amount and availability of local renewable energy to develop optimal load scheduling policy. The overall demand response policy is obtained by periodic federated aggregation of local models, helping to reduce communication latency. Experiments conducted on a simulated IIoT smart home environment, constructed using appliance consumption profiles derived from publicly available residential energy datasets and realistic solar irradiance-based renewable generation profiles, demonstrate that the proposed framework reduces peak energy consumption by 23% and improves demand response efficiency by 18% relative to centralized DRL baselines. Communication overhead generates roughly 65% lesser and user privacy is preserved throughout by design. The mechanism scales across nodes and supports real-time scheduling decisions under dynamically changing grid conditions. Therefore, it can be deployed practically for next-generation residential energy management.
Sundaram P, M. Jayaprakash· 2026 4th International Confe...· 0 citations
An AI-driven framework integrates hybrid energy harvesting mechanisms with Deep Reinforcement Learning (DRL) to optimize energy efficiency in IoT systems and achieves up to 300% improvement in network lifetime under low-energy harvesting conditions.
Elkhatim Abuelysar Elmobarak Mohammed Ali· Islamic University Journal o...· 0 citations
Results confirm that reinforcement learning–based resource allocation provides a scalable and effective solution for IoT networks, particularly in environments characterized by large state spaces, dynamic network conditions, and stochastic traffic patterns.
L. Hoang, Van-Tam Hoang, Huu-Huy Ngo· International journal of Com...· 1 citation
: Home Energy Management Systems (HEMS) is becoming an essential part of the low-carbon economy and smart cities due to the global energy crisis and climate change issues. Conventional Home Energy Management Systems have significant difficulties in dealing with the complexity and heterogeneity of energy data, which hinders their practicality and wide adoption. In this comprehensive paper, the survey and review the recent progress in machine learning techniques for smart home energy management applications, highlighting key developments. The systematically categorize and thoroughly analyze the performance of various machine learning models across multiple critical tasks: energy consumption prediction, user behavior analysis, demand response optimization, and Heating, Ventilation, and Air Conditioning (HVAC) control. The findings show that deep learning methods and reinforcement learning approaches consistently achieve better prediction accuracy and enhanced adaptivity; however, persistent issues exist, such as data heterogeneity, the weak generalization ability of models across different settings, and difficulties in practical real-world deployment and implementation. To address these challenges, the strongly suggest that future research should concentrate on cross-task joint optimization strategies, multi-source data fusion techniques, and long-term adaptive model frameworks to significantly facilitate practical, intelligent energy management in homes.
Xuan-Ming Zhou· Proceedings of the 3rd Inter...· 0 citations
This study integrates deep reinforcement learning (DRL) with lean management for renewable energy project scheduling. The problem is formulated as a constrained Markov decision process with weather‑dependent dynamics and a composite reward such as schedule, cost, quality, and constraints. A four‑layer architecture (convolutional, recurrent, and attention layers) and a masked proximal policy optimization actor-critic enhanced with prioritized experience replay and meta-learning learn adaptive policies; four lean principles map directly onto the DRL loop. A three-stage feasibility filter-hard action masking, soft resource rescaling, and penalty fallback-ensures that every executed decision satisfies project-network, resource, weather-window, grid, and environmental constraints. Validation on 186 Northwest China projects shows 33.0% shorter duration, 29.2% lower cost, and 87.2% resource utilization while preserving quality. Compared with eight state-of-the-art deep-learning and classical baselines, the proposed approach delivers an average improvement of approximately 2.9 percentage points in schedule efficiency over the strongest deep‑learning baselines, and reduces mean absolute error by approximately 41%-50% on large‑scale projects (>200 activities). The proposed method retains ∼80% performance under 30% noise and ≥97.8% on CPU‑only hardware, supporting practical adoption by small-to-medium enterprises and on-site project offices.
Yan Yao· Annals of the New York Acade...· 0 citations
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