Digital twin enabled federated reinforcement learning for energy efficient spectrum allocation in heterogeneous vehicular networks

Communication-Aware Federated Learning for Energy Management in Edge-Cloud Autonomous Systems

A Federated Deep Reinforcement Learning Framework for Real-Time Demand Response Optimization in IIoT-based Smart Home Automation

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.

Energy-Optimized Lightweight DRL for Computational Offloading in UAV-IoV Systems

The integration of 5G/6G networks with the Internet of Vehicles (IoV) requires efficient computational offloading for data-intensive applications such as autonomous driving and augmented reality. Although Unmanned Aerial Vehicles (UAVs) offer agile mobile edge computing (MEC) capabilities, their operational efficiency is hampered by high mobility, limited battery life, and the complexity of joint resource optimization. Existing offloading strategies often fail to simultaneously optimize latency, energy consumption, and resource utilization under dynamic IoV conditions. This paper proposes a novel Energy-Optimized Lightweight Deep Reinforcement Learning (DRL) framework for intelligent task offloading in UAV-assisted IoV networks. Our approach leverages a simplified Double Deep Q-Network (DDQN) to dynamically manage task partitioning by intelligent offloading decisions, UAV trajectory planning through optimized path forecasting, and resource allocation through adaptive computation distribution. Key innovations include a streamlined state-space design that reduces computational overhead by 30% and a composite reward function that balances latency and energy objectives. These are realized by a prioritized experience replay mechanism and a target network separation strategy that enhances learning stability. Experimental results demonstrate that our framework achieves a task success rate of 98.5%, reduces latency by 40%, and maintains a 78.1%. The results confirm the framework’s superiority, demonstrating significant improvements over its base architecture (DQN), its enhanced variant (DDQN), and other state-of-the-art baselines like MADDPG and game-theoretic approaches, thereby providing a robust solution for practical UAV-IoV deployments.

Reinforcement Learning for Resource Allocation in Energy-Harvesting Cooperative IoT Networks

This work exploits the concept of cooperative communication and radio frequency-based energy-harvesting to improve the network throughput while maintaining power supply to the IoTDs and employs the reinforcement learning frameworks, particularly state–action–reward–state–action (SARSA) and Q-learning.

Parked electric vehicle‐assisted distributed edge intelligence in the vehicle‐to‐grid platform

The Internet of Electric Vehicles (IoEV) has emerged as a key component of future networks. However, some computation‐intensive vehicular applications cannot be executed locally owing to IoEV infrastructure limitations and computing resource bottlenecks. In this study, we propose a task offloading and resource management scheme based on Parked Electric Vehicle (PEV)‐assisted distributed edge intelligence (DEI), making use of underutilized PEV resources to handle offloaded tasks. In the proposed scheme, distributional reinforcement learning, the normalized average bargaining solution (NABS), and V2G charging scheduling are jointly combined to dynamically control availability prediction, resource sharing, and scheduling. The proposed scheme maximizes hybrid optimization benefits through PEV and edge server cooperation. Simulation results confirm performance improvements of 10%, 10%, and 15% in normalized service payoff, system throughput, and task failure rate, respectively, compared with existing benchmark protocols. Open issues and research directions for PEV‐DEI systems are also discussed.

Intelligent Resource Allocation in Smart Cities Using Multi-Agent Reinforcement Learning

The rapid integration of Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, edge computing, and advanced communication technologies is transforming traditional urban infrastructure into intelligent smart cities. Conventional resource allocation methods struggle to manage dynamic urban environments, creating a need for adaptive and decentralized decision-making systems. This study proposes a Multi-Agent Reinforcement Learning (MARL) framework for intelligent resource allocation across transportation, energy, water, healthcare, emergency response, and communication systems. Each urban subsystem functions as an autonomous learning agent that optimizes local decisions while coordinating to improve overall city performance. The framework combines IoT sensing, edge intelligence, cloud analytics, and deep reinforcement learning to enable real-time, adaptive resource management. It enhances resource utilization, reduces energy consumption and response time, improves system reliability, and supports scalable urban operations. The proposed approach also provides a foundation for future smart city technologies, including digital twins, federated learning, autonomous edge intelligence, and 6G networks, promoting sustainable, resilient, and efficient urban development.