With the advancement of autonomous driving and smart navigation, Internet of Vehicles (IoV) systems face stringent requirements for real-time data delivery and processing reliability. Traditional metrics cannot fully capture information timeliness due to network dynamics and packet loss. Existing approaches also struggle with the coupling between task offloading and resource allocation, lacking adaptability in dynamic IoV environments. To address these issues, we propose a joint optimization scheme using a deep Q-network (DQN). Specifically, we build an IoV system model incorporating V2V and V2I communication, and formulate an optimization problem to minimize the average age of information (AAoI) under delay, bandwidth, computing, and energy constraints. We then design a mixed-action DQN algorithm with dual-network architecture, experience replay, and an action mask mechanism to enhance training stability and environmental adaptability. Simulation results show that our DQN-based scheme achieves the lowest AAoI among Random, Greedy, A2C, and DDQN, with reductions of 29.5%, 8.9 %, 7.1 %, and $\mathbf{7. 6 \%}$, respectively. It also exhibits superior delay and energy performance, confirming its effectiveness for dynamic IoV task offloading and resource allocation.
Chao He, Wanting Wang, Dongfeng Fu et al.· 2026 International Conferenc...· 0 citations
Low-carbon smart energy systems increasingly rely on dense sensing, distributed energy resources, virtual power plants, digital twins and demand response. These services require cloud-edge intelligence, but practical deployment is constrained by latency, reliability, privacy, cybersecurity and the energy and carbon cost of computation. This review examines how large language models can be introduced into the power internet of things without shifting them into the role of direct grid control agents. The literature is organised around five technical themes: task offloading, dynamic edge resource allocation, low-latency communication and collaborative computing, security and privacy protection, and green computing. The review then evaluates intelligent inspection, digital-twin assistance, virtual power plants, demand response, and load forecasting through an explicit evidence-maturity hierarchy. Across the reviewed studies, the most practical deployment pattern places large language models between heterogeneous operational evidence and verified engineering tools. Language models can organise evidence, invoke approved tools, and assist operator judgement; authority over physical control and market execution remains with deterministic models. Claims of low-carbon benefit should be based on the joint assessment of service performance, reliability, security, energy consumption, and carbon emissions.
Chao He, Yun-Jie Su, Si-Rui Zhang et al.· Clean Energy· 0 citations
