With the increasing global demand for renewable energy and heightened environmental awareness, electric vehicles (EVs) are rapidly becoming a popular clean and efficient mode of transportation. However, the widespread adoption of EVs has presented several challenges, such as the lagging development of charging infrastructure, the impact on the power grid, and the dynamic changes in user charging behavior. To address these issues, this paper first proposes a vehicle-to-grid (V2G) optimization framework that responds to regional dynamic pricing. It also considers power balancing in charging and discharging stations when a large number of EVs are involved in scheduling, with the aim of maximizing the benefits for EV owners. Next, by leveraging the interaction between environmental states and the dynamic behavior of EVs, we design an optimization algorithm that combines the recurrent proximal policy optimization (RPPO) algorithm and long short-term memory (LSTM) networks. This approach enhances system convergence and improves grid stability while maximizing benefits for EV owners. Finally, a simulation platform is used to validate the practical application of the RPPO algorithm in optimizing V2G and grid-to-vehicle (G2V) charging strategies, providing significant theoretical foundations and technical support for the development of smart grids and sustainable transportation systems.

With the emergence of smart mobile devices (SMDs) and mobile applications, the cloud-MEC collaborative computation offloading (CMCCO) scheme, i.e., offloading the computation-intensive task from the local SMD to either the mobile edge computing server (MEC) , or the remote mobile cloud computing server (MCC), is widely identified as a promising candidate under the conflict between SMD’s limited computing ability and computing-intensive application requiring higher energy consumption. Meanwhile, the existing CMCCO scenario over integrated coud-MEC Fiber Wireless broadband access networks (CM-FiWi) architecture, by generally fixing computing ability and transmitting power, still achieves higher computation offloading overhead in terms of task’s aggregate response time and SMD’s energy consumption. In light of this, the energy-aware collaborative computation offloading (EA-CCO) paradigm with very diverse types of computation tasks over CM-FiWi broadband access network is provided in this paper. An iterative searching algorithm for collaborative computation offloading scheme (ISA-CCO) is proposed as a solution to obtain minimized task offloading overhead, which jointly takes scaling computing ability, variable transmit power, and residual battery rate into considerations. Extensive numerical results demonstrate that the proposed solution outperforms the traditional paradigms, e.g., optimal enumeration collaborative computation offloading scheme (OECCO), approximation collaborative computation offloading algorithm (ACCO), and game theoretic collaborative computation offloading scheme (GTCCO). More specially, the proposed ISA-CCO scheme obviously achieves lower overall task offloading overhead than those fixed transmit power and computational frequency scaling.

In recent years, the Internet of Vehicles (IoV) has emerged as a pivotal driving force within intelligent transportation systems, offering users immersive interactive experiences. Meanwhile, unmanned aerial vehicles (UAVs) have demonstrated substantial potential for widespread application within the IoV domain, attributed to their high flexibility, low cost, and ease of deployment. However, as the complexity of IoV tasks increases, complex dependencies among tasks give rise to notable delay issues, which are further exacerbated by the uneven distribution of computational resources. In response to the previously mentioned challenges, we suggest a strategy for resource distribution and task offloading aided by UAVs for IoV. Firstly, by constructing a complex task dependency model, tasks are topologically sorted to clarify the dependencies among tasks, thereby optimizing task execution order. Secondly, focusing on the core issues of task offloading and resource allocation, we present the multi-agent deep deterministic policy gradient (MADDPG) algorithm to devise a dependency-aware scheduling strategy. This strategy integrates task dependencies and UAV mobility characteristics, enabling intelligent decision-making for UAV trajectory planning and task scheduling by analyzing actor and critic network action rewards at each timeslot. To further tackle non-convex optimization problems, we design a federated learning (FL)-based intelligent data caching and computation offloading (Fed-IDCCO) algorithm, leveraging deep reinforcement learning (DRL) techniques. This approach handles large-scale and continuous state and action spaces to obtain optimal task offloading strategies within IoV environments. This methodology not only effectively reduces task processing delays and energy consumption but also significantly enhances the overall system performance. Extensive experimental results demonstrate that, compared to several existing benchmark algorithms, the suggested method offers unique benefits in diminishing delays in task processing, lowering energy usage, controlling costs, and improving cache hit rates.

Integration of sensing, communication, computing, and intelligence (ISCCI) represents a pivotal advancement in B5G and 6G technologies, offering transformative potential for the Internet of Vehicles (IoV). As IoV systems become increasingly integral to intelligent transportation and autonomous driving, these systems also face escalating security challenges across multiple layers, including physical, network, application, and system dimensions. (1) This paper comprehensively surveys these security issues, systematically analyzing the threats encountered at each layer and proposing targeted countermeasures to mitigate risks. (2) Furthermore, the paper explores future trends in IoV security, emphasizing the roles of 6G networks, blockchain technology, and digital twins in addressing emerging challenges. (3) Finally, based on a comprehensive review of current research and insights, this paper aims to serve as a foundational reference for advancing secure and sustainable IoV ecosystems.

With the increasing deployment of environment-aware services in the Internet of Vehicles (IoV), vehicles are required to execute multiple computational tasks in real time. However, resource allocation and task offloading in unmanned aerial vehicles (UAVs)-assisted IoV systems remain challenging due to the growing number of vehicle terminals (VTs), potential privacy leakage, and resource-constrained edge devices. This paper proposes a digital twin (DT) and generative artificial intelligence (GAI)-powered hierarchical aerial-ground cooperative architecture (DTG-HACA) that achieves dynamic resource optimization through a three-layer framework. The DT layer enables real-time synchronization of vehicle/UAV states and simulated trajectory planning. The high altitude platforms (HAPs) layer provides low-latency offloading channels through stratospheric wide-area coverage and solar-powered endurance, while the physical entity layer executes energy-efficient edge computing via UAV-vehicle-roadside units (RSUs) collaboration. For UAV trajectory optimization, we introduce the multi-agent deep deterministic policy gradient (MADDPG)-improved prioritized experience replay (MADDPG-IPER) algorithm that minimizes communication overhead and energy consumption while integrating DT-simulated trajectory planning. For the joint challenge of edge caching and task offloading under privacy preservation constraints, we develop a federated deep reinforcement learning (FDRL) based generative adversarial network (FDRL-GAN) algorithm. This solution addresses critical challenges in dynamic task offloading and resource allocation for UAV-assisted IoV by leveraging GAI to predict task demands for cache hit rate optimization, while implementing FDRL for distributed privacy-preserving decision-making without raw data sharing, thereby achieving global resource allocation optimality. Extensive simulation experiments confirm that our proposed scheme demonstrates significant advantages over existing benchmark algorithms across five critical performance metrics, including training stability, computational capacity, task offloading efficiency, cache hit rate, and energy consumption.