Integrated sensing and communications (ISAC) is a pillar technology of 6G to enable intelligent environment awareness and hardware cost efficiencies. In ISAC-aided 6G vehicle-to-everything (V2X) networks, the perception data fusion from multiple sources can make a reliable and efficient data sharing to guarantee driving safety. However, the contradiction between task delay requirement and energy consumption becomes more and more prominent with the growth of computing task amount. Therefore, this paper proposes a joint computation offloading and resource allocation strategy to build greener V2X networks with mobile-edge computing (MEC) and ISAC technologies. A data fusion architecture for cooperative perception is first introduced to support fusing massive perception data from wireless infrastructures and vehicles. Then, a minimization problem of queuing latency is formulated with long-term latency and energy consumption constraints for data fusion computing tasks. The problem is further reformulated by the Lyapunov optimization method to transfer delay and energy constraints into queue stability problems. Finally, a joint computation offloading and resource allocation (JCORA) scheme is proposed to obtain the optimal computation offloading and resource allocation decision, achieving the balance between task delay and energy consumption. Extensive simulations validate the effectiveness of the proposed strategy compared with other baseline schemes.

Integrated Sensing and Communications (ISAC) technology can jointly design radio sensing and communication functionalities, which enable 6G Ere to have the ability to “see” the physical world rather than communication-only. Benefitting from ISAC, vehicular-to-everything (V2X) networks may efficiently complete high-precision traffic environment perception. Furthermore, with the assistance of flexibly deployed Unmanned Aerial Vehicles (UAVs), the V2X networks overcome the limited sensing range of sensors equipped on a vehicle and guarantee safe driving. This paper proposes an energy-efficient computation offloading strategy for multiple sensor data fusion in UAV Aided V2X Network supported by Integrated Sensing and Communication. Firstly, a vehicle-UAV cooperative perception architecture is proposed to perceive a wide range of traffic environments. Secondly, we introduce a computation offloading strategy jointly considering offloading decisions and dynamic computing resource allocation. Finally, a successive convex approximation (SCA) algorithm transforms a non-convex formulation problem into a tractable convex approximation problem. The simulation results show that the strategy proposed in this paper reduces the UAV energy consumption and data fusion task processing delay.

Ultra-dense networking (UDN) is a promising technology to improve the network capacity in the next-generation mobile communication system. The virtualization paradigm is tightly integrated into UDN to address the problem of interference management. However, mobility management based on virtual cells meets significant challenges in UDN due to the frequent handovers and massive signaling overhead. These problems become severe for vehicles owing to their high-speed movement. In this paper, driven by trajectory prediction using a real-world vehicle mobility dataset, we propose a proactive mobility management solution based on virtual cells. Four modules are designed in the centralized Software-Defined Networking controller to support the proposed solution. The proposed LSTM- DR framework predicts the next locations of vehicles by integrating Long Short-Term Memory (LSTM) networks and Dead Reckoning (DR) method. The active gNBs selection algorithm selects the serving gNBs to form virtual cells according to predicted locations and mobility preferences. The corresponding signaling procedure is then carefully designed to further reduce the signaling overhead. Simulation results show that the proposed prediction framework can achieve higher accuracy and robustness in trajectory prediction. The proposed proactive solution reduces the handover frequency and handover failure rate and thereby saves the handover signaling overhead significantly.

With the rapid development of Internet of Vehicles (IoV), particularly the introduction of Mobile Edge Computing (MEC), vehicles can efficiently share data with one another. However, edge computing nodes are vulnerable to various network attacks, posing security risks to data storage and sharing. Moreover, the presence of abnormal vehicles during the sharing process poses significant security threats to the entire network. To address these issues, this paper proposes a novel reputation management scheme, which proposes an improved multi-source multi-weight subjective logic algorithm. This algorithm fuses the direct and indirect opinion feedback of nodes through the subjective logic trust model while considering factors such as event validity, familiarity, timeliness, and trajectory similarity. Vehicle reputation values are periodically updated, and abnormal vehicles are identified through reputation thresholds. Finally, blockchain technology is employed to ensure the security of data storage and sharing. By analyzing real vehicle trajectory datasets, the algorithm is proven to effectively improve the differentiation and detection rate of abnormal vehicles.

Integrated sensing and communication (ISAC) has attracted great attention with the gains of spectrum efficiency and deployment costs through the coexistence of sensing and communication functions. Meanwhile, federated learning (FL) has great potential to apply to large-scale multiagent systems (LSMAS) in ISAC due to the attractive privacy protection mechanism. Nonindependent identically distribution (non-IID) is a fundamental challenge in FL and seriously affects the convergence performance. To deal with the non-IID issue in FL, a data augmentation optimization algorithm (DAOA) is proposed based on reinforcement learning (RL), where an augmented dataset is generated based on a generative adversarial network (GAN) and the local model parameters are inputted into a deep Q-network (DQN) to learn the optimal number of augmented data. Different from the existing works that only optimize the training performance, the number of augmented data is also considered to improve the sample efficiency in the article. In addition, to alleviate the high-dimensional input challenge in DQN and reduce the communication overhead in FL, a lightweight model is applied to the client based on deep separable convolution (DSC). Simulation results indicate that our proposed DAOA algorithm acquires considerable performance with significantly fewer augmented data, and the communication overhead is reduced greatly compared with benchmark algorithms.

The UAV/HAP-assisted Artificial Intelligence of Things (AIoT) integrates Artificial Intelligence (AI) technology into the Internet of Things (IoT) system, which provides ubiquitous global communications and intelligent computation services for mobile terminal devices (MTDs) by combining terrestrial and non-terrestrial communication networks. Efficient and energy-saving communication and computing resource support are crucial to improving the performance of AIoT systems. Therefore, an energy-efficient joint computation offloading and resource allocation strategy is proposed for AIoT supported by UAVs/HAPs. The strategy combines the advantages of Lyapunov optimization theory and the deep reinforcement learning (DRL)-based model to obtain optimal energy-efficient computation offloading and resource allocation decisions while guaranteeing long-term queue stability under dynamic network conditions. Firstly, the joint optimization problem is modeled as a multi-stage stochastic mixed integer nonlinear programming (MINLP) problem and reformulated into deterministic sub-problems under long-term performance constraints by the Lyapunov optimization theory. Then, a hybrid DRL-based online optimization algorithm is proposed to solve these sub-problems. This algorithm minimizes the overall system energy consumption by jointly optimizing computation offloading and resource allocation decisions. The simulation results show that the proposed strategy improves energy efficiency, decreases algorithm computation complexity, and guarantees the stability of task queues.