The deep integration of IoT and vehicular networks demands ultra-reliable, low-latency computing paradigms to support emerging applications like autonomous driving and smart traffic management. Existing Mobile Edge Computing (MEC) frameworks, however, struggle with dynamic resource heterogeneity, intermittent connectivity, and inefficient coordination among distributed nodes. To address these challenges, this paper proposes Vehicular Computing Power Networks (VCPN), an IoT-driven edge intelligence framework that orchestrates computational resources from mobile user equipments (MUEs), connected vehicles, and edge servers. We formulate a joint optimization problem to minimize end-to-end task latency by finding optimal task offloading decisions and resource allocation (e.g., CPU, bandwidth) policies under time-varying IoT channel conditions and node mobility. To enable decentralized coordination in IoT environment, we model the problem as a multi-agent Markov decision process (MDP) and propose a multi-agent deep deterministic policy gradient (MA-DDPG) algorithm in which agents (MUEs, vehicles, servers) collaboratively learn policies to optimize task scheduling and resource sharing. Furthermore, we design a robust MA-DDPG variant with error-resilient experience replay and channel-adaptive reward mechanisms to ensure reliable training under packet loss and unstable connectivity. Numerical results demonstrate that VCPN reduces average task latency and improves energy efficiency compared to federated MEC baselines. The proposed MA-DDPG algorithm achieves convergence stability in high-mobility scenarios, outperforming conventional deep reinforcement learning methods.

The development of industry 4.0 relies on emerging technologies of digital twin, machine learning, blockchain and Internet of Things (IoT) to build autonomous self-configuring systems that maximize manufactory efficiency, precision and accuracy. In this paper, we propose a new distributed and secure digital twin driven IIoT framework that integrates federated learning and Directed Acyclic Graph (DAG) blockchain with sharding. The proposed framework includes three planes: the data plane, the blockchain plane and the digital twin plane. Specifically, the data plane performs federated learning through a set of cluster heads to train models at network edges for twin model construction. The blockchain plane, which supports sharding, utilizes a hierarchical consensus scheme based on DAG blockchain to verify both local model updates and global model updates. The digital twin plane is responsible for constructing and maintaining twin model. Then, an efficient resource scheduling scheme is designed by considering performance of both federated learning and DAG blockchain with sharding. Accordingly, an optimization problem is formulated to maximize long-term utility of the digital twin driven IIoT. To cope with mapping error in the digital twin plane, a multi-agent Proximal Policy Optimization (MAPPO) approach is developed to solve the optimization problem. Numerical results illustrate that comparing with traditional approach, the proposed MAPPO improves utility by about 37 %, and reduces time latency by about 14%. Moreover, it also can well adapt to the mapping error.

Space–air–ground integrated edge computing is expecting to provide pervasive computation services for Internet of Things (IoT), especially in remote areas. However, the offloading process of power-limited IoT devices is a challenge issue due to unreliable communications in an aerial environment. In this article, we propose an energy-efficient space–air–ground integrated edge computing network architecture, in which the IoT devices choose the most appropriate LEO satellites or unmanned aerial vehicles (UAVs) for task offloading according to their energy level, communication conditions and computing capabilities. In order to providing efficient task offloading and energy-saving policy under an uncertainty aerial environment, a constrained Markov decision process is employed to formulate the task offloading decision problem and a deep reinforcement learning (DRL)-based algorithm is devised to solve the proposed problem. An adaptive federated DRL-based offloading method is further proposed to find suboptimal offloading decisions by considering the privacy protection and communication failure in the proposed network. Numerical results confirm the effectiveness of the proposed schemes on energy saving and computation efficiency.

With the rapid development of Internet of Things (IoT), the digital twin is emerging as one of the most promising technologies to connect physical components with digital space for better optimization of physical systems. However, the limited wireless resource and security concerns impede the deployment of the digital twin in IoT. In this article, we exploit blockchain to propose a new digital twin edge networks framework for enabling flexible and secure digital twin construction. We first develop cooperative federated learning through an access point (AP) to help resource-limited smart devices in constructing digital twin at the network edges belonging to different mobile network operators (MNOs). Then, we propose a model update chain by leveraging directed acyclic graph (DAG) blockchain to secure both local model updates and global model updates. In order to incentivize the APs to help in local models training for resource-limited smart devices and also encourage the APs to contribute resource in local model update verification, we design an iterative double auction-based joint cooperative federated learning and local model update verification scheme. The optimal unified time for cooperative federated learning and local model update verification is solved to maximize social welfare. Numerical results illustrate that the proposed scheme is efficient in digital twin construction.

The explosion of intelligent mobile applications has generated an unprecedented increase in the demand for diverse social content access. Device-to-device (D2D) communication, which enables direct transmission of content, can partly support these needs by reusing cellular spectrum resources. Most existing works focus on mitigating interference in D2D communication only in physical space. However, the effect of user content preference on interference in social space is ignored. In this article, a satisfied matching-embedded Social Internet of Things (IoT) is proposed for content preference-aware resource allocation (SIoT-RA). The Social IoT architecture is explored for user social characteristic analysis scenarios to ensure credible data management. Specifically, the user’s content preference characteristics are modeled as the probability of selecting similar content in the social IoT by employing the Dirichlet process. A satisfied matching-based model is proposed and embedded into the architecture as the core algorithm to achieve content preference-aware resource allocation. This design not only helps to reduce interference from the perspective of social space but also realizes maximized and satisfactory resource allocation performance by a matching algorithm that can quantify satisfaction as perceived utility. Extensive simulation results demonstrate the significant performance gains and high cost effectiveness of the proposed core algorithm of SIoT-RA in terms of the weighted sum data rate and users matching satisfaction.

The air ground integrated networks can leverage unmanned aerial vehicle (UAV) communications to tackle the ever-increasing and unbalanced traffic load in future communication systems. This paper investigates the UAV enabled traffic offloading problem in air ground integrated networks with mixed user traffic. The problem jointly maximizes the system load balance and the total UAV reward, which can be formulated under a two-layer network graph model. In the cellular network graph, the association between the delay-sensitive users and the access points (APs) as well as the association between the UAVs and the APs are formulated. In the UAV network graph, the association between the delay-insensitive users and the UAVs is formulated. By observing the coupling relationship of the decision variables, we decouple the problem into three sub-problems and solve the first two sub-problems with reduced complexity. Then, we devise a Deep Neural Network (DNN) empowered genetic algorithm to solve the last sub-problem. The DNN can be leveraged to filter out the non-optimal solutions in the initialization operator of the genetic algorithm for improving the efficiency. Performance comparisons are provided between the proposed traffic offloading scheme and the existing ones, which validate the advantages of the DNN empowered genetic algorithm regarding its convergence, accuracy, and robustness.

Device-to-Device (D2D) communication has been considered as a promising technique to provide better wireless services in local area. In this paper, we propose an improved intra-cluster D2D multicast protocol to enable data sharing among users. In our scheme, several D2D users take turns to multicast the corresponding packets. The sequence of packet transmissions is determined according to the data demand. In addition, we introduce relay based transmission into the multicast process so as to overcome transmission rate restriction caused by link heterogeneity. Greedy algorithm is applied to select relay nodes inspired by small world phenomenon. Through simulation, we show that the proposed method not only improves the delay performance and capacity gain, but also expands the D2D transmission range.