With the increasing computational demands of intelligent connected vehicles (ICVs), traditional roadside mobile edge computing (MEC) faces resource and coverage limitations. As a mobile computing entity, a vehicle platoon enables coordinated resource scheduling and low-latency communication, serving as an effective supplement to edge computing. However, limited onboard resources make vehicle platoons insufficient for handling heavy task loads. This study focuses on the joint task offloading and resource allocation between vehicle platoons and MEC servers. A MEC-assisted vehicle platoon computing network (MVPCN) is constructed, where both platoon vehicles (PVs) and MEC servers serve as computing nodes. We formulate a joint optimization problem of task offloading and resource allocation in the vehicle platoon, aiming to minimize the long-term weighted cost of time and energy consumption. Given the complexity of the problem and the limitations of conventional solution methods, we design a Discrete Two-Stage Stackelberg Game (DTSG) to decompose the problem into two subproblems: leader (platoon leader) level and follower (computing nodes) level, and prove the existence of a Stackelberg equilibrium (SE). Based on the game model, we propose a Nested Platoon Task Offloading and Resource Allocation (NPTORA) algorithm. The leader employs the multi-agent proximal policy optimization (MAPPO) to generate offloading decisions for the platoon, while the followers allocate computational resources using a Dynamic Priority-based Hybrid Optimization (DPHO) algorithm based on the received offloading decisions. Simulation results show that NPTORA outperforms baseline methods in task success, cost efficiency, and adaptability, demonstrating strong performance and practical potential.

Time-sensitive networking (TSN) is increasingly applied in automotive and industrial Internet fields due to its low latency and deterministic communication. Gate control list (GCL) is foundational for deploying TSN. Currently, most scheduling research focuses on frame-to-window-based scheduling. This scheduling approach typically generates a specific window for each frame, leading to a proliferation of GCL in large networks, which increases the complexity of implementing TSN. To simplify deployment and enhance scheduling reliability, this article introduces a systematic flexible-window-based scheduling framework. Utilizing a gapless GCL design approach, it optimizes flow’s worst-case end-to-end (e2e) delays through window length design, with delays obtained through network calculus analysis. A generic solving framework based on metaheuristic algorithms is established to address this optimization problem. The scheduling framework also features a load-balanced turn prohibition routing strategy to balance link loads and avoid cyclic dependencies, alongside a K-means priority clustering method based on routing overlap to reduce the number of priorities. Simulation validation in a high-level autonomous driving vehicle’s in-vehicle network shows that the proposed method can decrease GCL numbers by nearly 90% against frame-to-window scheduling. In common industrial Internet scenario, it significantly reduces worst-case e2e delays and enhances scheduling success rates compared to the analogous scheduling method. Large-scale complex network scenario further demonstrates its scalability.

As the Internet of Things proliferates, cloud-assisted mobile-edge computing (MEC) enables intelligent connected vehicles (ICVs) to offload their computationally intensive tasks to servers within the Internet of Vehicles, thereby reducing delay and energy consumption. However, most existing research on edge computing offloading overlooks the dependency relationships between subtasks. These dependencies significantly increase the complexity of task offloading, making it difficult to devise general solutions for scenarios of varying scales a challenging endeavor. To tackle this challenge, we present a heterogeneous graph attention network (HGAT) augmented deep reinforcement learning dependency-aware task offloading framework, aiming to achieve minimal task completion time and energy consumption. The dynamic system of vehicles and servers is modeled as an undirected graph, with nodes corresponding to servers/vehicles and edges capturing the intensity of task competition. Tasks are modeled as directed acyclic graphs, where nodes denote subtasks and directed edges define their dependencies. An HGAT-based encoder is then introduced to effectively capture the intricate relationships between subtasks and each servercores. Subtask selection and servercores assignment are formulated as a Markov decision process and solved using the proximal policy optimization method. Simulation results demonstrate that the proposed algorithm outperforms existing ones across various scenarios, showcasing superior adaptability and performance benefits.

In-vehicle time-sensitive networking (TSN) delivers highly secure, ultralow latency deterministic communication for intelligent connected vehicles (ICVs). To tackle the traffic scheduling problem of in-vehicle TSN, this study establishes in-vehicle network topologies and flow models, abstracts the traffic scheduling problem as a job-shop scheduling problem (JSSP), and formulates a priority-based optimization function capable of various end-to-end (E2E) delay requirements. A joint routing and scheduling optimization strategy based on improved grey wolf optimization (IGWO) is proposed, which incorporates acrlong LF, historical experience learning, and acrlong TS operators to significantly enhance search capabilities and optimization efficiency. This strategy can rapidly solve large-scale in-vehicle network scheduling and generate scheduling results with outstanding delay performance. Dynamic routing that combines load-balanced and shortest path effectively minimizes interference between flows, further reducing E2E delay. Simulation experiments grounded in realistic ICV scenarios demonstrate the effectiveness of the proposed strategy. Furthermore, the simulation results verify the impact of flow period parameters and network topologies on E2E delay, offering guidance for in-vehicle TSN engineering design.