Digital twin-assisted large AI task-aware edge offloading and resource allocation for low-altitude wireless sensor networks

Emerging vehicle-to-everything (V2X) applications, especially for smart transportation, require high-accuracy sensing and low-latency communications and computation; however, existing V2X architectures that deal with sensing, communication, and computation separately are ill-equipped to meet these coupled requirements. In this article, we propose a framework that combines integrated sensing and communications and digital twins with low-altitude edge intelligence for V2X, and formulate an optimization problem to minimize the total service delay (i.e., the transmission delay plus the task computation delay) of all vehicular users by jointly optimizing task offloading decisions, communication and computation resource allocation, and the association between unmanned aerial vehicles (UAVs) and vehicular users. To solve this problem under fast varying mobility and high-dimensional coupled constraints, we propose a task-aware multiagent resource allocation optimization algorithm, which enables scalable cooperative decision-making, including UAV-vehicular user association, and communication and computation resource allocation. Simulation results show substantial reductions in total service delay over the traditional deep reinforcement learning benchmark, especially in dense, dynamic low-altitude edge intelligent V2X scenarios.