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 paper, we propose a framework that combines integrated sensing and communications (ISAC) and digital twins (DTs) with low-altitude edge intelligence for V2X, and formulate an optimisation problem to minimise the total service delay (i.e., the transmission delay plus the task computation delay) of all vehicular users by jointly optimising task offloading decisions, communication and computation resource allocation, and the association between unmannedaerial vehicles (UAVs) and vehicular users. To solve this problem under fast varying mobility and high-dimensional coupled constraints, we propose a task-aware multi-agent resource allocation optimisation (TM-RAO) 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.