Collaborative Sensing-Aware Task Offloading and Resource Allocation for Integrated Sensing-Communication- and Computation-Enabled Internet of Vehicles (IoV)

Abstract

Integrated Sensing, Communication, and Computation (ISCC) has become a key technology driving the development of the Internet of Vehicles (IoV) by enabling real-time environmental sensing, low-latency communication, and collaborative computing. However, the increasing sensing data within the IoV leads to demands of fast data transmission in the context of limited communication resources. To address this issue, we propose a Collaborative Sensing-Aware Task Offloading (CSTO) mechanism for ISCC to reduce the sensing tasks transmission delay. We formulate a joint task offloading and communication resource allocation optimization problem to minimize the total processing delay of all vehicular sensing tasks. To solve this mixed-integer nonlinear programming (MINLP) problem, we design a two-stage iterative optimization algorithm that decomposes the original optimization problem into a task offloading subproblem and a resource allocation subproblem, which are solved iteratively. In the first stage, a Deep Reinforcement Learning algorithm is used to determine task offloading decisions based on the initial setting. In the second stage, a convex optimization algorithm is employed to allocate communication bandwidth according to the current task offloading decisions. We conduct simulation experiments by varying different crucial parameters, and the results demonstrate the superiority of our scheme over other benchmark schemes.