With the rapid development of vehicular networks, ever-growing number of on-board sensors makes vehicle applications/tasks to be not only computation-intensive but also data-intensive. To this end, Vehicular Cloud Computing (VCC) has been convinced as a promising paradigm to offer valuable computing and sensing services to vehicles. However, considering the heterogeneity of on-board computation and sensing capabilities, how to efficiently determine the appropriate vehicle to process the task is challenging. Also, the allocation of transmission power can significantly impact the corresponding energy consumption. Therefore, to minimize the weighted sum of execution delay and energy consumption of vehicle tasks, in this paper, we propose a Collaborative Sensing-Assisted Task Offloading and Resource Al-location (CSTR) algorithm based on the Integrated Sensing and Communication (ISAC) mechanism. The optimization problem is formulated as a mixed integer nonlinear programming problem (MINLP), which is proven to be NP-hard. To achieve this, the original problem is decoupled into two sub-problems namely the task offloading problem and transmission power allocation problem, which are solved by Genetic Algorithm (GA) and convex optimization technique, respectively. Validation through several simulations based on real-world road networks has demonstrated that our proposed CSTR can outperform existing benchmark solutions under various settings.


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    Title :

    Collaborative Sensing-Assisted Task Offloading and Resource Allocation for ISAC-Based Vehicular Clouds


    Contributors:
    Lin, Junzhe (author) / Liu, Zhang (author) / Chen, Ning (author) / Zhao, Yifeng (author) / Huang, Lianfen (author)


    Publication date :

    2024-06-24


    Size :

    1158537 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English