CAVE: Crowdsourcing Passing-By Vehicles for Reliable In-Vehicle Edge Computing
<|reference_start|>CAVE: Crowdsourcing Passing-By Vehicles for Reliable In-Vehicle Edge Computing: In-vehicle edge computing is a much anticipated paradigm to serve ever-increasing computation demands originated from the ego vehicle, such as passenger entertainments. In this paper, we explore the unique idea of crowdsourcing passing-by vehicles to augment computing of the ego vehicle. The challenges lie in the high dynamics of passing-by vehicles, time-correlated task computation, and the stringent requirement of computing reliability for individual user tasks. To this end, we formulate an optimization problem to minimize the end-to-end latency by optimizing the task assignment and resource allocation of user tasks. To address the complex problem, we propose a new algorithm (named CAVE) with multiple key designs. We build an end-to-end network and compute simulator and conduct extensive simulation to evaluate the performance of the proposed algorithm.<|reference_end|>
@article{cao2024cave:,
    title={CAVE: Crowdsourcing Passing-By Vehicles for Reliable In-Vehicle Edge
  Computing},
    author={Jiahe Cao, Qiang Liu, Dawei Chen, Kyungtae Han},
    journal={arXiv preprint arXiv:2408.00621},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00621},
    primaryClass={cs.NI}
}