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.
@article{arxiv.2408.00621,
title = {CAVE: Crowdsourcing Passing-By Vehicles for Reliable In-Vehicle Edge Computing},
author = {Jiahe Cao and Qiang Liu and Dawei Chen and Kyungtae Han},
journal= {arXiv preprint arXiv:2408.00621},
year = {2024}
}