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. First, we reformulate the original problem into two subproblems while incorporating not only incoming but also in-progress tasks. Second, we solve the task assignment subproblem with reliability constraints by using particle swarm optimization with the adaptive barrier function. Third, we solve the resource allocation subproblem by deriving the optimal allocation with Karush–Kuhn–Tucker (KKT) condition. We build an end-to-end network and compute simulator and conduct extensive simulation to evaluate the performance of the proposed algorithm. Simulation results show that, our CAVE algorithm reduces more than 15% end-to-end latency than state-of-the-art solutions, without degrading the reliability performance.