CAVE: Crowdsourcing Passing-By Vehicles for Reliable In-Vehicle Edge Computing

TL;DR

This paper introduces CAVE, a novel crowdsourcing approach using passing-by vehicles to enhance in-vehicle edge computing, addressing challenges of high vehicle dynamics and reliability through an optimized algorithm.

Contribution

The paper proposes a new algorithm, CAVE, for reliable task offloading in in-vehicle edge computing by crowdsourcing passing vehicles, with extensive simulation validation.

Findings

CAVE reduces end-to-end latency effectively.

The algorithm improves task reliability under high vehicle mobility.

Simulation results demonstrate significant performance gains.

Abstract

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.