Big Data Driven Computing Offloading Scheme with Driverless Vehicles Assistance
Chengling Chen, Zhou Su, Weiwei Li, Yuntao Wang · 2019
In the era of big data, edge computing is emerged as a promising paradigm to alleviate the pressure on the backbone network and facilitate vehicular services on the road. As edge nodes deployed for vehicular applications, roadside units (RSUs) need to undertake a large number of local computing tasks. However, due to the uncertainty of the vehicular network topology, static RSU deployments are subject to short-term overload and cannot handle various delay-sensitive computing tasks concurrently. To address the problem, we propose a big data driven computing offloading scheme to dispatch idle driverless vehicles to enhance the capacities of RSUs dynamically. First, we present a trust assessment model to evaluate the credibility of driverless vehicles. Then, a multi-attribute reverse auction is applied to maximize the utilities of RSUs and driverless vehicles. In addition, a secure forwarding method is developed to protect the privacy of computing tasks.