Computation Offloading for Energy and Delay Trade-Offs With Traffic Flow Prediction in Edge Computing-Enabled IoV

Abstract

An unprecedented prosperity in artificial intelligence promotes the development of Internet of Vehicles (IoV). Assisted by edge computing, vehicles enable to offload data to edge servers in close proximity to users for processing, thus making up for the shortage of local computing resources. However, due to the uneven space-time distribution of traffic flow, edge servers of a certain road segment may be overwhelmed by the surge of service requests. Furthermore, IoV system will incur significant additional energy consumption and time delay because of the absence of a proper computation offloading scheme between edge servers. To cope with above challenges, a computing offloading method for energy and delay trade-offs with traffic flow prediction in edge computing-enabled IoV is proposed. We first design the graph weighted convolution network (GWCN) that can fully excavate the connectivity and distance relation information between road segments to conduct traffic flow prediction. The short-term prediction results are utilized as the basis for adjusting the resource allocation of edge resources in different regions. Then, a computation offloading method driven by deep deterministic policy gradient (DDPG) is leveraged to obtain an optimal computation offloading scheme for edge servers. Finally, extensive comparative experiments demonstrate the low prediction error of GWCN and superior performance of DDPG-driven method in reducing total time delay and energy consumption.