A Queueing Model-Assisted Traffic Conditions Estimation Scheme for Supporting Vehicular Edge Computing
In recent years, with the development of the Internet of Things (IoT) and the Vehicular Networks (VNets), a large number of computers and sensors equipped on different vehicles (e.g., onboard CPU, camera, GPS, etc.) can not only help the vehicle to collect its own surrounding environment information, but also share those information with other participants in the transportation system through Vehicle-to-everything (V2X) technique. This ability to share information further makes VNets a precious resource for information and resources, which can support the vehicular edge computing (VEC) environment. However, due to the high moving speed of vehicles and the relative motion between vehicles, the topology of vehicle networking is highly dynamic. How to estimate the number of vehicles and the time period that they can form a vehicular cloudlet in a road segment is a challenging task for enabling VEC. Hence, in the paper, we present a queueing model-assisted traffic density estimation scheme to derive and analyze some essential parameters for implementing VEC, i.e., the vehicular cloudlet existence probability and the corresponding lifetime. We further demonstrate the results derived by the proposed scheme.
