Dynamic Edge-Twin Computing for Vehicle Tracking

Yuanda Wang, Shigang Chen, Ye Xia, Dimitrios Melissourgos, Haibo Wang

Abstract: Internet-connected devices have been surging rapidly during the past years. Many important applications based upon such devices have emerged, such as vehicle tracking systems. These applications often require real-time execution of a large number of computation tasks. Edge computing has shown great potential in processing frequent but less-demanding tasks. Additionally, cloud computing allows for great scalability when substantial computing resources are needed. Edge-cloud computing is a paradigm that combines edge computing and cloud computing. A key problem in edge-cloud computing is how to determine the execution location for each computation task. We propose a dynamic edge-twin computing model in the context of edge-cloud computing. It uses an evaluation mechanism to predict the completion times of the task at both an edge device and a cloud server. The completion time includes data transfer time and computing time and it is determined based on real-time information about the task and the computing environment. The task will be executed by the device with a shorter completion time. We have implemented a vehicle tracking system under the edge-twin model. The experimental results show that the edge-twin model outperforms edge-alone computing and cloud-alone computing.