Decision tree‐based task offloading in vehicle edge computing

Abstract

Summary There are significant developments in the Internet of Vehicles (IoV) field, and the requirements needed in this area are increasing rapidly. When these needs are examined in the near future, it appears that the demand for connected, autonomous, shared, and electric vehicles will increase. Therefore, fundamental problems such as big data flow and storage will arise in the IoV field. Another problem is the delay sensitivity of IoVs and the need to minimize data loss. The use of edge computing (EC) tools can play an important role in obtaining effective solutions to overcome these problems. Delay, bandwidth, and energy consumption rate, which are important qualities in EC systems, emerge as a problem that needs to be improved for delay‐sensitive systems. These improvements belong to the category of nonlinear challenging problems. Effective optimization or machine learning methods can be used to improve these types of problems. In this study, a two‐stage machine learning method is proposed for a more efficient task completion rate and service time. According to the proposed method, in the first stage, the decision tree algorithm is used to select the computing tool to which the task will be sent, and the decision is made on which computing tool to send it to. In the second stage, a linear regression‐based classification method is used to select a delay‐sensitive computing tool. The performance analysis of the proposed method was made using the edgeCloudSim simulation tool, and according to the results obtained, the proposed method provides better results than other algorithms in the literature.