Autonomous Vehicle Health Monitoring Based on Cloud-Fog Computing

Abstract

Almost every part of the economy relies on the transportation industry. The road transportation sector is one of the numerous types of transport sectors. As a vehicle's continual use causes damage, inefficiency, and faults in numerous parts, etc. Timely maintenance prevents or fixes these issues. Aiming at the requirements of vehicle health monitoring, this article studies and designs the cloud computing collaborative vehicle health monitoring network system and proposes the allocation of data transmission paths with an ant colony algorithm as the core. On this basis, it studies the construction of autonomous vehicle health status detection systems based on cloud and fog collaborative computing and gives the system level composition and system framework scheme. In the system, data is collected by sensor equipment and then transmitted, stored, and comprehensively processed. The obtained data provides a technical basis for automobile health detection. Through the construction of a simulation environment, it shows that the algorithm can adjust to the dynamic features of cloud system networks in the environment of road strength construction. The ant colony optimization (ACO) algorithm is a type of probabilistic algorithm that determines the best route through a graph. As compared with the shortest path first (SPF) algorithm and the brute force (BF) storm algorithm in terms of average throughput and response time, it is more suitable for cloud and fog collaborative network transmission environments.