Vehicular Edge Computing Model for Fault Detection and Diagnosis of High-Speed Train

Fault detection and diagnosis of high-speed train (HST) plays a pivotal role in intelligent rail transportation system. It aims at making real-time operation decisions based on evaluation results of train states to guarantee the operation safety. Thanks to the rapid development of edge computing technology, the performance of fault detection and diagnosis application can be improved dramatically through offloading computing tasks. In this paper, the fault detection and diagnosis service of HST is modeled as a vehicular edge computing (VEC) application where the application is partitioned into multiple tasks. Then, the edge computing model of fault detection and diagnosis is proposed, in which wireless transmission models and cost models are modeled. Based on the proposed application's models, the task offloading problem on minimizing the execution cost is formulated. Then, an offloading strategy with minimum objective cost is proposed. The effectiveness of the proposed offloading strategy is validated by extensive simulation results. The simulation results show that the execution costs can be reduced up to about 70%.