Fault Identification of Vehicles in IOT using Advanced Fault Detection Method (AFDM)

Abstract

Present in worldwide environment, Internet of Things (IoT) is become largest network in vehicular intelligent organizational constitute essential parts. Integration of different intelligent vehicles enabling spectrum based promising applications and also it is still difficult to realize because of their characteristics relates to intrinsic and define unexpected challenges in different communication systems like privacy and security. So that it is necessary to develop an efficient approach/method to provide solution from integrated intelligent vehicle systems because of increasing wireless medium, mobility, vehicle data faults in IoT systems. In this paper, we propose and develop Advanced Fault Detection and Modified Approach (AFDMA) based on Bayesian Network model which is used to explore spatial vehicle data for real time fault identification and modification. We also use calculation method to identify best fault identification based on threshold. Experimental results of proposed approach give efficient fault identification and modification with respect to traditionally available methods.