Vehicular Ad-hoc NETwork (VANET) is a meteoric growing research area due to the technological advancement in sensing, computation, communication, and radio wireless technology. The demand for VANET is increasing day by day due to the numerous diverse applications. Applications of VANET include safety applications like reduction in the road accident by broadcasting messages to the driver, convenience applications like automatic parking service, commercial applications like selling and buying of products through a network of wheels, productive applications like automatic environmental parameters monitoring, vehicular cloud service like secure toll transaction, etc. The aforementioned VANET applications will become non-operational if the communication unit of the vehicle becomes inoperative or malfunctioning. This paper proposes a composite fault diagnosis methodology by detecting and classifying the fault of the On Board Unit (OBU) of the vehicular ad hoc network. The fault detection includes the detection of two categories of the fault i.e. hard fault in which the communication unit is in the inactive state and not able to communicate with other communication units and soft fault in which the communication unit is in the active state but communicates incorrect data with other communication devices. The hard fault is detected by the repeated time out mechanism and by a statistical method called Kolmogorov–Smirnov test (K-S test). The detection of soft fault (permanent, intermittent, and transient) is performed by a statistical method called the Chi-square test. Apart from the detection of the soft fault, the classification of soft fault is carried out by ECOC-SVM. Finally, the result of the fault is transmitted to the faulty vehicle. The parameters such as Fault Detection Accuracy (FDA) and False Alarm Rate (FAR) are used to validate the proposed fault detection protocol. The parameter False classification rate (FCR) is used to measure the performance of the soft fault classification. Finally, the performance of the transmission of the result to the faulty vehicle is evaluated through the parameters end-to-end (E2E) delay, number of hops, and network gaps by comparing the proposed work with existing routing protocols of VANET like GSR, A-STAR, Bhoi et al.

由于传感，计算，通信和无线电无线技术的技术进步，车载专用网络（VANET）是一个飞速发展的研究领域。由于应用程序众多，对VANET的需求日益增加。VANET的应用包括安全应用，例如通过向驾驶员广播消息来减少道路交通事故；便利应用，例如自动停车服务；商业应用，例如通过车轮网络买卖产品；生产性应用，例如自动环境参数监控；车辆云。如果车辆的通信单元出现故障或出现故障，上述VANET应用程序将无法运行。通过对车载自组织网络的车载单元（OBU）的故障进行检测和分类，提出了一种复合故障诊断方法。故障检测包括对两类故障的检测，即通信单元处于不活动状态并且不能与其他通信单元通信的硬故障和通信单元处于活动状态但是通信不正确的软故障。与其他通信设备的数据。硬故障通过重复超时机制和称为Kolmogorov–Smirnov检验（KS检验）的统计方法进行检测。软故障（永久性，间歇性和瞬态）的检测是通过称为卡方检验的统计方法执行的。除了检测软故障，软故障的分类是通过ECOC-SVM进行的。最后，故障结果被传输到故障车辆。故障检测准确度（FDA）和误报率（FAR）等参数用于验证建议的故障检测协议。参数False分类率（FCR）用于测量软故障分类的性能。最后，通过将端到端（E2E）延迟，跳数和网络间隙等参数与拟议的工作与VANET的现有路由协议（如GSR）进行比较，可以评估结果传输到故障车辆的性能， Bhoi等人的A-STAR。故障检测准确度（FDA）和误报率（FAR）等参数用于验证建议的故障检测协议。参数False分类率（FCR）用于测量软故障分类的性能。最后，通过将端到端（E2E）延迟，跳数和网络间隙等参数与拟议的工作与VANET现有的路由协议（如GSR）进行比较，评估了将结果传输至故障车辆的性能， Bhoi等人的A-STAR。故障检测准确度（FDA）和误报率（FAR）等参数用于验证建议的故障检测协议。参数False分类率（FCR）用于测量软故障分类的性能。最后，通过将端到端（E2E）延迟，跳数和网络间隙等参数与拟议的工作与VANET的现有路由协议（如GSR）进行比较，可以评估结果传输到故障车辆的性能， Bhoi等人的A-STAR。