Exploring Anomaly Detection Techniques for Enhancing VANET Availability

In VANETs the quicker an anomaly can be detected and properly classified, the faster the issue it causes can be dealt with. In this paper, we propose deploying Vehicular Edge Computing (VEC) to detect anomalies characterized by the absence of message exchange between vehicles. The detection of anomalies spread across an urban area can possibly benefit from the processing and storage capacity of the VAC. VANETs are dynamic networks, whose vehicle density varies considerably over time. VANETs components do not usually store much information, making it difficult to efficiently detect the loss of messages from multiple vehicles across the neighborhoods of a city. The article aims to investigate whether the use of VEC and conventional anomaly detection techniques benefits loss of messages detection by increasing its fault coverage capacity. To measure fault coverage, fault injection experiments were conducted using simulation.