An AI fuzzy clustering-based routing protocol for vehicular image recognition in vehicular ad hoc IoT networks

A vehicular ad hoc IoT network (VA-IoT) plays a key role in exchanging the constrained networked vehicle information through IPv6-enabled sensor nodes. It is noteworthy to understand that vehicular IoT is interconnection of vehicular ad hoc networks with the support of constrained IoT devices. Routing protocols in VAN-IoT are designed to route the vehicular traffic in the distributed environments. In addition, VAN-IoT is designed to enhance road safety by reducing the number of road accidents through reliable data transmission. Routing in VAN-IoT has a unique dynamic topology, frequent spectrum, and node handover with restricted versatility. Hence, it is very crucial to design the hybrid reactive routing protocols to ensure the network throughput and data reliability of the VAN-IoT networks. This paper aims to propose an AI-based reactive routing protocol to enhance the performance of the network throughput, minimize the end-to-end delay with respect to node mobility, spectrum mobility, link traffic load and end-to-end network traffic load while transmitting the vehicular images. In addition, the performance of the proposed routing protocol in terms of image transmission time is being compared with the existing initiative-taking- and reactive-based routing protocols in vehicular ad hoc IoT (VA-IoT) networks.