Improved Quality of Service by canine olfactory route finding algorithm for Vehicular Ad Hoc Network

QoS aware vehicular ad-hoc network (VANET) routing protocols address the increasing demand for delay-sensitive vehicular applications to establish intelligent transportation. Primary challenge with ad-hoc nature of VANET is that the communication between vehicle-to-vehicle and vehicle-to-infrastructure is prone to link failure. Bio-inspired algorithms offer robust solutions to secure VANET links. Accordingly in this article, novel canine olfactory route-finding algorithm (CORFA) is proposed for VANET to achieve the best possible route with the minimum transmission of packets, which ensures enhanced QoS. The underlying architecture utilizes the exceptional ability of canines to evaluate and memorize the environment and pass the message to neighbors. It makes canines a great choice to model their behavior for VANET routing. RSUs are fundamental components of VANET that supply contents to the proceeding vehicles from their cache. As RSUs carry out information dissemination tasks efficiently, the already traversed and discovered routing paths in the recent past can be cached in RSU's storage and through back-haul can be circulated to neighboring RSUs. Vehicles request the pre-cached route and use them for data transmission. Thereby avoiding the route discovery and maintenance procedure reduces the routing overhead. In absence of a pre-cached route, the fittest vehicle from each hop is selected as next forwarder for packet transmission. The proposed routing algorithm forms multiple routes during route discovery to handle link failure scenario. Traffic simulator, SUMO is used to generate the mobility model and integrated with MATLAB for analysing the performance of CORFA with related geographical and topological routing protocols that confirm the performance superiority of CORFA under several metrics such as packet delivery ratio, network latency, throughput, and overhead.