An Intelligent Fuzzy Inference System for High Priority Vehicles in Vehicular Named Data Networks

Abstract

With the tremendous increase in the number of on-road vehicles, travel time for High Priority vehicles is touching alarming levels. In case of an emergency, this can lead to loss of life. To cater or propose a solution to this problem an efficient model can be developed which focuses on selecting vehicles obstructing the path of high-priority vehicles (HPV) and making communication with selected vehicles. This paper proposes a solution based on a hybrid approach using communication handled by Vehicular Named Data Networks (V-NDN) and decision support through a fuzzy inference system. Considering the basic V-NDN capabilities of transmitting packets and developing communication with vehicles in the periphery, a model is developed which will send Interest Packets to all vehicles in range and prioritize vehicles using directional values to find out which vehicle is obstructing the path of HPV. The decision support is provided through the fuzzy inference system which utilizes “If-Then” rules to implement directional identification of vehicles. Simulations have been run on the ndnSim simulator to verify the suggested scheme's performance. The results have been analyzed using the ndnSim simulator on different scenarios and their efficiency has been observed in terms of average delay, data retrieval performance and data delivery efficiency.