ACO and PSO Algorithms for Developing a New Communication Model for VANET Applications in Smart Cities

Abstract

Traffic congestion is a major mobility problem, which generates enormous economic resources and causes serious problems for the most dense of cities. Intelligent transportation systems based on vehicular ad-doc network (VANET) communications will improve many services, expressively, related to transport, security, reliability, management and including the assistance in the reduction of traffic congestion. In this context, we propose an intelligent system based, firstly, on a new clustering technique to control and maintain the stability of routes during inter-vehicular communications, and secondly, a bio-inspired systematically conducting mobility measurement of agent, intervening in cases of traffic congestion, in order to find new routes to those offered by the global positioning system. Obviously, by providing an appropriate route selection process, such routing concept could be helpful in exchanging control messages to inform the nearest medics willing to give first aid and routing of another alert to the nearest ambulance. Thus, in order to deal with these sophisticated optimization techniques, we evaluate in this article means of simulation experiments. According to that, we have anticipated particular incidents suitable to estimate the pertinence of the proposed system. The analysis and implementation in VANET will be based on three simulators, especially, SUMO, MOVE and NS2. The results prove the effectiveness of the approach by reducing fuel consumption and $$\hbox {CO}_{2}$$CO2 emissions along with the rest of pollutant emissions in the case of an incident.