Markov-History Based Modeling for Realistic Mobility of Vehicles in VANETs

Vehicular ad-hoc networks (VANETs) can be considered as complex dynamic systems where simulation modeling plays a crucial role in conducting research due to the difficulties in the realization of such a network, like high costs, risks and dangers. During the last decade, the results of VANET simulations were considered successful and acceptable however, most prior VANET simulators were based on non realistic mobility when describing node positions, distribution, frequency and movement, model paths, traffic signals etc. To deal with this challenge, we propose in this paper a Markov-History based modeling for realistic mobility of vehicles in VANETs. It starts with a geographic area digitization, followed by a vehicle positions initialization. The proposed model predicts directions of the vehicles using a Markov chain, as well as it predicts the vehicle velocities using a History-based sub-model. Moreover, the density of the network is updated according to the peak or off-peak hours. The emerged model can be used as mobility model to simulate VANET scenarios. To validate our proposal, a concrete mobility model has been designed using the proposed model that represents the downtown of Biskra City located in Algeria. The performances of the resulting network have been proved against the others obtained using Random Waypoint as a standard mobility model in terms of packet delivery ratio, network overhead, average end-to-end delay, and dropped packets.