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A study on modeling vehicles mobility with MLC for enhancing vehicle-to-vehicle connectivity in VANET - 2020

Research Area:  Vehicular Ad Hoc Networks

Abstract:

In Vehicular ad hoc networks (VANETs), vehicle-to-vehicle (V2V) is a significant mode of communication in which vehicles communicate with other moving vehicles with the aid of wireless transceivers. Due to the rapid mobility of vehicles, network connectivity over VANETs is frequently unstable, especially in sparse highways. This paper analyzes V2V connectivity dynamics by designing the microscopic mobility and lane changing decision model using an adaptive cursive control mechanism and recurrent neural network. Extensive simulators like SUMO and NS2 analyze the validity of this proposed model. The proposed analytical model provides a framework for examining the impact of mobility dependent metrics such as vehicle velocity, acceleration/deceleration, safety gap, vehicle arrival rate, vehicle density and network metric data delivery rate for characterizing the VANET connectivity of the proposed network. The simulation results synchronized those of the proposed model, which illustrated that the developed analytical model of this work is effective.

Author(s) Name:  J. Naskath, B. Paramasivan & Hamza Aldabbas

Journal name:  Journal of Ambient Intelligence and Humanized Computing

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s12652-020-02559-x

Volume Information:  volume 12, pages 8255–8264 (2021)