Analysis of alert message propagation on the highway in VANET assuming Markovian vehicle arrival process. (27th June 2020)

• Record Type: Journal Article
• Authors: Mahmood, Dhari Ali; Horvath, Gabor
• Is Part Of: International journal of communication systems. Volume 33:Number 14(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-27
• Subjects: ITS -- Markovian arrival process -- message propagation -- transient analysis -- V2V -- VANET; Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.4501
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: Vehicular ad hoc networks (VANETs) provide the infrastructure for the intelligent transportation system (ITS). The number of vehicles that are capable of communicating with each other through VANET is increasing. In this paper, we provide several analytical results regarding the message propagation on the highway in VANET. In our scenario, there is a stationary message source (i.e., alert messages are generated at an accident). Vehicles can receive the message and get informed when they reach the radio transmission range of the message source or another informed vehicle. Most papers published on the analysis of message propagation assume that the inter arrival times between vehicles follow a Poisson process; there are very few results available with more general traffic model. In this paper, we show that the Poisson process is not always suitable for modeling vehicle traffic. Instead of the Poisson process, we propose to use the more general Markovian arrival process (MAP) to model the vehicle headway times and derive the probability that the message propagates beyond a certain distance from the accident under this traffic assumption. Through several numerical examples, we demonstrate how much the statistics of the vehicle arrival process impacts the message propagation distance. Abstract : Most papers published on the analysis of message propagation in VANETs assume that the interarrival times between vehicles follow a Poisson process. We show that the Poisson process is not always suitable for modeling vehicle traffic. We propose to use the more general Markovian arrival process to model the vehicle headway times and provide analytical results on both the stationary and the transient properties of the message propagation distance, assuming Markovian vehicle arrival process.