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A vehicular ad hoc network (VANET) is a promising addition to our future intelligent transportation systems, which supports various safety and infotainment applications. The high node mobility and frequent topology changes in VANETs impose new challenges in maintaining a long-lasting connection between network nodes. As a result, the lifetime of communication links is a crucial issue in VANET development and operation. This paper presents a probabilistic analysis of the communication link in VANETs for three vehicle density ranges. First, we present the stationary distribution of the communication link length using mesoscopic mobility models. Second, we propose a stochastic microscopic mobility model that captures time variations of intervehicle distances (distance headways). A discrete-time finitestate Markov chain with state-dependent transition probabilities is proposed to model the distance headway. Third, the proposed stochastic microscopic model and first passage time analysis are used to derive the probability distribution of the communication link lifetime. Numerical results are presented to evaluate the proposed model, which demonstrate a close agreement between analytical and simulation results.

Node clustering is a potential approach to improve the scalability of networking protocols in vehicular ad hoc networks (VANETs). High relative vehicle mobility and frequent network topology changes inflict new challenges on maintaining stable clusters. As a result, cluster stability is a crucial measure of the efficiency of clustering algorithms for VANETs. This paper presents a stochastic analysis of the vehicle mobility impact on single-hop cluster stability. A stochastic mobility model is adopted to capture the time variations of intervehicle distances (distance headways). Firstly, we propose a discrete-time lumped Markov chain to model the time variations of a system of distance headways. Secondly, the first passage time analysis is used to derive probability distributions of the time periods of invariant clusteroverlap state and cluster-membership as measures of cluster stability. Thirdly, queueing theory is utilized to model the limiting behaviors of the numbers of common and unclustered nodes between neighbouring clusters. Numerical results are presented to evaluate the proposed models, which demonstrate a close agreement between analytical and simulation results.

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