Recent advances in mobility modeling for mobile ad hoc network research

Zheng, Qunwei; Hong, Xiaobin; Ray, Sid

doi:10.1145/986537.986554

Cited by 89 publications

(47 citation statements)

References 32 publications

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“…Several surveys have focused on mobility models [5] [38] and their effects on routing performance [9]. Due to the popularity of the random waypoint model for evaluating routing protocol performance, many researchers have focused on analyzing its characteristics [7] [29].…”

Section: Related Workmentioning

confidence: 99%

An integrated mobility and traffic model for vehicular wireless networks

Choffnes

Bustamante

2005

Proceedings of the 2nd ACM International Workshop on Vehicular Ad Hoc Networks

348

180

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Ad-hoc wireless communication among highly dynamic, mobile nodes in a urban network is a critical capability for a wide range of important applications including automated vehicles, real-time traffic monitoring and vehicular safety applications. When evaluating application performance in simulation, a realistic mobility model for vehicular ad-hoc networks (VANETs) is critical for accurate results. This paper analyzes ad-hoc wireless network performance in a vehicular network in which nodes move according to a simplified vehicular traffic model on roads defined by real map data. We show that when nodes move according to our street mobility model, STRAW, network performance is significantly different from that of the commonly used random waypoint model. We also demonstrate that protocol performance varies with the type of urban environment. Finally, we use these results to argue for the development of integrated vehicular and network traffic simulators to evaluate vehicular ad-hoc network applications, particularly when the information passed through the network affects node mobility.

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Section: Related Workmentioning

confidence: 99%

An integrated mobility and traffic model for vehicular wireless networks

Choffnes

Bustamante

2005

Proceedings of the 2nd ACM International Workshop on Vehicular Ad Hoc Networks

348

180

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“…To differentiate various mobility patterns and these mobility pattern Qunwei Zheng et al [4] classify mobility metrics in two categories: direct and derived metrics. First evaluates the phenomena of clear physical correspondence (such as speed or acceleration) like the temporal dependence, spatial dependence and geographic restrictions.…”

Section: Mobility Metricsmentioning

confidence: 99%

Estimation of Mobility Models Based on Mobility Metrics and Their Impact on Routing Protocols in MANET

Sharma¹,

Kumar²

2013

IJCCE

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“…The reason is that some old nodes have left, some new nodes have come into the transmission range with some old nodes still remaining. The link load is highly dependent upon the mobility pattern [3,4] followed by node N i , N j and their surrounding nodes and the routing protocol used. The number of control packets sent due to use of reactive and proactive routing algorithm is different.…”

Section: Introductionmentioning

confidence: 99%

Temporal Modeling of Link Characteristic in Mobile Ad hoc Network

Singh¹,

Dutta

2011

CIT

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Ad hoc network consists of a set of identical nodes that move freely and independently and communicate among themselves via wireless links. The most interesting feature of this network is that they do not require any existing infrastructure of central administration and hence is very suitable for temporary communication links in an emergency situation. This flexibility, however, is achieved at a price of communication uncertainty induced due to frequent topology changes. In this article, we have tried to identify the system dynamics using the proven concepts of time series modeling. Here, we have analyzed variation of link utilization between any two particular nodes over a fixed area for different mobility patterns under different routing algorithm. We have considered four different mobility models -(i) Gauss-Markov mobility model, (ii) Manhattan Grid Mobility model and (iii) Random Way Point mobility model and (iv) Reference Point Group mobility model. The routing protocols under which, we carried out our experiments are (i) Ad hoc On demand Distance Vector routing (AODV), (ii) Destination Sequenced Distance Vector routing (DSDV) and (iii) Dynamic Source Routing (DSR). The value of link load between two particular nodes behaves as a random variable for any mobility pattern under a routing algorithm. The pattern of link load for every combination of mobility model and for every routing protocol can be well modeled as an autoregressive model of order p i.e. AR(p). The order of p is estimated and it is found that most of them are of order 1 only.

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Recent advances in mobility modeling for mobile ad hoc network research

Cited by 89 publications

References 32 publications

An integrated mobility and traffic model for vehicular wireless networks

An integrated mobility and traffic model for vehicular wireless networks

Estimation of Mobility Models Based on Mobility Metrics and Their Impact on Routing Protocols in MANET

Temporal Modeling of Link Characteristic in Mobile Ad hoc Network

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