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Lookup NU author(s): Osama Younes, Dr Nigel Thomas
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Mobile Ad hoc Networks (MANETs) are becoming very attractive and useful in many kinds of communication and networking applications. Due to the advantage of quick construction and numerical analysis of analytical modelling techniques, such as Stochastic Petri nets, Queueing Networks and Process Algebra, have been broadly used for performance analysis of computer networks. In addition, analytical modelling techniques generally provide the best insight into the effects of various parameters and their interactions. To the best of our knowledge, there is no analytical study that investigates the effect of various factors of multihop ad hoc networks, such as communication range, density of nodes, random access behavior, mobility patterns, speed of nodes, traffic patterns, and traffic load, on the performance indices such as packet delay and network capacity. The main objective of this work is designing an analytical framework that can be used to study the effect of all these factors on the performance of MANETs, where nodes move according to random waypoint mobility model. We employ a verbose modelling approach which includes organizing a framework into several models to break up the complexity of modelling the complete network, and make it easier to analysis each model of the framework as required. The proposed framework can be used to evaluate any of transport, network, or data link layer protocols. The proposed models are validated using extensive simulations.
Author(s): Younes O, Thomas N
Editor(s): Tribastone, M., Gilmore, S.
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Computer Performance Engineering: EPEW/UKPEW
Year of Conference: 2012
Pages: 140-155
ISSN: 0302-9743
Publisher: Springer
URL: http://dx.doi.org/10.1007/978-3-642-36781-6_10
DOI: 10.1007/978-3-642-36781-6_10
Notes: E-ISBN: 9783642367816
Library holdings: Search Newcastle University Library for this item
Series Title: Lecture Notes in Computer Science
ISBN: 9783642367809