Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.comnet.2005.12.013
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dc.titlePerformance analysis of mobility-based d-hop (MobDHop) clustering algorithm for mobile ad hoc networks
dc.contributor.authorEr, I.I.
dc.contributor.authorSeah, W.K.G.
dc.date.accessioned2013-07-04T07:50:27Z
dc.date.available2013-07-04T07:50:27Z
dc.date.issued2006
dc.identifier.citationEr, I.I., Seah, W.K.G. (2006). Performance analysis of mobility-based d-hop (MobDHop) clustering algorithm for mobile ad hoc networks. Computer Networks 50 (17) : 3375-3399. ScholarBank@NUS Repository. https://doi.org/10.1016/j.comnet.2005.12.013
dc.identifier.issn13891286
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39825
dc.description.abstractThis paper presents the performance analysis of the mobility-based d-hop (MobDHop) clustering algorithm, which forms variable-diameter clusters based on node mobility patterns in MANETs. Unlike existing clustering algorithms, the diameter of clusters is not restricted by any preset value. Instead, the diameter of clusters is flexible and determined by the stability of clusters. Nodes which have similar moving patterns are grouped into one cluster in order to achieve maximum cluster stability. Unlike existing multihop clustering algorithms, MobDHop only requires 1-hop neighbourhood knowledge instead of multihop neighbourhood knowledge. This makes MobDHop a truly adaptive, distributed and localized algorithm. This paper first presents the empirical results of MobDHop based on a series of extensive NS-2 simulations. The simulation results show that MobDHop forms clusters which are more stable than those formed by Lowest-ID and Max Connectivity Clustering Algorithm in both Random Waypoint and Reference Point Group Mobility Model. Subsequently, the performance of MobDHop is examined from a theoretical perspective where both the time and message complexities are derived. A comparison of MobDHop and four other clustering algorithms is presented. We show that the overhead incurred by multihop clustering has a similar asymptotic bound as 1-hop clustering while being able to reap the benefits of multihop clusters. © 2006 Elsevier B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.comnet.2005.12.013
dc.sourceScopus
dc.subjectGroup mobility pattern
dc.subjectMobile ad hoc networks
dc.subjectMobility-based clustering
dc.subjectPerformance analysis
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1016/j.comnet.2005.12.013
dc.description.sourcetitleComputer Networks
dc.description.volume50
dc.description.issue17
dc.description.page3375-3399
dc.description.codenCNETD
dc.identifier.isiut000241154900009
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