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dc.contributor.authorKumar, Neerajen_US
dc.contributor.authorLin, Chun-Chengen_US
dc.date.accessioned2015-12-02T02:59:26Z-
dc.date.available2015-12-02T02:59:26Z-
dc.date.issued2015-01-01en_US
dc.identifier.issn1743-8225en_US
dc.identifier.urihttp://dx.doi.org/10.1504/IJAHUC.2015.070594en_US
dc.identifier.urihttp://hdl.handle.net/11536/128191-
dc.description.abstractVehicular ad hoc networks (VANETs) are offering lot of services for the benefits of community of users. But, due to the dynamic nature of VANETs, it is a challenging task to perform reliable multicast. To address this issue, this paper proposes a new approach called reliable multicasting in non-stationary environment as a Bayesian coalition game using learning automata (RMBCG-LA) for VANETs. A new metric, probabilistic reliability index (PRI) is computed by each player. A coalition among the players of the game is formed using Bayesian network with a threshold in each coalition is based upon the conditional probability. For each action performed by the automaton, its action is rewarded or penalised by the non-stationary environment in which it is operates. The performance of the proposed scheme is evaluated in comparison with the well-known existing schemes. The results obtained show that our proposed scheme is better than the other schemes of its category.en_US
dc.language.isoen_USen_US
dc.subjectvehicular ad hoc networken_US
dc.subjectlearning automataen_US
dc.subjectcoalition gameen_US
dc.subjectBayesian networken_US
dc.titleReliable multicast as a Bayesian coalition game for a non-stationary environment in vehicular ad hoc networks: a learning automata-based approachen_US
dc.typeArticleen_US
dc.identifier.doi10.1504/IJAHUC.2015.070594en_US
dc.identifier.journalINTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTINGen_US
dc.citation.volume19en_US
dc.citation.issue3-4en_US
dc.citation.spage168en_US
dc.citation.epage182en_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000360155900004en_US
dc.citation.woscount0en_US
Appears in Collections:Articles