Bayesian Coalition Game for Contention-Aware Reliable Data Forwarding in Vehicular Mobile Cloud

Neeraj Kumar, Rahat Iqbal, Sudip Misra, Joel J.P.C. Rodrigues

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    93 Citations (Scopus)


    The exponential growth in the demands of users to access various resources during mobility has led to the popularity of Vehicular Mobile Cloud. Vehicular users may access various resources on road from the cloud which acts as a service provider for them. Most of the existing proposals on vehicular cloud use unicast sender-based data forwarding, which results in an overall performance degradation with respect to the metrics such as packet delivery ratio, end-to-end delay, and reliable data transmission. Most of the applications for vehicular cloud have tight upper bounds with respect to reliable transmission. In view of the above, in this paper, we formulate the problem of reliable data forwarding as a Bayesian Coalition Game (BCG) using Learning Automata concepts. Learning Automata (LA) are assumed as the players in the game stationed on the vehicles. For taking adaptive decisions about reliable data forwarding, each player observes the moves of the other players in the game. For this purpose, a coalition game is formulated among the players of the game for taking adaptive decisions. For each action taken by a player in the game, it gets a reward or a penalty from the environment, and accordingly, it updates its action probability vector. An adaptive Learning Automata based Contention Aware Data Forwarding (LACADF) is also proposed. The proposed scheme is evaluated in different network scenarios with respect to parameters such as message overhead, throughput, and delay by varying the density and mobility of the vehicles. The results obtained show that the proposed scheme is better than the other conventional schemes with respect to the above metrics.
    Original languageEnglish
    Pages (from-to)60-72
    JournalFuture Generation Computer Systems
    Publication statusPublished - Jul 2015

    Bibliographical note

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    This work was partially supported by Instituto de Telecomunicações, Next Generation Networks and Applications Group (NetGNA), Covilhã Delegation, by Government of Russian Federation, Grant 074-U01, and by National Funding from the FCT—Fundação para a Ciência e a Tecnologia through the Pest-OE/EEI/LA0008/2013 Project.


    • Bayesian Coalition Game
    • Data dissemination
    • Learning automata
    • Vehicular cloud


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