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Improved security and routing path learning in MANETs using Beer–Quiche game theoritical model in cloud computing

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Abstract

In anAdhoc Cloud as a Service (Adhoc Cloud is used over MANETs), the attackers try to interrupt the data packets being broadcasted in the network between the nodes, which travels from source to destination. To solve this problem, Beer–Quiche theoretical routing model is utilized for stochastic multipath routing in MANETs. Initially, the path setup and route selection is regulated using proposed approach. During each stage, the source node tracks the available paths, path residual bandwidth and policy of the attackers, who gathers information from previous stage to provide an attack. Depending on such selection, the optimal paths are selected by the source node for broadcasting the packets. This is selected using a proper switching mechanism in multiple paths which is established between the source and destination. Further, an optimal routing selection policy is proposed to broadcast the packet to its destination node during each stage of the Beer–Quiche game. The numerical analysis proves that the proposed scheme attains better performance than AODV, AOMDV, CA-AOMDV, FS and MR in terms of reduced end-to-end delay, better residual bandwidth utilization, routing overhead, packet delivery ratio and increased path security. The results shows that the packet transmission has increased nearly to 68%, with 17% reduction in packet delay transmission. This has resulted in reducing the overhead by 25% than the AODV method.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Vivekanandha College of Technology for Women, Elayampalayam, Tamil Nadu, India

    D. Sathiya

  2. Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathiyamangalam, Tamil Nadu, India

    B. Gomathy

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  1. D. Sathiya
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  2. B. Gomathy
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Correspondence to D. Sathiya.

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Sathiya, D., Gomathy, B. Improved security and routing path learning in MANETs using Beer–Quiche game theoritical model in cloud computing. Cluster Comput 22 (Suppl 1), 21–31 (2019). https://doi.org/10.1007/s10586-017-1599-4

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  • DOI: https://doi.org/10.1007/s10586-017-1599-4

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