Integrated Poisson and Hyper-exponential Bayesian Probabilistic Factor-oriented Efficient Routing Mechanism for MANETs
|V.Vijayagopal 1 , K.Prabu 2|
1 PG & Research Department of Computer Science, Sudharsan College of Arts & Science, Pudukkottai, Tamilnadu, India.
2 PG & Research Department of Computer Science, Sudharsan College of Arts & Science, Pudukkottai, Tamilnadu, India.
Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-7 , Page no. 14-21, Jul-2018
Online published on Jul 31, 2018
Copyright © V.Vijayagopal, K.Prabu . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: V.Vijayagopal, K.Prabu, “Integrated Poisson and Hyper-exponential Bayesian Probabilistic Factor-oriented Efficient Routing Mechanism for MANETs”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.14-21, 2018.
MLA Style Citation: V.Vijayagopal, K.Prabu "Integrated Poisson and Hyper-exponential Bayesian Probabilistic Factor-oriented Efficient Routing Mechanism for MANETs." International Journal of Computer Sciences and Engineering 6.7 (2018): 14-21.
APA Style Citation: V.Vijayagopal, K.Prabu, (2018). Integrated Poisson and Hyper-exponential Bayesian Probabilistic Factor-oriented Efficient Routing Mechanism for MANETs. International Journal of Computer Sciences and Engineering, 6(7), 14-21.
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|Reputation is considered as the significant reliability index for measuring the degree of participation rendered by each mobile node towards co-operation. Reputation decreases drastically under the influence of byzantine nodes as they intentionally drop maximum number of packets. Byzantine nodes decrease packet delivery rate and throughput of the network in spite of consuming network resources. The existing Bayesian conditional probability based mitigation approaches fail to utilize the benefits of conditional probabilistic distributions like Poisson and Hyper-exponential distribution for detection. This paper presents an Integrated Poisson and Hyper-exponential Bayesian Probabilistic Factor-based Mitigation Mechanism (IPHBFMM) for investigating the influence of byzantine nodes and mitigate them for ensuring better routing process. IPHBFMM is potential in combining two independent variables for discriminating co-operative nodes from byzantine nodes based on past and present behaviour. Simulation results proved that the energy consumptions and communication overhead of IPHBFMM is excellently minimized by 26% and 34% compared to the existing Bayesian probability-oriented techniques considered for analysis.|
|Key-Words / Index Term :|
|Byzantine nodes, Poisson Factor, Hyper-Exponential distribution, Bayesian Probability. Co-operative Packet Forwarding Factor, Packet Forwarding Normalization Factor|
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