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An Experimental Analysis on Selfish Node Detection Techniques for MANET Based on MSD and MBD-SNDT

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Computer Networks, Big Data and IoT

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 66))

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Abstract

Mobile ad hoc network (MANET) is a network that permits mobile servers and customers to convey without fixed infrastructure. MANET is a promptly developing region of investigation as it employs a group of uses. To encourage effective information access and update data sets are conveyed on MANET. Since information openness is influenced by the portability and power imperatives of the servers and customers, the information in MANET is reproduced. As in ad hoc network, since portable host moves unreservedly network panel happens habitually in this way information accessibility is decreased bringing about execution debasement. The majority of the replication strategies expect that each node advances each packet provided to it and combine completely as far as sharing the memory space. A portion of the nodes may go about as selfish nodes which may selfishly conclude and participate incompletely with all the different nodes. In this manner, selfish conduct of nodes could lessen the general information openness in the network. At first, a design model of a MANET is built and the correspondence between the versatile is started. The packet drop can occur in MANET because of the selfish node or network clog. In this paper, MSD-SNDT and MBD-SNDT method is proposed to recognize the selfish nodes effectively in MANET. The reproduction study shows that the proposed MSD-SNDT and MBD-SNDT strategy improves the selfish node detection ratio, packet delivery proportion (PDP), and normal packet drop proportion.

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Correspondence to C. Suresh Kumar .

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Ramesh, V., Suresh Kumar, C. (2021). An Experimental Analysis on Selfish Node Detection Techniques for MANET Based on MSD and MBD-SNDT. In: Pandian, A., Fernando, X., Islam, S.M.S. (eds) Computer Networks, Big Data and IoT. Lecture Notes on Data Engineering and Communications Technologies, vol 66. Springer, Singapore. https://doi.org/10.1007/978-981-16-0965-7_7

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  • DOI: https://doi.org/10.1007/978-981-16-0965-7_7

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