This paper is published in Volume-6, Issue-6, 2020
Area
Computer Networks
Author
Suma R., Dr. C. Siddaraju
Org/Univ
Dr. Ambedkar Institute of Technology, Bengaluru, Karnataka, India
Pub. Date
23 December, 2020
Paper ID
V6I6-1248
Publisher
Keywords
Particle Swarm Optimization, K-Means Clustering, Mobile Adhoc Networks

Citationsacebook

IEEE
Suma R., Dr. C. Siddaraju. Integration of particle swarm optimization with an adaptive K-Nearest Neighbor for energy-efficient clustering in MANET, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Suma R., Dr. C. Siddaraju (2020). Integration of particle swarm optimization with an adaptive K-Nearest Neighbor for energy-efficient clustering in MANET. International Journal of Advance Research, Ideas and Innovations in Technology, 6(6) www.IJARIIT.com.

MLA
Suma R., Dr. C. Siddaraju. "Integration of particle swarm optimization with an adaptive K-Nearest Neighbor for energy-efficient clustering in MANET." International Journal of Advance Research, Ideas and Innovations in Technology 6.6 (2020). www.IJARIIT.com.

Abstract

The objective of the proposed work is to increase the lifetime of mobile ad hoc networks. The energy efficiency issue associated with the mobile Adhoc network is the critical factor for the success of any MANET system and hence we aim to develop an energy-efficient MANET system suitable for any kind of real environment. This paper addresses the nodes mobility issue based on a well-known particle swarm optimization (PSO) technique and as well designs a clustering algorithm based on an adaptive k-nearest neighbor algorithm. The cluster formation is achieved by considering a multi-objective fitness function of PSO and extensive experimentation in the simulated networked environment reveals the performance of the proposed method. In mobile Adhoc networks (MANET), optimal energy is one of the critical components and the random movement of mobile nodes within a region of interest made it more complex. The provision to have frequent changes in the topology of mobile nodes in addition to keeping the battery life for a longer duration is much more complex. The standard metrics such as network lifetime, the average number of clusters formed, energy usage, and packet transfer ratio are estimated to exhibit the performance of the proposed method. A comparative analysis is carried out with the recently proposed variant of particle swarm optimization based methods to reveal the accuracy and energy efficiency nature of the proposed method. The novelty of the proposed approach includes the exploration of an optimization algorithm integrating with a clustering strategy to increase the energy efficiency of the MANET thereby increasing the lifetime of the network. The proposed approach exhibit better accuracy and possess energy efficient even under scaled environment.
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