Open Access

ARTICLE

Energy-Efficient Secure Adaptive Neuro Fuzzy Based Clustering Technique for Mobile Adhoc Networks

by Maganti Srinivas^*, M. Ramesh Patnaik

Department of Instrument Technology, A. U. College of Engineering, Andhra University, Visakhapatnam, Andhra Pradesh, 530003, India

* Corresponding Author: Maganti Srinivas. Email:

Intelligent Automation & Soft Computing 2022, 34(3), 1755-1767. https://doi.org/10.32604/iasc.2022.026355

Received 23 December 2021; Accepted 10 February 2022; Issue published 25 May 2022

Abstract

In recent times, Mobile Ad Hoc Network (MANET) becomes a familiar research field owing to its applicability in distinct scenarios. MANET comprises a set of autonomous mobile nodes which independently move and send data through wireless channels. Energy efficiency is considered a critical design issue in MANET and can be addressed by the use of the clustering process. Clustering is treated as a proficient approach, which partitions the mobile nodes into groups called clusters and elects a node as cluster head (CH). On the other hand, the nature of wireless links poses security as a major design issue. Therefore, this paper proposes a non-probabilistic and energy-efficient secure adaptive neuro fuzzy-based clustering scheme (NPEE-SANFC) for MANET. The proposed NPEE-SANFC techniques elects CHs in two levels such as tentative CH election and final CH election. Besides, a non-probabilistic way of Tentative CH (TCH) selection takes place by the use of a back-off timer. In addition, ANFC technique is applied for the election of Final CH (FCH)s. The presented model involves a set of input parameters such as residual energy, intra-cluster distance, inter-cluster distance, and trust degree. The incorporation of the trust degree of the node enables to elect secure CHs. Furthermore, the application of two processes for optimal CH selection will result in enhanced network lifetime and energy efficiency. To validate the results regarding the effectiveness of the presented NPEE-SANFC technique, a set of experiments was performed; and the results were determined using distinct measures such as the energy consumption, network lifetime, throughput, and end-to-end delay.

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Keywords

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