Energy efficient routing using fuzzy neural network in wireless sensor networks

Varun, R.K., Gangwar, R.C., Kaiwartya, O. ORCID: 0000-0001-9669-8244 and Aggarwal, G. ORCID: 0000-0002-8338-2504, 2021. Energy efficient routing using fuzzy neural network in wireless sensor networks. Wireless Communications and Mobile Computing, 2021: 5113591. ISSN 1530-8669

1454296_a1806_Aggarwal.pdf - Published version

Download (857kB) | Preview


In wireless sensor networks, energy is a precious resource that should be utilized wisely to improve its life. Uneven distribution of load over sensor devices is also the reason for the depletion of energy that can cause interruptions in network operations as well. For the next generation’s ubiquitous sensor networks, a single artificial intelligence methodology is not able to resolve the issue of energy and load. Therefore, this paper proposes an energy-efficient routing using a fuzzy neural network (ERFN) to minimize the energy consumption while fairly equalizing energy consumption among sensors thus as to prolong the lifetime of the WSN. The algorithm utilizes fuzzy logic and neural network concepts for the intelligent selection of cluster head (CH) that will precisely consume equal energy of the sensors. In this work, fuzzy rules, sets, and membership functions are developed to make decisions regarding next-hop selection based on the total residual energy, link quality, and forward progress towards the sink. The developed algorithm ERFN proofs its efficiency as compared to the state-of-the-art algorithms concerning the number of alive nodes, percentage of dead nodes, average energy decay, and standard deviation of residual energy.

Item Type: Journal article
Publication Title: Wireless Communications and Mobile Computing
Creators: Varun, R.K., Gangwar, R.C., Kaiwartya, O. and Aggarwal, G.
Publisher: Hindawi Publishing Corporation
Date: 10 August 2021
Volume: 2021
ISSN: 1530-8669
Rights: ©2021 Rajesh Kumar Varun et al. 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.
Divisions: Schools > School of Science and Technology
Record created by: Jonathan Gallacher
Date Added: 26 Jul 2021 08:40
Last Modified: 12 Aug 2021 14:21

Actions (login required)

Edit View Edit View


Views per month over past year


Downloads per month over past year