Khan_Bilal_Young_Fuzzy_TOPSIS.pdf (4.69 MB)
Fuzzy-TOPSIS based Cluster Head selection in mobile sensor networks
Version 2 2023-06-12, 08:36
Version 1 2023-06-09, 04:36
journal contribution
posted on 2023-06-12, 08:36 authored by Bilal Muhammad Khan, Rabia Bilal, Rupert YoungRupert YoungOne of the critical parameters of Wireless Sensor Networks (WSNs) is node lifetime. There are various methods to increase WSN node lifetime, the clustering technique is being one of them. In clustering, selection of a desired percentage of Cluster Heads (CHs) is performed among the sensor nodes (SNs). Selected CHs are responsible for collecting data from their member nodes, aggregating the data and finally sending it to the sink. In this paper, we propose a Fuzzy-TOPSIS technique, based on multi criteria decision making, to choose CH efficiently and effectively to maximize the WSN lifetime. We will consider several criteria including: residual energy; node energy consumption rate; number of neighbor nodes; average distance between neighboring nodes; and distance from the sink. A threshold based intra-cluster and inter-cluster multi-hop communication mechanism is used to decrease energy consumption. We have also analyzed the impact of node density and different types of mobility strategies in order to investigate impact over WSN lifetime. In order to maximize the load distribution in the WSN, a predictable mobility with octagonal trajectory is proposed. This results in improvement of overall network lifetime and latency. Results shows that the proposed scheme improves the network lifetime by 60%, conserve energy by 80%, a significant reduction of frequent Cluster Head (CH) per round selection by 25% is achieved as compared to the conventional Fuzzy and LEACH protocols.
History
Publication status
- Published
File Version
- Published version
Journal
Journal of Electrical Systems and Information TechnologyISSN
2314-7172Publisher
ElsevierExternal DOI
Issue
3Volume
5Page range
928-943Department affiliated with
- Engineering and Design Publications
Research groups affiliated with
- Industrial Informatics and Signal Processing Research Group Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2017-01-09First Open Access (FOA) Date
2017-02-27First Compliant Deposit (FCD) Date
2017-01-08Usage metrics
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