Hesitant Fuzzy Entropy-Based Opportunistic Clustering and Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks

Limited energy resources of sensor nodes in Wireless Sensor Networks (WSNs) make energy consumption the most significant problem in practice. This paper proposes a novel, dynamic, self-organizing Hesitant Fuzzy Entropy-based Opportunistic Clustering and data fusion Scheme (HFECS) in order to overcom...

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Published in:Sensors (Basel, Switzerland) Vol. 20; no. 3; p. 913
Main Authors: Anees, Junaid, Zhang, Hao-Chun, Baig, Sobia, Guene Lougou, Bachirou, Robert Bona, Thomas Gasim
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 08.02.2020
MDPI
Subjects:
Algorithms
Clustering
Communication
data fusion
Decision making
Energy consumption
Energy efficiency
Fuzzy sets
hesitant fuzzy entropy
heterogeneous clustering
multi-attribute decision modeling
opportunistic routing
Probability distribution
Random variables
Sensors
Wireless networks
wireless sensor networks
data fusion
opportunistic routing
wireless sensor networks
heterogeneous clustering
multi-attribute decision modeling
hesitant fuzzy entropy
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Limited energy resources of sensor nodes in Wireless Sensor Networks (WSNs) make energy consumption the most significant problem in practice. This paper proposes a novel, dynamic, self-organizing Hesitant Fuzzy Entropy-based Opportunistic Clustering and data fusion Scheme (HFECS) in order to overcome the energy consumption and network lifetime bottlenecks. The asynchronous working-sleeping cycle of sensor nodes could be exploited to make an opportunistic connection between sensor nodes in heterogeneous clustering. HFECS incorporates two levels of hierarchy in the network and energy heterogeneity is characterized using three levels of energy in sensor nodes. HFECS gathers local sensory data from sensor nodes and utilizes multi-attribute decision modeling and the entropy weight coefficient method for cluster formation and the cluster head election procedure. After cluster formation, HFECS uses the same techniques for performing data fusion at the first hierarchical level to reduce the redundant information flow from the first-second hierarchical levels, which can lead to an improvement in energy consumption, better utilization of bandwidth and extension of network lifetime. Our simulation results reveal that HFECS outperforms the existing benchmark schemes of heterogeneous clustering for larger network sizes in terms of half-life period, stability period, average residual energy, network lifetime, and packet delivery ratio.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s20030913