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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Vydané v:	Sensors (Basel, Switzerland) Ročník 20; číslo 3; s. 913
Hlavní autori:	Anees, Junaid, Zhang, Hao-Chun, Baig, Sobia, Guene Lougou, Bachirou, Robert Bona, Thomas Gasim
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 08.02.2020 MDPI
Predmet:	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
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Abstract	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.
AbstractList	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. 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.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.
Author	Guene Lougou, Bachirou Anees, Junaid Baig, Sobia Zhang, Hao-Chun Robert Bona, Thomas Gasim
AuthorAffiliation	4 School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China; thomasrobert@stu.hit.edu.cn 3 Department of Electrical and Computer Engineering, Lahore Campus, COMSATS University Islamabad (CUI), Lahore 54000, Pakistan; drsobia@cuilahore.edu.pk 1 School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China; j.anees@hit.edu.cn (J.A.); 15bf02043@hit.edu.cn (B.G.L.) 2 Satellite Control Facility (SCF-L) directorate, SE&T wing, Space & Upper Atmosphere Research Commission, Lahore 54000, Pakistan
AuthorAffiliation_xml	– name: 4 School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China; thomasrobert@stu.hit.edu.cn – name: 2 Satellite Control Facility (SCF-L) directorate, SE&T wing, Space & Upper Atmosphere Research Commission, Lahore 54000, Pakistan – name: 3 Department of Electrical and Computer Engineering, Lahore Campus, COMSATS University Islamabad (CUI), Lahore 54000, Pakistan; drsobia@cuilahore.edu.pk – name: 1 School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China; j.anees@hit.edu.cn (J.A.); 15bf02043@hit.edu.cn (B.G.L.)
Author_xml	– sequence: 1 givenname: Junaid orcidid: 0000-0001-5768-7526 surname: Anees fullname: Anees, Junaid – sequence: 2 givenname: Hao-Chun orcidid: 0000-0002-7712-623X surname: Zhang fullname: Zhang, Hao-Chun – sequence: 3 givenname: Sobia orcidid: 0000-0002-1951-4568 surname: Baig fullname: Baig, Sobia – sequence: 4 givenname: Bachirou orcidid: 0000-0001-7252-2797 surname: Guene Lougou fullname: Guene Lougou, Bachirou – sequence: 5 givenname: Thomas Gasim surname: Robert Bona fullname: Robert Bona, Thomas Gasim
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Snippet	Limited energy resources of sensor nodes in Wireless Sensor Networks (WSNs) make energy consumption the most significant problem in practice. This paper...
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StartPage	913
SubjectTerms	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
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Title	Hesitant Fuzzy Entropy-Based Opportunistic Clustering and Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks
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