A Dual Cluster-Head Energy-Efficient Routing Algorithm Based on Canopy Optimization and K-Means for WSN
Wireless sensor networks (WSN) are widely used in various applications, such as environmental monitoring, healthcare, event detection, agriculture, disaster management, and so on. Due to their small size, sensors are limited power sources and are often deployed in special environments where frequent...
Saved in:
| Published in: | Sensors (Basel, Switzerland) Vol. 22; no. 24; p. 9731 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Switzerland
MDPI AG
12.12.2022
MDPI |
| Subjects: | |
| ISSN: | 1424-8220, 1424-8220 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Wireless sensor networks (WSN) are widely used in various applications, such as environmental monitoring, healthcare, event detection, agriculture, disaster management, and so on. Due to their small size, sensors are limited power sources and are often deployed in special environments where frequent battery replacement is not feasible. Therefore, it is important to reduce the energy consumption of sensors and extend the network lifetime. An effective way to achieve this is clustering. This paper proposes a dual cluster-head energy-efficient algorithm (DCK-LEACH), which is based on K-means and Canopy optimization. Considering that the K-means algorithm is sensitive to the location of the initial clustering centers, this paper uses both the dynamic Canopy algorithm and K-means algorithm for clustering. For cluster-head election, this algorithm uses a hierarchy to minimize the cluster-head burden and balance the network load. The primary cluster-head is selected by two objectives: the node’s residual energy and the distance from the node to the clustering center. The vice cluster-head is selected by the residual energy of the node, and the distance from the nodes to the base station. Simulator results show that DCK-LEACH significantly prolongs the energy-critical node lifetime and the network lifetime compared with existing protocols. |
|---|---|
| AbstractList | Wireless sensor networks (WSN) are widely used in various applications, such as environmental monitoring, healthcare, event detection, agriculture, disaster management, and so on. Due to their small size, sensors are limited power sources and are often deployed in special environments where frequent battery replacement is not feasible. Therefore, it is important to reduce the energy consumption of sensors and extend the network lifetime. An effective way to achieve this is clustering. This paper proposes a dual cluster-head energy-efficient algorithm (DCK-LEACH), which is based on K-means and Canopy optimization. Considering that the K-means algorithm is sensitive to the location of the initial clustering centers, this paper uses both the dynamic Canopy algorithm and K-means algorithm for clustering. For cluster-head election, this algorithm uses a hierarchy to minimize the cluster-head burden and balance the network load. The primary cluster-head is selected by two objectives: the node's residual energy and the distance from the node to the clustering center. The vice cluster-head is selected by the residual energy of the node, and the distance from the nodes to the base station. Simulator results show that DCK-LEACH significantly prolongs the energy-critical node lifetime and the network lifetime compared with existing protocols. Wireless sensor networks (WSN) are widely used in various applications, such as environmental monitoring, healthcare, event detection, agriculture, disaster management, and so on. Due to their small size, sensors are limited power sources and are often deployed in special environments where frequent battery replacement is not feasible. Therefore, it is important to reduce the energy consumption of sensors and extend the network lifetime. An effective way to achieve this is clustering. This paper proposes a dual cluster-head energy-efficient algorithm (DCK-LEACH), which is based on K-means and Canopy optimization. Considering that the K-means algorithm is sensitive to the location of the initial clustering centers, this paper uses both the dynamic Canopy algorithm and K-means algorithm for clustering. For cluster-head election, this algorithm uses a hierarchy to minimize the cluster-head burden and balance the network load. The primary cluster-head is selected by two objectives: the node's residual energy and the distance from the node to the clustering center. The vice cluster-head is selected by the residual energy of the node, and the distance from the nodes to the base station. Simulator results show that DCK-LEACH significantly prolongs the energy-critical node lifetime and the network lifetime compared with existing protocols.Wireless sensor networks (WSN) are widely used in various applications, such as environmental monitoring, healthcare, event detection, agriculture, disaster management, and so on. Due to their small size, sensors are limited power sources and are often deployed in special environments where frequent battery replacement is not feasible. Therefore, it is important to reduce the energy consumption of sensors and extend the network lifetime. An effective way to achieve this is clustering. This paper proposes a dual cluster-head energy-efficient algorithm (DCK-LEACH), which is based on K-means and Canopy optimization. Considering that the K-means algorithm is sensitive to the location of the initial clustering centers, this paper uses both the dynamic Canopy algorithm and K-means algorithm for clustering. For cluster-head election, this algorithm uses a hierarchy to minimize the cluster-head burden and balance the network load. The primary cluster-head is selected by two objectives: the node's residual energy and the distance from the node to the clustering center. The vice cluster-head is selected by the residual energy of the node, and the distance from the nodes to the base station. Simulator results show that DCK-LEACH significantly prolongs the energy-critical node lifetime and the network lifetime compared with existing protocols. |
| Audience | Academic |
| Author | Lu, Tao Li, Zhengliang Min, Qiusha Wu, Mei Chen, Jing |
| AuthorAffiliation | 3 School of Educational Information Technology, Central China Normal University, Wuhan 430079, China 1 School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China 2 School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan 430205, China |
| AuthorAffiliation_xml | – name: 2 School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan 430205, China – name: 1 School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China – name: 3 School of Educational Information Technology, Central China Normal University, Wuhan 430079, China |
| Author_xml | – sequence: 1 givenname: Mei orcidid: 0000-0001-8676-9432 surname: Wu fullname: Wu, Mei – sequence: 2 givenname: Zhengliang orcidid: 0000-0003-1471-8462 surname: Li fullname: Li, Zhengliang – sequence: 3 givenname: Jing orcidid: 0000-0001-9929-5846 surname: Chen fullname: Chen, Jing – sequence: 4 givenname: Qiusha surname: Min fullname: Min, Qiusha – sequence: 5 givenname: Tao orcidid: 0000-0001-8117-2012 surname: Lu fullname: Lu, Tao |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36560099$$D View this record in MEDLINE/PubMed |
| BookMark | eNplkklv1DAUgCNURBc48AeQJS5wSOstdnxBGoaBVhQqsYij5XhJPUrswU6Qhl-P22mrtsgHW8_f-56Xd1jthRhsVb1E8JgQAU8yxpgKTtCT6gBRTOsWY7h3b71fHea8hhATQtpn1T5hDYNQiIOqX4APsxrAcpjzZFN9apUBq2BTv61XznntbZjAtzhPPvRgMfQx-elyBO9VtgbEAJYqxM0WXGwmP_q_avIlpoIBn-svVoUMXEzg1_evz6unTg3ZvriZj6qfH1c_lqf1-cWns-XivNYNbKdaaAxFSxmBmmpFHTFcMdQ5yGEnlNMNZ0K0UBncYQgVYkgLxokSrUUFQuSoOtt5TVRruUl-VGkro_LyOhBTL1WavB6sJJ0mXQM7btqOIi6UdswgIboOlQrcFNe7nWszd6M1urxEUsMD6cOd4C9lH_9IwVtKKSyCNzeCFH_PNk9y9FnbYVDBxjlLzJsWQdqwpqCvH6HrOKdQnuqKYkUICS7U8Y7qVbmADy6WuroMY0evS1M4X-ILTouxgYSVhFf3r3B39tsGKMDJDtAp5pysk9pP179YzH6QCMqrFpN3LVYy3j7KuJX-z_4D3rbOBQ |
| CitedBy_id | crossref_primary_10_1007_s12083_024_01675_1 crossref_primary_10_3390_s24196303 crossref_primary_10_3390_s23063261 crossref_primary_10_1002_dac_5963 crossref_primary_10_3390_s23115177 crossref_primary_10_1109_ACCESS_2023_3345218 crossref_primary_10_1088_2631_8695_ade050 crossref_primary_10_1007_s11760_024_03313_y crossref_primary_10_1007_s12083_024_01880_y crossref_primary_10_1016_j_aej_2025_02_018 crossref_primary_10_3390_telecom5040062 crossref_primary_10_7717_peerj_cs_2243 crossref_primary_10_1007_s00500_025_10563_6 crossref_primary_10_1109_JSEN_2024_3416961 |
| Cites_doi | 10.1109/ICDS47004.2019.8942253 10.1007/s11277-021-09028-4 10.1108/SR-06-2016-0104 10.1016/j.cogsys.2018.10.021 10.1109/ACCESS.2022.3142082 10.1109/ACCESS.2019.2956068 10.1016/j.adhoc.2021.102692 10.1142/S0218126621500638 10.1007/s00521-019-04441-0 10.1109/EDiS49545.2020.9296444 10.1109/ACCESS.2019.2911190 10.1109/JIOT.2022.3195677 10.1504/IJES.2020.105935 10.3390/electronics11060869 10.1109/TCYB.2016.2526683 10.1109/ICCICT.2012.6398214 10.1109/INCET51464.2021.9456313 10.1109/ACCESS.2022.3163254 10.1109/ICE348803.2020.9122962 10.1109/CYBER.2015.7288132 10.1002/dac.4354 10.1002/dac.3407 10.3390/s19204579 10.1109/ACCESS.2020.2985495 10.1109/FCST.2015.61 10.3390/s22166085 10.1109/ICSESS.2017.8343043 10.1109/ICNSC.2016.7478994 10.1109/JSYST.2020.3010868 10.1109/LISAT.2016.7494136 10.1109/SAI.2015.7237270 10.1007/s00500-020-04955-z 10.1109/ICAA53760.2021.00090 10.1109/IEMCON.2015.7344478 10.1109/CENTCON52345.2021.9688050 10.3390/s20205865 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2022 MDPI AG 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 by the authors. 2022 |
| Copyright_xml | – notice: COPYRIGHT 2022 MDPI AG – notice: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 by the authors. 2022 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.3390/s22249731 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni Edition) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) Medical Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE Publicly Available Content Database MEDLINE - Academic CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1424-8220 |
| ExternalDocumentID | oai_doaj_org_article_3bc3b50b7d8b4179acf6d199bb180a7d PMC9784440 A746535036 36560099 10_3390_s22249731 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Science Research Project of Hubei Provincial Department of Education grantid: D20211503 – fundername: Innovation and entrepreneurship project for undergraduate student of Hubei Province grantid: 20201049001 – fundername: Wuhan Institute of Technology project on Science Research Fund grantid: K201843 – fundername: Hubei Provincial Department of Education research grantid: B2022056 – fundername: National Natural Science Foundation of China grantid: 62007011 – fundername: MOE (Ministry of Education in China) Project of Humanities and Social Sciences grantid: 22YJC910007 – fundername: Wuhan Institute of Technology project on Science Research Fund grantid: K201843; 20QD48 – fundername: innovation and entrepreneurship project for undergraduate student of Hubei Province grantid: 20201049001 |
| GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE IAO ITC KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M 3V. ABJCF ALIPV ARAPS CGR CUY CVF ECM EIF HCIFZ KB. M7S NPM PDBOC 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 5PM |
| ID | FETCH-LOGICAL-c508t-9c20984630c4ca4f3d7a61bf070b9afc5769980ad2b200a161c9673a98e1f0713 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 18 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000904115100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1424-8220 |
| IngestDate | Fri Oct 03 12:43:55 EDT 2025 Tue Nov 04 02:07:11 EST 2025 Sun Nov 09 11:04:27 EST 2025 Tue Oct 07 07:34:19 EDT 2025 Tue Nov 04 18:15:20 EST 2025 Wed Feb 19 02:25:00 EST 2025 Sat Nov 29 07:09:09 EST 2025 Tue Nov 18 20:45:11 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 24 |
| Keywords | K-means algorithm energy-efficient wireless sensor networks network lifetime canopy algorithm |
| Language | English |
| License | Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c508t-9c20984630c4ca4f3d7a61bf070b9afc5769980ad2b200a161c9673a98e1f0713 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-8117-2012 0000-0003-1471-8462 0000-0001-9929-5846 0000-0001-8676-9432 |
| OpenAccessLink | https://doaj.org/article/3bc3b50b7d8b4179acf6d199bb180a7d |
| PMID | 36560099 |
| PQID | 2756784032 |
| PQPubID | 2032333 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_3bc3b50b7d8b4179acf6d199bb180a7d pubmedcentral_primary_oai_pubmedcentral_nih_gov_9784440 proquest_miscellaneous_2758104565 proquest_journals_2756784032 gale_infotracacademiconefile_A746535036 pubmed_primary_36560099 crossref_citationtrail_10_3390_s22249731 crossref_primary_10_3390_s22249731 |
| PublicationCentury | 2000 |
| PublicationDate | 20221212 |
| PublicationDateYYYYMMDD | 2022-12-12 |
| PublicationDate_xml | – month: 12 year: 2022 text: 20221212 day: 12 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland – name: Basel |
| PublicationTitle | Sensors (Basel, Switzerland) |
| PublicationTitleAlternate | Sensors (Basel) |
| PublicationYear | 2022 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Panchal (ref_25) 2021; 30 Qin (ref_32) 2017; 47 Gul (ref_27) 2022; 9 ref_14 ref_36 Lin (ref_40) 2019; 7 ref_35 Kuo (ref_38) 2015; 46 ref_12 ref_34 Lin (ref_3) 2021; 15 ref_33 Thangaramya (ref_39) 2020; 24 ref_10 Park (ref_9) 2021; 67 ref_31 He (ref_11) 2019; 7 ref_19 ref_18 ref_17 ref_16 ref_15 ref_37 Pattnaik (ref_41) 2020; 33 Gamal (ref_29) 2022; 10 Sathyamoorthy (ref_24) 2022; 122 Baz (ref_4) 2018; 31 Gherbi (ref_1) 2017; 37 Pitchaimanickam (ref_13) 2020; 32 Wu (ref_5) 2020; 14 ref_21 Panchal (ref_26) 2021; 123 ref_28 Arya (ref_30) 2022; 10 Mei (ref_20) 2020; 12 Lata (ref_2) 2020; 8 ref_8 Jesudurai (ref_22) 2019; 57 Bakaraniya (ref_23) 2013; 4 ref_7 ref_6 |
| References_xml | – ident: ref_8 doi: 10.1109/ICDS47004.2019.8942253 – volume: 122 start-page: 2745 year: 2022 ident: ref_24 article-title: Improved K-Means based Q Learning algorithm for optimal clustering and node balancing in WSN publication-title: Wireless Pers. Commun. doi: 10.1007/s11277-021-09028-4 – volume: 37 start-page: 12 year: 2017 ident: ref_1 article-title: A survey on clustering routing protocols in wireless sensor networks publication-title: Sens. Rev. doi: 10.1108/SR-06-2016-0104 – volume: 4 start-page: 1521 year: 2013 ident: ref_23 article-title: K-LEACH: An improved LEACH protocol for lifetime improvement in WSN publication-title: Int. J. Eng. Trends Technol. – volume: 14 start-page: 514 year: 2020 ident: ref_5 article-title: A many-objective optimization WSN energy balance model publication-title: KSII Trans. Internet Inf. Syst. – volume: 57 start-page: 101 year: 2019 ident: ref_22 article-title: An improved energy efficient cluster-head selection protocol using the double cluster-heads and data fusion methods for IoT applications publication-title: Cogn. Syst. Res. doi: 10.1016/j.cogsys.2018.10.021 – volume: 10 start-page: 9340 year: 2022 ident: ref_30 article-title: Performance analysis of deep learning-based routing protocol for an efficient data transmission in 5G WSN Communication publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3142082 – volume: 7 start-page: 172505 year: 2019 ident: ref_11 article-title: Energy saving algorithm and simulation of wireless sensor networks based on clustering routing protocol publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2956068 – volume: 123 start-page: 1 year: 2021 ident: ref_26 article-title: EEHCHR: Energy efficient hybrid clustering and hierarchical routing for wireless sensor networks publication-title: Ad Hoc Netw. doi: 10.1016/j.adhoc.2021.102692 – volume: 30 start-page: 2150063 year: 2021 ident: ref_25 article-title: EADCR: Energy aware distance based cluster-head selection and routing protocol for wireless sensor networks publication-title: J. Circuits Syst. Comput. (World Sci.) doi: 10.1142/S0218126621500638 – volume: 32 start-page: 7709 year: 2020 ident: ref_13 article-title: A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster-head selection in wireless sensor networks publication-title: Neural Comput. Appl. doi: 10.1007/s00521-019-04441-0 – ident: ref_35 doi: 10.1109/EDiS49545.2020.9296444 – volume: 7 start-page: 49894 year: 2019 ident: ref_40 article-title: An energy-efficient clustering algorithm combined game theory and dual-cluster-head mechanism for WSNs publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2911190 – volume: 9 start-page: 25150 year: 2022 ident: ref_27 article-title: UAV-Driven sustainable and Quality-Aware data collection in robotic wireless sensor networks publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2022.3195677 – volume: 12 start-page: 177 year: 2020 ident: ref_20 article-title: A hybrid optimisation algorithm based on GA algorithm and ACO algorithm improvements for routing selection in heterogeneous sensor networks publication-title: Int. J. Embed. Syst. doi: 10.1504/IJES.2020.105935 – ident: ref_18 doi: 10.3390/electronics11060869 – volume: 47 start-page: 772 year: 2017 ident: ref_32 article-title: Distributed K-Means algorithm and fuzzy C-Means algorithm for sensor networks based on multiagent consensus theory publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2016.2526683 – ident: ref_6 – ident: ref_19 doi: 10.1109/ICCICT.2012.6398214 – ident: ref_7 doi: 10.1109/INCET51464.2021.9456313 – volume: 10 start-page: 36935 year: 2022 ident: ref_29 article-title: Enhancing the lifetime of wireless sensor networks using fuzzy logic LEACH technique-based particle swarm optimization publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3163254 – ident: ref_16 doi: 10.1109/ICE348803.2020.9122962 – ident: ref_34 doi: 10.1109/CYBER.2015.7288132 – volume: 33 start-page: e4354 year: 2020 ident: ref_41 article-title: Assimilation of fuzzy clustering approach and EHO-Greedy algorithm for efficient routing in WSN publication-title: Int. J. Commun. Syst. doi: 10.1002/dac.4354 – volume: 31 start-page: e3407 year: 2018 ident: ref_4 article-title: A new algorithm for cluster-head selection in LEACH protocol for wireless sensor networks publication-title: Int. J. Commun. Syst. doi: 10.1002/dac.3407 – ident: ref_33 doi: 10.3390/s19204579 – volume: 8 start-page: 66013 year: 2020 ident: ref_2 article-title: Fuzzy clustering algorithm for enhancing reliability and network lifetime of wireless sensor networks publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2985495 – ident: ref_10 doi: 10.1109/FCST.2015.61 – ident: ref_31 doi: 10.3390/s22166085 – ident: ref_21 doi: 10.1109/ICSESS.2017.8343043 – ident: ref_37 doi: 10.1109/ICNSC.2016.7478994 – volume: 15 start-page: 4492 year: 2021 ident: ref_3 article-title: A social welfare theory-based energy-efficient cluster-head election scheme for WSNs publication-title: IEEE Syst. J. doi: 10.1109/JSYST.2020.3010868 – volume: 67 start-page: 4019 year: 2021 ident: ref_9 article-title: Enhanced KOCED routing protocol with k-means algorithm publication-title: Comput. Mater. Contin. – volume: 46 start-page: 1014 year: 2015 ident: ref_38 article-title: A CSMA-based MAC protocol for WLANs with automatic synchronization capability to provide hard quality of service guarantees publication-title: Comput. Netw. – ident: ref_15 doi: 10.1109/LISAT.2016.7494136 – ident: ref_36 doi: 10.1109/SAI.2015.7237270 – volume: 24 start-page: 16483 year: 2020 ident: ref_39 article-title: Intelligent fuzzy rule-based approach with outlier detection for secured routing in WSN publication-title: Soft Comput. doi: 10.1007/s00500-020-04955-z – ident: ref_12 doi: 10.1109/ICAA53760.2021.00090 – ident: ref_14 doi: 10.1109/IEMCON.2015.7344478 – ident: ref_17 doi: 10.1109/CENTCON52345.2021.9688050 – ident: ref_28 doi: 10.3390/s20205865 |
| SSID | ssj0023338 |
| Score | 2.5275085 |
| Snippet | Wireless sensor networks (WSN) are widely used in various applications, such as environmental monitoring, healthcare, event detection, agriculture, disaster... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 9731 |
| SubjectTerms | Algorithms canopy algorithm Clustering Communication Computer Communication Networks Energy consumption Energy management systems Energy use energy-efficient Environmental Monitoring K-means algorithm network lifetime Optimization Physical Phenomena Sensors Wireless sensor networks Wireless Technology Workloads |
| SummonAdditionalLinks | – databaseName: Health & Medical Collection dbid: 7X7 link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lj9MwEB7BwgEOvB-BBRmEBJdokziN4xPqlq5WAgoSIHqL_Ep3pW5S-kDi3zPjutlGIC5IPTUjZax5frH9DcArLUQiMkVHdfggxgpVx0ogaq2NS_Paaq5rz67_QUwm5XQqP4cPbqtwrHKXE32itq2hb-RHRFMuEI3w7O3iR0xTo2h3NYzQuArXaGw2-bmYXgIujvhryybEEdofrbAW5jSqqVeDPFX_nwl5ryL1T0vulZ-T2_-r-B24FRpPNtx6yl244pp7cHOPjvA-zIbs3QZlRvMN0SfEp2h_NvaXA-Oxp5pA3RidIUJxNpzP8DXrswt2jJXQsrZhI9W0i1_sE6ahi3C_k6nGsvfxR4clkWGDzL5_mTyAbyfjr6PTOAxiiA32b-tYmiyR2KjwxORG5TW3QhWprjFdaKlqg5gFUVuibKYx6BQ2kUYWgitZurQmGPwQDpq2cY-B5dbaxClZ1LrIS2NVUiSOdqWxMUNo6CJ4szNNZQJLOQ3LmFeIVsiKVWfFCF52oostNcffhI7Jvp0AsWn7P9rlrArBWXFtuB4kWthS00Q2ZerCplJqneKqhI3gNXlHRTGPyhgVri7gkog9qxoKYqkbYDMQweHOCaqQDFbVpQdE8KJ7jGFMezOqce3Gy5Spb68jeLT1t05nTgRJ2MlHIHqe2FtU_0lzfuapwiW-N8-TJ_9W6yncyOhWR0q_QzhYLzfuGVw3P9fnq-VzH1O_ASBxKt0 priority: 102 providerName: ProQuest |
| Title | A Dual Cluster-Head Energy-Efficient Routing Algorithm Based on Canopy Optimization and K-Means for WSN |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/36560099 https://www.proquest.com/docview/2756784032 https://www.proquest.com/docview/2758104565 https://pubmed.ncbi.nlm.nih.gov/PMC9784440 https://doaj.org/article/3bc3b50b7d8b4179acf6d199bb180a7d |
| Volume | 22 |
| WOSCitedRecordID | wos000904115100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lj9MwEB7BwgEOiDeBpTIICS7ROnEaJ8e2ZLUIWioeopwiP5LdSt1k1QcSF347M04aNQKJC1LkgzOKHM-MZz7Z_gbglZaSy1DRUR0x9DFClb6SiFpLUwRRabXQpWPX_yBns2SxSOcHpb7oTFhDD9xM3InQRugh19ImmqplKVPGNkhTrYOEK2lp9eUy3YOpFmoJRF4Nj5BAUH-ywSgYUZGmXvRxJP1_LsUHsah_TvIg8JzehTttxshGzUjvwbWiug-3D3gEH8D5iL3docxktSPeA_8MFccyd6vPzxxHBH6a0eEfFGej1Xm9Xm4vLtkYQ5hldcUmqqqvfrKPuH5cthczmaose-9PC4xlDDNb9u3z7CF8Pc2-TM78toKCbzDx2vqpCXmKGYbgJjIqKoWVKg50iX6uU1UaBBsIt7iyoUZvUZj9mTSWQqVJEZSEXx_BUVVXxRNgkbWWFyqNSx1HibGKx7yg7WTMqBDTFR682c9sblp6capyscoRZpAS8k4JHrzsRK8aTo2_CY1JPZ0A0WC7DjSOvDWO_F_G4cFrUm5OzoqDMaq9c4C_RLRX-UgSvdwQo7gHx3v9560Xb3KixpeIgEXowYvuNfofbaqoqqh3TiYJXF7swePGXLoxC2I2whTcA9kzpN5P9d9UywvH8Y3gPooi_vR_zMIzuBXSpY2AnmM42q53xXO4aX5sl5v1AK7LhXRtMoAb42w2_zRwzoTt9FeGffN30_n330jDI_Y |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VggQceD8MBRYEgotV2-t4vQeE0jRVqqQBiSJyM_tyWim1Qx6g_il-IzOOkyYCcesBKSd75Ow4387Ml939BuC1FiIQkaKtOrzhY4bKfSWQtebGhXFuNdd5pa7fE_1-OhjIT1vwa3kWhrZVLmNiFahtaeg_8l2SKRfIRnj0Yfzdp65RtLq6bKGxgEXXnf9EyjZ9f7iPv--bKDpoH7c6ft1VwDdYjMx8aaJAYtblgYmNinNuhUpCnSP2tVS5wQIcKUigbKQRQQorIiMTwZVMXZgTp8PnXoGrGMcFkT0xuCB4HPneQr2IcxnsTjH3xtQaaiPnVa0B_kwAaxlwc3fmWro7uP2_vag7cKsurFlzMRPuwpYr7sHNNbnF-zBssv052rRGc5KH8DuIb9auDj_67UpKA98Foz1SaM6aoyG6NTs5Y3uY6S0rC9ZSRTk-Zx8xzJ7V51eZKizr-kcOUz5DAsC-fu4_gC-X4ulD2C7Kwj0GFltrA6dkkuskTo1VQRI4WnXHwhOpr_Pg3RIKmalV2KkZyChDNkaoyVao8eDVynS8kB75m9Ee4WllQGrh1YVyMszq4JNxbbhuBFrYVFPHOWXyxIZSah2iV8J68JbQmFFMw8EYVR_NQJdIHSxrClLha2Cx48HOEnRZHeym2QXiPHi5uo1hitaeVOHKeWWThhV98ODRAt-rMXMSgEKm4oHYQP6GU5t3itOTSgpd4vfGcfDk38N6Adc7x0e9rHfY7z6FGxGdYAnpswPbs8ncPYNr5sfsdDp5Xs1nBt8ue178Bu2Rhbo |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Jb9NAFH4qKUJwYF8MBQYEgosV2-N44gNCaRY1ShsiQUU5mVk8aaXUDllA_Wv8Ot5zHJMIxK0HpJzsJ2fG-d7yZWa-B_BKCeGJQNJWHd5wMUNZVwpkrVanfmiN4soW6vqHYjhsnpzEox34uT4LQ9sq1zGxCNQm1_QfeZ1kygWyER7UbbktYtTpvZ9-c6mDFK20rttprCAySC9-IH2bv-t38Ld-HQS97qf2gVt2GHA1FiYLN9aBF2MG5p4OtQwtN0JGvrLoByqWVmMxjnTEkyZQiCaJ1ZGOI8Fl3Ex9S_wOn3sFdrEkD4Ma7I76R6MvFd3jyP5WWkacx159jpk4pEZRWxmwaBTwZzrYyIfbezU3kl_v1v_82m7DzbLkZq2Vj9yBnTS7Czc2hBjvwbjFOku0aU-WJBzhHiDyWbc4Ful2C5ENfC-Mdk-hOWtNxjitxek528cawLA8Y22Z5dML9gED8Hl5spXJzLCBe5RiMcCQGrDPH4f34fhSZvoAalmepY-AhcYYL5VxZFUUNrWRXuSltB6PJSmS4tSBt2tYJLrUZ6c2IZMEeRohKKkQ5MDLynS6EiX5m9E-YasyIB3x4kI-GydlWEq40lw1PCVMU1EvOqltZPw4VsrHWQnjwBtCZkLRDgejZXloA6dEumFJS5A-XwPLIAf21gBMyjA4T36jz4EX1W0MYLQqJbM0XxY2Tb8gFg48XGG9GjMnaSjkMA6ILS_YmtT2nezstBBJj_F7w9B7_O9hPYdr6A7JYX84eALXAzra4tNnD2qL2TJ9Clf198XZfPasdG4GXy_bMX4BZkOQCQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Dual+Cluster-Head+Energy-Efficient+Routing+Algorithm+Based+on+Canopy+Optimization+and+K-Means+for+WSN&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Wu%2C+Mei&rft.au=Li%2C+Zhengliang&rft.au=Chen%2C+Jing&rft.au=Min%2C+Qiusha&rft.date=2022-12-12&rft.issn=1424-8220&rft.eissn=1424-8220&rft.volume=22&rft.issue=24&rft_id=info:doi/10.3390%2Fs22249731&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon |