Multiobjective Multiple Mobile Sink Scheduling via Evolutionary Fuzzy Rough Neural Network for Wireless Sensor Networks
The sensor nodes in wireless sensor networks have the deficiency of limited energy, and the multihop transmission of information will lead to a premature paralysis of nodes near the sink. The use of the mobile sink can balance the energy consumption and greatly prolong the lifetime. Therefore, this...
Uložené v:
| Vydané v: | IEEE transactions on fuzzy systems Ročník 30; číslo 11; s. 4630 - 4641 |
|---|---|
| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
IEEE
01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Predmet: | |
| ISSN: | 1063-6706, 1941-0034, 1941-0034 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | The sensor nodes in wireless sensor networks have the deficiency of limited energy, and the multihop transmission of information will lead to a premature paralysis of nodes near the sink. The use of the mobile sink can balance the energy consumption and greatly prolong the lifetime. Therefore, this article studies the scheduling strategy of multiple mobile sinks and proposes a heuristic strategy based on interval type-2 fuzzy rough neural network. The energy and lifetime of sensor nodes, as well as location information of the mobile sink and special nodes are taken as input features. Through neural network learning, the outputs determine whether to move, moving direction, moving distance, and residence time, which can complete the scheduling task. The scheduling problem is regarded as a multiobjective optimization problem, and the network lifetime, the moving path length, and the network interpretability are optimized at the same time, so as to obtain a lightweight network with good interpretability and performance. Based on the parallel multiobjective evolutionary algorithm, a multiobjective neural evolutionary framework is constructed. This framework can balance multiple objectives and complete complex scheduling tasks. Compared with static sinks, random-moving sinks, sinks with manually designed strategy, gene expression programming-based sinks, as well as the other state-of-the-art multiobjective evolutionary algorithms, the proposed framework can achieve superior results. |
|---|---|
| AbstractList | The sensor nodes in wireless sensor networks have the deficiency of limited energy, and the multihop transmission of information will lead to a premature paralysis of nodes near the sink. The use of the mobile sink can balance the energy consumption and greatly prolong the lifetime. Therefore, this article studies the scheduling strategy of multiple mobile sinks and proposes a heuristic strategy based on interval type-2 fuzzy rough neural network. The energy and lifetime of sensor nodes, as well as location information of the mobile sink and special nodes are taken as input features. Through neural network learning, the outputs determine whether to move, moving direction, moving distance, and residence time, which can complete the scheduling task. The scheduling problem is regarded as a multiobjective optimization problem, and the network lifetime, the moving path length, and the network interpretability are optimized at the same time, so as to obtain a lightweight network with good interpretability and performance. Based on the parallel multiobjective evolutionary algorithm, a multiobjective neural evolutionary framework is constructed. This framework can balance multiple objectives and complete complex scheduling tasks. Compared with static sinks, random-moving sinks, sinks with manually designed strategy, gene expression programming-based sinks, as well as the other state-of-the-art multiobjective evolutionary algorithms, the proposed framework can achieve superior results. The sensor nodes in wireless sensor networks have the deficiency of limited energy, and the multi-hop transmission of information will lead to premature paralysis of nodes near the sink. The use of the mobile sink can balance the energy consumption and greatly prolong the lifetime. Therefore, this paper studies the scheduling strategy of multiple mobile sinks and proposes a heuristic strategy based on interval type-2 fuzzy rough neural network. The energy and lifetime of sensor nodes, as well as location information of the mobile sink and special nodes are taken as input features. Through neural network learning, the outputs of whether to move, moving direction, moving distance and residence time can complete the scheduling task. The scheduling problem is regarded as a multiobjective optimization problem, and the network lifetime, the moving path length and the network interpretability are optimized at the same time, so as to obtain a lightweight network with good interpretability and performance. Based on the parallel multiobjective evolutionary algorithm, a neural evolutionary framework is constructed. Compared with static sinks, random- moving sinks, sinks with manually designed strategy and gene expression programming-based sinks as well as other state- of-the-art multiobjective evolutionary algorithms, the proposed framework can achieve superior results. IEEE |
| Author | Cao, Bin Lv, Zhihan Liu, Xin Yang, Peng Singh, Amit Kumar Zhao, Jianwei |
| Author_xml | – sequence: 1 givenname: Jianwei orcidid: 0000-0003-0424-4056 surname: Zhao fullname: Zhao, Jianwei email: 201422102003@stu.hebut.edu.cn organization: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China – sequence: 2 givenname: Bin orcidid: 0000-0003-4558-9501 surname: Cao fullname: Cao, Bin email: caobin@scse.hebut.edu.cn organization: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China – sequence: 3 givenname: Xin surname: Liu fullname: Liu, Xin email: 202011701002@stu.hebut.edu.cn organization: School of Economics and Management, Hebei University of Technology, Tianjin, China – sequence: 4 givenname: Peng orcidid: 0000-0002-1129-8485 surname: Yang fullname: Yang, Peng email: yangp@hebut.edu.cn organization: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China – sequence: 5 givenname: Amit Kumar orcidid: 0000-0001-7359-2068 surname: Singh fullname: Singh, Amit Kumar email: amit.singh@nitp.ac.in organization: Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, India – sequence: 6 givenname: Zhihan orcidid: 0000-0003-2525-3074 surname: Lv fullname: Lv, Zhihan email: lvzhihan@gmail.com organization: Uppsala University, Uppsala, Sweden |
| BackLink | https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-472684$$DView record from Swedish Publication Index (Uppsala universitet) |
| BookMark | eNp9kU9PGzEQxa2KSg3QL0Avlnrtpv63XvuIKGmRKJVIUqRcLGczGxy262CvE8GnxyERhx44vRn5_cZjv2N01PkOEDqjZEgp0d8no-lsNmSEsSGnkmuiP6AB1YIWhHBxlGsieSErIj-h4xhXhFBRUjVA29-p7Z2fr6Du3Qbwa7tuc-HnLsvYdQ94XN_DIrWuW-KNs_hy49uUoc6GJzxKz89P-Nan5T2-gRRsm6Xf-vCAGx_wnQvQQox4DF3M_eEsnqKPjW0jfD7oCZqOLicXv4rrPz-vLs6vi5pz1hes0rWgjWJSC7KgnDUVscySRjVS7CwNLEQNhMNclKXWtM5vVlopUTLFNecn6Nt-btzCOs3NOrh_eW3jrTM_3N9z48PSpGRExaQS2f51b18H_5gg9mblU-jyhoZVnDLBhZDZxfauOvgYAzRvYykxuzzMax5ml4c55JEh9R9Uu97uvrEP1rXvo1_2qAOAt7t0JUpJGX8BVVib9Q |
| CODEN | IEFSEV |
| CitedBy_id | crossref_primary_10_1109_TFUZZ_2024_3397728 crossref_primary_10_1007_s11227_024_06630_8 crossref_primary_10_1088_1361_6501_adf133 crossref_primary_10_3390_w16203001 crossref_primary_10_1007_s11432_023_4011_4 crossref_primary_10_1007_s12652_023_04535_7 crossref_primary_10_3390_app13116631 crossref_primary_10_1016_j_adhoc_2023_103350 |
| Cites_doi | 10.1007/s11277-015-2998-6 10.1016/j.asoc.2015.04.061 10.1109/TFUZZ.2019.2930488 10.1007/s11276-007-0017-x 10.1109/JPROC.2019.2921977 10.1177/1059712314536909 10.1016/j.future.2017.10.015 10.1109/TEVC.2018.2868770 10.1007/978-3-319-68759-9_63 10.1016/j.swevo.2019.04.008 10.1109/TFUZZ.2016.2574915 10.1145/3369798 10.1109/TWC.2002.804190 10.1002/0471656895.ch8 10.1109/TFUZZ.2004.832538 10.1109/ICSMC.1998.728196 10.1109/TFUZZ.2020.2972207 10.1109/TCYB.2015.2490669 10.1007/s12652-018-0901-5 10.1002/widm.1402 10.1109/ICCV.2019.00138 10.1109/4235.996017 10.1109/TII.2018.2803758 10.1155/2021/6577492 10.1016/j.neucom.2013.04.005 10.1016/j.swevo.2020.100697 10.1177/1475921719854528 10.32604/cmc.2020.08674 10.1007/978-3-662-44599-0 10.1016/j.neucom.2020.01.114 10.1109/TEVC.2007.892759 10.1109/TEVC.2015.2455812 10.1109/UKRCON.2017.8100357 10.1007/s11277-019-06717-z 10.5004/dwt.2021.26871 10.1016/j.engappai.2020.103924 10.1038/s42256-020-00278-8 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 |
| DBID | 97E RIA RIE AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D ADTPV AOWAS DF2 |
| DOI | 10.1109/TFUZZ.2022.3163909 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional SwePub SwePub Articles SWEPUB Uppsala universitet |
| DatabaseTitle | CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Computer and Information Systems Abstracts |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 1941-0034 |
| EndPage | 4641 |
| ExternalDocumentID | oai_DiVA_org_uu_472684 10_1109_TFUZZ_2022_3163909 9745612 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 61976242 funderid: 10.13039/501100001809 – fundername: Hebei University of Technology grantid: JBKYTD2002 funderid: 10.13039/501100010850 – fundername: Natural Science Fund of Hebei Province for Distinguished Young Scholars grantid: F2021202010 |
| GroupedDBID | -~X .DC 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD HZ~ H~9 ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RNS TAE TN5 VH1 AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D ADTPV AOWAS DF2 |
| ID | FETCH-LOGICAL-c332t-279c41f826940d132f70a2a0f8f64c332fed4ce03eb455991c941898845283933 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 10 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000878174700012&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1063-6706 1941-0034 |
| IngestDate | Tue Nov 04 16:35:14 EST 2025 Sun Nov 30 03:55:16 EST 2025 Tue Nov 18 22:28:58 EST 2025 Sat Nov 29 03:12:42 EST 2025 Wed Aug 27 02:14:21 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 11 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c332t-279c41f826940d132f70a2a0f8f64c332fed4ce03eb455991c941898845283933 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0003-2525-3074 0000-0001-7359-2068 0000-0002-1129-8485 0000-0003-0424-4056 0000-0003-4558-9501 |
| PQID | 2731243446 |
| PQPubID | 85428 |
| PageCount | 12 |
| ParticipantIDs | ieee_primary_9745612 crossref_citationtrail_10_1109_TFUZZ_2022_3163909 crossref_primary_10_1109_TFUZZ_2022_3163909 swepub_primary_oai_DiVA_org_uu_472684 proquest_journals_2731243446 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-11-01 |
| PublicationDateYYYYMMDD | 2022-11-01 |
| PublicationDate_xml | – month: 11 year: 2022 text: 2022-11-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE transactions on fuzzy systems |
| PublicationTitleAbbrev | TFUZZ |
| PublicationYear | 2022 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | Lu (ref32) 2018 ref35 ref12 ref34 ref15 ref37 ref36 ref31 ref30 ref11 ref33 ref2 ref1 ref17 ref39 ref16 ref19 ref18 Antonio (ref38) Zhao (ref24) 2020; 46 ref23 ref26 ref25 ref20 ref42 ref41 ref22 ref21 Eaton (ref10) 2015 ref28 ref27 Ha (ref14) ref29 ref8 ref7 Gaier (ref13) 2019 ref9 ref4 ref3 ref6 ref5 ref40 |
| References_xml | – ident: ref5 doi: 10.1007/s11277-015-2998-6 – ident: ref30 doi: 10.1016/j.asoc.2015.04.061 – ident: ref23 doi: 10.1109/TFUZZ.2019.2930488 – ident: ref8 doi: 10.1007/s11276-007-0017-x – ident: ref18 doi: 10.1109/JPROC.2019.2921977 – ident: ref12 doi: 10.1177/1059712314536909 – ident: ref36 doi: 10.1016/j.future.2017.10.015 – start-page: 5364 volume-title: Proc. 33rd Annu. Conf. Neural Inf. Process. Syst. year: 2019 ident: ref13 article-title: Weight agnostic neural networks – ident: ref29 doi: 10.1109/TEVC.2018.2868770 – ident: ref9 doi: 10.1007/978-3-319-68759-9_63 – ident: ref26 doi: 10.1016/j.swevo.2019.04.008 – ident: ref20 doi: 10.1109/TFUZZ.2016.2574915 – ident: ref19 doi: 10.1145/3369798 – start-page: 2758 volume-title: Proc. IEEE Congr. Evol. Comput. ident: ref38 article-title: Use of cooperative coevolution for solving large-scale multi-objective optimization problems – ident: ref31 doi: 10.1109/TWC.2002.804190 – ident: ref34 doi: 10.1002/0471656895.ch8 – year: 2018 ident: ref32 article-title: Research on rough modeling of type-2 fuzzy sets – ident: ref22 doi: 10.1109/TFUZZ.2004.832538 – ident: ref33 doi: 10.1109/ICSMC.1998.728196 – ident: ref17 doi: 10.1109/TFUZZ.2020.2972207 – ident: ref39 doi: 10.1109/TCYB.2015.2490669 – ident: ref4 doi: 10.1007/s12652-018-0901-5 – ident: ref15 doi: 10.1002/widm.1402 – ident: ref27 doi: 10.1109/ICCV.2019.00138 – ident: ref42 doi: 10.1109/4235.996017 – ident: ref35 doi: 10.1109/TII.2018.2803758 – ident: ref1 doi: 10.1155/2021/6577492 – ident: ref11 doi: 10.1016/j.neucom.2013.04.005 – ident: ref37 doi: 10.1016/j.swevo.2020.100697 – ident: ref2 doi: 10.1177/1475921719854528 – ident: ref6 doi: 10.32604/cmc.2020.08674 – volume-title: Evolutionary Humanoid Robotics year: 2015 ident: ref10 doi: 10.1007/978-3-662-44599-0 – ident: ref28 doi: 10.1016/j.neucom.2020.01.114 – volume: 46 start-page: 2350 issue: 11 year: 2020 ident: ref24 article-title: Deep neural fuzzy system algorithm and its regression application publication-title: Acta Automatica Sinica – ident: ref41 doi: 10.1109/TEVC.2007.892759 – ident: ref40 doi: 10.1109/TEVC.2015.2455812 – ident: ref21 doi: 10.1109/UKRCON.2017.8100357 – ident: ref7 doi: 10.1007/s11277-019-06717-z – ident: ref3 doi: 10.5004/dwt.2021.26871 – start-page: 2450 volume-title: Proc. 32nd Conf. Neural Inf. Process. Syst. ident: ref14 article-title: Recurrent world models facilitate policy evolution – ident: ref16 doi: 10.1016/j.engappai.2020.103924 – ident: ref25 doi: 10.1038/s42256-020-00278-8 |
| SSID | ssj0014518 |
| Score | 2.4495919 |
| Snippet | The sensor nodes in wireless sensor networks have the deficiency of limited energy, and the multihop transmission of information will lead to a premature... The sensor nodes in wireless sensor networks have the deficiency of limited energy, and the multi-hop transmission of information will lead to premature... |
| SourceID | swepub proquest crossref ieee |
| SourceType | Open Access Repository Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 4630 |
| SubjectTerms | Artificial neural networks Energy consumption Energy utilization Evolutionary algorithms Fuzzy inference Fuzzy logic Fuzzy neural networks Fuzzy rough neural network Fuzzy rough neural network (FRNN) Fuzzy sets Gene expression Genetic algorithms Multi-Objective Evolutionary Algorithm multi-objective evolutionary algorithm (MOEA) multiobjective evolutionary algorithm Multiobjective evolutionary algorithms Multiobjective optimization multiple mobile sink scheduling Multiple mobile sinks Multiple objective analysis network lifetime neural evolution Neural evolutions Neural networks Nodes Optimisations Optimization Paralysis Rough neural networks Rough sets Scheduling Sensor nodes Sensors Task complexity Task scheduling Wireless communication Wireless communications Wireless networks Wireless sensor networks |
| Title | Multiobjective Multiple Mobile Sink Scheduling via Evolutionary Fuzzy Rough Neural Network for Wireless Sensor Networks |
| URI | https://ieeexplore.ieee.org/document/9745612 https://www.proquest.com/docview/2731243446 https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-472684 |
| Volume | 30 |
| WOSCitedRecordID | wos000878174700012&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: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1941-0034 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014518 issn: 1941-0034 databaseCode: RIE dateStart: 19930101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fT9swED4VtIftgbKyiTJAftieICM_3Nh5rDYqXqim8UOIF8txHFFUtahtispfvzvHjYqEJvGWKHZk6bvYd7m77wP43stIttrawIScBzzt5YHktgxyra0pddJLjXFiE2I4lHd32Z8WnDa9MNZaV3xmf9Kly-UXU1PRr7Iz9H1JzHELtoQQda9WkzHgvahue0uTIBVhum6QCbOz68HN_T2GgnGMESqeyFR8uHEIOVWV1w7mJmmoO2gG7fctcRd2vEPJ-rUFfIaWnXSgvRZrYP7b7cCnDebBPXh2jbfT_LHe79ilrytkl9Mc9wl2hSEqzn3Ag4j61dlypNn50pupnq3YoHp5WbG_pPHDiOADlzCsK8oZusGMimrHuImyKwyT8d4_m3-Bm8H59a-LwGswBCZJ4kUQi8zwqJTU8BoWGLqWItSxDktZppyGlLbgpDlmc07kZZHJeCQzKTmxxmRJ8hW2J9OJ3QcmrRRSa0r0lDzSRR7HBU85JVoFYsK7EK1BUcYTlJNOxli5QCXMlANSEZDKA9mFk2bOU03P8d_Re4RYM9KD1YXDNfbKf8FzhW4duj4JRstd-FHbQzOPKLl_j277CqFXVaW4IM6cg7ff_g0-0hrq_sVD2F7MKnsEH8xyMZrPjp0R_wNnOe_r |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fb9MwED6NgcT2wGBjojDAD_AEYflxSZzHCVYNsVaIdWjai-U4jtZpalHbdNr-eu4cNyoSQuItUezI0nex73J33wfwLi1YttrawISIAWZpGUi0dVBqbU2tkzQzxolN5MOhvLgovm_Ax64Xxlrris_sJ750ufxqahr-VXZIvi-LOT6AhyliHLXdWl3OANOobXzLkiDLw2zVIhMWh6P--eUlBYNxTDEqnclcfrh2DDldlT9dzHXaUHfU9Hf-b5FP4Yl3KcVRawPPYMNOdmFnJdcg_Ne7C9tr3IN7cOtab6fldbvjiYGvLBSDaUk7hTijIJXmXtFRxB3rYjnW4njpDVXP7kS_ub-_Ez9Y5UcwxQctYdjWlAtyhAWX1d7QNirOKFCme_9s_hzO-8ejzyeBV2EITJLEiyDOC4NRLbnlNawoeK3zUMc6rGWdIQ-pbYWsOmZLZPqyyBQYyUJKZN6YIkn2YXMyndgXIKSVudSaUz01Rroq47jCDDnVmhMm2INoBYoynqKclTJulAtVwkI5IBUDqTyQPfjQzfnVEnT8c_QeI9aN9GD14GCFvfLf8FyRY0fOT0Lxcg_et_bQzWNS7i_jn0eKoFdNozBn1pyXf3_7W3h8MhqcqtOvw2-vYIvX03YzHsDmYtbY1_DILBfj-eyNM-jfBDPzMg |
| 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=Multiobjective+Multiple+Mobile+Sink+Scheduling+via+Evolutionary+Fuzzy+Rough+Neural+Network+for+Wireless+Sensor+Networks&rft.jtitle=IEEE+transactions+on+fuzzy+systems&rft.au=Zhao%2C+J.&rft.au=Cao%2C+B.&rft.au=Liu%2C+X.&rft.au=Yang%2C+P.&rft.date=2022-11-01&rft.issn=1941-0034&rft.volume=30&rft.issue=11&rft.spage=4630&rft_id=info:doi/10.1109%2FTFUZZ.2022.3163909&rft.externalDocID=oai_DiVA_org_uu_472684 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1063-6706&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1063-6706&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1063-6706&client=summon |