Machine Learning Based Power Efficient Optimized Communication Ensemble Model with Intelligent Fog Computing for WSNs

Abstract Wireless sensor networks have evolved in recent years. Energy consumption is one of the major parameters to access the performance of the network. This study proposes an algorithm which is based on Ant lion optimization along with three different clustering techniques. This study is motivat...
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Autor*in:	Jain, Abhishek [verfasserIn] Bhardwaj, Amit Kumar

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	k-means Fuzzy c-means SOM Ant lion optimization FOG layer

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Wireless personal communications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 131(2023), 1 vom: 20. Apr., Seite 415-429
Übergeordnetes Werk:	volume:131 ; year:2023 ; number:1 ; day:20 ; month:04 ; pages:415-429

Links:	Volltext

DOI / URN:	10.1007/s11277-023-10436-x

Katalog-ID:	SPR052038769

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allfields	10.1007/s11277-023-10436-x doi (DE-627)SPR052038769 (SPR)s11277-023-10436-x-e DE-627 ger DE-627 rakwb eng Jain, Abhishek verfasserin aut Machine Learning Based Power Efficient Optimized Communication Ensemble Model with Intelligent Fog Computing for WSNs 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Wireless sensor networks have evolved in recent years. Energy consumption is one of the major parameters to access the performance of the network. This study proposes an algorithm which is based on Ant lion optimization along with three different clustering techniques. This study is motivated from fog computing in which Ant lion optimization is applied in the bottom layer and an ensemble model of k-means, SOM and Fuzzy c-means techniques is used for clustering in FOG layer. To access model’s performance, results are compared to the traditional methods. This model has shown significant improvement in terms of energy consumption as compared to traditional model ECCM. Proposed model has also shown improvement in terms of load balancing. k-means (dpeaa)DE-He213 Fuzzy c-means (dpeaa)DE-He213 SOM (dpeaa)DE-He213 Ant lion optimization (dpeaa)DE-He213 FOG layer (dpeaa)DE-He213 Bhardwaj, Amit Kumar aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 131(2023), 1 vom: 20. Apr., Seite 415-429 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:131 year:2023 number:1 day:20 month:04 pages:415-429 https://dx.doi.org/10.1007/s11277-023-10436-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 131 2023 1 20 04 415-429
spelling	10.1007/s11277-023-10436-x doi (DE-627)SPR052038769 (SPR)s11277-023-10436-x-e DE-627 ger DE-627 rakwb eng Jain, Abhishek verfasserin aut Machine Learning Based Power Efficient Optimized Communication Ensemble Model with Intelligent Fog Computing for WSNs 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Wireless sensor networks have evolved in recent years. Energy consumption is one of the major parameters to access the performance of the network. This study proposes an algorithm which is based on Ant lion optimization along with three different clustering techniques. This study is motivated from fog computing in which Ant lion optimization is applied in the bottom layer and an ensemble model of k-means, SOM and Fuzzy c-means techniques is used for clustering in FOG layer. To access model’s performance, results are compared to the traditional methods. This model has shown significant improvement in terms of energy consumption as compared to traditional model ECCM. Proposed model has also shown improvement in terms of load balancing. k-means (dpeaa)DE-He213 Fuzzy c-means (dpeaa)DE-He213 SOM (dpeaa)DE-He213 Ant lion optimization (dpeaa)DE-He213 FOG layer (dpeaa)DE-He213 Bhardwaj, Amit Kumar aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 131(2023), 1 vom: 20. Apr., Seite 415-429 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:131 year:2023 number:1 day:20 month:04 pages:415-429 https://dx.doi.org/10.1007/s11277-023-10436-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 131 2023 1 20 04 415-429
allfields_unstemmed	10.1007/s11277-023-10436-x doi (DE-627)SPR052038769 (SPR)s11277-023-10436-x-e DE-627 ger DE-627 rakwb eng Jain, Abhishek verfasserin aut Machine Learning Based Power Efficient Optimized Communication Ensemble Model with Intelligent Fog Computing for WSNs 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Wireless sensor networks have evolved in recent years. Energy consumption is one of the major parameters to access the performance of the network. This study proposes an algorithm which is based on Ant lion optimization along with three different clustering techniques. This study is motivated from fog computing in which Ant lion optimization is applied in the bottom layer and an ensemble model of k-means, SOM and Fuzzy c-means techniques is used for clustering in FOG layer. To access model’s performance, results are compared to the traditional methods. This model has shown significant improvement in terms of energy consumption as compared to traditional model ECCM. Proposed model has also shown improvement in terms of load balancing. k-means (dpeaa)DE-He213 Fuzzy c-means (dpeaa)DE-He213 SOM (dpeaa)DE-He213 Ant lion optimization (dpeaa)DE-He213 FOG layer (dpeaa)DE-He213 Bhardwaj, Amit Kumar aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 131(2023), 1 vom: 20. Apr., Seite 415-429 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:131 year:2023 number:1 day:20 month:04 pages:415-429 https://dx.doi.org/10.1007/s11277-023-10436-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 131 2023 1 20 04 415-429
allfieldsGer	10.1007/s11277-023-10436-x doi (DE-627)SPR052038769 (SPR)s11277-023-10436-x-e DE-627 ger DE-627 rakwb eng Jain, Abhishek verfasserin aut Machine Learning Based Power Efficient Optimized Communication Ensemble Model with Intelligent Fog Computing for WSNs 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Wireless sensor networks have evolved in recent years. Energy consumption is one of the major parameters to access the performance of the network. This study proposes an algorithm which is based on Ant lion optimization along with three different clustering techniques. This study is motivated from fog computing in which Ant lion optimization is applied in the bottom layer and an ensemble model of k-means, SOM and Fuzzy c-means techniques is used for clustering in FOG layer. To access model’s performance, results are compared to the traditional methods. This model has shown significant improvement in terms of energy consumption as compared to traditional model ECCM. Proposed model has also shown improvement in terms of load balancing. k-means (dpeaa)DE-He213 Fuzzy c-means (dpeaa)DE-He213 SOM (dpeaa)DE-He213 Ant lion optimization (dpeaa)DE-He213 FOG layer (dpeaa)DE-He213 Bhardwaj, Amit Kumar aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 131(2023), 1 vom: 20. Apr., Seite 415-429 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:131 year:2023 number:1 day:20 month:04 pages:415-429 https://dx.doi.org/10.1007/s11277-023-10436-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 131 2023 1 20 04 415-429
allfieldsSound	10.1007/s11277-023-10436-x doi (DE-627)SPR052038769 (SPR)s11277-023-10436-x-e DE-627 ger DE-627 rakwb eng Jain, Abhishek verfasserin aut Machine Learning Based Power Efficient Optimized Communication Ensemble Model with Intelligent Fog Computing for WSNs 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Wireless sensor networks have evolved in recent years. Energy consumption is one of the major parameters to access the performance of the network. This study proposes an algorithm which is based on Ant lion optimization along with three different clustering techniques. This study is motivated from fog computing in which Ant lion optimization is applied in the bottom layer and an ensemble model of k-means, SOM and Fuzzy c-means techniques is used for clustering in FOG layer. To access model’s performance, results are compared to the traditional methods. This model has shown significant improvement in terms of energy consumption as compared to traditional model ECCM. Proposed model has also shown improvement in terms of load balancing. k-means (dpeaa)DE-He213 Fuzzy c-means (dpeaa)DE-He213 SOM (dpeaa)DE-He213 Ant lion optimization (dpeaa)DE-He213 FOG layer (dpeaa)DE-He213 Bhardwaj, Amit Kumar aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 131(2023), 1 vom: 20. Apr., Seite 415-429 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:131 year:2023 number:1 day:20 month:04 pages:415-429 https://dx.doi.org/10.1007/s11277-023-10436-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 131 2023 1 20 04 415-429
language	English
source	Enthalten in Wireless personal communications 131(2023), 1 vom: 20. Apr., Seite 415-429 volume:131 year:2023 number:1 day:20 month:04 pages:415-429
sourceStr	Enthalten in Wireless personal communications 131(2023), 1 vom: 20. Apr., Seite 415-429 volume:131 year:2023 number:1 day:20 month:04 pages:415-429
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	k-means Fuzzy c-means SOM Ant lion optimization FOG layer
isfreeaccess_bool	false
container_title	Wireless personal communications
authorswithroles_txt_mv	Jain, Abhishek @@aut@@ Bhardwaj, Amit Kumar @@aut@@
publishDateDaySort_date	2023-04-20T00:00:00Z
hierarchy_top_id	271179120
id	SPR052038769
language_de	englisch
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author	Jain, Abhishek
spellingShingle	Jain, Abhishek misc k-means misc Fuzzy c-means misc SOM misc Ant lion optimization misc FOG layer Machine Learning Based Power Efficient Optimized Communication Ensemble Model with Intelligent Fog Computing for WSNs
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topic_title	Machine Learning Based Power Efficient Optimized Communication Ensemble Model with Intelligent Fog Computing for WSNs k-means (dpeaa)DE-He213 Fuzzy c-means (dpeaa)DE-He213 SOM (dpeaa)DE-He213 Ant lion optimization (dpeaa)DE-He213 FOG layer (dpeaa)DE-He213
topic	misc k-means misc Fuzzy c-means misc SOM misc Ant lion optimization misc FOG layer
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title	Machine Learning Based Power Efficient Optimized Communication Ensemble Model with Intelligent Fog Computing for WSNs
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title_full	Machine Learning Based Power Efficient Optimized Communication Ensemble Model with Intelligent Fog Computing for WSNs
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doi_str_mv	10.1007/s11277-023-10436-x
title_sort	machine learning based power efficient optimized communication ensemble model with intelligent fog computing for wsns
title_auth	Machine Learning Based Power Efficient Optimized Communication Ensemble Model with Intelligent Fog Computing for WSNs
abstract	Abstract Wireless sensor networks have evolved in recent years. Energy consumption is one of the major parameters to access the performance of the network. This study proposes an algorithm which is based on Ant lion optimization along with three different clustering techniques. This study is motivated from fog computing in which Ant lion optimization is applied in the bottom layer and an ensemble model of k-means, SOM and Fuzzy c-means techniques is used for clustering in FOG layer. To access model’s performance, results are compared to the traditional methods. This model has shown significant improvement in terms of energy consumption as compared to traditional model ECCM. Proposed model has also shown improvement in terms of load balancing. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Wireless sensor networks have evolved in recent years. Energy consumption is one of the major parameters to access the performance of the network. This study proposes an algorithm which is based on Ant lion optimization along with three different clustering techniques. This study is motivated from fog computing in which Ant lion optimization is applied in the bottom layer and an ensemble model of k-means, SOM and Fuzzy c-means techniques is used for clustering in FOG layer. To access model’s performance, results are compared to the traditional methods. This model has shown significant improvement in terms of energy consumption as compared to traditional model ECCM. Proposed model has also shown improvement in terms of load balancing. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Wireless sensor networks have evolved in recent years. Energy consumption is one of the major parameters to access the performance of the network. This study proposes an algorithm which is based on Ant lion optimization along with three different clustering techniques. This study is motivated from fog computing in which Ant lion optimization is applied in the bottom layer and an ensemble model of k-means, SOM and Fuzzy c-means techniques is used for clustering in FOG layer. To access model’s performance, results are compared to the traditional methods. This model has shown significant improvement in terms of energy consumption as compared to traditional model ECCM. Proposed model has also shown improvement in terms of load balancing. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Machine Learning Based Power Efficient Optimized Communication Ensemble Model with Intelligent Fog Computing for WSNs
url	https://dx.doi.org/10.1007/s11277-023-10436-x
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doi_str	10.1007/s11277-023-10436-x
up_date	2024-07-04T01:00:10.158Z
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