Multi-objective Service Composition Optimization in Smart Agriculture Using Fuzzy-Evolutionary Algorithm
Abstract Agricultural applications can take advantage of improved services provided by the Internet of Things paradigms to manage data effectively. It is necessary to manage Quality of Service (QoS) characteristics to effectively monitor and measure the given services. Given how challenging it is to...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sharma, Shalini [verfasserIn] Pathak, Bhupendra Kumar [verfasserIn] Kumar, Rajiv [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. 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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| Übergeordnetes Werk: |
Enthalten in: Operations research forum - Springer International Publishing, 2020, 5(2024), 2 vom: 09. Mai |
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| Übergeordnetes Werk: |
volume:5 ; year:2024 ; number:2 ; day:09 ; month:05 |
| Links: |
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| DOI / URN: |
10.1007/s43069-024-00319-7 |
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| Katalog-ID: |
SPR055802931 |
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| 520 | |a Abstract Agricultural applications can take advantage of improved services provided by the Internet of Things paradigms to manage data effectively. It is necessary to manage Quality of Service (QoS) characteristics to effectively monitor and measure the given services. Given how challenging it is to satisfy a user’s complicated requirements with a single service, this paper presents a QoS-aware method for sending agricultural information as a service and then combining those services, thus, known as service composition. The proposed work is divided into two phases. In the first phase, a fuzzy inference set is used to initialize the population whereas, in the second phase, the multi-objective evolutionary algorithm NSGA-II (Non-dominated sorting genetic algorithm) has been used to optimize the cost and time of services involved in apple crop production. Since evolutionary algorithms have a problem dealing with uncertainties so modification using fuzzy logic has been proposed to check its effectiveness in Service Composition Problem (SCP). In order to demonstrate the persuasiveness of our work, the proposed method is compared with the multi-objective genetic algorithm (MOGA), Gaining sharing knowledge (GSK) algorithm, and NSGA-II and it has been found that NSGA-II is giving more diversified and near to true Pareto solutions. | ||
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10.1007/s43069-024-00319-7 doi (DE-627)SPR055802931 (SPR)s43069-024-00319-7-e DE-627 ger DE-627 rakwb eng 510 VZ Sharma, Shalini verfasserin aut Multi-objective Service Composition Optimization in Smart Agriculture Using Fuzzy-Evolutionary Algorithm 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. 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 Agricultural applications can take advantage of improved services provided by the Internet of Things paradigms to manage data effectively. It is necessary to manage Quality of Service (QoS) characteristics to effectively monitor and measure the given services. Given how challenging it is to satisfy a user’s complicated requirements with a single service, this paper presents a QoS-aware method for sending agricultural information as a service and then combining those services, thus, known as service composition. The proposed work is divided into two phases. In the first phase, a fuzzy inference set is used to initialize the population whereas, in the second phase, the multi-objective evolutionary algorithm NSGA-II (Non-dominated sorting genetic algorithm) has been used to optimize the cost and time of services involved in apple crop production. Since evolutionary algorithms have a problem dealing with uncertainties so modification using fuzzy logic has been proposed to check its effectiveness in Service Composition Problem (SCP). In order to demonstrate the persuasiveness of our work, the proposed method is compared with the multi-objective genetic algorithm (MOGA), Gaining sharing knowledge (GSK) algorithm, and NSGA-II and it has been found that NSGA-II is giving more diversified and near to true Pareto solutions. Service composition (dpeaa)DE-He213 Fuzzy logic controller (dpeaa)DE-He213 NSGA-II (dpeaa)DE-He213 Internet of Things (dpeaa)DE-He213 Smart agriculture (dpeaa)DE-He213 Pathak, Bhupendra Kumar verfasserin aut Kumar, Rajiv verfasserin aut Enthalten in Operations research forum Springer International Publishing, 2020 5(2024), 2 vom: 09. Mai (DE-627)1669255581 (DE-600)2978290-9 2662-2556 nnns volume:5 year:2024 number:2 day:09 month:05 https://dx.doi.org/10.1007/s43069-024-00319-7 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-WIW 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_65 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_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_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_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 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_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 5 2024 2 09 05 |
| spelling |
10.1007/s43069-024-00319-7 doi (DE-627)SPR055802931 (SPR)s43069-024-00319-7-e DE-627 ger DE-627 rakwb eng 510 VZ Sharma, Shalini verfasserin aut Multi-objective Service Composition Optimization in Smart Agriculture Using Fuzzy-Evolutionary Algorithm 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. 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 Agricultural applications can take advantage of improved services provided by the Internet of Things paradigms to manage data effectively. It is necessary to manage Quality of Service (QoS) characteristics to effectively monitor and measure the given services. Given how challenging it is to satisfy a user’s complicated requirements with a single service, this paper presents a QoS-aware method for sending agricultural information as a service and then combining those services, thus, known as service composition. The proposed work is divided into two phases. In the first phase, a fuzzy inference set is used to initialize the population whereas, in the second phase, the multi-objective evolutionary algorithm NSGA-II (Non-dominated sorting genetic algorithm) has been used to optimize the cost and time of services involved in apple crop production. Since evolutionary algorithms have a problem dealing with uncertainties so modification using fuzzy logic has been proposed to check its effectiveness in Service Composition Problem (SCP). In order to demonstrate the persuasiveness of our work, the proposed method is compared with the multi-objective genetic algorithm (MOGA), Gaining sharing knowledge (GSK) algorithm, and NSGA-II and it has been found that NSGA-II is giving more diversified and near to true Pareto solutions. Service composition (dpeaa)DE-He213 Fuzzy logic controller (dpeaa)DE-He213 NSGA-II (dpeaa)DE-He213 Internet of Things (dpeaa)DE-He213 Smart agriculture (dpeaa)DE-He213 Pathak, Bhupendra Kumar verfasserin aut Kumar, Rajiv verfasserin aut Enthalten in Operations research forum Springer International Publishing, 2020 5(2024), 2 vom: 09. Mai (DE-627)1669255581 (DE-600)2978290-9 2662-2556 nnns volume:5 year:2024 number:2 day:09 month:05 https://dx.doi.org/10.1007/s43069-024-00319-7 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-WIW 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_65 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_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_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_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 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_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 5 2024 2 09 05 |
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10.1007/s43069-024-00319-7 doi (DE-627)SPR055802931 (SPR)s43069-024-00319-7-e DE-627 ger DE-627 rakwb eng 510 VZ Sharma, Shalini verfasserin aut Multi-objective Service Composition Optimization in Smart Agriculture Using Fuzzy-Evolutionary Algorithm 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. 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 Agricultural applications can take advantage of improved services provided by the Internet of Things paradigms to manage data effectively. It is necessary to manage Quality of Service (QoS) characteristics to effectively monitor and measure the given services. Given how challenging it is to satisfy a user’s complicated requirements with a single service, this paper presents a QoS-aware method for sending agricultural information as a service and then combining those services, thus, known as service composition. The proposed work is divided into two phases. In the first phase, a fuzzy inference set is used to initialize the population whereas, in the second phase, the multi-objective evolutionary algorithm NSGA-II (Non-dominated sorting genetic algorithm) has been used to optimize the cost and time of services involved in apple crop production. Since evolutionary algorithms have a problem dealing with uncertainties so modification using fuzzy logic has been proposed to check its effectiveness in Service Composition Problem (SCP). In order to demonstrate the persuasiveness of our work, the proposed method is compared with the multi-objective genetic algorithm (MOGA), Gaining sharing knowledge (GSK) algorithm, and NSGA-II and it has been found that NSGA-II is giving more diversified and near to true Pareto solutions. Service composition (dpeaa)DE-He213 Fuzzy logic controller (dpeaa)DE-He213 NSGA-II (dpeaa)DE-He213 Internet of Things (dpeaa)DE-He213 Smart agriculture (dpeaa)DE-He213 Pathak, Bhupendra Kumar verfasserin aut Kumar, Rajiv verfasserin aut Enthalten in Operations research forum Springer International Publishing, 2020 5(2024), 2 vom: 09. Mai (DE-627)1669255581 (DE-600)2978290-9 2662-2556 nnns volume:5 year:2024 number:2 day:09 month:05 https://dx.doi.org/10.1007/s43069-024-00319-7 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-WIW 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_65 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_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_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_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 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_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 5 2024 2 09 05 |
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10.1007/s43069-024-00319-7 doi (DE-627)SPR055802931 (SPR)s43069-024-00319-7-e DE-627 ger DE-627 rakwb eng 510 VZ Sharma, Shalini verfasserin aut Multi-objective Service Composition Optimization in Smart Agriculture Using Fuzzy-Evolutionary Algorithm 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. 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 Agricultural applications can take advantage of improved services provided by the Internet of Things paradigms to manage data effectively. It is necessary to manage Quality of Service (QoS) characteristics to effectively monitor and measure the given services. Given how challenging it is to satisfy a user’s complicated requirements with a single service, this paper presents a QoS-aware method for sending agricultural information as a service and then combining those services, thus, known as service composition. The proposed work is divided into two phases. In the first phase, a fuzzy inference set is used to initialize the population whereas, in the second phase, the multi-objective evolutionary algorithm NSGA-II (Non-dominated sorting genetic algorithm) has been used to optimize the cost and time of services involved in apple crop production. Since evolutionary algorithms have a problem dealing with uncertainties so modification using fuzzy logic has been proposed to check its effectiveness in Service Composition Problem (SCP). In order to demonstrate the persuasiveness of our work, the proposed method is compared with the multi-objective genetic algorithm (MOGA), Gaining sharing knowledge (GSK) algorithm, and NSGA-II and it has been found that NSGA-II is giving more diversified and near to true Pareto solutions. Service composition (dpeaa)DE-He213 Fuzzy logic controller (dpeaa)DE-He213 NSGA-II (dpeaa)DE-He213 Internet of Things (dpeaa)DE-He213 Smart agriculture (dpeaa)DE-He213 Pathak, Bhupendra Kumar verfasserin aut Kumar, Rajiv verfasserin aut Enthalten in Operations research forum Springer International Publishing, 2020 5(2024), 2 vom: 09. Mai (DE-627)1669255581 (DE-600)2978290-9 2662-2556 nnns volume:5 year:2024 number:2 day:09 month:05 https://dx.doi.org/10.1007/s43069-024-00319-7 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-WIW 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_65 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_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_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_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 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_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 5 2024 2 09 05 |
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10.1007/s43069-024-00319-7 doi (DE-627)SPR055802931 (SPR)s43069-024-00319-7-e DE-627 ger DE-627 rakwb eng 510 VZ Sharma, Shalini verfasserin aut Multi-objective Service Composition Optimization in Smart Agriculture Using Fuzzy-Evolutionary Algorithm 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. 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 Agricultural applications can take advantage of improved services provided by the Internet of Things paradigms to manage data effectively. It is necessary to manage Quality of Service (QoS) characteristics to effectively monitor and measure the given services. Given how challenging it is to satisfy a user’s complicated requirements with a single service, this paper presents a QoS-aware method for sending agricultural information as a service and then combining those services, thus, known as service composition. The proposed work is divided into two phases. In the first phase, a fuzzy inference set is used to initialize the population whereas, in the second phase, the multi-objective evolutionary algorithm NSGA-II (Non-dominated sorting genetic algorithm) has been used to optimize the cost and time of services involved in apple crop production. Since evolutionary algorithms have a problem dealing with uncertainties so modification using fuzzy logic has been proposed to check its effectiveness in Service Composition Problem (SCP). In order to demonstrate the persuasiveness of our work, the proposed method is compared with the multi-objective genetic algorithm (MOGA), Gaining sharing knowledge (GSK) algorithm, and NSGA-II and it has been found that NSGA-II is giving more diversified and near to true Pareto solutions. Service composition (dpeaa)DE-He213 Fuzzy logic controller (dpeaa)DE-He213 NSGA-II (dpeaa)DE-He213 Internet of Things (dpeaa)DE-He213 Smart agriculture (dpeaa)DE-He213 Pathak, Bhupendra Kumar verfasserin aut Kumar, Rajiv verfasserin aut Enthalten in Operations research forum Springer International Publishing, 2020 5(2024), 2 vom: 09. Mai (DE-627)1669255581 (DE-600)2978290-9 2662-2556 nnns volume:5 year:2024 number:2 day:09 month:05 https://dx.doi.org/10.1007/s43069-024-00319-7 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-WIW 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_65 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_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_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_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 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_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 5 2024 2 09 05 |
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Sharma, Shalini |
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multi-objective service composition optimization in smart agriculture using fuzzy-evolutionary algorithm |
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Multi-objective Service Composition Optimization in Smart Agriculture Using Fuzzy-Evolutionary Algorithm |
| abstract |
Abstract Agricultural applications can take advantage of improved services provided by the Internet of Things paradigms to manage data effectively. It is necessary to manage Quality of Service (QoS) characteristics to effectively monitor and measure the given services. Given how challenging it is to satisfy a user’s complicated requirements with a single service, this paper presents a QoS-aware method for sending agricultural information as a service and then combining those services, thus, known as service composition. The proposed work is divided into two phases. In the first phase, a fuzzy inference set is used to initialize the population whereas, in the second phase, the multi-objective evolutionary algorithm NSGA-II (Non-dominated sorting genetic algorithm) has been used to optimize the cost and time of services involved in apple crop production. Since evolutionary algorithms have a problem dealing with uncertainties so modification using fuzzy logic has been proposed to check its effectiveness in Service Composition Problem (SCP). In order to demonstrate the persuasiveness of our work, the proposed method is compared with the multi-objective genetic algorithm (MOGA), Gaining sharing knowledge (GSK) algorithm, and NSGA-II and it has been found that NSGA-II is giving more diversified and near to true Pareto solutions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. 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 Agricultural applications can take advantage of improved services provided by the Internet of Things paradigms to manage data effectively. It is necessary to manage Quality of Service (QoS) characteristics to effectively monitor and measure the given services. Given how challenging it is to satisfy a user’s complicated requirements with a single service, this paper presents a QoS-aware method for sending agricultural information as a service and then combining those services, thus, known as service composition. The proposed work is divided into two phases. In the first phase, a fuzzy inference set is used to initialize the population whereas, in the second phase, the multi-objective evolutionary algorithm NSGA-II (Non-dominated sorting genetic algorithm) has been used to optimize the cost and time of services involved in apple crop production. Since evolutionary algorithms have a problem dealing with uncertainties so modification using fuzzy logic has been proposed to check its effectiveness in Service Composition Problem (SCP). In order to demonstrate the persuasiveness of our work, the proposed method is compared with the multi-objective genetic algorithm (MOGA), Gaining sharing knowledge (GSK) algorithm, and NSGA-II and it has been found that NSGA-II is giving more diversified and near to true Pareto solutions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. 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 Agricultural applications can take advantage of improved services provided by the Internet of Things paradigms to manage data effectively. It is necessary to manage Quality of Service (QoS) characteristics to effectively monitor and measure the given services. Given how challenging it is to satisfy a user’s complicated requirements with a single service, this paper presents a QoS-aware method for sending agricultural information as a service and then combining those services, thus, known as service composition. The proposed work is divided into two phases. In the first phase, a fuzzy inference set is used to initialize the population whereas, in the second phase, the multi-objective evolutionary algorithm NSGA-II (Non-dominated sorting genetic algorithm) has been used to optimize the cost and time of services involved in apple crop production. Since evolutionary algorithms have a problem dealing with uncertainties so modification using fuzzy logic has been proposed to check its effectiveness in Service Composition Problem (SCP). In order to demonstrate the persuasiveness of our work, the proposed method is compared with the multi-objective genetic algorithm (MOGA), Gaining sharing knowledge (GSK) algorithm, and NSGA-II and it has been found that NSGA-II is giving more diversified and near to true Pareto solutions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. 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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Multi-objective Service Composition Optimization in Smart Agriculture Using Fuzzy-Evolutionary Algorithm |
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Pathak, Bhupendra Kumar Kumar, Rajiv |
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10.1007/s43069-024-00319-7 |
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| score |
7.1683083 |