Multiple Parameter Based Energy Balanced and Optimized Clustering for WSN to Enhance the Lifetime Using MADM Approaches
Abstract Efficient utilization of power has recently emerged as a critical issue in sensor networks that is addressed by efficient clustering techniques. In WSN, clustering process selects cluster heads (CHs) to control the topology and consumes the power effectively. The comprehensive evolution of...
Ausführliche Beschreibung
Autor*in: |
Rajpoot, Prince [verfasserIn] Dwivedi, Pragya [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2019 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Wireless personal communications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 106(2019), 2 vom: 02. März, Seite 829-877 |
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Übergeordnetes Werk: |
volume:106 ; year:2019 ; number:2 ; day:02 ; month:03 ; pages:829-877 |
Links: |
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DOI / URN: |
10.1007/s11277-019-06192-6 |
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Katalog-ID: |
SPR018606024 |
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520 | |a Abstract Efficient utilization of power has recently emerged as a critical issue in sensor networks that is addressed by efficient clustering techniques. In WSN, clustering process selects cluster heads (CHs) to control the topology and consumes the power effectively. The comprehensive evolution of CH selection process increases the lifetime of sensor nodes resulting in total enhancement of the lifetime of WSN. The efficiency of clustering is affected by many attributes like higher residual energy, distance from a normal node to CH, distance from CH to the Base Station, etc. The conflicting nature of these attributes makes it difficult to find the cooperation among these attributes for optimal clustering. In this paper, we have applied MADM approaches for optimal CH selection to enhance the lifetime of WSN by utilizing eleven attributes, these attributes have very important role in efficient power consumption during data set collection. The MADM approaches employed for ranking and choosing optimal CHs are: Technique for Order Preference by Similarity to Ideal Solution, Preference Ranking Organization METHod for Enrichment Evaluations, and Analytic Hierarchy Process. Results reveal that these eleven attributes helps the proposed approach to outperform over the other approaches such as LEACH, LEACH-C and EECS in terms of lifetime. | ||
650 | 4 | |a Wireless sensor network (WSN) |7 (dpeaa)DE-He213 | |
650 | 4 | |a Clustering |7 (dpeaa)DE-He213 | |
650 | 4 | |a MADM methods |7 (dpeaa)DE-He213 | |
650 | 4 | |a WSN lifetime |7 (dpeaa)DE-He213 | |
700 | 1 | |a Dwivedi, Pragya |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Wireless personal communications |d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 |g 106(2019), 2 vom: 02. März, Seite 829-877 |w (DE-627)271179120 |w (DE-600)1479327-1 |x 1572-834X |7 nnns |
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10.1007/s11277-019-06192-6 doi (DE-627)SPR018606024 (SPR)s11277-019-06192-6-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Rajpoot, Prince verfasserin aut Multiple Parameter Based Energy Balanced and Optimized Clustering for WSN to Enhance the Lifetime Using MADM Approaches 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Efficient utilization of power has recently emerged as a critical issue in sensor networks that is addressed by efficient clustering techniques. In WSN, clustering process selects cluster heads (CHs) to control the topology and consumes the power effectively. The comprehensive evolution of CH selection process increases the lifetime of sensor nodes resulting in total enhancement of the lifetime of WSN. The efficiency of clustering is affected by many attributes like higher residual energy, distance from a normal node to CH, distance from CH to the Base Station, etc. The conflicting nature of these attributes makes it difficult to find the cooperation among these attributes for optimal clustering. In this paper, we have applied MADM approaches for optimal CH selection to enhance the lifetime of WSN by utilizing eleven attributes, these attributes have very important role in efficient power consumption during data set collection. The MADM approaches employed for ranking and choosing optimal CHs are: Technique for Order Preference by Similarity to Ideal Solution, Preference Ranking Organization METHod for Enrichment Evaluations, and Analytic Hierarchy Process. Results reveal that these eleven attributes helps the proposed approach to outperform over the other approaches such as LEACH, LEACH-C and EECS in terms of lifetime. Wireless sensor network (WSN) (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 MADM methods (dpeaa)DE-He213 WSN lifetime (dpeaa)DE-He213 Dwivedi, Pragya verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 106(2019), 2 vom: 02. März, Seite 829-877 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:106 year:2019 number:2 day:02 month:03 pages:829-877 https://dx.doi.org/10.1007/s11277-019-06192-6 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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 53.00 ASE AR 106 2019 2 02 03 829-877 |
spelling |
10.1007/s11277-019-06192-6 doi (DE-627)SPR018606024 (SPR)s11277-019-06192-6-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Rajpoot, Prince verfasserin aut Multiple Parameter Based Energy Balanced and Optimized Clustering for WSN to Enhance the Lifetime Using MADM Approaches 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Efficient utilization of power has recently emerged as a critical issue in sensor networks that is addressed by efficient clustering techniques. In WSN, clustering process selects cluster heads (CHs) to control the topology and consumes the power effectively. The comprehensive evolution of CH selection process increases the lifetime of sensor nodes resulting in total enhancement of the lifetime of WSN. The efficiency of clustering is affected by many attributes like higher residual energy, distance from a normal node to CH, distance from CH to the Base Station, etc. The conflicting nature of these attributes makes it difficult to find the cooperation among these attributes for optimal clustering. In this paper, we have applied MADM approaches for optimal CH selection to enhance the lifetime of WSN by utilizing eleven attributes, these attributes have very important role in efficient power consumption during data set collection. The MADM approaches employed for ranking and choosing optimal CHs are: Technique for Order Preference by Similarity to Ideal Solution, Preference Ranking Organization METHod for Enrichment Evaluations, and Analytic Hierarchy Process. Results reveal that these eleven attributes helps the proposed approach to outperform over the other approaches such as LEACH, LEACH-C and EECS in terms of lifetime. Wireless sensor network (WSN) (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 MADM methods (dpeaa)DE-He213 WSN lifetime (dpeaa)DE-He213 Dwivedi, Pragya verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 106(2019), 2 vom: 02. März, Seite 829-877 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:106 year:2019 number:2 day:02 month:03 pages:829-877 https://dx.doi.org/10.1007/s11277-019-06192-6 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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 53.00 ASE AR 106 2019 2 02 03 829-877 |
allfields_unstemmed |
10.1007/s11277-019-06192-6 doi (DE-627)SPR018606024 (SPR)s11277-019-06192-6-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Rajpoot, Prince verfasserin aut Multiple Parameter Based Energy Balanced and Optimized Clustering for WSN to Enhance the Lifetime Using MADM Approaches 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Efficient utilization of power has recently emerged as a critical issue in sensor networks that is addressed by efficient clustering techniques. In WSN, clustering process selects cluster heads (CHs) to control the topology and consumes the power effectively. The comprehensive evolution of CH selection process increases the lifetime of sensor nodes resulting in total enhancement of the lifetime of WSN. The efficiency of clustering is affected by many attributes like higher residual energy, distance from a normal node to CH, distance from CH to the Base Station, etc. The conflicting nature of these attributes makes it difficult to find the cooperation among these attributes for optimal clustering. In this paper, we have applied MADM approaches for optimal CH selection to enhance the lifetime of WSN by utilizing eleven attributes, these attributes have very important role in efficient power consumption during data set collection. The MADM approaches employed for ranking and choosing optimal CHs are: Technique for Order Preference by Similarity to Ideal Solution, Preference Ranking Organization METHod for Enrichment Evaluations, and Analytic Hierarchy Process. Results reveal that these eleven attributes helps the proposed approach to outperform over the other approaches such as LEACH, LEACH-C and EECS in terms of lifetime. Wireless sensor network (WSN) (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 MADM methods (dpeaa)DE-He213 WSN lifetime (dpeaa)DE-He213 Dwivedi, Pragya verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 106(2019), 2 vom: 02. März, Seite 829-877 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:106 year:2019 number:2 day:02 month:03 pages:829-877 https://dx.doi.org/10.1007/s11277-019-06192-6 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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 53.00 ASE AR 106 2019 2 02 03 829-877 |
allfieldsGer |
10.1007/s11277-019-06192-6 doi (DE-627)SPR018606024 (SPR)s11277-019-06192-6-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Rajpoot, Prince verfasserin aut Multiple Parameter Based Energy Balanced and Optimized Clustering for WSN to Enhance the Lifetime Using MADM Approaches 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Efficient utilization of power has recently emerged as a critical issue in sensor networks that is addressed by efficient clustering techniques. In WSN, clustering process selects cluster heads (CHs) to control the topology and consumes the power effectively. The comprehensive evolution of CH selection process increases the lifetime of sensor nodes resulting in total enhancement of the lifetime of WSN. The efficiency of clustering is affected by many attributes like higher residual energy, distance from a normal node to CH, distance from CH to the Base Station, etc. The conflicting nature of these attributes makes it difficult to find the cooperation among these attributes for optimal clustering. In this paper, we have applied MADM approaches for optimal CH selection to enhance the lifetime of WSN by utilizing eleven attributes, these attributes have very important role in efficient power consumption during data set collection. The MADM approaches employed for ranking and choosing optimal CHs are: Technique for Order Preference by Similarity to Ideal Solution, Preference Ranking Organization METHod for Enrichment Evaluations, and Analytic Hierarchy Process. Results reveal that these eleven attributes helps the proposed approach to outperform over the other approaches such as LEACH, LEACH-C and EECS in terms of lifetime. Wireless sensor network (WSN) (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 MADM methods (dpeaa)DE-He213 WSN lifetime (dpeaa)DE-He213 Dwivedi, Pragya verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 106(2019), 2 vom: 02. März, Seite 829-877 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:106 year:2019 number:2 day:02 month:03 pages:829-877 https://dx.doi.org/10.1007/s11277-019-06192-6 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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 53.00 ASE AR 106 2019 2 02 03 829-877 |
allfieldsSound |
10.1007/s11277-019-06192-6 doi (DE-627)SPR018606024 (SPR)s11277-019-06192-6-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Rajpoot, Prince verfasserin aut Multiple Parameter Based Energy Balanced and Optimized Clustering for WSN to Enhance the Lifetime Using MADM Approaches 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Efficient utilization of power has recently emerged as a critical issue in sensor networks that is addressed by efficient clustering techniques. In WSN, clustering process selects cluster heads (CHs) to control the topology and consumes the power effectively. The comprehensive evolution of CH selection process increases the lifetime of sensor nodes resulting in total enhancement of the lifetime of WSN. The efficiency of clustering is affected by many attributes like higher residual energy, distance from a normal node to CH, distance from CH to the Base Station, etc. The conflicting nature of these attributes makes it difficult to find the cooperation among these attributes for optimal clustering. In this paper, we have applied MADM approaches for optimal CH selection to enhance the lifetime of WSN by utilizing eleven attributes, these attributes have very important role in efficient power consumption during data set collection. The MADM approaches employed for ranking and choosing optimal CHs are: Technique for Order Preference by Similarity to Ideal Solution, Preference Ranking Organization METHod for Enrichment Evaluations, and Analytic Hierarchy Process. Results reveal that these eleven attributes helps the proposed approach to outperform over the other approaches such as LEACH, LEACH-C and EECS in terms of lifetime. Wireless sensor network (WSN) (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 MADM methods (dpeaa)DE-He213 WSN lifetime (dpeaa)DE-He213 Dwivedi, Pragya verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 106(2019), 2 vom: 02. März, Seite 829-877 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:106 year:2019 number:2 day:02 month:03 pages:829-877 https://dx.doi.org/10.1007/s11277-019-06192-6 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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 53.00 ASE AR 106 2019 2 02 03 829-877 |
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Rajpoot, Prince |
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Rajpoot, Prince ddc 620 bkl 53.00 misc Wireless sensor network (WSN) misc Clustering misc MADM methods misc WSN lifetime Multiple Parameter Based Energy Balanced and Optimized Clustering for WSN to Enhance the Lifetime Using MADM Approaches |
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620 ASE 53.00 bkl Multiple Parameter Based Energy Balanced and Optimized Clustering for WSN to Enhance the Lifetime Using MADM Approaches Wireless sensor network (WSN) (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 MADM methods (dpeaa)DE-He213 WSN lifetime (dpeaa)DE-He213 |
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Multiple Parameter Based Energy Balanced and Optimized Clustering for WSN to Enhance the Lifetime Using MADM Approaches |
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Multiple Parameter Based Energy Balanced and Optimized Clustering for WSN to Enhance the Lifetime Using MADM Approaches |
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multiple parameter based energy balanced and optimized clustering for wsn to enhance the lifetime using madm approaches |
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Multiple Parameter Based Energy Balanced and Optimized Clustering for WSN to Enhance the Lifetime Using MADM Approaches |
abstract |
Abstract Efficient utilization of power has recently emerged as a critical issue in sensor networks that is addressed by efficient clustering techniques. In WSN, clustering process selects cluster heads (CHs) to control the topology and consumes the power effectively. The comprehensive evolution of CH selection process increases the lifetime of sensor nodes resulting in total enhancement of the lifetime of WSN. The efficiency of clustering is affected by many attributes like higher residual energy, distance from a normal node to CH, distance from CH to the Base Station, etc. The conflicting nature of these attributes makes it difficult to find the cooperation among these attributes for optimal clustering. In this paper, we have applied MADM approaches for optimal CH selection to enhance the lifetime of WSN by utilizing eleven attributes, these attributes have very important role in efficient power consumption during data set collection. The MADM approaches employed for ranking and choosing optimal CHs are: Technique for Order Preference by Similarity to Ideal Solution, Preference Ranking Organization METHod for Enrichment Evaluations, and Analytic Hierarchy Process. Results reveal that these eleven attributes helps the proposed approach to outperform over the other approaches such as LEACH, LEACH-C and EECS in terms of lifetime. |
abstractGer |
Abstract Efficient utilization of power has recently emerged as a critical issue in sensor networks that is addressed by efficient clustering techniques. In WSN, clustering process selects cluster heads (CHs) to control the topology and consumes the power effectively. The comprehensive evolution of CH selection process increases the lifetime of sensor nodes resulting in total enhancement of the lifetime of WSN. The efficiency of clustering is affected by many attributes like higher residual energy, distance from a normal node to CH, distance from CH to the Base Station, etc. The conflicting nature of these attributes makes it difficult to find the cooperation among these attributes for optimal clustering. In this paper, we have applied MADM approaches for optimal CH selection to enhance the lifetime of WSN by utilizing eleven attributes, these attributes have very important role in efficient power consumption during data set collection. The MADM approaches employed for ranking and choosing optimal CHs are: Technique for Order Preference by Similarity to Ideal Solution, Preference Ranking Organization METHod for Enrichment Evaluations, and Analytic Hierarchy Process. Results reveal that these eleven attributes helps the proposed approach to outperform over the other approaches such as LEACH, LEACH-C and EECS in terms of lifetime. |
abstract_unstemmed |
Abstract Efficient utilization of power has recently emerged as a critical issue in sensor networks that is addressed by efficient clustering techniques. In WSN, clustering process selects cluster heads (CHs) to control the topology and consumes the power effectively. The comprehensive evolution of CH selection process increases the lifetime of sensor nodes resulting in total enhancement of the lifetime of WSN. The efficiency of clustering is affected by many attributes like higher residual energy, distance from a normal node to CH, distance from CH to the Base Station, etc. The conflicting nature of these attributes makes it difficult to find the cooperation among these attributes for optimal clustering. In this paper, we have applied MADM approaches for optimal CH selection to enhance the lifetime of WSN by utilizing eleven attributes, these attributes have very important role in efficient power consumption during data set collection. The MADM approaches employed for ranking and choosing optimal CHs are: Technique for Order Preference by Similarity to Ideal Solution, Preference Ranking Organization METHod for Enrichment Evaluations, and Analytic Hierarchy Process. Results reveal that these eleven attributes helps the proposed approach to outperform over the other approaches such as LEACH, LEACH-C and EECS in terms of lifetime. |
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2 |
title_short |
Multiple Parameter Based Energy Balanced and Optimized Clustering for WSN to Enhance the Lifetime Using MADM Approaches |
url |
https://dx.doi.org/10.1007/s11277-019-06192-6 |
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author2 |
Dwivedi, Pragya |
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Dwivedi, Pragya |
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doi_str |
10.1007/s11277-019-06192-6 |
up_date |
2024-07-03T20:54:29.454Z |
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score |
7.398386 |