Prediction Models for Energy Efficient Data Aggregation in Wireless Sensor Network
Abstract In sensor networks, the periodically aggregated data often exhibit high temporal coherency. Huge energy consumption incurred in transmitting these redundant information results in network disconnection thereby leading to service disruption. In order to effectively manage the energy consumpt...
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| Autor*in: |
Sinha, Adwitiya [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2015 |
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| Übergeordnetes Werk: |
Enthalten in: Wireless personal communications - Springer US, 1994, 84(2015), 2 vom: 23. Mai, Seite 1325-1343 |
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| Übergeordnetes Werk: |
volume:84 ; year:2015 ; number:2 ; day:23 ; month:05 ; pages:1325-1343 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11277-015-2690-x |
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OLC2053792809 |
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| 520 | |a Abstract In sensor networks, the periodically aggregated data often exhibit high temporal coherency. Huge energy consumption incurred in transmitting these redundant information results in network disconnection thereby leading to service disruption. In order to effectively manage the energy consumption in concurrent data gathering rounds, temporal data prediction model is proposed. The proposed model provides near accurate predictions that successfully restricts redundant transmissions. The communication energy conserved owing to successful predictions helps to increase the number of data cycles considerably. In addition, an energy prediction-based cluster head rotation algorithm is also presented for load balancing within clusters. Experimental outcomes show that the proposed prediction model significantly improves energy conservation by providing successful predictions per data gathering cycle. Results reveal lower magnitude of prediction error as compared to certain existing prediction methods. | ||
| 650 | 4 | |a Wireless sensor network | |
| 650 | 4 | |a Temporal prediction | |
| 650 | 4 | |a Data aggregation | |
| 650 | 4 | |a Energy prediction | |
| 650 | 4 | |a Energy consumption | |
| 650 | 4 | |a Prediction accuracy | |
| 700 | 1 | |a Lobiyal, D. K. |4 aut | |
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10.1007/s11277-015-2690-x doi (DE-627)OLC2053792809 (DE-He213)s11277-015-2690-x-p DE-627 ger DE-627 rakwb eng 620 VZ Sinha, Adwitiya verfasserin aut Prediction Models for Energy Efficient Data Aggregation in Wireless Sensor Network 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract In sensor networks, the periodically aggregated data often exhibit high temporal coherency. Huge energy consumption incurred in transmitting these redundant information results in network disconnection thereby leading to service disruption. In order to effectively manage the energy consumption in concurrent data gathering rounds, temporal data prediction model is proposed. The proposed model provides near accurate predictions that successfully restricts redundant transmissions. The communication energy conserved owing to successful predictions helps to increase the number of data cycles considerably. In addition, an energy prediction-based cluster head rotation algorithm is also presented for load balancing within clusters. Experimental outcomes show that the proposed prediction model significantly improves energy conservation by providing successful predictions per data gathering cycle. Results reveal lower magnitude of prediction error as compared to certain existing prediction methods. Wireless sensor network Temporal prediction Data aggregation Energy prediction Energy consumption Prediction accuracy Lobiyal, D. K. aut Enthalten in Wireless personal communications Springer US, 1994 84(2015), 2 vom: 23. Mai, Seite 1325-1343 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:84 year:2015 number:2 day:23 month:05 pages:1325-1343 https://doi.org/10.1007/s11277-015-2690-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_4266 AR 84 2015 2 23 05 1325-1343 |
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10.1007/s11277-015-2690-x doi (DE-627)OLC2053792809 (DE-He213)s11277-015-2690-x-p DE-627 ger DE-627 rakwb eng 620 VZ Sinha, Adwitiya verfasserin aut Prediction Models for Energy Efficient Data Aggregation in Wireless Sensor Network 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract In sensor networks, the periodically aggregated data often exhibit high temporal coherency. Huge energy consumption incurred in transmitting these redundant information results in network disconnection thereby leading to service disruption. In order to effectively manage the energy consumption in concurrent data gathering rounds, temporal data prediction model is proposed. The proposed model provides near accurate predictions that successfully restricts redundant transmissions. The communication energy conserved owing to successful predictions helps to increase the number of data cycles considerably. In addition, an energy prediction-based cluster head rotation algorithm is also presented for load balancing within clusters. Experimental outcomes show that the proposed prediction model significantly improves energy conservation by providing successful predictions per data gathering cycle. Results reveal lower magnitude of prediction error as compared to certain existing prediction methods. Wireless sensor network Temporal prediction Data aggregation Energy prediction Energy consumption Prediction accuracy Lobiyal, D. K. aut Enthalten in Wireless personal communications Springer US, 1994 84(2015), 2 vom: 23. Mai, Seite 1325-1343 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:84 year:2015 number:2 day:23 month:05 pages:1325-1343 https://doi.org/10.1007/s11277-015-2690-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_4266 AR 84 2015 2 23 05 1325-1343 |
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10.1007/s11277-015-2690-x doi (DE-627)OLC2053792809 (DE-He213)s11277-015-2690-x-p DE-627 ger DE-627 rakwb eng 620 VZ Sinha, Adwitiya verfasserin aut Prediction Models for Energy Efficient Data Aggregation in Wireless Sensor Network 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract In sensor networks, the periodically aggregated data often exhibit high temporal coherency. Huge energy consumption incurred in transmitting these redundant information results in network disconnection thereby leading to service disruption. In order to effectively manage the energy consumption in concurrent data gathering rounds, temporal data prediction model is proposed. The proposed model provides near accurate predictions that successfully restricts redundant transmissions. The communication energy conserved owing to successful predictions helps to increase the number of data cycles considerably. In addition, an energy prediction-based cluster head rotation algorithm is also presented for load balancing within clusters. Experimental outcomes show that the proposed prediction model significantly improves energy conservation by providing successful predictions per data gathering cycle. Results reveal lower magnitude of prediction error as compared to certain existing prediction methods. Wireless sensor network Temporal prediction Data aggregation Energy prediction Energy consumption Prediction accuracy Lobiyal, D. K. aut Enthalten in Wireless personal communications Springer US, 1994 84(2015), 2 vom: 23. Mai, Seite 1325-1343 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:84 year:2015 number:2 day:23 month:05 pages:1325-1343 https://doi.org/10.1007/s11277-015-2690-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_4266 AR 84 2015 2 23 05 1325-1343 |
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10.1007/s11277-015-2690-x doi (DE-627)OLC2053792809 (DE-He213)s11277-015-2690-x-p DE-627 ger DE-627 rakwb eng 620 VZ Sinha, Adwitiya verfasserin aut Prediction Models for Energy Efficient Data Aggregation in Wireless Sensor Network 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract In sensor networks, the periodically aggregated data often exhibit high temporal coherency. Huge energy consumption incurred in transmitting these redundant information results in network disconnection thereby leading to service disruption. In order to effectively manage the energy consumption in concurrent data gathering rounds, temporal data prediction model is proposed. The proposed model provides near accurate predictions that successfully restricts redundant transmissions. The communication energy conserved owing to successful predictions helps to increase the number of data cycles considerably. In addition, an energy prediction-based cluster head rotation algorithm is also presented for load balancing within clusters. Experimental outcomes show that the proposed prediction model significantly improves energy conservation by providing successful predictions per data gathering cycle. Results reveal lower magnitude of prediction error as compared to certain existing prediction methods. Wireless sensor network Temporal prediction Data aggregation Energy prediction Energy consumption Prediction accuracy Lobiyal, D. K. aut Enthalten in Wireless personal communications Springer US, 1994 84(2015), 2 vom: 23. Mai, Seite 1325-1343 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:84 year:2015 number:2 day:23 month:05 pages:1325-1343 https://doi.org/10.1007/s11277-015-2690-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_4266 AR 84 2015 2 23 05 1325-1343 |
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10.1007/s11277-015-2690-x doi (DE-627)OLC2053792809 (DE-He213)s11277-015-2690-x-p DE-627 ger DE-627 rakwb eng 620 VZ Sinha, Adwitiya verfasserin aut Prediction Models for Energy Efficient Data Aggregation in Wireless Sensor Network 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract In sensor networks, the periodically aggregated data often exhibit high temporal coherency. Huge energy consumption incurred in transmitting these redundant information results in network disconnection thereby leading to service disruption. In order to effectively manage the energy consumption in concurrent data gathering rounds, temporal data prediction model is proposed. The proposed model provides near accurate predictions that successfully restricts redundant transmissions. The communication energy conserved owing to successful predictions helps to increase the number of data cycles considerably. In addition, an energy prediction-based cluster head rotation algorithm is also presented for load balancing within clusters. Experimental outcomes show that the proposed prediction model significantly improves energy conservation by providing successful predictions per data gathering cycle. Results reveal lower magnitude of prediction error as compared to certain existing prediction methods. Wireless sensor network Temporal prediction Data aggregation Energy prediction Energy consumption Prediction accuracy Lobiyal, D. K. aut Enthalten in Wireless personal communications Springer US, 1994 84(2015), 2 vom: 23. Mai, Seite 1325-1343 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:84 year:2015 number:2 day:23 month:05 pages:1325-1343 https://doi.org/10.1007/s11277-015-2690-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_4266 AR 84 2015 2 23 05 1325-1343 |
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Abstract In sensor networks, the periodically aggregated data often exhibit high temporal coherency. Huge energy consumption incurred in transmitting these redundant information results in network disconnection thereby leading to service disruption. In order to effectively manage the energy consumption in concurrent data gathering rounds, temporal data prediction model is proposed. The proposed model provides near accurate predictions that successfully restricts redundant transmissions. The communication energy conserved owing to successful predictions helps to increase the number of data cycles considerably. In addition, an energy prediction-based cluster head rotation algorithm is also presented for load balancing within clusters. Experimental outcomes show that the proposed prediction model significantly improves energy conservation by providing successful predictions per data gathering cycle. Results reveal lower magnitude of prediction error as compared to certain existing prediction methods. © Springer Science+Business Media New York 2015 |
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Abstract In sensor networks, the periodically aggregated data often exhibit high temporal coherency. Huge energy consumption incurred in transmitting these redundant information results in network disconnection thereby leading to service disruption. In order to effectively manage the energy consumption in concurrent data gathering rounds, temporal data prediction model is proposed. The proposed model provides near accurate predictions that successfully restricts redundant transmissions. The communication energy conserved owing to successful predictions helps to increase the number of data cycles considerably. In addition, an energy prediction-based cluster head rotation algorithm is also presented for load balancing within clusters. Experimental outcomes show that the proposed prediction model significantly improves energy conservation by providing successful predictions per data gathering cycle. Results reveal lower magnitude of prediction error as compared to certain existing prediction methods. © Springer Science+Business Media New York 2015 |
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Abstract In sensor networks, the periodically aggregated data often exhibit high temporal coherency. Huge energy consumption incurred in transmitting these redundant information results in network disconnection thereby leading to service disruption. In order to effectively manage the energy consumption in concurrent data gathering rounds, temporal data prediction model is proposed. The proposed model provides near accurate predictions that successfully restricts redundant transmissions. The communication energy conserved owing to successful predictions helps to increase the number of data cycles considerably. In addition, an energy prediction-based cluster head rotation algorithm is also presented for load balancing within clusters. Experimental outcomes show that the proposed prediction model significantly improves energy conservation by providing successful predictions per data gathering cycle. Results reveal lower magnitude of prediction error as compared to certain existing prediction methods. © Springer Science+Business Media New York 2015 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2053792809</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504075134.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11277-015-2690-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2053792809</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11277-015-2690-x-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sinha, Adwitiya</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Prediction Models for Energy Efficient Data Aggregation in Wireless Sensor Network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media New York 2015</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In sensor networks, the periodically aggregated data often exhibit high temporal coherency. Huge energy consumption incurred in transmitting these redundant information results in network disconnection thereby leading to service disruption. In order to effectively manage the energy consumption in concurrent data gathering rounds, temporal data prediction model is proposed. The proposed model provides near accurate predictions that successfully restricts redundant transmissions. The communication energy conserved owing to successful predictions helps to increase the number of data cycles considerably. In addition, an energy prediction-based cluster head rotation algorithm is also presented for load balancing within clusters. Experimental outcomes show that the proposed prediction model significantly improves energy conservation by providing successful predictions per data gathering cycle. 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K.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Wireless personal communications</subfield><subfield code="d">Springer US, 1994</subfield><subfield code="g">84(2015), 2 vom: 23. 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