Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Wireless Personal Communications
Erschienen in: Wireless Personal Communications 2/2015

01.09.2015

Prediction Models for Energy Efficient Data Aggregation in Wireless Sensor Network

verfasst von: Adwitiya Sinha, D. K. Lobiyal

Erschienen in: Wireless Personal Communications | Ausgabe 2/2015

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.
Vorheriger Artikel Opportunistic Sub-channel and Transmit Power Allocation in an OFDMA Based Cognitive Femtocell Network
Nächster Artikel Secret Key Cryptosystem Based on Non-systematic Polar Codes

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Akkaya, K., Demirbas, M., & Aygun, R. S. (2008). The impact of data aggregation on the performance of wireless ssensor networks. Wireless Communications and Mobile Computing, 8, 171–193.CrossRef
2.
Rajagopalan, R., & Varshney, P. K. (2006). Data-aggregation techniques in sensor networks: A survey. IEEE Communications Surveys and Tutorials, 4th Quarter, 8(4), 48–63.CrossRef
3.
Chen, Y., Shu, J., Zhang, S., Liu, L., & Sun, L. (2009). Data fusion in wireless sensor networks. In IEEE second international symposium on electronic commerce and security, pp. 504–509.
4.
Pal, R., Gupta, B., Prasad, N R., & Prasad, R. (2009). Efficient data processing in ultralow power wireless networks: Ideas from compressed sensing. In 2nd international symposium on applied sciences in biomedical and communication technologies, pp. 1–2.
5.
Guo, W., Xiong, N., Vasilakos, A. V., Chen, G., & Cheng, H. (2011). Multi-source temporal data aggregation in wireless sensor networks. Wireless Personal Communications, 56, 359–370.CrossRef
6.
Chatterjea, S., Nieberg, T., Meratnia, N., & Havinga, P. (2008). A distributed and self-organizing scheduling algorithm for energy-efficient data aggregation in wireless sensor networks. ACM Transactions on Sensor Networks, 4(4), 20:1–20:41.CrossRef
7.
Kalpakis, K. (2010). Everywhere sparse approximately optimal minimum energy data gathering and aggregation in sensor networks. ACM Transaction on Sensor Network, 7(1), 9-2–9-26.
8.
Boulis, A., Ganeriwal, S., & Srivastava, M. B. (2003). Aggregation in sensor networks: An energy–accuracy trade-off. Ad Hoc Networks, 1, 317–331.CrossRef
9.
Yao, Y., & Giannakis, B. B. (2005). Energy-efficient scheduling for wireless sensor networks. IEEE Transactions on Communications, 53(8), 54–61.MathSciNetCrossRef
10.
Wang, X., Ma, J.-J., Wang, S., & Bi, D.-W. (2007). Prediction-based dynamic energy management in wireless sensor networks. Sensors, 7, 251–266.CrossRef
11.
Zhao, J., Liu, H., Li, Z., & Li, W. (2013). Periodic data prediction algorithm in wireless sensor networks. Advances in Wireless Sensor Networks Communications in Computer and Information Science, 334, 695–701.CrossRef
12.
Borgnea, Y.-A. L., Santinib, S., & Bontempi, G. (2007). Adaptive model selection for time series prediction in wireless sensor networks. Signal Processing, 87(12), 3010–3020.CrossRef
13.
Hastie, T., Tibsharani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.CrossRef
14.
Navon, I. M. (2009). Data Assimilation for numerical weather prediction: A review. In Data assimilation for atmospheric, oceanic and hydrologic applications, pp. 21–65.
15.
Wang, G., Wang, H., Cao, J., & Guo, M. (2007). Energy-efficient dual prediction-based data gathering for environmental monitoring applications. In IEEE WCNC, pp. 3516–3521.
16.
Blaß, E.-O., Horneber, J., & Zitterbart, M. (2008). Analyzing data prediction in wireless sensor networks. In IEEE vehicular technology conference, pp. 86–87.
17.
Kang, J., Tang, L., Zuo, X., & Li, H. (2009). Grey kernel partial least squares-based prediction for temporal data aggregation in sensor networks. IEEE International Conference Intelligent Computing and Intelligent Systems, 3, 38–42.CrossRef
18.
Meng, L., Zhang, H., & Zou, Y. (2011). A data aggregation transfer protocol based on clustering and data prediction in wireless sensor networks. In 7th IEEE international conference on wireless communications, networking and mobile computing (WiCOM), pp. 1–5.
19.
Jiang, H., Jin, S., & Wang, C. (2011). Prediction or not? An energy-efficient framework for clustering-based data collection in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 22(6), 1064–1071.CrossRef
20.
Tulone, D., & Madden, S. (2006). PAQ: Time series forecasting for approximate query answering in sensor networks. In 3rd European conference on wireless sensor networks, pp. 21–37.
21.
Tulone, D., & Madden, S. (2006). An energy-efficient querying framework in sensor networks for detecting node similarities. In 9th international ACM symposium on modeling, analysis and simulation of wireless and mobile systems, pp. 291–300.
22.
Wei, G., Ling, Y., Guo, B., Xiao, B., & Vasilakos, A. V. (2011). Prediction based data aggregation in wireless sensor network: Combining grey model and Kalman Filter. Computer Communications, 34, 793–802.CrossRef
23.
Edara, P., Limaye, A., & Ramamritham, K. (2008). Asynchronous in-network prediction: Efficient aggregation in sensor networks. ACM Transactions on Sensor Networks, 4(4), 25–34.CrossRef
24.
Sinha, A., & Lobiyal, D. K. (2014). Probabilistic data aggregation in information-based clustered sensor network. Wireless Personal Communications, 77(2), 1287–1310.CrossRef
25.
Fall, K., & Varadhan, K. (2009). The NS manual. The VINT project.
26.
Altman, E., & Jemenez, T. (2003). NS simulator for beginners.
27.
Issariyakul, T., & Hossain, E. (2009). Introduction to network simulator NS2.
28.
Metadaten
Titel
Prediction Models for Energy Efficient Data Aggregation in Wireless Sensor Network
verfasst von
Adwitiya Sinha
D. K. Lobiyal
Publikationsdatum
01.09.2015
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 2/2015
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-015-2690-x

Weitere Artikel der Ausgabe 2/2015

Wireless Personal Communications 2/2015 Zur Ausgabe

OriginalPaper

Globally Optimal Cooperation in Dense Cognitive Radio Networks

OriginalPaper

Channel Modeling and Analysis for Wireless Underground Sensor Networks in Water Medium Using Electromagnetic Waves in the 300–700 MHz Range

OriginalPaper

TCPNC-DGSA: Efficient Network Coding Scheme for TCP in Multi-hop Cognitive Radio Networks

OriginalPaper

Investigation on Cylindrical Dielectric Resonator Antenna with Metamaterial Superstrate

OriginalPaper

A Clustering Routing Protocol for WSN Based on Type-2 Fuzzy Logic and Ant Colony Optimization

OriginalPaper

Wideband Power Spectrum Sensing for Cognitive Radios Based on Sub-Nyquist Sampling

Neuer Inhalt

    Bildnachweise