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
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Telecommunication Systems

23.04.2024

Data fusion algorithm of wireless sensor network based on clustering and fuzzy logic

verfasst von: Xiuwu Yu, Wei Peng, Ke Zhang, Zixiang Zhou, Yong Liu

Erschienen in: Telecommunication Systems

Einloggen

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

search-config
loading …

Abstract

In order to reduce network energy consumption and prolong the network lifetime in wireless sensor networks, a data fusion algorithm named CFLDF is proposed. Firstly, upon completion of the arrangement of network nodes, network clustering is achieved using fuzzy c-means optimized by the improved butterfly optimization algorithm, and a data fusion model is established on the clustering structure. Then, reliable data is sent to the cluster head by the nodes with the assistance of a fuzzy logic controller, and data fusion is performed by the cluster head using a fuzzy logic algorithm. Finally, cluster heads transmit the fused data to the base station. Finally, the fused data is transmitted to the base station by the cluster heads. Simulation experiments are conducted to evaluate the CFLDF algorithm against the LEACH, LEACH-C, and SEECP algorithms. The results demonstrate that network energy consumption is effectively reduced and the network lifetime is extended by the CFLDF algorithm.

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

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 "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"

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.
Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.CrossRef
2.
Xiu-Wu, Y. U., Hao, Y. U., Yong, L., & Ren-rong, X. (2020). A clustering routing algorithm based on wolf pack algorithm for heterogeneous wireless sensor networks. Computer Networks, 167, 106994.CrossRef
3.
Izadi, D., Abawajy, J. H., Ghanavati, S., & Herawan, T. (2015). A data fusion method in wireless sensor networks. Sensors, 15(2), 2964–2979.CrossRef
4.
Dhanaraj, R. K., Lalitha, K., Anitha, S., Khaitan, S., Gupta, P., & Goyal, M. K. (2021). Hybrid and dynamic clustering based data aggregation and routing for wireless sensor networks. Journal of Intelligent & Fuzzy Systems, 40(6), 10751–10765.CrossRef
5.
Sun, G., Zhang, Z., Zheng, B., & Li, Y. (2019). Multi-sensor data fusion algorithm based on trust degree and improved genetics. Sensors, 19(9), 2139.CrossRef
6.
Zhang, Y., Yang, W., Han, D., & Kim, Y. I. (2014). An integrated environment monitoring system for underground coal mines—Wireless sensor network subsystem with multi-parameter monitoring. Sensors, 14(7), 13149–13170.CrossRef
7.
Xiao, X., Huang, H., & Wang, W. (2020). Underwater wireless sensor networks: An energy-efficient clustering routing protocol based on data fusion and genetic algorithms. Applied Sciences, 11(1), 312.CrossRef
8.
Liu, X. (2012). A survey on clustering routing protocols in wireless sensor networks. Sensors, 12(8), 11113–11153.CrossRef
9.
Goyal, N., Dave, M., & Verma, A. K. (2017). Improved data aggregation for cluster based underwater wireless sensor networks. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 87, 235–245.CrossRef
10.
Sun, Y., Luo, H., & Das, S. K. (2012). A trust-based framework for fault-tolerant data aggregation in wireless multimedia sensor networks. IEEE Transactions on Dependable and Secure Computing, 9(6), 785–797.CrossRef
11.
Ounoughi, C., & Yahia, S. B. (2023). Data fusion for ITS: A systematic literature review. Information Fusion, 89, 267–291.CrossRef
12.
Abdulsalam, H. M., Ali, B. A., & AlRoumi, E. (2018). Usage of mobile elements in internet of things environment for data aggregation in wireless sensor networks. Computers & Electrical Engineering, 72, 789–807.CrossRef
13.
Liu, J., Huang, J., Sun, R., Yu, H., & Xiao, R. (2020). Data fusion for multi-source sensors using GA-PSO-BP neural network. IEEE Transactions on Intelligent Transportation Systems, 22(10), 6583–6598.CrossRef
14.
Hégarat-Mascle, L., Richard, D., & Ottlé, C. (2003). Multi-scale data fusion using Dempster–Shafer evidence theory. Integrated Computer-Aided Engineering, 10(1), 9–22.CrossRef
15.
Sasiadek, J. Z., & Hartana, P. (2000). Sensor data fusion using Kalman filter. In Proceedings of the third international conference on information fusion (vol. 2, pp. WED5–19). IEEE.
16.
Koks, D., & Challa, S. (2003). An introduction to Bayesian and Dempster–Shafer data fusion. DSTO Systems Sciences Laboratory.
17.
Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences (p. 10). IEEE.
18.
Zhang, K., Zhang, G., Yu, X., Hu, S., & Li, M. (2022). Clustering the sensor networks based on energy-aware affinity propagation. Computer Networks, 207, 108853.CrossRef
19.
Tang, X., Zhang, M., Yu, P., Liu, W., Cao, N., & Xu, Y. (2020). A nonuniform clustering routing algorithm based on an improved K-means algorithm. Computers, Materials & Continua, 64(3), 66.CrossRef
20.
Chang, L., Li, F., Niu, X., & Zhu, J. (2022). On an improved clustering algorithm based on node density for WSN routing protocol. Cluster Computing, 25(4), 3005–3017.CrossRef
21.
Anzola, J., Pascual, J., Tarazona, G., & Gonzalez Crespo, R. (2018). A clustering WSN routing protocol based on kd tree algorithm. Sensors, 18(9), 2899.CrossRef
22.
Collotta, M., Pau, G., & Bobovich, A. V. (2017). A fuzzy data fusion solution to enhance the QoS and the energy consumption in wireless sensor networks. Wireless Communications and Mobile Computing, 66, 7.
23.
Larios, D. F., Barbancho, J., Rodríguez, G., Sevillano, J. L., Molina, F. J., & León, C. (2012). Energy efficient wireless sensor network communications based on computational intelligent data fusion for environmental monitoring. IET Communications, 6(14), 2189–2197.CrossRef
24.
Luo, J., & Cai, J. (2015). A dynamic virtual force-based data aggregation algorithm for wireless sensor networks. International Journal of Distributed Sensor Networks, 11(5), 814184.CrossRef
25.
Rahman, H., Ahmed, N., & Hussain, I. (2016). Comparison of data aggregation techniques in Internet of Things (IoT). In 2016 International conference on wireless communications, signal processing and networking (WiSPNET) (pp. 1296–1300). IEEE.
26.
Ruspini, E. H. (1970). Numerical methods for fuzzy clustering. Information Sciences, 2(3), 319–350.CrossRef
27.
Arora, S., & Singh, S. (2019). Butterfly optimization algorithm: A novel approach for global optimization. Soft Computing, 23, 715–734.CrossRef
28.
Mirjalili, S. (2016). SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120–133.CrossRef
29.
Mendel, J. M. (1995). Fuzzy logic systems for engineering: A tutorial. Proceedings of the IEEE, 83(3), 345–377.CrossRef
30.
Heinzelman, W. B. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 2, 66.
31.
Mittal, N., Singh, U., & Sohi, B. S. (2017). A stable energy efficient clustering protocol for wireless sensor networks. Wireless Networks, 23, 1809–1821.CrossRef
Metadaten
Titel
Data fusion algorithm of wireless sensor network based on clustering and fuzzy logic
verfasst von
Xiuwu Yu
Wei Peng
Ke Zhang
Zixiang Zhou
Yong Liu
Publikationsdatum
23.04.2024
Verlag
Springer US
Erschienen in
Telecommunication Systems
Print ISSN: 1018-4864
Elektronische ISSN: 1572-9451
DOI
https://doi.org/10.1007/s11235-024-01141-6