Skip to main content

2018 | OriginalPaper | Buchkapitel

Structural Strength Recognizing System with Efficient Clustering Technique

verfasst von : Sumedha Sirsikar, Manoj Chandak

Erschienen in: Intelligent Computing and Information and Communication

Verlag: Springer Singapore

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

search-config
loading …

Abstract

Internet of Things (IoT) visualizes future, in which the objects of everyday life are equipped with sensor technology for digital communication. IoT supports the concept of smart city, which aims to provide different services for the administration of the city and for the citizens. The important application of IoT is Structural Strength Recognition (SSR). This approach is becoming popular to increase the safety of buildings and human life. Proper maintenance of historical buildings requires continuous monitoring and current conditions of it. Sensor nodes are used to collect data of these historical buildings or large structures. Structural strength recognition covers huge geographical area and it requires continuous monitoring of it. It involves more energy consumption during these activities. Hence, there is need for efficient energy management technique. Clustering is one of the important techniques for energy management in Wireless Sensor Networks (WSN). It helps in reducing the energy consumed in wireless data transmission. In this paper, SSR system is designed with efficient clustering algorithm for wide network and also finds out optimum number of clusters.

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!

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!

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!

Literatur
1.
Zurück zum Zitat Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014), Internet of things for smart cities. IEEE Internet of Things Journal, 1(1), pp. 22–32. Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014), Internet of things for smart cities. IEEE Internet of Things Journal, 1(1), pp. 22–32.
2.
Zurück zum Zitat Sasikumar, P., & Khara, S. (2012), K-means clustering in wireless sensor networks. Fourth international conference on Computational Intelligence and Communication Networks (CICN), IEEE, pp. 140–144. Sasikumar, P., & Khara, S. (2012), K-means clustering in wireless sensor networks. Fourth international conference on Computational Intelligence and Communication Networks (CICN), IEEE, pp. 140–144.
3.
Zurück zum Zitat Navjot Kaur Jassi, Sandeep Singh Wraich, (2014), A Review: An Improved K-means Clustering Technique in WSN, Proceedings of the International Conference on Advances in Engineering and Technology (ICAET). Navjot Kaur Jassi, Sandeep Singh Wraich, (2014), A Review: An Improved K-means Clustering Technique in WSN, Proceedings of the International Conference on Advances in Engineering and Technology (ICAET).
4.
Zurück zum Zitat Bholowalia, P., & Kumar, A. (2014). EBK-means: A clustering technique based on elbow method and k-means in Wireless Sensor Network, International Journal of Computer Applications, 105(9). Bholowalia, P., & Kumar, A. (2014). EBK-means: A clustering technique based on elbow method and k-means in Wireless Sensor Network, International Journal of Computer Applications, 105(9).
5.
Zurück zum Zitat Alrabea, A., Senthilkumar, A. V., Al-Shalabi, H., & Bader, A. (2013), Enhancing k-means algorithm with initial cluster centers derived from data partitioning along the data axis with PCA. Journal of Advances in Computer Networks, 1(2), pp. 137–142. Alrabea, A., Senthilkumar, A. V., Al-Shalabi, H., & Bader, A. (2013), Enhancing k-means algorithm with initial cluster centers derived from data partitioning along the data axis with PCA. Journal of Advances in Computer Networks, 1(2), pp. 137–142.
6.
Zurück zum Zitat Bhawna, Pathak, T., & Ranga, V., (2014), A Comprehensive Survey of Clustering Approaches in Wireless Sensor Networks, Elsevier Publications. Bhawna, Pathak, T., & Ranga, V., (2014), A Comprehensive Survey of Clustering Approaches in Wireless Sensor Networks, Elsevier Publications.
7.
Zurück zum Zitat Solaiman, B. F., & Sheta, A. F. Energy optimization in wireless sensor networks using a hybrid K-means PSO clustering algorithm. Solaiman, B. F., & Sheta, A. F. Energy optimization in wireless sensor networks using a hybrid K-means PSO clustering algorithm.
8.
Zurück zum Zitat Tong, W., Jiyi, W., He, X., Jinghua, Z., & Munyabugingo, C. (2013), A cross unequal clustering routing algorithm for sensor network. Measurement Science Review, 13(4), pp. 200–205. Tong, W., Jiyi, W., He, X., Jinghua, Z., & Munyabugingo, C. (2013), A cross unequal clustering routing algorithm for sensor network. Measurement Science Review, 13(4), pp. 200–205.
9.
Zurück zum Zitat Alahakoon, S., Preethichandra, D. M., & Ekanayake, E. M. (2009), Sensor network applications in structures–a survey. EJSE Special Issue: Sensor Network on Building Monitoring: From Theory to Real Application, pp. 1–10. Alahakoon, S., Preethichandra, D. M., & Ekanayake, E. M. (2009), Sensor network applications in structures–a survey. EJSE Special Issue: Sensor Network on Building Monitoring: From Theory to Real Application, pp. 1–10.
10.
Zurück zum Zitat Sirsikar, S., Chunawale, A., & Chandak, M. (2014), Self-organization Architecture and Model for Wireless Sensor Networks. International Conference on Electronic Systems, Signal Processing and Computing Technologies (ICESC), IEEE, pp. 204–208. Sirsikar, S., Chunawale, A., & Chandak, M. (2014), Self-organization Architecture and Model for Wireless Sensor Networks. International Conference on Electronic Systems, Signal Processing and Computing Technologies (ICESC), IEEE, pp. 204–208.
Metadaten
Titel
Structural Strength Recognizing System with Efficient Clustering Technique
verfasst von
Sumedha Sirsikar
Manoj Chandak
Copyright-Jahr
2018
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-7245-1_4

Premium Partner