Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Wireless Personal Communications
Published in: Wireless Personal Communications 1/2020

11-01-2020

Adaptive Buffering and Fuzzy Based Multilevel Clustering for Energy Efficient Wireless Sensor Network

Authors: T. Shankar, A. Rajesh, R. Mageshvaran

Published in: Wireless Personal Communications | Issue 1/2020

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Wireless sensor network consists of a number of power constrained sensor nodes that sense data from the environment. The collected data is directed to the base station in a harmonized way. Under such circumstances, the foremost challenges of sensor networks are limited energy, system lifetime, latency, quality of information, and limited communication bandwidth. Clustering methods enable to reuse the bandwidth and better resource allocation in order to maintain stable power control. In this paper the election of cluster head among the region cluster members is carried out through Adaptive Buffering with Fuzzy based Multilevel clustering (ABFMC). The proposed algorithm facilitates all nodes to communicate with the base station through a unique number of buffer nodes. Here, the decision based on distance factor is made by the selection of transmission through cluster head. Simulation results show that the proposed ABFMC algorithm provides better network lifetime and efficient energy distribution among the nodes.
previous article An Expert Approach for Data Flow Prediction: Case Study of Wireless Sensor Networks
next article Load Balanced Clustering Based on Imperialist Competitive Algorithm in Wireless Sensor Networks

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
Literature
1.
Akyildiz, F., & Kasimoglu, I. H. (2004). Wireless sensor and actor networks: Research challenges. Ad Hoc Networks,2(4), 351–367.CrossRef
2.
Poonguzhali, P. K., Ananthamoorthy, N. P., & Jabakani, S. J. (2015). Design challenges and comprehensive study on cluster based routing protocol in wireless sensor network. Int. Journal of Advanced Research in Computer and Communication Engineering, 4(1), 5–11.CrossRef
3.
Kumar, P., & Chand, N. (2013). Clustering in wireless multimedia sensor networks. Journal of Sensor Technology,3, 126–132. (Published Online December 2013).CrossRef
4.
Liu, X. (2012). A survey on clustering routing algorithms in wireless sensor networks. Guangzhou: School of Electronic and Information Engineering, South China University of Technology.
5.
Heinzelman, W. R., Chandrakasan, A., & Balakrishan, H. (2002). Energy efficient communication algorithm for wireless micro sensor networks. Communications Review,6, 28–36. (Proceedings of IEEE HICSS (January 2000)).
6.
SEP: A Stable Election Algorithm for clustered heterogeneous wireless sensor networks GEORGIOS SMARAGDAKIS IBRAHIM MATTA AZER BESTAVROS Computer Science Department Boston University (2004).
7.
Qin, M., & Zimmermann, R. (2007). VCA: An energy efficient voting based clustering algorithm for sensor networks. Journal of Universal Computer Science,13(1), 87–109.
8.
Chatterjee, M., Das, S. K., & Turgut, D. (2002). WCA: A weighted clustering algorithm for mobile ad hoc networks. Journal of Cluster Computing (Special Issue on Mobile Ad hoc Networks),5(2), 193–204.
9.
Banerjee, S., & Khuller, S. (2001). A clustering scheme for hierarchical control in multi-hop wireless networks. IEEE INFOCOM, pp. 1028–1037.
10.
Ding, P., Holliday, J., & Celik, A. (2005). Distributed energy-efficient hierarchical clustering for wireless sensor networks. In International conference on distributed computing in sensor systems (pp. 322–339).
11.
Ye, M., Li, C., Chen, G., & Wu, J. (2005). EECS: An energy efficient clustering scheme in wireless sensor networks. In Proceedings of the 24th IEEE international performance, computing, and communications conference (IPCCC), Phoenix, AZ, USA, 7–9 April 2005, pp. 535–540.
12.
Soro, S., & Heinzelman, W. (2005). Prolonging the lifetime of wireless sensor networks via unequal clustering. In Proceedings of the 5th IEEE international workshop on algorithms for wireless, mobile, ad hoc and sensor networks (WMAN), Denver, CO, USA, 4–8 April 2005, pp. 236–243.
13.
Li, C. F., Ye, M., Chen, G. H., & Wu, J. (2005). An energy-efficient unequal clustering mechanism for wireless sensor networks. In Proceedings of the 2nd IEEE international conference on mobile ad hoc and sensor systems conference (MASS), Washington, DC, 7–10 November 2005, pp. 596–604.
14.
Kim, J. M., Park, S. H., Han, Y. J., & Chung, T. M. (2008). CHEF, cluster head election mechanism using fuzzy logic in wireless sensor networks. In Proceedings of the ICACT, pp. 654–659.
15.
Gupta, I., Riordan, D., & Sampalli, S. (2005). Cluster-head election using fuzzy logic for wireless sensor networks. In Proceedings of the 3rd annual communication networks and services research conference, 2005, pp. 255–260.
16.
Buttyan, L., & Schaffer, P. (2010). PANEL: Position-based aggregator node election in wireless sensor networks. International Journal of Distributed Sensor Networks,2010, 1–16.
17.
Chan, H., & Perrig, A. (2004). ACE: An emergent algorithm for highly uniform cluster formation. In Proceedings of the 1st European workshop on sensor networks (EWSN), Berlin, Germany, 19–21 January 2004, pp. 154–171.
18.
Murugunathan, S. D., Ma, D. C. F., Bhasin, R. I., & Fapajuwo, A. O. (2005). A centralized energy-efficient routing algorithm for wireless sensor networks. IEEE Radio Communications,43, S8–S13.CrossRef
19.
Manjeshwar, E., & Agrawal, D. P. (2001). TEEN: A routing algorithm for enhanced efficiency in wireless sensor networks. In Proceedings of the 15th international parallel and distributed processing symposium (IPDPS), San Francisco, CA, USA, 23–27 April 2001, pp. 2009–2015.
20.
Jung, S., Han, Y., & Chung, T. (2007). The concentric clustering scheme for efficient energy consumption in the PEGASIS. In Proceedings of the 9th international conference on advanced communication technology, Gangwon-Do, Korea, 12–14 February 2007, pp. 260–265.
21.
Shankar, T., Karthikeyan, A., Sivasankar, P., & Rajeev, R. N. (2015). Implementation of smart sleep mechanism and hybrid data collection technique for maximizing network lifetime in WSN’s. Indian Journal of Science and Technology, 8(S9), 1–8.CrossRef
22.
Shankar, T., Karthikeyan, A., Sivasankar, P., & Rajesh, A. (2017). Hybrid approach for optimal cluster head selection in wsn using leach and monkey search algorithms. Journal of Engineering Science and Technology,12(2), 506–517.
23.
Shankar, T., & Shanmugavel, S. (2013). Hybrid approach for energy optimization in wireless sensor network using ABC and firefly. International Review on Computers and Software,8(10), 2335–2341.
24.
Shankar, T., & Shanmugavel, S. (2014). Energy optimization in cluster based wireless sensor networks. Journal of Engineering Science and Technology, School of Engineering, Taylor’s University,9(2), 246–260.
Metadata
Title
Adaptive Buffering and Fuzzy Based Multilevel Clustering for Energy Efficient Wireless Sensor Network
Authors
T. Shankar
A. Rajesh
R. Mageshvaran
Publication date
11-01-2020
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 1/2020
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-020-07029-3

Other articles of this Issue 1/2020

Wireless Personal Communications 1/2020 Go to the issue

OriginalPaper

Low Complexity Linear Detection for Uplink Multiuser MIMO SC-FDMA Systems

OriginalPaper

A Mathematical Model for Energy Efficient Secured CH Clustering Protocol for Mobile Wireless Sensor Network

OriginalPaper

Performance Analysis of Microstriplines Interconnect Structure with Novel Guard Trace as Parallel Links for High Speed Dram Interfaces

OriginalPaper

RSSI Fingerprinting Based iPhone Indoor Localization System Without Apple API

OriginalPaper

Energy Harvesting for Cooperative Cognitive Radio Networks

OriginalPaper

Transmission of Audio over LTE Packet Based Wireless Networks Using Wavelets