CFP last date
20 December 2024
Reseach Article

Data Aggregation and Life Time Improvement in wireless Sensor Networks using Dynamic Clustering

by Reepu Daman, Preety Chaudhary, Rakesh Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 156 - Number 11
Year of Publication: 2016
Authors: Reepu Daman, Preety Chaudhary, Rakesh Kumar
10.5120/ijca2016912551

Reepu Daman, Preety Chaudhary, Rakesh Kumar . Data Aggregation and Life Time Improvement in wireless Sensor Networks using Dynamic Clustering. International Journal of Computer Applications. 156, 11 ( Dec 2016), 6-10. DOI=10.5120/ijca2016912551

@article{ 10.5120/ijca2016912551,
author = { Reepu Daman, Preety Chaudhary, Rakesh Kumar },
title = { Data Aggregation and Life Time Improvement in wireless Sensor Networks using Dynamic Clustering },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 156 },
number = { 11 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 6-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume156/number11/26751-2016912551/ },
doi = { 10.5120/ijca2016912551 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:02:19.092187+05:30
%A Reepu Daman
%A Preety Chaudhary
%A Rakesh Kumar
%T Data Aggregation and Life Time Improvement in wireless Sensor Networks using Dynamic Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 156
%N 11
%P 6-10
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Wireless sensor network is an emerging area of research due to vulnerability of sensing information from not approachable areas. In WSN sensor nodes have been deployed for sensing information and transmit this information to base station so that various decisions can be developed. In the processing of WSN data aggregation and energy consumption are major issues. In this paper a new approach has been purposed for data management and energy consumption reduction using dynamic clustering and avoidance ofredundant information transmission over the network. This approach use check sum approach for data redundancy checking and discard redundant or repeated information. This approach provides better results than previous approaches.

References
  1. QiulingTang; Changyin Sun; Huan Wen; Ye Liang “Cross-layer energy efficiency analysis and optimization in WSN”IEEE conference on Networking, Sensing and Control (ICNSC), 2010, pp. 138 – 142.
  2. Sharawi, M.; Emary, E.; Saroit, I.A.; El-Mahdy, H. “WSN's energy-aware coverage preserving optimization model based on multi-objective bat algorithm” IEEE conference on Evolutionary Computation (CEC), 2015, pp. 472 – 479.
  3. Darif, A.; Aboutajdine, D.; Saadane, R. “Energy consumption optimization in real time applications for WSN using IR-UWB technology” IEEE conference on Renewable and Sustainable Energy Conference (IRSEC), 2013, pp. 379 – 384.
  4. Ganhão, F. Pereira, M.; Bernardo, L.; Dinis, R. “Energy per Useful Packet Optimization on a TDMA WSN Channel” IEEE conference on Computer Communications and Networks (ICCCN), 2010, pp. 1 – 6.
  5. Abusaimeh, H.; Shuang-Hua Yang “Energy-aware optimization of the number of clusters and cluster-heads in WSN” IEEE conference on Innovations in Information Technology (IIT), 2012, pp. 178 – 183.
  6. Bojan, S.; Nikola, Z. “Genetic algorithm as energy optimization method in WSN” IEEE conference on Telecommunications Forum (TELFOR), 2013, pp. 97 – 100.
  7. Jackulin, T.; Ramya, M.; Subashini, C. “Energy optimization for WSN architecture and self-test Embedded processor” IEEE conference on Emerging Trends in Electrical Engineering and Energy Management (ICETEEEM), 2012, pp. 253 – 256.
  8. Tanevski, M.; Boegli, A.; Farine, P. “Power supply energy optimization for ultra-low-power wireless sensor nodes” IEEE conference on Sensors Applications Symposium (SAS), 2013, pp.176 – 181.
  9. Elshaikh, M. ; Bin MohdWarip, M.N. ; Lynn, O.B. ; Ahmad, R.B. “Energy consumption optimization with Ichi Taguchi method for Wireless Sensor Networks” IEEE conference on Electronic Design (ICED),2014,pp 493 – 498.
  10. Elhabyan, R. S.; Yagoub, M.C.E. “Particle swarm optimization protocol for clustering in wireless sensor networks: A realistic approach” IEEE conference on Information Reuse and Integration (IRI), 2014, pp. 345 – 350.
Index Terms

Computer Science
Information Sciences

Keywords

Energy optimization WSN data sensing clusters.