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Reseach Article

Energy Efficient Clustering Method for Wireless Sensor Network By Using Compressive Sensing and MEMAC

Published on December 2014 by Swati Gawand, Santosh Kumar
Innovations and Trends in Computer and Communication Engineering
Foundation of Computer Science USA
ITCCE - Number 4
December 2014
Authors: Swati Gawand, Santosh Kumar
70bb2e1e-0258-4428-9892-cac61584afca

Swati Gawand, Santosh Kumar . Energy Efficient Clustering Method for Wireless Sensor Network By Using Compressive Sensing and MEMAC. Innovations and Trends in Computer and Communication Engineering. ITCCE, 4 (December 2014), 23-28.

@article{
author = { Swati Gawand, Santosh Kumar },
title = { Energy Efficient Clustering Method for Wireless Sensor Network By Using Compressive Sensing and MEMAC },
journal = { Innovations and Trends in Computer and Communication Engineering },
issue_date = { December 2014 },
volume = { ITCCE },
number = { 4 },
month = { December },
year = { 2014 },
issn = 0975-8887,
pages = { 23-28 },
numpages = 6,
url = { /proceedings/itcce/number4/19064-2031/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Innovations and Trends in Computer and Communication Engineering
%A Swati Gawand
%A Santosh Kumar
%T Energy Efficient Clustering Method for Wireless Sensor Network By Using Compressive Sensing and MEMAC
%J Innovations and Trends in Computer and Communication Engineering
%@ 0975-8887
%V ITCCE
%N 4
%P 23-28
%D 2014
%I International Journal of Computer Applications
Abstract

In the Wireless sensor network, there may be possibility of failure of nodes because of the power drained or addition of new nodes or may be change in location of nodes due to physical movement. So to accommodate these types of dynamic changes in sensor nodes . MEMAC (Mobile Energy Aware Medium Acces Control) protocol presents hybrid scheme of contention based and scheduled based scheme of previous MAC protocol having the purpose of overcome the drawbacks. . To avoid collision and energy consumption it must uses mobility information and acquires schedule according to mobility conditions and it also needs proper designing of mobility model for real life setting. Compressive sensing (CS) can reduce the number of data transmissions and balance the traffic load of the networks. Hence, the total number of data transmissions for collection of data by using pure CS is still large. The hybrid method of using CS to reduce the number of transmissions in sensor networks. Hence, the previous work use the CS method on routing trees. In this paper, a clustering method that uses hybrid CS for sensor networks. The nodes are form in the clusters within that one sensor act as cluster head and other are cluster member . Within a cluster, nodes send data to cluster head (CH) without using CS. CHs use CS to transmit data to sink. In this paper Compressive sensing and MEMAC protocol is used for reducing the energy consumption of sensor nodes and also to reduce the congestion in the network. .

References
  1. Ruitao Xie and Xiaohua Jia, Fellow, IEEE, Computer Society" Transmission-Efficient Clustering Method for Wireless Sensor Networks Using Compressive Sensing" ieee transactions on parallel and distributed systems, vol. 25, no. 3, march 2014
  2. Shivendra Dubey and Chetan Agrawal"a survey of data collection techniques in wireless sensor network",. 2013. ISSN: 22311963,International Journal of Advances in Engineering Technology, Sept. 2013
  3. Fengyuan Ren, Member, IEEE, Jiao Zhang, Tao He, Chuang Lin, Senior Member, IEEE, and Sajal K. Das, Senior Member, ieee,"EBRP: Energy-Balanced Routing Protocol for Data Gathering in Wireless Sensor Networks", ieee transactionsonparallelanddistributed systems, vol. 22, no. 12, december 2011.
  4. L. Xiang, J. Luo, and A. Vasilakos,"Compressed Data Aggregation for Energy Efficient Wireless Sensor Networks," Proc. IEEE Sensor, Mesh, and Ad Hoc Comm. and Networks (SECON '11), pp. 46-54, June 2011.
  5. C. Luo, F. Wu, J. Sun, and C. W. Chen,"Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gather- ing",IEEE Trans. Wireless Comm. , vol. 9, no. 12, pp. 3728-3738, Dec. 2010.
  6. C. Luo, F. Wu, J. Sun, and C. W. Chen, "Compressive Data Gathering for Large-Scale Wireless Sensor Networks", Proc. ACM MobiCom, pp. 145-156, Sept. 2009.
  7. S. Chen, Y. Wang, X. -Y. Li, and X. Shi, "Data Collection Capacity of Random-Deployed Wireless Sensor Networks," Proc. IEEE GLOBECOM, pp. 1-6, Dec. 2009.
  8. S. Lee, S. Pattem, M. Sathiamoorthy, B. Krishnamachari, and A. Ortega, "Spatially-Localized Compressed Sensing and Routing inMulti-Hop Sensor Networks," Proc. Third Int'l Conf. GeoSensor Networks (GSN '09), pp. 11-20, 2009.
  9. Bashir Yahya will appear in Wiley series, "Energy efficient MAC protocols in Wireless Sensor Networks" in 2009
  10. J. Haupt, W. Bajwa, M. Rabbat, and R. Nowak, "Compressed Sensing for Networked Data," IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 92-101, Mar. 2008.
  11. D Zeinalipour-Yazti, H. Papadakis, M. D. Dikaiakos, "Mobile sensor network Data Management" Parallel processing letter journal, sept. 2008.
  12. Allred J. , Hasan A. B. Gray P, Mohseni K. , "SensorFlock: An Airborne Wireless sensor network of Micro-air Vehicles", in 2007.
  13. Nittel S. , Trigoni N. , Nural A. , "A drift-tolerant model for data management in ocean sensor networks", in 2007.
  14. D. Donoho, "Compressed Sensing," IEEE Trans. Information Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.
  15. O. Younis and S. Fahmy, "HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks" IEEE Trans. Mobile Computing, vol. 3, no. 4, pp. 366-379, Oct. -Dec. 2004.
  16. R. Szewczyk, A. Mainwaring, J. Polastre, J. Anderson, and D. Culler, "An Analysis of a Large Scale Habitat Monitoring Application," Proc. ACM Second Int'l Conf. Embedded Networked Sensor Systems (SenSys '04), pp. 214-226, Nov. 2004.
  17. Lingxuan Hu and David, "Localization for Mobile sensor networks", International conference on mobile computing and networking, 2004.
  18. S. Guha, A. Meyerson, N. Mishra, R. Motwani, and L. O'Callaghan, "Clustering Data Streams: Theory and Practice," IEEE Trans. Knowledge and Data Eng. , vol. 15, no. 3, pp. 515- 528, May/June 2003.
  19. S. Bandyopadhyay and E. Coyle, "An Energy Efficient Hierarch- ical Clustering Algorithm for Wireless Sensor Networks", Proc. IEEE INFOCOM, vol. 3, pp. 1713-1723, Mar. 2003.
  20. T Dam and K Langendoen, "An adaptive energy efficient MAC protocol for Wireless Sensor Networks" in 2003
  21. K. T. Jin; D-H Cho, "A new MAC algorithm based on reservation and scheduling for energy limited ad-hoc networks", in 2003.
  22. I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "Wireless Sensor Networks: A Survey," Computer Networks, vol. 38, no. 4, pp. 393-422, 2002
  23. A. Nasipuri and K. Li, "A Directionality Based Location Discovery Scheme for Wireless Sensor Networks," Proc. First ACM Int'l Workshop Wireless Sensor Networks and Applications (WSNA '02), pp. 105-111, 2002.
  24. W. Heinzelman and H. Balakrishanan, "An Application- Specific protocol Architecture for wireless Microsensor networks", IEEE Transaction on Wireless Communications, in 2002.
Index Terms

Computer Science
Information Sciences

Keywords

Compressive Sensing Clustering Data Collection Memac Wireless Sensor Networks.