Effect of Clustering on Energy Efficiency and Network Lifetime in Wireless Sensor Networks
Wireless Sensor Network is Multi hop Self-configuring
Wireless Network consisting of sensor nodes. The deployment of
wireless sensor networks in many application areas, e.g., aggregation
services, requires self-organization of the network nodes into clusters.
Efficient way to enhance the lifetime of the system is to partition the
network into distinct clusters with a high energy node as cluster head.
The different methods of node clustering techniques have appeared in
the literature, and roughly fall into two families; those based on the
construction of a dominating set and those which are based solely on
energy considerations. Energy optimized cluster formation for a set
of randomly scattered wireless sensors is presented. Sensors within a
cluster are expected to be communicating with cluster head only. The
energy constraint and limited computing resources of the sensor nodes
present the major challenges in gathering the data. In this paper we
propose a framework to study how partially correlated data affect the
performance of clustering algorithms. The total energy consumption
and network lifetime can be analyzed by combining random geometry
techniques and rate distortion theory. We also present the relation
between compression distortion and data correlation.
[1] D. Baker and A. Ephremides, "The architectural organization of a
mobile radio network via a distributed algorithm," Transactions on
communications, vol. 29, pp. 1694-1701, Nov 1981.
[2] A. D. Amis, R. Prakash, T. H. P. Vuong, and D. T. Huynh, "Max-min dcluster
formation in wireless ad hoc networks," INFOCOM, pp. 32-41,
March 2000.
[3] W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, "An
application-specific protocol architecture for wireless microsensor network,"
IEEE Trans. on Wireless Communications, vol. 1, pp. 660-670,
Oct 2002.
[4] S. Bandyopadhyay and E. J. Coyle, "An energy efficient hierarchical
clustering algorithm for wireless sensor networks," INFOCOM, vol. 3,
pp. 1713-1723, April 2003.
[5] Z. J. Haas and B. Liang, "Ad hoc mobility management with uniform
quorum systems," IEEE/ACM Transactions on Networking, vol. 7,
pp. 228-240, April 1999.
[6] A. Boulis, S. Ganeriwal, and M. B. Srivastava, "Aggregation in sensor
networks: An energy acuracy trade-off," Proceedings of the First IEEE.
2003 IEEE International Workshop, pp. 128-138, May 2003.
[7] J. Chou, D. Petrovic, and K. Ramachandran, "A distributed and adaptive
signal processing approach to reducing energy consumption in sensor
networks," INFOCOM, vol. 2, pp. 1054-1062, April 2003.
[8] D. Petrovic, R. C. Shah, K. Ramchandran, and J. Rabaey, "Data
funneling: routing with aggregation and compression for wireless sensor
networks," IEEE International Workshop on Sensor Network Protocols
and Applications, pp. 156-162, May 2003.
[9] E. Duarte-Melo and M. Liu, "Analysis of energy consumption and
lifetime of heterogeneous wireless sensor networks," GLOBECOM -02,
vol. 1, pp. 21-25, 2002.
[10] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "A survey
on sensor networks," IEEE communications magazine, vol. 40, pp. 102-
114, Aug 2002.
[1] D. Baker and A. Ephremides, "The architectural organization of a
mobile radio network via a distributed algorithm," Transactions on
communications, vol. 29, pp. 1694-1701, Nov 1981.
[2] A. D. Amis, R. Prakash, T. H. P. Vuong, and D. T. Huynh, "Max-min dcluster
formation in wireless ad hoc networks," INFOCOM, pp. 32-41,
March 2000.
[3] W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, "An
application-specific protocol architecture for wireless microsensor network,"
IEEE Trans. on Wireless Communications, vol. 1, pp. 660-670,
Oct 2002.
[4] S. Bandyopadhyay and E. J. Coyle, "An energy efficient hierarchical
clustering algorithm for wireless sensor networks," INFOCOM, vol. 3,
pp. 1713-1723, April 2003.
[5] Z. J. Haas and B. Liang, "Ad hoc mobility management with uniform
quorum systems," IEEE/ACM Transactions on Networking, vol. 7,
pp. 228-240, April 1999.
[6] A. Boulis, S. Ganeriwal, and M. B. Srivastava, "Aggregation in sensor
networks: An energy acuracy trade-off," Proceedings of the First IEEE.
2003 IEEE International Workshop, pp. 128-138, May 2003.
[7] J. Chou, D. Petrovic, and K. Ramachandran, "A distributed and adaptive
signal processing approach to reducing energy consumption in sensor
networks," INFOCOM, vol. 2, pp. 1054-1062, April 2003.
[8] D. Petrovic, R. C. Shah, K. Ramchandran, and J. Rabaey, "Data
funneling: routing with aggregation and compression for wireless sensor
networks," IEEE International Workshop on Sensor Network Protocols
and Applications, pp. 156-162, May 2003.
[9] E. Duarte-Melo and M. Liu, "Analysis of energy consumption and
lifetime of heterogeneous wireless sensor networks," GLOBECOM -02,
vol. 1, pp. 21-25, 2002.
[10] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "A survey
on sensor networks," IEEE communications magazine, vol. 40, pp. 102-
114, Aug 2002.
@article{"International Journal of Information, Control and Computer Sciences:63754", author = "Prakash G L and Chaitra K Meti and Poojitha K and Divya R.K.", title = "Effect of Clustering on Energy Efficiency and Network Lifetime in Wireless Sensor Networks", abstract = "Wireless Sensor Network is Multi hop Self-configuring
Wireless Network consisting of sensor nodes. The deployment of
wireless sensor networks in many application areas, e.g., aggregation
services, requires self-organization of the network nodes into clusters.
Efficient way to enhance the lifetime of the system is to partition the
network into distinct clusters with a high energy node as cluster head.
The different methods of node clustering techniques have appeared in
the literature, and roughly fall into two families; those based on the
construction of a dominating set and those which are based solely on
energy considerations. Energy optimized cluster formation for a set
of randomly scattered wireless sensors is presented. Sensors within a
cluster are expected to be communicating with cluster head only. The
energy constraint and limited computing resources of the sensor nodes
present the major challenges in gathering the data. In this paper we
propose a framework to study how partially correlated data affect the
performance of clustering algorithms. The total energy consumption
and network lifetime can be analyzed by combining random geometry
techniques and rate distortion theory. We also present the relation
between compression distortion and data correlation.", keywords = "Clusters, multi hop, random geometry, rate distortion.", volume = "3", number = "7", pages = "1870-6", }