A Dynamic Aggregation Protocol for Energy Efficient Data Fusion in Wireless Sensor Network
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10.5120/1991-2683 |
Adwitiya Sinha and D K Lobiyal. Article: A Dynamic Aggregation Protocol for Energy Efficient Data Fusion in Wireless Sensor Network. International Journal of Computer Applications 16(3):32–38, February 2011. Full text available. BibTeX
@article{key:article,
author = {Adwitiya Sinha and D. K. Lobiyal},
title = {Article: A Dynamic Aggregation Protocol for Energy Efficient Data Fusion in Wireless Sensor Network},
journal = {International Journal of Computer Applications},
year = {2011},
volume = {16},
number = {3},
pages = {32--38},
month = {February},
note = {Full text available}
}
Abstract
Effective data fusion principally prolongs the survival of a Wireless Sensor Network (WSN) and largely determines the degree of its performance in terms of energy utilization. In our research work, we propose a data fusion protocol based on clustering technique. The protocol computes the correlation-dominating set by exploiting spatial and temporal correlation among the data sensed by the sensor nodes in the network. On the basis of the dominating set the network correlation graph is derived, which is further applied to form clusters. Moreover, an efficient energy model is taken into consideration for electing a sensor node from the dominating set as the cluster head. Finally within a cluster, the cluster head aggregates data from the remaining dominating nodes and transmits them to the data processing node. It can be observed that with the application of correlation and aggregation in our protocol, the size of the set of actually transmitting nodes is reduced significantly. We have used Network Simulator (ns-2.34) to simulate our work. The results are obtained in terms of three metrics: energy consumption, success rate and network lifespan. The results are obtained by taking average of five runs, to ensure precision in the experimentation.
Reference
- Alzaid, H., Foo, E. and Nieto, J. G. 2008. Secure data aggregation in wireless sensor. Information Security Institute, Qeensland University of Technology. ACSC2008 conference, Wollongong, Australia.
- Buratti, C., Giorgetti, A. and Verdone, R. 2005. Cross-layer design of an energy-efficient cluster formation algorithm with carrier-sensing multiple access for wireless sensor network. EURASIP Journal on Wireless Communications and Networking.
- Gupta, H., Navda, V., Das, S. and Chowdhury, V. 2008. Efficient gathering of correlated data in sensor networks,” State University of New York, Stony Brook, ACM Transactions on Sensor Networks, Vol. 4, No. 1, Article 4.
- Sharaf, M. A., Beaver, J., Labrindis, A. and Chrysanthis, P. K. 2003. TiNA: A scheme for temporal coherency-aware in-network aggregation. ACM Workshop on Data Engineering for Wireless& Mobile Access (MobiDe).
- Goel, S. and Imielinski, T. 2001. Prediction-based monitoring in sensor networks. ACM Computer Communications Rev. (CCR).
- Fall, K. and Varadhan, K. The NS Manual.
- Pattem, S., Krishnamachari, B. and Govindan, R. 2008. The impact of spatial correlation on routing with compression in wireless sensor networks. University of Southern California, ACM Transactions on Sensor Networks, Vol. 4, No. 4, Article 24.
- Gallo, G., Longo, G. and Nguyen, S. and Pallottino, S. 1992. Direced hypergraphs and applications. National Research Council of Canada.
- Li, J., Foh, C. H., Andrew, L. H. L. and Zukerman, M. 2008. Sizes of minimum connected dominating sets of a class of wireless sensor networks. State University of New York, Stony Brook. ACM Trans. on Sensor Networks.
- Ros, F. J. and Ruiz, P. M. 2004. Implementing a new manet unicast routing protocol in NS2. Dept. of Information and Communications Engineering, University of Murcia.
- Chakraverty, S., Batra, A. and Rathi, A. 2006. Directed convergence heuristic: A fast and novel approach to steiner tree construction”, IFIP International Conference on Very Large Scale Integration.