A reliable and data aggregation aware routing protocol for wireless sensor networks

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A reliable and data aggregation aware routing protocol for wireless sensor networks

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International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems archive
Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems table of contents

Tenerife, Canary Islands, Spain

SESSION: Dissemination, multicast, routing table of contents

Pages 245-252

Year of Publication: 2009

ISBN:978-1-60558-616-8

Authors

Leandro A. Villas	Federal University of Minas Gerais, Belo Horizonte, Brazil
Azzedine Boukerche	University of Ottawa, Ottawa, ON, Canada
Regina B. Araujo	Federal University of Sao Carlos, Sao Carlos, Brazil
Antonio A.F. Loureiro	Federal University of Minas Gerais, Belo Horizonte, Brazil

Sponsor

SIGSIM: ACM Special Interest Group on Simulation and Modeling

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ACM New York, NY, USA

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ABSTRACT

This paper presents the Data-Aggregation Aware Routing Protocol, DAARP, for wireless sensor networks. This novel protocol reduces the number of messages necessary to set up a routing tree, maximizes the number of overlapping routes, selects routes with the highest aggregation rate, and performs reliable data aggregation transmission. DAARP was compared to three existing solutions reported in the literature regarding communication costs, delivery efficiency, aggregation rate and aggregated data delivery rate. The results show that DAARP outperforms these solutions for different scenarios in all evaluations performed.

REFERENCES

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	1	I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cyirci. Wireless sensor networks: A survey. Computer Networks, 38(4):393--422, March 2002.
	2	A. Baddeley. Spatial point processes and their application. In W. Weil, editor, Stochastic Geometry, volume 1892 of Lecture Notes in Mathematics, pages 1--75. Springer, Berlin, 2006.
	3	F. Bauer and A. Varma. Distributed algorithms for multicast path setup in data networks. IEEE/ACM Transaction on Networking, 4(2):181--191, 1996.
	4	A. Boukerche, R. B. Araujo, and L. Villas. Optimal route selection for highly dynamic wireless sensor and actor networks environment. In MSWiM '07: Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems, pages 21--27, New York, NY, USA, 2007. ACM.
	5	A. C. Frery, H. Ramos, J. Alencar-Neto, and E. Nakamura. Error estimation in wireless sensor networks. In SAC'08: Proceedings of ACM Symposium on Applied Computing, volume 3, pages 1927--1932, 2008.
	6	J. Hill, R. Szewczyk, A. Woo, S. Hollar, D. Culler, and K. Pister. System architecture directions for networked sensors. SIGPLAN Not., 35(11):93--104, 2000.
	7	S. Hougardy and H. J. Prömel. A 1.598 approximation algorithm for the steiner problem in graphs. In SODA '99: Proceedings of the 10th annual ACM-SIAM symposium on Discrete algorithms, pages 448--453, Philadelphia, PA, USA, 1999. Society for Industrial and Applied Mathematics.
	8	C. Intanagonwiwat, R. Govindan, and D. Estrin. Directed diffusion: a scalable and robust communication paradigm for sensor networks. In MobiCom '00: Proceedings of the 6th annual international conference on Mobile computing and networking, pages 56--67, New York, NY, USA, 2000. ACM.
	9	B. Krishnamachari, D. Estrin, and S. B. Wicker. The impact of data aggregation in wireless sensor networks. In ICDCSW '02: Proceedings of the 22nd International Conference on Distributed Computing Systems, pages 575--578, Washington, DC, USA, 2002. IEEE Computer Society.
	10	E. F. Nakamura, H. A. B. F. de Oliveira, L. F. Pontello, and A. A. F. Loureiro. On demand role assignment for event-detection in sensor networks. In ISCC '06: Proceedings of the 11th IEEE Symposium on Computers and Communications, pages 941--947, Washington, DC, USA, 2006. IEEE Computer Society.
	11	E. F. Nakamura, A. A. F. Loureiro, and A. C. Frery. Information fusion for wireless sensor networks: Methods, models, and classifications. ACM Computing Surveys, 39(3):9-1/9-55, 2007.
	12	G. Robins and A. Zelikovsky. Improved steiner tree approximation in graphs. In SODA '00: Proceedings of the 11th annual ACM-SIAM symposium on Discrete algorithms, pages 770--779, Philadelphia, PA, USA, 2000. Society for Industrial and Applied Mathematics.
	13	K. Romer and F. Mattern. The design space of wireless sensor networks. IEEE Wireless Communications, 11(6):54--61, December 2004.
	14	Sinalgo. Simulator for network algorithms, 2008. Distributed Computing Group - ETH-Zurich, last visited in October, 2008.
	15	O. Younis, M. Krunz, and S. Ramasubramanina. Node clustering in wireless sensor networks: Recent developments and deployment challenges. IEEE Network, 20(3):20--25, December 2006.