CFP last date
20 May 2024
Reseach Article

Data Routing In-Network Aggregation for Wireless Sensor Network

by G.M. Joshi, B.M. Patil
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 137 - Number 13
Year of Publication: 2016
Authors: G.M. Joshi, B.M. Patil
10.5120/ijca2016908902

G.M. Joshi, B.M. Patil . Data Routing In-Network Aggregation for Wireless Sensor Network. International Journal of Computer Applications. 137, 13 ( March 2016), 11-16. DOI=10.5120/ijca2016908902

@article{ 10.5120/ijca2016908902,
author = { G.M. Joshi, B.M. Patil },
title = { Data Routing In-Network Aggregation for Wireless Sensor Network },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 137 },
number = { 13 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 11-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume137/number13/24334-2016908902/ },
doi = { 10.5120/ijca2016908902 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:38:15.434174+05:30
%A G.M. Joshi
%A B.M. Patil
%T Data Routing In-Network Aggregation for Wireless Sensor Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 137
%N 13
%P 11-16
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

WSNs have limited computational power, limited memory and battery power this increases the complexity and leads to need for data aggregation method. The main goal of data aggregation algorithms is to gather and aggregate data in an energy efficient manner, so that lifetime of network is increased. When WSN sensing an event the redundant data will be detected and collected this need to increase in communication cost and energy consumption of network so, in this work the DRINA (a novel data routing for In-network Aggregation) protocol has some advantages like a reduced number of messages for setting up a routing tree, high aggregation rate, maximized number of overlapping routes. The DRINA algorithm was compared with two other algorithms: (InFRA) The Information Fusion-based Role Assignment and Shortest Path Tree (SPT) algorithms it provides best result.

References
  1. I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cyirci, “Wireless Sensor Networks: A Survey,” Computer Networks, vol. 38, no. 4, pp. 393-422, Mar. 2002.
  2. K. Romer and F. Mattern, “The Design Space of Wireless Sensor Networks,” IEEE Wireless Comm.,vol. 11, no. 6, pp. 54-61, Dec. 2004.
  3. L. A. Villas, A. Boukerche, H. S. Ramos, Horacio A.B. F. de Oliveira, R. B. de Araujo, and A. A. F. Loureiro, “DRINA: a lightweight and reliable routing approach for in-network aggregation in wireless sensor networks,” IEEE Trans. on Computers, vol. 62, no. 4, pp. 676-689, April 2013.
  4. I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cyirci, “Wireless sensor networks: a survey,” Computer Networks, vol. 38, pp. 393-422, 2002.
  5. O. Younis, M. Krunz, and S. Ramasubramanina, “Node Clustering in Wireless Sensor Networks: Recent Developments and Deployment Challenges,” IEEE Network, vol. 20, no. 3, pp. 20-25, Dec. 2006.
  6. I. Chatzigiannakis, T. Dimitriou, S.E. Nikoletseas, and P.G. Spirakis, “A Probabilistic Algorithm for Efficient and Robust Data Propagation in Wireless Sensor Networks,” Ad Hoc Networks, vol. 4, no. 5, pp. 621-635, 2006.
  7. I. Chatzigiannakis, S. Nikoletseas, and P.G. Spirakis, “Efficient and Robust Protocols for Local Detection and Propagation in Smart Dust Networks,” Mobile Networks and Applications, vol. 10, Nos.1/2, pp. 133-149, 2005.
  8. E.F. Nakamura, A.A.F. Loureiro, and A.C. Frery, “Information Fusion for Wireless Sensor Networks:Methods, Models, and Classifications,” ACM Computing Surveys, vol. 39, no. 3,pp. 9-1/9-55, 2007.
  9. F. Hu, X. Cao, and C. May, “Optimized Scheduling for Data Aggregation in Wireless Sensor Networks,” Proc. Int’l Conf. Information Technology: Coding and Computing (ITCC ’05), pp. 557-561, 2005.
  10. I. Solis and K. Obraczka, “The Impact of Timing in Data Aggregation for Sensor Networks,” IEEE Int’l Conf. Comm., vol. 6, pp. 3640-3645, June 2004.
  11. B. Krishnamachari, D. Estrin, and S.B. Wicker, “The Impact of Data Aggregation in Wireless Sensor Networks,” Proc. 22nd Int’l Conf. Distributed Computing Systems (ICDCSW ’02), pp. 575-578, 2002.
  12. C. Intanagonwiwat, D. Estrin, R. Govindan, and J. Heidemann, “Impact of Network Density on Data Aggregation in Wireless Sensor Networks,” Proc. 22nd Int’l Conf. Distributed Computing Systems, pp. 457-458, 2002.
  13. 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,” Proc. IEEE 11th Symp. Computers and Comm. (ISCC ’06), pp. 941-947, 2006.
  14. S. Madden, M.J. Franklin, J.M. Hellerstein, and W. Hong, “Tag: A Tiny Aggregation Service for Ad-Hoc Sensor Networks,” ACM SIGOPS Operating Systems Rev., vol. 36, no. SI, pp. 131-146, 2002.
  15. S. Madden, R. Szewczyk, M.J. Franklin, and D. Culler, “Supporting Aggregate Queries over Ad-Hoc Wireless Sensor Networks,” Proc. IEEE Fourth Workshop Mobile Computing Systems and Applications (WMCSA ’02), pp. 49-58, 2002.
  16. A.P. Chandrakasan, A.C. Smith, and W.B. Heinzelman, “An Application-Specific Protocol Architecture for Wireless Micro sensor Networks,” IEEE Trans. Wireless Comm., vol. 1, no. 4,pp. 660-670, Oct. 2002.
  17. K.-W. Fan, S. Liu, and P. Sinha, “On the Potential Of Structure-Free Data Aggregation in Sensor Networks,” Proc. IEEE INFOCOM,pp. 1-12, Apr. 2006.
  18. C. Intanagonwiwat, R. Govindan, and D. Estrin, “Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks,” Proc. MobiCom, pp. 56-67, 2000.
  19. J. Hill, R. Szewczyk, A. Woo, S. Hollar, D. Culler, and K. Pister, “System Architecture Directions for Networked Sensors,” ACM SIGPLAN Notices, vol. 35, no. 11, pp. 93-104, 2000.
Index Terms

Computer Science
Information Sciences

Keywords

Wireless sensor networks DRINA InFRA SPT.