Advertisement

Wireless Networks

, Volume 19, Issue 3, pp 285–299 | Cite as

On reducing delay in mobile data collection based wireless sensor networks

  • Arun K. Kumar
  • Krishna M. Sivalingam
  • Adithya Kumar
Article

Abstract

In a wireless sensor network, battery power is a limited resource on the sensor nodes. Hence, the amount of power consumption by the nodes determines the node and network lifetime. This in turn has an impact on the connectivity and coverage of the network. One way to reduce power consumed is to use a special mobile data collector (MDC) for data gathering, instead of multi-hop data transmission to the sink. The MDC collects the data from the nodes and transfers it to the sink. Various kinds of MDC approaches have been explored for different assumptions and constraints. But in all the models proposed, the data latency is usually high, due to the slow speed of the mobile nodes. In this paper, we propose a new model of mobile data collection that reduces the data latency significantly. Using a combination of a new touring strategy based on clustering and a data collection mechanism based on wireless communication, we show that the delay can be reduced significantly without compromising on the advantages of MDC based approach. Using extensive simulation studies, we analyze the performance of the proposed approach and show that the packet delay reduces by more than half when compared to other existing approaches.

Keywords

Wireless sensor networks Mobile data collector Clustering Wireless communication 

Notes

Acknowledgments

Part of this work was supported by IIT Madras/DRDO Memorandum of Cooperation 2008. The first author is currently a graduate student at The University of Wisconsin-Madison, USA.

References

  1. 1.
    Raghavendra, C. S., Sivalingam, K. M., & Znati, T. (2004). Wireless sensor networks. Berlin: Springer.CrossRefGoogle Scholar
  2. 2.
    Ekici, E., Gu, Y., & Bozdag, D. (2006). Mobility-based communication in wireless sensor networks. IEEE Communications Magazine, 44(7), 56–62.Google Scholar
  3. 3.
    Wu, Q., Rao, N., Barhen, J., Iyengar, S., Vaishnavi, V., Qi, H., et al. (2004). On computing mobile agent routes for data fusion in distributed sensor networks. IEEE Transactions on Knowledge and Data Engineering, 16(6), 740–753.Google Scholar
  4. 4.
    Asok, A., Sivalingam, K. M., & Agrawal, P. (2009). Mobility in wireless sensor networks. In: D. Agrawal, B. Xie (Eds.), Encyclopedia on ad hoc and ubiquitous computing. Singapore: World Scientific Press.Google Scholar
  5. 5.
    Qi, H., Iyengar, S., & Chakrabarty, K. (2001). Multi-resolution data integration using mobile agents in distributed sensor networks. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Applications and Review, 31(3), 383–391.Google Scholar
  6. 6.
    Bote, D., Sivalingam, K. M., & Agrawal, P. (2007). Data gathering in ultra wide band based wireless sensor networks using a mobile node. In International conference on broadband communications and networks (BROADNETS), Raleigh, NC.Google Scholar
  7. 7.
    Jain, S., Shah, R., Brunette, W., Borriello, G., & Roy, S. (2006). Exploiting mobility for energy efficient data collection in wireless sensor networks. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Applications and Review, 31(3), 327–339.Google Scholar
  8. 8.
    Tong, L., Zhao, Q., & Adireddy, S. (2003). Sensor networks with mobile agents. In Proceedings of IEEE MILCOM, Boston, MA.Google Scholar
  9. 9.
    Shah, R., Roy, S., Jain, S., & Brunette, W. (2003). Data MULEs: modeling a three-tier architecture for sparse sensor networks. in IEEE workshop on sensor network protocols and applications (SNPA) (pp. 30–41). Alaska: Anchorage.Google Scholar
  10. 10.
    Somasundara, A. A., Ramamoorthy, A., & Srivastava, M. B. (2004). Mobile element scheduling for efficient data collection in wireless sensor networks with dynamic deadlines. In IEEE International Real-Time Systems Symposium (RTSS) (pp. 296–305). Portugal: Lisbon.Google Scholar
  11. 11.
    Jea, D., Somasundara, A. A., & Srivastava, M. B. (2005). Multiple controlled mobile elements (data mules) for data collection in sensor networks. In IEEE distributed computing in sensor systems (DCOSS) (pp. 244–257). CA: Marina Del Ray.Google Scholar
  12. 12.
    Shah, P., Sivalingam, K. M., & Agrawal, P. (2008). Efficient data gathering in distributed hybrid sensor networks using multiple mobile agents. In Proceedings of third international conference on communication system software and middleware (COMSWARE) Bangalore, India.Google Scholar
  13. 13.
    Ma, M., & Yang, Y. (2007). SenCar: An energy-efficient data gathering mechanism for large-scale multihop sensor networks. IEEE Transaction on Parallel and Distributed Systems, 18(10).Google Scholar
  14. 14.
    Boukerche, A., & Fei, X. (2008). Adaptive data gathering protocols with mobile collectors for vehicular ad-hoc and sensor networks. In IEEE international conference on wireless & mobile computing, networking & communication. http://doi.ieeecomputersociety.org/10.1109/WiMob.2008.124.
  15. 15.
    Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques. Amsterdam: Elsevier.Google Scholar
  16. 16.
    StatSoft. (2012). Cluster analysis, [Online]. From http://www.statsoft.com/textbook/stcluan.html.
  17. 17.
    MilanPolyTech. (2009). A tutorial on clustering algorithms, [Online]. From http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/.
  18. 18.
    Heyer, L., Kruglyak, S., & Yooseph, S. (1999). Exploring expression data: Identification and analysis of coexpressed genes. Genome Research, 9, 1106–1115.Google Scholar
  19. 19.
    Applegate, D. L., Bixby, R., Chv\(\tilde{A}\)ątal, V., & Cook, W. (2006). The traveling salesman problem: a computational study. Princeton: Princeton University Press.Google Scholar
  20. 20.
    Cook, W. (2005). Concorde TSP solver, [Online]. From http://www.tsp.gatech.edu/concorde/index.html.
  21. 21.
    IEEE. The IEEE 802.11 working group on wireless LAN standards, [Online]. From http://www.ieee802.org/11/.
  22. 22.
    The Wi-Fi alliance, [Online]. From http://wi-fi.org/ (2009).
  23. 23.
    Schiller, J. (2006). Mobile communications. Reading: Addison Wesley Publishers.Google Scholar
  24. 24.
    The IEEE 802.16 working group on broadband wireless access standards, [Online]. From http://grouper.ieee.org/groups/802/16/ (2009).
  25. 25.
    The WiMax forum, [Online]. From http://www.wimaxforum.org/ (2009).
  26. 26.
    Heinzelman, W. R., Kulik, J., & Balakrishnan, H. (1999). Adaptive protocols for information dissemination in wireless sensor networks. In ACM MOBICOM. USA: Seattle.Google Scholar
  27. 27.
    Bandyopadhyay, S., & Coyle, E. J. (2003). An energy efficient hierarchical clustering algorithm for wireless sensor networks. In IEEE INFOCOM. USA: San Francisco.Google Scholar
  28. 28.
    Durresi, A., Paruchuri, V., & Barolli, L. (2006). Clustering protocol for sensor networks. In IEEE international conference on advanced information networking and applications (AINA). Austria: Vienna.Google Scholar
  29. 29.
    Lee, S., Yoo, J., & Chung, T. C. (2004). Distance-based energy efficient clustering for wireless sensor networks. In IEEE conference on local computer networks (LCN). USA: Tampa.Google Scholar
  30. 30.
    Elzinga J., & Hearn, D. (1972). The minimum covering sphere problem. Management Science, 19(1), 96–104.MathSciNetzbMATHCrossRefGoogle Scholar
  31. 31.
    Elzinga J., & Hearn, D. (1972). Geometrical solutions for some minimax location problems. Transportation Science, 6(4), 379–394.Google Scholar
  32. 32.
    Gartner, B. (1999). Fast and robust smallest enclosing balls. In European Symposium on Algorithms (ESA (pp. 325–338).Google Scholar
  33. 33.
    Gartner, B. (2012). Miniball 2.0: Smallest enclosing balls of points, [Online]. From http://www.inf.ethz.ch/personal/gaertner/miniball.html.
  34. 34.
    Kershner, R. (1939). The number of circles covering a set. American Journal of Mathematics, 61(3), 665–671.MathSciNetCrossRefGoogle Scholar
  35. 35.
    OMNET++ network simulation framework. From http://www.omnetpp.org (2011).
  36. 36.
    MiXiM (mixed simulator) simulation framework. From http://mixim.sourceforge.net (2011).

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Arun K. Kumar
    • 1
  • Krishna M. Sivalingam
    • 1
  • Adithya Kumar
    • 2
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology MadrasChennaiIndia
  2. 2.Department of Computer Science and EngineeringNational Institute of TechnologyTiruchirappalliIndia

Personalised recommendations