Balancing Overhearing Energy and Latency in Wireless Sensor Networks

  • Byoungyong Lee
  • Kyungseo Park
  • Ramez Elmasri
Part of the IFIP – The International Federation for Information Processing book series (IFIPAICT, volume 264)


A WSN (Wireless Sensor Networks) consists of a large number of sensor nodes. Each sensor node has limited battery, small storage, and short radio range. Many researchers have proposed various methods to reduce energy consumption in sensor nodes, since it is difficult to replace sensor node power sources. Generally, a sensor node consumes its energy during processing, receiving, transmitting and overhearing of messages that are directed to other nodes. Among those, overhearing is not necessary for correct operation of sensor networks. In this paper we propose a new synchronized wakeup scheme to reduce the overhearing energy consumption using different wakeup time scheduling for extending sensor network lifetime. The results of our simulation show that there is a trade-off between reducing overhearing energy and delay time. Therefore we propose Double Trees Structure, called DTS, having two routing trees, one based on Short Rings Topology and the other on Long Rings Topology. DTS has multi routing paths from base station to children nodes. If a node which is on the next routing path does not wakeup in time to receive the data, the sender node selects another path to connect to the destination. We can save the wait time until the next destination node wakes up. In the simulation result, our wakeup scheduling reduces overhearing energy consumption more than the S-MAC protocol. Using the double trees structure reduces the delay time.


Sensor Network Wakeup Scheduling Overhearing Energy. 


  1. 1.
    W. Ye, J. Heidemann, D. Estrin, “An energy-efficient MAC protocol for wireless sensor netowkrs”in INFOCOM 2002: Proceedings of the Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies, Vol. 3, IEEE, June 2002 .Google Scholar
  2. 2.
    Mohamed A. Sharaf, Jonathan Beaver, Alexandros Labrinidis, Panos K. Chrysanthis, “Balancing energy efficiency and quality of aggregate data in sensor networks” VLDB journal, 2004 13:384-403.CrossRefGoogle Scholar
  3. 3.
    Ramanan Subramanian, Faramarz Fekri, “Sleep scheduling and lifetime maximization in sensor networks: fundamental limits and optimal solutions” Proceedings of the fifth international conference on Information processing in sensor networks, IPSN 06.Google Scholar
  4. 4.
    Ossama Younis, Sonia Fahmy, “Distributed clustering in ad-hoc sensor networks:a hybrid, energy-efficient approach,” IEEE Transaction on Mobile Computing Vol.3, No.4, 2004.Google Scholar
  5. 5.
    Byoungyong Lee, Kyungseo Park, Ramez Elmasri, “Energy Balanced In-Network Aggregation Using Multiple Trees in Wireless Sensor Networks”, Consumer Communications and Networking Conference, Jan. 2007.Google Scholar
  6. 6.
    Prithwish Basu, Jason Redi, “Effect of overhearing transmissions on energy efficiency in dense sensor networks”, Proceedings of the third international symposium on Information processing in sensor networks, IPSN 04.Google Scholar
  7. 7.
    W.R. Heinzelman, A. Chandrakasan, H.Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks”, Proceedings of the 33rd Hawaii International Conference on System Sciences 2000.Google Scholar
  8. 8.
    Mark Stemm, Randy H Katz, “Measuring and reducing energy consumption of network interfaces in hand-held devices,” IEICE Transactions on Communications, vol. E80-B, no.8, pp1125-1131, Aug.1997.Google Scholar
  9. 9.
    Oliver Kasten, Energy Consumption,˜ kasten/ research/bathtub/ energy_consumption.html, Eldgenossische Technische Hochschule Zurich.Google Scholar
  10. 10.
    Habib M. Ammari and Sajal K. Das, “Trade-off between energy savings and source-to-sink delay in data dissemination for wireless sensor networks,” Proceedings of the 8th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems, MSWiM ’05.Google Scholar
  11. 11.
    S. Nath, P. Gibbons, S. Seshan, Z. Anderson, “Synopsis Diffusion for Robust Aggregation in Sensor Networks,” In Proc. 2nd ACM SenSys, pp 250-262,2004.Google Scholar
  12. 12.
    Qin Wang, Zygmunt J. Haas, “BASS: an adaptive sleeping scheme for wireless sensor network with bursty arrival,” Proceedings of the 2006 international conference on wireless communications and mobile computing, IWCMC ’06.Google Scholar
  13. 13.
    Wei Ye, John Heidemann, Deborah Estrin, “Medium access control with coordinated adaptive sleeping for wireless sensor networks,” IEEE/ACM Transaction on Networking, vol. 12, No.3, June 2004.Google Scholar
  14. 14.
    A. Keshavarzian, H. Lee, L. Venkatranman, “Wakeup Scheduling in Wireless Sensor Networks,” MobiHoc ’06, 2006, Florence, Italy.Google Scholar
  15. 15.
    S. Madden, M. Franklin, J. Hellerstein, W. Hong “TAG: a Tiny Aggregation Service for Ad-Hoc Sensor Networks,” In OSDI, 2002.Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Byoungyong Lee
    • 1
  • Kyungseo Park
    • 1
  • Ramez Elmasri
    • 1
  1. 1.Computer Science & Engineering DepartmentUniversity of Texas at ArlingtonArlingtonUSA

Personalised recommendations