Energy Usage in Biomimetic Models for Massively-Deployed Sensor Networks

  • Kennie H. Jones
  • Kenneth N. Lodding
  • Stephan Olariu
  • Larry Wilson
  • Chunsheng Xin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3759)


Promises of ubiquitous control of the physical environment by sensor networks open avenues that will redefine the way we live and work. Due to the small size and low cost of sensors, visionaries promise smart systems enabled by deployment of huge numbers of sensors working in concert. At the moment, sensor network research is concentrating on developing techniques for performing simple tasks with minimal energy expense, assuming some form of centralized control. Centralized control does not scale to large networks and simple tasks in small-scale networks will not lead to the sophisticated applications predicted. Recently, the authors have proposed a new way of looking at sensor networks, motivated by lessons learned from the way biological ecosystems are organized. Here we demonstrate that in such a model, fully distributed data aggregation can be performed efficiently, without synchronization, in a scalable fashion, where individual motes operate autonomously based on local information, cooperating with neighbors to make local decisions that are aggregated across the network achieving globally-meaningful effects.


Sensor Network Wireless Sensor Network Cellular Automaton Local Decision Energy Usage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Akyildiz, F., Su, W., Sankarasubramanian, Y., Cayirci, E.: Wireless sensor networks: A survey. Computer Networks 38(4), 393–422 (2002)CrossRefGoogle Scholar
  2. 2.
    Jones, K.H., Lodding, K.N., Olariu, S., Wilson, L., Xin, C.: Biology-inspired distributed consensus in massively-deployed sensor networks. In: Proc. 4th International Conference on Ad hoc Networks and Wireless, Cancun, Mexico, October 6-8 (2005)Google Scholar
  3. 3.
    Culler, D., Estrin, D., Srivastava, M.: Overview of sensor networks. IEEE Computer 37(8), 41–49 (2004)Google Scholar
  4. 4.
    Lynch, N.A.: Distributed Algorithms. Morgan Kaufmann Publishers, San Francisco (1996)zbMATHGoogle Scholar
  5. 5.
    Hemingway, B., Brunette, W., Anderl, T., Boriello, G.: The flock: Mote sensors sing in undergraduate curriculum. IEEE Computer 37(8), 72–78 (2004)Google Scholar
  6. 6.
    Kahn, J.M., Katz, R.H., Pister, K.S.J.: Next century challenges: Mobile support for Smart Dust. In: Proc. ACM MOBICOM, Seattle, WA, pp. 271–278 (August 1999)Google Scholar
  7. 7.
    Lammers, D.: Embedded projects take a share of Intel’s research dollars. EE Times, August 28 (2001), from,, Retrieved April 5 (2004),
  8. 8.
    Martinez, K., Hart, J.K., Ong, R.: Environmental sensor networks. IEEE Computer 37(8), 50–56 (2004)Google Scholar
  9. 9.
    Olariu, S., Wadaa, A., Wilson, L., Eltoweissy, M.: Wireless sensor networks: leveraging the virtual infrastructure. IEEE Network 18(4), 51–56 (2004)CrossRefGoogle Scholar
  10. 10.
    Olariu, S., Xu, Q.: A simple self-organization protocol for massively deployed sensor networks, Computer Communications (2005) (to appear)Google Scholar
  11. 11.
    Park, S., Locher, I., Savvides, A., Srivastava, M.B., Chen, A., Muntz, R., Yue, S.: Design of a wearable sensor badge for smart kindergarten. In: Proc. 6th International Symposium on Wearable Computers, Seattle, WA (October 2002)Google Scholar
  12. 12.
    Ryokai, K., Cassell, J.: StoryMat: A play space for collaborative storytelling. In: Proc. CHI 1999 (October 1999)Google Scholar
  13. 13.
    Sohrabi, K., Gao, J., Ailawadhi, V., Pottie, G.: Protocols for self-organization of a wireless sensor network. IEEE Personal Communications 7(5), 16–27 (2000)CrossRefGoogle Scholar
  14. 14.
    Wadaa, A., Olariu, S., Wilson, L., Eltoweissy, M., Jones, K.: Training a wireless sensor network. Mobile Networks and Applications 10, 151–167 (2005)CrossRefGoogle Scholar
  15. 15.
    Warneke, B., Last, M., Leibowitz, B., Pister, K.: SmartDust: communicating with a cubic-millimeter computer. IEEE Computer 34(1), 44–55 (2001)Google Scholar
  16. 16.
    Zhirnov, V.V., Herr, D.J.C.: New frontiers: self-assembly and nano-electronics. IEEE Computer 34(1), 34–43 (2001)Google Scholar
  17. 17.
    Kelly, K.: Out of Control: The New Biology of Machines, Social Systems, and the Economic World, Perseus Books (1994)Google Scholar
  18. 18.
    MPR/MIB User’s Manual, Crossbow Technology, Inc., Retrieved May 5 (2005), from

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Kennie H. Jones
    • 1
  • Kenneth N. Lodding
    • 1
  • Stephan Olariu
    • 2
  • Larry Wilson
    • 2
  • Chunsheng Xin
    • 3
  1. 1.NASA Langley Research CenterHamptonUSA
  2. 2.Old Dominion UniversityNorfolkUSA
  3. 3.Norfolk State UniversityNorfolkUSA

Personalised recommendations