Wireless Networks

, Volume 12, Issue 6, pp 691–707 | Cite as

Energy balanced data propagation in wireless sensor networks

  • Charilaos Efthymiou
  • Sotiris Nikoletseas
  • Jose Rolim


We study the problem of energy-balanced data propagation in wireless sensor networks. The energy balance property guarantees that the average per sensor energy dissipation is the same for all sensors in the network, during the entire execution of the data propagation protocol. This property is important since it prolongs the network’:s lifetime by avoiding early energy depletion of sensors.

We propose a new algorithm that in each step decides whether to propagate data one-hop towards the final destination (the sink), or to send data directly to the sink. This randomized choice balances the (cheap) one-hop transimssions with the direct transimissions to the sink, which are more expensive but “bypass” the sensors lying close to the sink. Note that, in most protocols, these close to the sink sensors tend to be overused and die out early.

By a detailed analysis we precisely estimate the probabilities for each propagation choice in order to guarantee energy balance. The needed estimation can easily be performed by current sensors using simple to obtain information. Under some assumptions, we also derive a closed form for these probabilities.

The fact (shown by our analysis) that direct (expensive) transmissions to the sink are needed only rarely, shows that our protocol, besides energy-balanced, is also energy efficient.


Wireless sensor networks Data propagation Energy balance Randomized algorithms 


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  1. [1]
    I.F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, Wireless sensor networks: A survey, The Journal of Computer Networks 38 (2002) 393–422.CrossRefGoogle Scholar
  2. [2]
    I.F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, A survey on sensor networks, The IEEE Communications Magazine (August 2002) 102–114.Google Scholar
  3. [3]
    A. Boukerche, I. Chatzigiannakis and S. Nikoletseas, A new energy efficient and fault-tolerant protocol for data propagation in smart dust networks using varying transmission range, Accepted in the Computer Communications Journal, Elsevier, (2004).Google Scholar
  4. [4]
    A. Boukerche, X. Cheng and J. Linus, Energy-aware data-centric routing in microsensor networks, ACM Madeling Analysis and Simulation of Wireless and Mobile Systems (MSWIM2003) (Paris, France, 2003) pp. 42–49.Google Scholar
  5. [5]
    A. Boukerche and S. Nikoletseas, Protocols for data propagation in Wireless Sensor Networks: A survey, chapter in the book wireless communications systems and networks, Mohsen Guizani, (Eds.) Kluwer Academic Publishers, Date Published (06/2004), ISBN: 0306481901, 718 p.Google Scholar
  6. [6]
    A. Boukerche and S. Nikoletseas, Energy efficient algorithms in wireless sensor networks invited book chapter, Springer Verlag, to appear in (2004).Google Scholar
  7. [7]
    I. Chatzigiannakis, S. Nikoletseas and P. Spirakis, Smart dust protocols for Local Detection and Propagation, in: Proc. 2nd ACM Workshop on Principles of Mobile Computing—POMC’2002. Also accepted in the ACM Mobile Networks (MONET) Journal, Special Issue on Algorithmic Solutions for Wireless, Mobile, Ad-Hoc and Sensor Networks, to appear.Google Scholar
  8. [8]
    I. Chatzigiannakis, T. Dimitriou, S. Nikoletseas and P. Spirakis, A probabilistic algorithm for efficient and robust data propagation in smart dust networks, in Proc. 5th European Wireless Conference (EW’04), Barcelona, Spain Also, invited in the Journal of Ad-Hoc Networks (under review) (February, 2004).Google Scholar
  9. [9]
    I. Chatzigiannakis, T. Dimitriou, M. Mavronicolas, S. Nikoletseas and P. Spirakis, A comparative study of protocols for efficient data propagation in smart dust networks, in Proc. International Conference on Parallel and Distributed ComputingEUPOPAR 2003. Also Accepted in the Parallel Processing Letters Journal (PPL), Vol. 13 (2003) pp. 615–627.Google Scholar
  10. [10]
    I. Chatzigiannakis and S. Nikoletseas, A sleep-awake protocol for information propagation in smart dust networks, in Proc. 3rd Workshop on Mobile and Ad-Hoc Networks (WMAN)–IPDPS Workshops, (IEEE Press, 2003) p. 225.Google Scholar
  11. [11]
    C. Efthymiou, S. Nikoletseas and J. Rolim, Energy balanced data propagation in wireless sensor networks, in Proc. 4th Workshop on Mobile, Ad-Hoc and Sensor Networks (WMAN)–IPDPS Workshops, (IEEE Press, 2004).Google Scholar
  12. [12]
    W. R. Heinzelman, A. Chandrakasan and H. Balakrishnan, Energy-efficient communication protocol for wireless microsensor Networks, in Proc. 33rd Hawaii International Conference on System Sciences–HICSS’ (2000).Google Scholar
  13. [13]
    C. Intanagonwiwat, R. Govindan and D. Estrin, Directed diffusion: A scalable and robust communication paradigm for sensor networks, in Proc. 6th ACM/IEEE International Conference on Mobile Computing–MOBICOM’ (2000).Google Scholar
  14. [14]
    C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann and F. Silva, Directed diffusion for wireless sensor networking, Extended version of [13].Google Scholar
  15. [15]
    J.M. Kahn, R.H. Katz and K.S.J. Pister, Next century challenges: Mobile networking for smart dust, in Proc. 5th ACM/IEEE International Conference on Mobile Computing (September 1999) pp. 271–278.Google Scholar
  16. [16]
    S. Nikoletseas, I. Chatzigiannakis, A. Antoniou, C. Efthymiou, A. Kinalis and G. Mylonas, Energy efficient protocols for sensing Multiple Events in Smart Dust Networks, in Proc. 37th Annual ACM/IEEE Simulation Symposium (ANSS’04), (IEEE Computer Society Press, 2004) pp. 15–24.Google Scholar
  17. [17]
    S. Nikoletseas, C. Raptopoulos and P. Spirakis, The existence and efficient construction of large independent sets in general random intersection graphs, in The Proceedings of the 31st International Colloquium on Automata, Languages and Programming (ICALP), Lecture Notes in Computer Science (Springer Verlag, 2004).Google Scholar
  18. [18]
    S. M. Ross, Stochastic processes, 2nd Edition. John Wiley and Sons, Inc., (1995).Google Scholar
  19. [19]
    C. Schurgers, V. Tsiatsis, S. Ganeriwal and M. Srivastava, Topology management for sensor networks: Exploiting latency and density, in Proc. MOBICOM (2002). Google Scholar
  20. [20]
    P. Triantafilloy, N. Ntarmos, S. Nikoletseas and P. Spirakis, NanoPeer networks and P2P worlds, in Proc. 3rd IEEE International Conference on Peer-to-Peer Computing, (2003).Google Scholar
  21. [21]
    M. Singh, and V. Prasanna, Energy-optimal and energy-balanced sorting in a single-hop wireless sensor network, in Proc. First IEEE International Conference on Pervasive Computing and Comminications – PERCOM (2003).Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Charilaos Efthymiou
    • 1
  • Sotiris Nikoletseas
    • 1
  • Jose Rolim
    • 2
  1. 1.Computer Technology Institute and Department of Computer Engineering & InformaticsUniversity of PatrasGreece
  2. 2.University of GenevaSwitzerland

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