Open Access

Extending the Lifetime of Sensor Networks through Adaptive Reclustering

EURASIP Journal on Wireless Communications and Networking20072007:031809

DOI: 10.1155/2007/31809

Received: 14 October 2006

Accepted: 30 March 2007

Published: 6 June 2007


We analyze the lifetime of clustered sensor networks with decentralized binary detection under a physical layer quality-of-service (QoS) constraint, given by the maximum tolerable probability of decision error at the access point (AP). In order to properly model the network behavior, we consider four different distributions (exponential, uniform, Rayleigh, and lognormal) for the lifetime of a single sensor. We show the benefits, in terms of longer network lifetime, of adaptive reclustering. We also derive an analytical framework for the computation of the network lifetime and the penalty, in terms of time delay and energy consumption, brought by adaptive reclustering. On the other hand, absence of reclustering leads to a shorter network lifetime, and we show the impact of various clustering configurations under different QoS conditions. Our results show that the organization of sensors in a few big clusters is the winning strategy to maximize the network lifetime. Moreover, the observation of the phenomenon should be frequent in order to limit the penalties associated with the reclustering procedure. We also apply the developed framework to analyze the energy consumption associated with the proposed reclustering protocol, obtaining results in good agreement with the performance of realistic wireless sensor networks. Finally, we present simulation results on the lifetime of IEEE 802.15.4 wireless sensor networks, which enrich the proposed analytical framework and show that typical networking performance metrics (such as throughput and delay) are influenced by the sensor network lifetime.


Authors’ Affiliations

Wireless Ad-Hoc and Sensor Networks (WASN) Laboratory, Department of Information Engineering, University of Parma


  1. Tsitsiklis JN: Decentralized detection. In Advanced Statistical Signal Processing. Volume 2. Edited by: Poor HV, Thomas JB. JAI Press, Greenwich, Conn, USA; 1993:297-344.Google Scholar
  2. Tenney RR, Sandell NR Jr.: Detection with distributed sensors. IEEE Transactions on Aerospace and Electronic Systems 1981,17(4):501-510.MathSciNetView ArticleGoogle Scholar
  3. Chong C-Y, Kumar SP: Sensor networks: evolution, opportunities, and challenges. Proceedings of the IEEE 2003,91(8):1247-1256. 10.1109/JPROC.2003.814918View ArticleGoogle Scholar
  4. Simic SN, Sastry S: Distributed environmental monitoring using random sensor networks. Proceedings of the 2nd International Workshop on Information Processing in Sensor Networks (IPSN '03), April 2003, Palo Alto, Calif, USA 582-592.View ArticleGoogle Scholar
  5. Viswanathan R, Varshney PK: Distributed detection with multiple sensors—part I: fundamentals. Proceedings of the IEEE 1997,85(1):54-63. 10.1109/5.554208View ArticleGoogle Scholar
  6. Shi W, Sun TW, Wesel RD: Quasi-convexity and optimal binary fusion for distributed detection with identical sensors in generalized Gaussian noise. IEEE Transactions on Information Theory 2001,47(1):446-450. 10.1109/18.904560MATHMathSciNetView ArticleGoogle Scholar
  7. Rappaport TS: Wireless Communications. Principles & Pratice. 2nd edition. Prentice-Hall, Upper Saddle River, NJ, USA; 2002.Google Scholar
  8. Ferrari G, Pagliari R: Decentralized binary detection with noisy communication links. IEEE Transactions on Aerospace and Electronic Systems 2006,42(4):1554-1563.View ArticleGoogle Scholar
  9. Kansal A, Ramamoorthy A, Srivastava MB, Pottie GJ: On sensor network lifetime and data distortion. Proceedings of IEEE International Symposium on Information Theory (ISIT '05), September 2005, Adelaide, Australia 6-10.Google Scholar
  10. Arnon S: Deriving an upper bound on the average operation time of a wireless sensor network. IEEE Communications Letters 2005,9(2):154-156. 10.1109/LCOMM.2005.02022View ArticleGoogle Scholar
  11. Ordóñez F, Krishnamachari B: Optimal information extraction in energy-limited wireless sensor networks. IEEE Journal on Selected Areas in Communications 2004,22(6):1121-1129. 10.1109/JSAC.2004.830930View ArticleGoogle Scholar
  12. Zhang H, Hou J:On deriving the upper bound of -lifetime for large sensor networks. Proceedings of the 5th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc '04), May 2004, Tokyo, Japan 121-132.View ArticleGoogle Scholar
  13. Hu Z, Li B: On the fundamental capacity and lifetime limits of energy-constrained wireless sensor networks. Proceedings of the 10th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS '04), May 2004, Toronto, Canada 2-9.Google Scholar
  14. Blough DM, Santi P: Investigating upper bounds on network lifetime extension for cell-based energy conservation techniques in stationary ad hoc networks. Proceedings of the 8th Annual International Conference on Mobile Computing and Networking (MOBICOM '02), September 2002, Atlanta, Ga, USA 183-192.View ArticleGoogle Scholar
  15. Bhardwaj M, Garnett T, Chandrakasan AP: Upper bounds on the lifetime of sensor networks. Proceedings of IEEE International Conference on Communications (ICC '01), June 2001, Helsinki, Finland 3: 785-790.Google Scholar
  16. Bhardwaj M, Chandrakasan AP: Bounding the lifetime of sensor networks via optimal role assignments. Proceedings of the 21st Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '02), June 2002, New York, NY, USA 3: 1587-1596.Google Scholar
  17. Rai V, Mahapatra RN: Lifetime modeling of a sensor network. Proceedings of Design, Automation and Test in Europe (DATE '05), March 2005, Munich, Germany 1: 202-203.View ArticleGoogle Scholar
  18. Chen Y, Zhao Q: On the lifetime of wireless sensor networks. IEEE Communications Letters 2005,9(11):976-978. 10.1109/LCOMM.2005.11010View ArticleGoogle Scholar
  19. Zhao Q, Swami A, Tong L: The interplay between signal processing and networking in sensor networks. IEEE Signal Processing Magazine 2006,23(4):84-93.View ArticleGoogle Scholar
  20. Kalpakis K, Dasgupta K, Namjoshi P: Maximum lifetime data gathering and aggregation in wireless sensor networks. In Tech. Rep. TR CS-02-12. University of Maryland, Baltimore, Md, USA; 2002. Scholar
  21. Coleri S, Ergen M, Koo TJ: Lifetime analysis of a sensor network with hybrid automata modelling. Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications (WSNA '02), September 2002, Atlanta, Ga, USA 98-104.View ArticleGoogle Scholar
  22. Franceschetti M, Meester R: Critical node lifetimes in random networks via the Chen-Stein method. IEEE Transactions on Information Theory 2006,52(6):2831-2837.MATHMathSciNetView ArticleGoogle Scholar
  23. Timmons NF, Scanlon WG: Analysis of the performance of IEEE 802.15.4 for medical sensor body area networking. Proceedings of the 1st Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON '04), October 2004, Santa Clara, Calif, USA 16-24.Google Scholar
  24. Gutierrez JA, Callaway EH Jr., Barrett RL Jr.: IEEE 802.15.4 Std: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs). IEEE Computer Society Press, Washington, DC, USA; 2003.Google Scholar
  25. Ferrari G, Martalò M, Sarti M: Reduced-complexity decentralized detection of spatially non-constant phenomena. Proceedings of the 2nd International Workshop on Distributed Cooperative Laboratories (INGRID '07), April 2007, Portofino, ItalyGoogle Scholar
  26. Ferrari G, Martalò M, Pagliari R: Clustered decentralized binary detection: an information-theoretic approach. Proceedings of the 2nd International Symposium on Communications, Control and Signal Processing (ISCCSP '06), March 2006, Marrakech, MoroccoGoogle Scholar
  27. Papoulis A: Probability, Random Variables and Stochastic Processes. McGraw-Hill, New York, NY, USA; 1991.Google Scholar
  28. Ferrari G, Martalò M, Pagliari R: On multi-level decentralized detection in sensor networks. Proceedings of International Conference on Intelligent Systems and Computing: Theory and Applications (ISYC '06), July 2006, Ayia Napa, CyprusGoogle Scholar
  29. Ziemer RE: Elements of Engineering Probability & Statistics. Prentice-Hall, Upper Saddle River, NJ, USA; 1997.Google Scholar
  30. Balakrishnan N, Cohen AC: Order Statistics and Inference, Estimation Methods. Academic Press, New York, NY, USA; 1991.MATHGoogle Scholar
  31. Conway JH, Guy RK: The Book of Numbers. Springer, New York, NY, USA; 1996.MATHView ArticleGoogle Scholar
  32. Ferrari G, Medagliani P, Di Piazza S, Martalò M: Wireless sensor networks: performance analysis in indoor scenarios. EURASIP Journal on Wireless Communications and Networking 2007, 2007: 14 pages.Google Scholar
  33. Ma J, Gao M, Zhang Q, Ni LM, Zhu W: Localized low-power topology control algorithms in IEEE 802.15.4-based sensor networks. Proceedings of the 25th IEEE International Conference on Distributed Computing Systems (ICDCS '05), June 2005, Columbus, Ohio, USA 27-36.Google Scholar
  34. Opnet
  35. National Institute of Standards and Technology (NIST),
  36. Ferrari G, Medagliani P, Martalò M: Performance analysis of Zigbee wireless sensor networks with relaying. Proceedings of the 2nd International Workshop on Distributed Cooperative Laboratories (INGRID '07), April 2007, Portofino, ItalyGoogle Scholar


© G. Ferrari and M. Martalò. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.