Node Failure Time Analysis for Maximum Stability Versus Minimum Distance Spanning Tree Based Data Gathering in Mobile Sensor Networks

  • Natarajan MeghanathanEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 284)


A mobile sensor network is a wireless network of sensor nodes that move arbitrarily. In this paper, we explore the use of a maximum stability spanning tree-based data gathering (Max.Stability-DG) algorithm and a minimum-distance spanning tree-based data gathering (MST-DG) algorithm for mobile sensor networks. We analyze the impact of these two algorithms on the node failure times, specifically with respect to the node lifetime (the time of first node failure) and network lifetime (the time of disconnection of the network of live sensor nodes due to one or more node failures). Both the Max.Stability-DG and MST-DG algorithms are based on a greedy strategy of determining a data gathering tree when one is needed and using that tree as long as it exists. The Max.Stability-DG algorithm assumes the availability of the complete knowledge of future topology changes and determines a data gathering tree whose corresponding spanning tree would exist for the longest time since the current time instant; whereas, the MST-DG algorithm determines a data gathering tree whose corresponding spanning tree is the minimum distance tree at the current time instant. We observe a node lifetime – network lifetime tradeoff: the Max.Stability-DG trees incur a lower node lifetime due to repeated use of a data gathering tree for a longer time; on the other hand, the Max.Stability-DG trees incur a longer network lifetime.


  1. 1.
    S. Lindsey, C. Raghavendra, K.M. Sivalingam, Data gathering algorithms in sensor networks using energy metrics. IEEE Trans. Parallel Distrib. Syst. 13(9), 924–935 (2002)CrossRefGoogle Scholar
  2. 2.
    W. Heinzelman, A. Chandrakasan, H. Balakarishnan, Energy-efficient communication protocols for wireless microsensor networks, in Proceedings of the Hawaiian International Conference on Systems Science, Maui, HI, USA, Jan 2000Google Scholar
  3. 3.
    N. Meghanathan, An algorithm to determine energy-aware maximal leaf nodes data gathering tree for wireless sensor networks. J. Theor. Appl. Inf. Tech. 15(2), 96–107 (2010)Google Scholar
  4. 4.
    N. Meghanathan, A data gathering algorithm based on energy-aware connected dominating sets to minimize energy consumption and maximize node lifetime in wireless sensor networks. Int. J. Interdiscip. Telecommun. Netw. 2(3), 1–17 (2010)CrossRefGoogle Scholar
  5. 5.
    N. Meghanathan, A comprehensive review and performance analysis of data gathering algorithms for wireless sensor networks. Int. J. Interdiscip. Telecommun. Netw. 4(2), 1–29 (2012)CrossRefMathSciNetGoogle Scholar
  6. 6.
    T.H. Cormen, C.E. Leiserson, R.L. Rivest, C. Stein, Introduction to algorithms, 3rd edn. (MIT Press, Cambridge, MA, 2009)zbMATHGoogle Scholar
  7. 7.
    H. Zhang, J.C. Hou, Maintaining sensing coverage and connectivity in large sensor networks. Wirel. Ad Hoc Sens. Netw. 1(1–2), 89–123 (2005)Google Scholar
  8. 8.
    A. Farago, V.R. Syrotiuk, MERIT: a scalable approach for protocol assessment. Mobile Netw. Appl. 8(5), 567–577 (2003)CrossRefGoogle Scholar
  9. 9.
    F. Kuhn, T. Moscibroda, R. Wattenhofer, Unit disk graph approximation, in Proceedings of the Workshop on Foundations of Mobile Computing, Philadelphia, PA, USA, Oct 2004, pp. 17–23Google Scholar
  10. 10.
    A.J. Viterbi, CDMA: principles of spread spectrum communication, 1st edn. (Prentice Hall, Englewood Cliffs, 1995)zbMATHGoogle Scholar
  11. 11.
    T.S. Rappaport, Wireless communications: principles and practice, 2nd edn. (Prentice Hall, Upper Saddle River, 2002)Google Scholar
  12. 12.
    C. Bettstetter, H. Hartenstein, X. Perez-Costa, Stochastic properties of the random-way point mobility model. Wirel. Netw. 10(5), 555–567 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceJackson State UniversityJacksonUSA

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