LazySync: A New Synchronization Scheme for Distributed Simulation of Sensor Networks

  • Zhong-Yi Jin
  • Rajesh Gupta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5516)


To meet the demands for high simulation fidelity and speed, parallel and distributed simulation techniques are widely used in building wireless sensor network simulators. However, accurate simulations of dynamic interactions of sensor network applications incur large synchronization overheads and severely limit the performance of existing distributed simulators. In this paper, we present LazySync, a novel conservative synchronization scheme that can significantly reduce such overheads by minimizing the number of clock synchronizations during simulations. We implement and evaluate this scheme in a cycle accurate distributed simulation framework that we developed based on Avrora, a popular parallel sensor network simulator. In our experiments, the scheme achieves a speedup of 4% to 53% in simulating single-hop sensor networks with 8 to 256 nodes and 4% to 118% in simulating multi-hop sensor networks with 16 to 256 nodes. The experiments also demonstrate that the speedups can be significantly larger as the scheme scales with both the number of packet transmissions and sensor network size.


Sensor Network Sensor Node Wireless Sensor Network Neighboring Node Wireless Channel 
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  1. 1.
    Chandy, K.M., Misra, J.: Asynchronous distributed simulation via a sequence of parallel computations. Commun. ACM 24(4), 198–206 (1981)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Levis, P., Lee, N., Welsh, M., Culler, D.: Tossim: accurate and scalable simulation of entire tinyos applications. In: SenSys 2003: Proceedings of the 1st international conference on Embedded networked sensor systems, pp. 126–137. ACM Press, New York (2003)Google Scholar
  3. 3.
    Shnayder, V., Hempstead, M., rong Chen, B., Allen, G.W., Welsh, M.: Simulating the power consumption of large-scale sensor network applications. In: SenSys 2004: Proceedings of the 2nd international conference on Embedded networked sensor systems, pp. 188–200. ACM, New York (2004)Google Scholar
  4. 4.
    Polley, J., Blazakis, D., McGee, J., Rusk, D., Baras, J.: Atemu: a fine-grained sensor network simulator. In: 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks. IEEE SECON 2004, October 4-7, 2004, pp. 145–152 (2004)Google Scholar
  5. 5.
    Landsiedel, O., Alizai, H., Wehrle, K.: When timing matters: Enabling time accurate and scalable simulation of sensor network applications. In: IPSN 2008: Proceedings of the 2008 International Conference on Information Processing in Sensor Networks, Washington, DC, USA, pp. 344–355. IEEE Computer Society Press, Los Alamitos (2008)Google Scholar
  6. 6.
    Jin, Z., Gupta, R.: Improved distributed simulation of sensor networks based on sensor node sleep time. In: Nikoletseas, S.E., Chlebus, B.S., Johnson, D.B., Krishnamachari, B. (eds.) DCOSS 2008. LNCS, vol. 5067, pp. 204–218. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Fujimoto, R.M.: Parallel and distributed simulation. In: WSC 1999: Proceedings of the 31st conference on Winter simulation, pp. 122–131. ACM Press, New York (1999)Google Scholar
  8. 8.
    Riley, G.F., Ammar, M.H., Fujimoto, R.M., Park, A., Perumalla, K., Xu, D.: A federated approach to distributed network simulation. ACM Trans. Model. Comput. Simul. 14(2), 116–148 (2004)CrossRefGoogle Scholar
  9. 9.
    Titzer, B.L., Lee, D.K., Palsberg, J.: Avrora: scalable sensor network simulation with precise timing. In: IPSN 2005: Proceedings of the 4th international symposium on Information processing in sensor networks, Piscataway, NJ, USA, pp. 477–482. IEEE Press, Los Alamitos (2005)Google Scholar
  10. 10.
    Wen, Y., Wolski, R., Moore, G.: Disens: scalable distributed sensor network simulation. In: PPoPP 2007: Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming, pp. 24–34. ACM Press, New York (2007)Google Scholar
  11. 11.
    Henderson, T.: NS-3 Overview (2008)Google Scholar
  12. 12.
    Jefferson, D.R.: Virtual time. ACM Trans. Program. Lang. Syst. 7(3), 404–425 (1985)CrossRefGoogle Scholar
  13. 13.
    Jin, Z., Gupta, R.: Improving the speed and scalability of distributed simulations of sensor networks. Technical Report CS2009-0935, UCSD (2009)Google Scholar
  14. 14.
    Filo, D., Ku, D.C., Micheli, G.D.: Optimizing the control-unit through the resynchronization of operations. Integr. VLSI J. 13(3), 231–258 (1992)CrossRefMATHGoogle Scholar
  15. 15.
    Liu, J., Nicol, D.M.: Lookahead revisited in wireless network simulations. In: PADS 2002: Proceedings of the sixteenth workshop on Parallel and distributed simulation, Washington, DC, USA, pp. 79–88. IEEE Computer Society Press, Los Alamitos (2002)Google Scholar
  16. 16.
    Hughes, J.: Why functional programming matters. Comput. J. 32(2), 98–107 (1989)CrossRefGoogle Scholar
  17. 17.
    Crossbow: MICA2 Datasheet (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Zhong-Yi Jin
    • 1
  • Rajesh Gupta
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of CaliforniaSan DiegoUSA

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