Implementation of Average Consensus Protocols for Commercial Sensor Networks Platforms

  • R. Pagliari
  • A. Scaglione
Conference paper
Part of the Signals and Communication Technology book series (SCT)

Abstract

In sensor networks, average consensus and gossiping algorithms, featuring only near neighbor communications, present advantages over flooding and epidemic algorithms in a number of distributed signal processing applications. This chapter looks into the implementation of average consensus algorithms within the constraints of current sensor network technology. Our event-based protocols work in the real event-based environment provided by a common Mica2 platform and use its wireless CSMA packet-switched network interface. Within this architecture our chapter derives different protocols according to an event-based software architecture that are suitable for an environment like TinyOS, the most used operating system for low-power mote platforms. Theoretical and simulation results are presented, and the main advantage over traditional routing protocols is given by the fully distributed and scalable nature this approach follows.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    D. Agrawal, A. El Abbadi, and R. C. Steinke, “Epidemic algorithms in replicated databases,” in PODS ’97: Proceedings of the Sixteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, New York, NY, USA, 1997, ACM Press, New York, pp. 161–172.Google Scholar
  2. [2]
    W. Ren and R. W. Beard, “Consensus seeking in multi-agent systems under dynamically changing interaction topologies,” IEEE Trans. Autom. Control, Technote, vol. 50, no. 5, pp. 655–661, 2003.CrossRefMathSciNetGoogle Scholar
  3. [3]
    S. Boyd, P. Diaconis, and L. Xiao, “Fastest mixing Markov chain on a graph,” SIAM Rev., vol. 46, no. 4, pp. 667–689, 2003.CrossRefMathSciNetGoogle Scholar
  4. [4]
    C. Y. Chong and S. P. Kumar, “Sensor networks: evolution, opportunities, and challenges,” in Proceedings of IEEE, San Antonio, TX, August 2003, pp. 1247–1256.Google Scholar
  5. [5]
    A. Demers, D. Greene, C. Hauser, W. Irish, J. Larson, S. Shenker, H. Sturgis, D. Swinehart, and D. Terry, ‘Epidemic algorithms for replicated database maintenance,” in PODC ’87: Proceedings of the Sixth Annual ACM Symposium on Principles of Distributed Computing, New York, NY, USA, 1987, ACM Press, New York, pp. 1–12.Google Scholar
  6. [6]
    J. Elson, L. Girod, and D. Estrin, “Fine-grained network time synchronization using reference broadcasts,” SIGOPS Oper. Syst. Rev., vol. 36, no. SI,p. 147–163, 2002.CrossRefGoogle Scholar
  7. [7]
    D. Ganesan, B. Krishnamachari, A. Woo, D. Culler, D. Estrin, and S. Wicker, “An empirical study of epidemic algorithms in large scale multihop wireless networks,” Tech. Rep., University of California, Los Angeles, 2002.Google Scholar
  8. [8]
    P. Gupta and P. Kumar, “Performance analysis of the IEEE 802.11 distributed coordination function,” IEEE Trans. Inf. Theory, vol. 18, no. 3, pp. 535–547, 2000.MathSciNetGoogle Scholar
  9. [9]
    P. Gupta and P. Kumar, “Capacity of wireless networks,” IEEE Trans. Inf. Theory, vol. 46, no. 2, pp. 388–404, 2000.MATHCrossRefMathSciNetGoogle Scholar
  10. [10]
    L. Lamport, “Time, clocks, and the ordering of events in a distributed system,” Commun. ACM, vol. 21, no. 7, pp. 558–565, 1978.MATHCrossRefGoogle Scholar
  11. [11]
    Q. Lindsey, D. Lymberopoulos, and A. Savvides, “An empirical analysis of radio signal strength variability in IEEE 802.15.4 networks using monopole antennas,” Tech. Rep., Yale University, ENALAB, 2006.Google Scholar
  12. [12]
    A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J. Anderson, “Wireless sensor networks for habitat monitoring,” in ACM WSNA, Atlanta, GA, 2002.Google Scholar
  13. [13]
    M. Maroti, “Directed flood-routing framework for wireless sensor networks,” in Middleware ’04: Proceedings of the 5th ACM/IFIP/USENIX International Conference on Middleware, New York, NY, USA, 2004, Springer, New York, pp. 99–114.Google Scholar
  14. [14]
    M. Maroti, B. Kusy, G. Simon, and A. Ledeczi, “The flooding time synchronization protocol,” in SenSys ’04: Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, New York, NY, USA, 2004, ACM Press, New York, pp. 39–49.Google Scholar
  15. [15]
    M. Mehyar, D. Spanos, J. Pongsajapan, S. Low, and R. Murray, “Asynchronous distributed averaging on communication networks,” IEEE Trans. Netw., vol. 18, no. 3, pp. 535–547, 2007.Google Scholar
  16. [16]
    D. L. Mills, “Internet time synchronization: the network time protocol,” IEEE Trans. Commun., vol. 39, no. 10, pp. 1482–1493, 1991.CrossRefGoogle Scholar
  17. [17]
    M. Mock, R. Frings, E. Nett, and S. Trikaliotis,“Continuous clock synchronization in wireless real-tim applications,” in The 19th IEEE Symposium on Reliable Distributed Systems, SRDS’00, Washington–Brussels–Tokyo, 2000, pp. 125–133.Google Scholar
  18. [18]
    L. Murray, “Stability of multiagent systems with time-dependent communication links,” IEEE Trans. Autom. Control, vol. 50, no. 2, pp. 169–182, 2005.CrossRefGoogle Scholar
  19. [19]
    R. Olfati Saber and R. M. Murray, “Consensus protocols for networks of dynamic agents,” in American Control Conference, 2003.Google Scholar
  20. [20]
    R. O. Saber and R. M. Murray, “Consensus problems in networks of agents with switching topology and time-delays,” IEEE Trans. Autom. Controls, vol. 49, no. 9, pp. 1520–1533, 2004.CrossRefGoogle Scholar
  21. [21]
    K. Romer, “Time synchronization in ad hoc networks,” in MobiHoc 2001, Long Beach, USA, 2001.Google Scholar
  22. [22]
    R. O. Saber and R. M. Murray, “Consensus roblems in networks of agents with switching topology and time-delays,” IEEE Trans. Autom. Controls, vol. 49, no. 9, pp. 1520–1533, 2004.CrossRefGoogle Scholar
  23. [23]
    M. L. Sichitiu and C. Veerarittiphan, “Simple, accurate time synchronization for wireless sensor networks,” in Proceedings of the IEEE Wireless Communications and Networking Conference, WCNC’2003, New Orleans, LA, USA, 2003.Google Scholar
  24. [24]
    W. Su and I. F. Akyildiz, “Time-diffusion synchronization protocol for wireless sensor networks,” IEEE/ACM Trans. Netw., vol. 13, no. 2, pp. 384–397, 2005.CrossRefGoogle Scholar
  25. [25]
    L. Xiao and S. Boyd, “Fast linear iterations for distributed averaging,” Syst. Control Lett., vol. 53, pp. 65–78, 2004.MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • R. Pagliari
    • 1
  • A. Scaglione
    • 2
  1. 1.University of GenovaGenovaItaly
  2. 2.School of Electrical and Computer EngineeringCornell UniversityIthacaUSA

Personalised recommendations