The Journal of Supercomputing

, Volume 61, Issue 3, pp 869–893 | Cite as

General criteria-based clustering method for multi-node computing system

  • Yu Niu
  • Brian J. d’Auriol
  • Sungyoung LeeEmail author


Synchronization/desynchronization and clustering are important techniques in multi-node computing systems, especially for sensor networks (SN) which is broadly considered to be a type of multi-node computing environment. However, most of the existing algorithms’ clustering criteria are limited to the node location information and ignore the nature and characteristics of the nodes as well as the requirements of the applications. In this paper, an autonomic concurrent General Criteria-based Clustering (GCC) method is proposed for multi-node computing systems. The GCC method is based on the neuron oscillator pulse-coupling model and its clustering criteria can come from any node-related data or properties. The cluster member nodes share similar physical or logical properties and represent those relationships in the form of Logical Clusters (LCs). Due to the neuron dynamic system basis of the method, there is concurrency that exists both on the whole network and on each individual node. The simulation shows that the GCC method can generate diverse logical clusters and synchronization/desynchronization coexistence results with acceptable time and energy usage.


Clustering Pulse-coupling oscillator Multi-node computing system 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Degesys J, Rose I, Patel A, Nagpal R (2007) Desync: self-organizing desynchronization and TDMA on wireless networks. In: Proc of the sixth international symposium on information processing in sensor networks (IPSN2007), Cambridge, MA, USA, Apr, pp 10–20 Google Scholar
  2. 2.
    Sekiyama K, Kubo Y, Fukunaga S, Date M (2005) Self-organizing communication timing control for sensor network. Complex Int 12 [Online]. Available:
  3. 3.
    Sekiyama K, Suzuki K, Fukunaga S, Date M (2005) Autonomous synchronization scheme access control for sensor network. In: Proceedings of 9th international conference on knowledge-based intelligent information and engineering systems, Melbourne, Australia, Sept, KES 2005, vol 4, pp 487–495 CrossRefGoogle Scholar
  4. 4.
    Tate J, Bate I (2009) An improved lightweight synchronisation primitive for sensornets. In: Proc 6th IEEE international conference on mobile ad-hoc and sensor systems, Macau, Oct, 2009. IEEE Computer Society, Los Alamitos, pp 448–457 CrossRefGoogle Scholar
  5. 5.
    Heinzelman W, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless sensor networks. In: Proceedings of the 33th Hawaii international conference on system sciences, Havaii, USA, Jan, p 10 CrossRefGoogle Scholar
  6. 6.
    Myoupo JF, Cheikhna AO, Sow I (2010) A randomized clustering of anonymous wireless ad hoc networks with an application to the initialization problem. J Supercomput 52(2):135–148 CrossRefGoogle Scholar
  7. 7.
    Younis O, Fahmy S (2004) Heed: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput 3(4):366–379 CrossRefGoogle Scholar
  8. 8.
    Kuhn F, Moscibroda T, Wattenhofer R (2004) Initializing newly deployed ad hoc and sensor networks. In: Proceedings of the 10th annual international conference on mobile computing and networking (MOBICOM), Philadelphia, PA, USA, Sept. Springer, Berlin, pp 260–274 CrossRefGoogle Scholar
  9. 9.
    Amis A, Prakash R, Vuong T, Huynh D (2000) Max-min d-cluster formation in wireless ad hoc networks. In: Proceedings nineteenth annual joint conference of the IEEE computer and communications societies INFORCOM 2000, Tel-Aviv, Israel, Mar, vol 2, pp 32–41 CrossRefGoogle Scholar
  10. 10.
    Chan H, Perrig A (2004) Ace: An emergent algorithm for highly uniform cluster formation. In: Proc first European workshop sensor networks (EWSN), Jan, vol 2920. Springer, Berlin, pp 154–171 Google Scholar
  11. 11.
    Liu Y, Kwok YK, Wang JZ (2008) On scheduling and clustering in hierarchical TH-PPM UWB wireless ad hoc networks. J Supercomput 46(1):58–83 CrossRefGoogle Scholar
  12. 12.
    Chatterjee M, Das SK, Turgut D (2002) WCA: a weighted clustering algorithm for mobile ad hoc networks. Clust Comput 5(2):193–204 CrossRefGoogle Scholar
  13. 13.
    Govindan R, Hellerstein JM, Hong W, Madden S, Franklin M, Shenker S (2002) The sensor network as a database. USC Computer Science Department, Tech. Rep. Technical Report 02-771 Google Scholar
  14. 14.
    Mirollo R, Strogatz S (1990) Synchronization of pulse-coupled biological oscillators. SIAM J Appl Math 50(6):1645–1662 MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Lucarelli D, Wang I (2004) Decentralized synchronization protocols with nearest neighbor communication. In: Proc 2nd ACM conference on embedded networked sensor systems (SenSys’04), Baltimore, Maryland, USA, Nov, pp 62–68 CrossRefGoogle Scholar
  16. 16.
    Hong YW, Scaglione A (2005) A scalable synchronization protocol for large scale sensor networks and its applications. IEEE J Sel Areas Commun 23(5):1085–1099 CrossRefGoogle Scholar
  17. 17.
    Niu Y, d’Auriol BJ, Wu XL, Wang J, Cho JS, Lee SY (2008) Selective pulse coupling synchronicity for sensor network. In: Second international conference on sensor technologies and applications 2008 (SENSORCOMM ’08), Cap Esterel, France, Aug, pp 123–128 CrossRefGoogle Scholar
  18. 18.
    Werner Allen G, Tewari G, Patel A, Welsh M, Nagpal R (2005) Firefly inspired sensor network synchronicity with realistic radio effects. In: Proc 3rd ACM conference on embedded networked sensor systems (SenSys’05), San Diego, California, USA, Nov. ACM Press, New York, pp 142–153 CrossRefGoogle Scholar
  19. 19.
    Tyrrell A, Auer G, Bettstetter C (2006) Fireflies as role models for synchronization in ad hoc networks. In: Proc of 1st bio-inspired models of network, information, and computing systems 2006 (BIONETICS), Madonna di Campiglio, Dec, pp 1–7 CrossRefGoogle Scholar
  20. 20.
    van Vreeswijk C, Abbott LF (1993) Self-sustained firing in populations of integrate-and-fire neurons. SIAM J Appl Math 53(1):253–264 MathSciNetCrossRefGoogle Scholar
  21. 21.
    van Vreeswijk C (1996) Partial synchronization in populations of pulse-coupled oscillators. Phys Rev E 54(5):5522–5537 CrossRefGoogle Scholar
  22. 22.
    Ernst U, Pawelzik K, Geisel T (1995) Synchronization induced by temporal delays in pulse-coupled oscillators. Phys Rev Lett 74(9):1570–1573 CrossRefGoogle Scholar
  23. 23.
    Ernst U, Pawelzik K, Geisel T (1998) Delay-induced multistable synchronization of biological oscillators. Phys Rev E 57(2):2150–2162 MathSciNetCrossRefGoogle Scholar
  24. 24.
    Niu Y, d’Auriol BJ, Lee YK, Lee SY (2009) An analysis method for dynamical system. In: Proceedings of the 32th Korea information processing society (KIPS) fall conference, Seoul, Korea, Nov, pp 45–P2–086 Google Scholar
  25. 25.
    Hong YW, Scaglione A (2003) Time synchronization with pulse-coupled oscillators for UWB wireless ad hoc networks. In: Proc IEEE conference on ultra wideband systems and technologies, Reston, Virginia, USA, Nov, pp 190–194 CrossRefGoogle Scholar
  26. 26.
    802.15.4a-2007 (2007) IEEE standard for local and metropolitan area networks—specific requirements—Part 15.4: Wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate wireless personal area networks (WPANs): Amendment 1: Add alternate phys.” IEEE, Tech Rep, Aug 2007, amendment to IEEE Std 802.15.4-2006. [Online]. Available:
  27. 27.
    Arslan H, Chen ZN, Benedetto M-GD (2006) Ultra wideband wireless communication. Wiley-InterScience, Hoboken, Chap 14, pp 318–339 CrossRefGoogle Scholar
  28. 28.
    Benedetto M-GD, Nardis LD, Junk M, Giancola G (2005) (UWB)2: Uncoordinated, wireless, baseborn medium access for UWB communication networks. Mob Netw Appl 10(5):663–674 CrossRefGoogle Scholar
  29. 29.
    d’Auriol BJ, Niu Y, Lee S, Lee Y-K (2009) The plasma free space optical model for ubiquitous systems. In: Proceedings of the 3rd international conference on ubiquitous information management and communication (ICUIMC-09), Sungkyunkwan University, Suwon, Korea, Jan. ACM Press, New York, pp 446–455 Google Scholar
  30. 30.
    IEEE 802.11g-2003 (2003) Further higher data rate extension in the 2.4 GHz band (PDF), IEEE, Tech Rep, O ct retrieved 2007-09-24. [Online]. Available:
  31. 31.
    Ghosh AK, Kunta S, Verma P, Huck RC (2010) Free-space optics based sensor network design using angle-diversity photodiode arrays. In: Proceedings of SPIE, San Diego, CA, USA, Aug, vol 7814, p 78140U Google Scholar
  32. 32.
    Verma P, Ghosh AK, Venugopalan A (2008) Free-space optics based wireless sensor network design. In: 1st Symposium on military communications, Prague, Czech Republic, April Google Scholar
  33. 33.
    Nakano H, Utani A, Miyauchi A, Yamamoto H (2008) Synchronization-based data gathering scheme using chaotic pulse-coupled neural networks in wireless sensor networks. In: IEEE International joint conference on neural networks, Hong Kong, Jun 2008. IEEE Press, New York, pp 1115–1121 Google Scholar
  34. 34.
    Kusy B, Maroti M (2004) Flooding time synchronization in wireless sensor networks. In: Proc 2nd ACM conference on embedded networked sensor systems (SenSys’04), Baltimore, Maryland, USA, Nov., ACM Press, New York, pp 39–49 Google Scholar
  35. 35.
    Lindesey S, Raghavendra C (2002) Pegasis: power-efficient gathering in sensor information systems. In: Aerospace conference proceedings, 2002, Montana, USA, Mar. IEEE Press, New York, pp 3-1125–3-1130 Google Scholar
  36. 36.
    Heinzelman W, Chandrakasan A, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Dept. of Computer EngineeringKyung Hee UniversityYonginSouth Korea

Personalised recommendations