Application oriented connected dominating set-based cluster formation in wireless sensor networks

Open Access
Original Paper

Abstract

Clustering is a fundamental mechanism used in the design of Wireless Sensor Network (WSN) protocols. The performance of WSNs can be improved by selecting the most suitable nodes to form a stable backbone structure with guaranteed network coverage. This paper proposes a base station-controlled centralized algorithm for static sensor networks and a distributed, weighted algorithm for dynamic sensor networks. The solutions are based on a (k,r)-Connected Dominating Set, which is suitable for cluster-based hierarchical routing. The clusterhead redundancy parameter k improves reliability, the multi-hop parameter r addresses the scalability issue and the combined weight metric improves the network lifespan and reduces the number of re-affiliations. To create a stable and efficient backbone structure, the backbone sensor nodes are selected based on quality, which is a function of the residual battery power, node degree, transmission range, and mobility of the sensor nodes. Simulation experiments are conducted to evaluate the performance of both the algorithms in terms of the number of elements in the backbone structure, re-affiliation frequency, load balancing, network lifespan, and the power dissipation. The results establish the potential of these algorithms for use in WSNs.

Keywords

Sensor network Weighted clustering Connected dominating set Load balancing Mobility 

References

  1. 1.
    Agre J, Clare L (2000) An integrated architecture for cooperative sensing networks. Computer 33(5):106–108CrossRefGoogle Scholar
  2. 2.
    Akkaya K, Younis M (2005) A survey on routing protocols for wireless sensor networks. Ad Hoc Netw 3(3):325–349CrossRefGoogle Scholar
  3. 3.
    Akyildiz IF, Su W, Sankaraubramaniam Y, Cayirci E (2002) A survey on sensor networks. IEEE Commun MagGoogle Scholar
  4. 4.
    Akylidiz IF, Su W, Sankarasubramanian Y, Cayirci E (2002) Wireless sensor network: A survey on sensor network. IEEE Commun Mag 40(8):102–114CrossRefGoogle Scholar
  5. 5.
    Anitha VS, Sebastian MP (2009) Scenario-based cluster formationand management in mobile ad hoc networks. Int J Mobile Comput Multimedia Commun 1(1):1–15 (IGI Journal)CrossRefGoogle Scholar
  6. 6.
    Anitha VS, Sebastian MP (2009) Scenario-based diameter-bounded algorithm for cluster creation and management in mobile ad hoc networks. In: 13th IEEE/ACM international symposium on distributed simulation and real time applications, pp 97–104Google Scholar
  7. 7.
    Blum J, Ding M, Thaeler A, Cheng X (2004) Connected dominating set in sensor networks and MANETs. In: Du D-Z, Pardalos P (eds) Handbook of combinatorial optimization. Kluwer Academic, AmsterdamGoogle Scholar
  8. 8.
    Bonnet P, Gehrke J, Seshadri P (2000) Querying the physical world. IEEE Pers Commun 7(5):10–15CrossRefGoogle Scholar
  9. 9.
    Bulusu N, Estrin D, Girod L, Heidemann J (2001) Scalable coordination for wireless sensor networks: self-configuring localization systems. In: International symposium on communication theory and applications (ISCTA 2001). Ambleside, UK, 2001Google Scholar
  10. 10.
    Buratti C, Conti A, Dardari D, Verdone R (2009) An overview on wireless sensor networks technology and evolution. Sensors 9(9):6869–6896CrossRefGoogle Scholar
  11. 11.
    Chatterjee M, Das SK, Turgut D (2002) Wca: a weighted clustering algorithm for mobile ad hoc networks. Clust Comput 5(1):193–204CrossRefGoogle Scholar
  12. 12.
    Chen Y, Lieshman AL (2002) Approximating minimum size weakly connected dominating sets for clustering mobile ad hoc networks. In: MobiHoc. ACM Press, New York, pp 165–172Google Scholar
  13. 13.
    Das B, Bhargavan V (1997) Routing in ad hoc networks using minimum connected dominating set. In: ICE, pp 371–380Google Scholar
  14. 14.
    Das B, Sivakumar E, Bhargavan V (1997) Routing in ad hoc networks using a virtual backbone. In: Proceedings for the 6th international conference on computer communications and networks (IC3N’97), Las Vegas, NV, USA, 1997, pp 22–25Google Scholar
  15. 15.
    Das B, Sivakumar R, Bharghavan V (1997) Routing in ad-hoc networks using a spine. In: Proc of international conference on computers and communications networks, ICCCN, Las Vegas, 1997Google Scholar
  16. 16.
    Ding P, Holliday J, Celek A (2005) Distributed energy efficient hierarchical clustering for wireless sensor networks. In: Proc of the IEEE international conference on distributed computing in sensor systemsGoogle Scholar
  17. 17.
    Friis: (1946) A note on simple transmission formula. In: Proceedings of IRE, pp 254–256Google Scholar
  18. 18.
    Garey M, Johnson D (1978) Computers and intractability: a guide to NP-completenessGoogle Scholar
  19. 19.
    Guha S, Khuller S (1998) Approximation algorithms for connected dominating sets. Algorithmica 20:374–387MathSciNetCrossRefGoogle Scholar
  20. 20.
    Halweil B (2001) Study finds modern farming is costly. World Watch 14(1):9–10Google Scholar
  21. 21.
    Heinzelman W, Chandrakasan A, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670CrossRefGoogle Scholar
  22. 22.
    Johnson P, Andrews DC (1996) Remote continuous physiological monitoring in the home. J Telemed Telecare 2(2):107–113CrossRefGoogle Scholar
  23. 23.
    Kahn J, Katz R, Pister K (1999) Next century challenges: mobile networking for smart dust. In: Proceedings of the ACM MobiCom’99. Washington, USA, 1999, pp 271–278Google Scholar
  24. 24.
    Li J, Andrew LL, Foh CH, Zukerman M, Chen HH (2009) Connectivity, coverage and placement in wireless sensor networks. SensorsGoogle Scholar
  25. 25.
    Lindsey S, Raghavendra CS (2002) Pegasis: power efficient gathering in sensor information systems. In: Proc of IEEE aerospace conference. IEEEGoogle Scholar
  26. 26.
    Noury N, Herve T, Rialle V, Virone G, Mercier E, Morey G, Moro A, Porcheron T (2000) Monitoring behavior in home using a smart fall sensor. In: IEEE-EMBS special topic conference on microtechnologies in medicine and biology, pp 607–610Google Scholar
  27. 27.
    Paruchuri V, Durresi A, Durresi M, Barolli L (2005) Routing through back bone structures in sensor networks. In: Proceedings ICPADS, ICPADS, Japan, 2005, pp 397–401Google Scholar
  28. 28.
    Rabaey J, Ammer M, da Silva J Jr, Patel D, Roundy S (2000) Picoradio supports ad hoc ultra-low power wireless networking. IEEE Comput MagGoogle Scholar
  29. 29.
    Ryl DS, Stojmenovic I, Wu J (2005) Energy-efficient backbone construction, broadcasting, and area coverage in sensor networks. Handbook of sensor networks. Wiley, New YorkGoogle Scholar
  30. 30.
    Sinha P, Sivakumar R, Bharghavan V (2001) Enhancing ad hoc routing with dynamic virtual infrastructures. In: 20th annual joint conference of the IEEE computer and communications societies, vol 3Google Scholar
  31. 31.
    Stojmenovic I, Wu J (2004) Broadcasting and activity scheduling in AD HOC networks. In: Basagni S, Conti M, Giordano S, Stojmenovic I (eds) Mobile ad hoc networking. IEEE, New YorkGoogle Scholar
  32. 32.
    Wan PJ, Alzoubi KM, Frieder O (2004) Distributed construction of connected dominating set in wireless ad hoc networks. Mobile Netw Appl 9:141–149CrossRefGoogle Scholar
  33. 33.
    Warneke B, Liebowitz B, Pister K (2001) Smart dust: communicating with a cubic-millimeter computer. IEEE Computer, New YorkGoogle Scholar
  34. 34.
    Wu J, Li H On calculating connected dominating set for efficient routing in ad hoc wireless networks. In: Proc of proceedings of the 3rd ACM international workshop on discrete algorithms and methods for mobile computing and communications, pp. 7–14. ACM, New YorkGoogle Scholar
  35. 35.
    Wu Y, Li Y (2008) Construction algorithms for k-connected m-dominating sets in wireless sensor networks. In: Mobihoc 2008Google Scholar
  36. 36.
    Wu Y, Wang F, Thai MT, Li Y (2007) Constructing k-connected m-dominating sets in wireless sensor networks. In: Military communications conference. MILCOM, Orlando, pp 29–31Google Scholar
  37. 37.
    Younis O, Fahmy S (2004) Heed: A hybrid energy-efficient distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput 3(4):366–379CrossRefGoogle Scholar

Copyright information

© The Brazilian Computer Society 2010

Authors and Affiliations

  1. 1.National Institute of TechnologyCalicutIndia
  2. 2.Indian Institute of Management KozhikodeKozhikodeIndia

Personalised recommendations