Threat-Aware Clustering in Wireless Sensor Networks

  • Ryan E. Blace
  • Mohamed Eltoweissy
  • Wael Abd-Almageed
Part of the IFIP – The International Federation for Information Processing book series (IFIPAICT, volume 264)


Technological advances in miniaturization and wireless networking have enabled the utilization of distributed wireless sensor networks (WSN) in many applications. WSNs often use clustering as a means of achieving scalable and efficient communications. Cluster head nodes are of increased importance in these network topologies because they are both communication and coordination hubs. Much of the research into maximizing WSN longevity and efficiency focuses on dynamically clustering the network according to the residual energy contained within each node. This is a result of the commonly held assumption that battery depletion is the primary cause of node failure. In this work, we consider that there are applications in which threats may significantly impact node survival. In order to cope with these applications, we present a threat-aware clustering algorithm, extending the Hybrid Energy Efficient Distributed clustering algorithm (HEED) that minimizes the exposure of cluster heads to threats in the network environment. Simulation results indicate that our extended threat-aware HEED, or t-HEED, improves both the longevity and energy efficiency of a WSN while incurring minimal additional overhead. Our research demonstrates and motivates the need for a general framework for adaptive context-aware clustering in WSNs.


context awareness threat model clustering sensor networks 


  1. [1]
    A. A. Abbasi and M. Younis, “A survey on clustering algorithms for wireless sensor networks.” vol. 30: Butterworth-Heinemann, 2007, pp. 2826-2841.Google Scholar
  2. [2]
    O. Younis and S. Fahmy, “HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks,” vol. 3, pp. 366-379, 2004.Google Scholar
  3. [3]
    S. Yi, J. Heo, Y. Cho, and J. Hong, “PEACH: Power-efficient and adaptive clustering hierarchy protocol for wireless sensor networks.” vol. 30: Butterworth-Heinemann, 2007, pp. 2842-2852.Google Scholar
  4. [4]
    W.-T. Su, K.-M. Chang, and Y.-H. Kuo, “eHIP: An energy-efficient hybrid intrusion prohibition system for cluster-based wireless sensor networks.” vol. 51: Elsevier North- Holland, Inc., 2007, pp. 1151-1168.Google Scholar
  5. [5]
    I. Stojmenovic, M. Seddigh, and J. Zunic, “Dominating sets and neighbor elimination-based broad-casting algorithms in wireless networks,” IEEE Transactions on Parallel and Distributed Systems, 13(1): 2002, pp.14–25.CrossRefGoogle Scholar
  6. [6]
    F. Bouhafs, M. Merabti, and H. Mokhtar, “A semantic clustering routing protocol for wireless sensor networks,” Consumer Communications and Networking Conference, IEEE Computer Society, 2006, pp. 351– 355.Google Scholar
  7. [7]
    F. Siegemund, “A context-aware communication platform for smart objects.” Pervasive, Elsevier, 2004, pp.69–86.Google Scholar
  8. [8]
    M. Strohbach and H. Gellersen, “Smart clustering - networking smart objects based on their physical relationships,” Proceedings of the 5th IEEE Int’l Workshop on Networked Appliances, IEEE Computer Society, 2002, pp. 151– 155.Google Scholar
  9. [9]
    R.M Perianu, C. Lombriser, P. Havinga, J Scholten, G. Tröster, “Tandem: A Context-Aware Method for Spontaneous Clustering of Dynamic Wireless Sensor Nodes,” Internet of Things, Int’l Conf. for Industry and Academia, March 2008.Google Scholar
  10. [10]
    M. Younis, W. Youssef, M. Eltoweissy, and S. Olariu, “Safety- and QoS-Aware Management of Heterogeneous Sensor Networks,” Journal of Inter- connection Networks, Vol. 7, No. 1, 2006, pp. 179-193.Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Ryan E. Blace
    • 1
  • Mohamed Eltoweissy
    • 2
  • Wael Abd-Almageed
    • 3
  1. 1.BBN Technologies LLCColombia
  2. 2.Virginia TechMohamed Eltoweissy, Electrical and Computer EngUSA
  3. 3.Wael Abd-Almageed, UMIACSUniversity of MarylandUSA

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