On the Effectiveness of Relaxation Theory for Controlling High Traffic Volumes in Body Sensor Networks

  • Naimah Yaakob
  • Ibrahim Khalil
  • Jiankun Hu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 83)


Congestion related issues are major concerns in any networking system including Body Sensor Networks (BSN). This is due to the number of disastrous effects (e.g. high packet loss rate and service interruption) it may cause on the system’s performance. BSN, which normally involves with life-threatening measurements, are found to be very much affected by this problem. The incorporation of its real-time applications with life-death matters may likely put people at high risk during congestion. To address this challenge and alleviate congestion in BSN, we explore the feasibility of a new rate limiting technique known as Relaxation Theory (RT). Uniquely distinctive from the typical rate limiting schemes, the novelty of our approach lies in the ability to ’relax’ or postpone the excessive incoming packets to a certain extent, and avoid congestion from occurring in the first place. An insight performance analysis on one of BSN applications in healthcare monitoring (Electrocardiogram - ECG) shows promising results.


Congestion control Relaxation Theory Engineering Level 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Yang, G.Z.: Body sensor networks, research challenges and applicationsGoogle Scholar
  2. 2.
    Minoli, D.: Broadband Network Analysis and Design. Artech House, Inc., Norwood (1993)Google Scholar
  3. 3.
    Woo, A., Culler, D.E.: A transmission control scheme for media access in sensor networks. In: Proceedings of the 7th Annual International Conference on Mobile Computing and Networking, MobiCom 2001, pp. 221–235. ACM, New York (2001), Google Scholar
  4. 4.
    Stann, F., Heidemann, J.: Rmst: reliable data transport in sensor networks. In: Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, pp. 102–112 (May 2003)Google Scholar
  5. 5.
    Akan, O., Akyildiz, I.: Event-to-sink reliable transport in wireless sensor networks. IEEE/ACM Transactions on Networking 13(5), 1003–1016 (2005)CrossRefGoogle Scholar
  6. 6.
    Wan, C.-Y., Campbell, A., Krishnamurthy, L.: Pump-slowly, fetch-quickly (psfq): a reliable transport protocol for sensor networks. IEEE Journal on Selected Areas in Communications 23(4), 862–872 (2005)CrossRefGoogle Scholar
  7. 7.
    Misra, S., Tiwari, V., Obaidat, M.: Lacas: learning automata-based congestion avoidance scheme for healthcare wireless sensor networks. IEEE Journal on Selected Areas in Communications 27(4), 466–479 (2009)CrossRefGoogle Scholar
  8. 8.
    Wan, C.-Y., Eisenman, S.B., Campbell, A.T.: Coda: congestion detection and avoidance in sensor networks. In: Proceedings of the 1st International Conference on Embedded Networked Sensor Systems, SenSys 2003, pp. 266–279. ACM, New York (2003), Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Naimah Yaakob
    • 1
  • Ibrahim Khalil
    • 1
  • Jiankun Hu
    • 2
  1. 1.School of Computer Science and Information TechnologyRMIT UniversityMelbourneAustralia
  2. 2.School of Engineering and Information TechnologyUniversity of New South WalesCanberraAustralia

Personalised recommendations