Probabilistic Bandwidth Assignment in Wireless Sensor Networks

  • Dawood Khan
  • Bilel Nefzi
  • Luca Santinelli
  • YeQiong Song
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7405)


With this paper we offer an insight in designing and analyzing wireless sensor networks in a versatile manner. Our framework applies probabilistic and component-based design principles for the wireless sensor network modeling and consequently analysis; while maintaining flexibility and accuracy. In particular, we address the problem of allocating and reconfiguring the available bandwidth. The framework has been successfully implemented in IEEE 802.15.4 using an Admission Control Manager (ACM); which is a module of the MAC layer that guarantees that the nodes respect their probabilistic bandwidth assignment as well as the bandwidth assignment policy applied. The proposed framework also aims to accurately analyze the behaviors of communication protocols for energy-consumption and reliability purposes. We evaluate the probabilistic bandwidth assignment methods using CSMA/CA access protocol of IEEE 802.15.4. Furthermore, we analyze the behavior of the ACM and compare the performance of the network using the ACM against the original standard. The simulation results show that the use of ACM increases the overall performance of the network.


Wireless Sensor Network Medium Access Control Medium Access Control Protocol Medium Access Control Layer Beacon Interval 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sastry, S., Radeva, T., Chen, J., Welch, J.L.: Reliable networks with unreliable sensors. In: Aguilera, M.K., Yu, H., Vaidya, N.H., Srinivasan, V., Choudhury, R.R. (eds.) ICDCN 2011. LNCS, vol. 6522, pp. 281–292. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Jung, C., Hwang, H., Sung, D., Hwang, G.: Enhanced markov chain model and throughput analysis of the slotted csma/ca for ieee 802.15.4 under unsaturated traffic conditions. IEEE Transactions on Vehicular Technology 58(1), 473–478 (2009)CrossRefGoogle Scholar
  3. 3.
    Wang, Y., Vuran, M., Goddard, S.: Cross-layer analysis of the end-to- end delay distribution in wireless sensor networks. In: 30th IEEE Real-Time Systems Symposium, pp. 138–147. IEEE Computer Society (2009)Google Scholar
  4. 4.
    He, W., Liu, X., Zheng, L., Yang, H.: Reliability calculus: A theoretical framework to analyze communication reliability. In: 30th International Conference on Distributed Computing Systems, ICDCS 2010, pp. 159–168. IEEE Computer Society (2010)Google Scholar
  5. 5.
    LAN-MAN Standards Committee of the IEEE Computer Society: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs). IEEE Press (2006)Google Scholar
  6. 6.
    Schmitt, J.B., Roedig, U.: Sensor Network Calculus – A Framework for Worst Case Analysis. In: Prasanna, V.K., Iyengar, S.S., Spirakis, P.G., Welsh, M. (eds.) DCOSS 2005. LNCS, vol. 3560, pp. 141–154. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    She, H., Lu, Z., Jantsch, A., Zheng, L.R., Zhou, D.: Deterministic worst-case performance analysis for wireless sensor networks. In: International Wireless Communications and Mobile Computing Conference, IWCMC 2008, pp. 1081–1086 (August 2008)Google Scholar
  8. 8.
    Le Boudec, J.Y., Thiran, P.: Network calculus: A Theory of Deterministic Queuing Systems for the Internet. Springer-Verlag New York, Inc. (2001)Google Scholar
  9. 9.
    Jurcík, P., Koubaa, A., Severino, R., Alves, M., Tovar, E.: Dimensioning and worst-case analysis of cluster-tree sensor networks. ACM Transactions on Sensor Networks 7 (2010)Google Scholar
  10. 10.
    Koubâa, A., Alves, M., Tovar, E., Cunha, A.: An implicit gts allocation mechanism in IEEE 802.15.4 for time-sensitive wireless sensor networks: theory and practice. Real-Time Syst. 39(1-3), 169–204 (2008)CrossRefGoogle Scholar
  11. 11.
    Na, C., Yang, Y., Mishra, A.: An optimal gts scheduling algorithm for time-sensitive transactions in IEEE 802.15.4 networks. Comput. Netw. 52(13), 2543–2557 (2008)zbMATHCrossRefGoogle Scholar
  12. 12.
    Huang, Y.-K., Pang, A.-C., Hung, H.-N.: An adaptive gts allocation scheme for IEEE 802.15.4. IEEE Transactions on Parallel and Distributed Systems 19(5), 641–651 (2008)CrossRefGoogle Scholar
  13. 13.
    Nastasi, C., Marinoni, M., Santinelli, L., Pagano, P., Lipari, G., Franchino, G.: BACCARAT: a Dynamic Real-Time Bandwidth Allocation Policy for IEEE 802.15.4. To appear in the Proceedings of IEEE Percom 2010, International Workshop on Sensor Networks and Systems for Pervasive Computing (PerSeNS 2010), Mannheim, Germany, March 29-April 2. IEEE (2010)Google Scholar
  14. 14.
    Santinelli, L., Chitnis, M., Nastasi, C., Checconi, F., Lipari, G., Pagano, P.: A component-based architecture for adaptive bandwidth allocation in wireless sensor networks. In: IEEE Symposium on Industrial Embedded Systems, SIES (2010)Google Scholar
  15. 15.
    Jiang, Y.: A basic stochastic network calculus. SIGCOMM Comput. Commun. Rev. 36(4), 123–134 (2006)CrossRefGoogle Scholar
  16. 16.
    Xie, J., Jiang, Y.: Stochastic network calculus models under max-plus algebra. In: Proceedings of the Global Communications Conference, GLOBECOM 2009, Honolulu, Hawaii, USA, November 30 - December 4, pp. 1–6 (2009)Google Scholar
  17. 17.
    Thiele, L., Chakraborty, S., Naedele, M.: Real-time calculus for scheduling hard real-time systems. In: ISCAS, vol. 4, pp. 101–104 (2000)Google Scholar
  18. 18.
    Khan, D., Navet, N., Bavoux, B., Migge, J.: Aperiodic traffic in response time analyses with adjustable safety level. In: 14th IEEE International Conference on Emerging Techonologies and Factory Automation - ETFA (2009)Google Scholar
  19. 19.
    Wandeler, E., Thiele, L.: Interface-based design of real-time systems with hierarchical scheduling. In: RTAS, pp. 243–252 (2006)Google Scholar
  20. 20.
    Santinelli, L., Meumeu Yomsy P., Maxim, D., Cucu-Grosjean, L.: A component-based framework for modeling and analysing probabilistic real-time systems. In: 16th IEEE International Conference on Emerging Technologies and Factory Automation (2011)Google Scholar
  21. 21.
    Zigbee Specification Document 053474r17 (January 2008),
  22. 22.
    OPNET, OPNET Simulator, v 15.0,
  23. 23.
    Micaz: Micaz datasheet (2011),
  24. 24.
    Polastre, J., Hill, J., Culler, D.: Versatile low power media access for wireless sensor networks. In: SenSys 2004: Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, pp. 95–107. ACM, New York (2004)CrossRefGoogle Scholar
  25. 25.
    Buettner, M., Yee, G.V., Anderson, E., Han, R.: X-mac: a short preamble mac protocol for duty-cycled wireless sensor networks. In: SenSys 2006: Proceedings of the 4th International Conference on Embedded Networked Sensor Systems, pp. 307–320. ACM, New York (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dawood Khan
    • 1
  • Bilel Nefzi
    • 2
  • Luca Santinelli
    • 3
  • YeQiong Song
    • 2
  1. 1.LAAS-CNRSToulouseFrance
  2. 2.Université de Lorraine – LORIAVillers-les-nancyFrance
  3. 3.INRIAVillers-les-nancyFrance

Personalised recommendations