Least Path Interference Beaconing Protocol (LIBP): A Frugal Routing Protocol for The Internet-of-Things

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8458)


This paper presents a frugal protocol for sensor readings dissemination in the Internet-of-Things (IoT). The protocol called Least Path Interference Beaconing (LIBP) is based on a lightweight path selection model that builds a routing spanning tree rooted at the sink node based on information disseminated through a periodic beaconing process. LIBPs frugality results from a routing process where the sensor nodes select the least path interfering parents on the routing spanning tree with the expectation of flow balancing the traffic routed from nodes to the sink of a sensor network. The simulation results produced by Cooja under the Contiki operating system are in agreement with previous results obtained under the TinyOS operating system. They reveal that LIBP outperforms different versions of the RPL protocol and the CTP protocol in terms of power consumption, scalability, throughput and recovery from failure as well as its frugality as a routing protocol.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Vasseur, J., Dunkels, A.: Interconnecting Smart Objects with IP, The Next Internet. Morgan Kaufmann (July 2010) ISBN: 9780123751652Google Scholar
  2. 2.
    Bagula, A., Djenouri, D., Karbab, E.B.: Ubiquitous sensor network management: The least interference beaconing model. In: Proceedings of the IEEE 24th International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), September 8-11, pp. 2352–2356 (2013) ISSN 2166-9570Google Scholar
  3. 3.
    Bagula, A.B., Djenouri, D., Karbab, E.: On the Relevance of Using Interference and Service Differentiation Routing in the Internet-of-Things. In: Balandin, S., Andreev, S., Koucheryavy, Y. (eds.) NEW2AN 2013 and ruSMART 2013. LNCS, vol. 8121, pp. 25–35. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Levis, P., Lee, N., Welsh, M., Culler, D.: TOSSIM: Simulating large wireless sensor networks of tinyos motes. In: Proc. of ACM SenSys 2003, Los Angeles, CA, pp. 126–137 (November 2003)Google Scholar
  5. 5.
    Dunkels, A., Gronvall, B., Voigt, T.: Contiki - a lightweight and flexible operating system for tiny networked sensors. In: 29th Annual IEEE International Conference on Local Computer Networks, pp. 455–462 (2004)Google Scholar
  6. 6.
    Gnawali, O., Fonseca, R., Jamieson, K., Moss, D., Levis, P.: Collection Tree Protocol. In: Proc. of ACM SenSys 2009, Berkeley, CA/USA, November 4-6 (2009)Google Scholar
  7. 7.
    Winter, T., et al.: RPL: IPv6 Routing protocol for Low-Power and Lossy Networks, RFC 6550 (March 2012)Google Scholar
  8. 8.
    Dunkels, A.: Poster Abstract: Rime: A Lightweight Layered Communication Stack for Sensor Networks. In: European Conference on Wireless Sensor Networks (EWSN), Delft, The Netherlands (January 2007)Google Scholar
  9. 9.
    Levis, P., Patel, N., Culler, D., Shenker, S.: Trickle: A self regulating algorithm for code maintenance and propagation in wireless sensor networks. In: Proc. of the USENIX NSDI Conf., San Francisco, CA (March 2004)Google Scholar
  10. 10.
    Thubert, P.: Objective Function Zero for the Routing Protocol for Low-Power and Lossy Networks (RPL). Internet Engineering Task Force (IETF), Request for Comments 6552, 1–14 (2012)Google Scholar
  11. 11.
    Dunkels, A., Osterlind, F., Tsiftes, N., He, Z.: Software-based Online Sensor Node Energy Estimation. In: ACM Proceeding of the 4th Workshop on Embedded Networked Sensors (EmNets 2007), pp. 28–32 (2007)Google Scholar
  12. 12.
    Bagula, A.: On Achieving Bandwidth-aware LSP/LambdaSP Multiplexing/Separation in Multi-layer Networks. IEEE Journal on Selected Areas in Communications (JSAC): Special issue on Traffic Engineering for Multi-Layer Networks 25(5) (June 2007)Google Scholar
  13. 13.
    Bagula, A.: Hybrid traffic engineering: the least path interference algorithm. In: Proc. of ACM Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists on IT Research in Developing Countries, pp. 89–96 (2004)Google Scholar
  14. 14.
    Bagula, A.B.: Hybrid routing in next generation IP networks. Elsevier Computer Communications 29(7), 879–892 (2006)CrossRefGoogle Scholar
  15. 15.
    Zennaro, M., Bagula, A.: Design of a flexible and robust gateway to collect sensor data in intermittent power environments. International Journal of Sensor Networks 8(3/4) (2010)Google Scholar
  16. 16.
    Bagula, A., Krzesinski, A.E.: Traffic engineering label switched paths in IP networks using a pre-planned flow optimization model. In: Proceedings of the Ninth International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS 2001), pp. 70–77 (August 2001)Google Scholar
  17. 17.
    Bagula, A.: Modelling and Implementation of QoS in Wireless Sensor Networks: A Multi-constrained Traffic Engineering Model. Eurasip Journal on Wireless Communications and Networking 2010, Article ID 468737, doi:10.1155/2010/468737Google Scholar
  18. 18.
    Bagula, A., Zennaro, M., Inggs, G., Scott, S., Gascon, D.: Ubiquitous Sensor Networking for Development (USN4D): An Application to Pollution Monitoring. MDPI Sensors 12(1), 391–414 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.ISAT Laboratory, Department of Computer ScienceUniversity of Cape TownCape TownSouth Africa
  2. 2.ISAT Laboratory, Department of Computer ScienceUniversity of Western CapeBellville , Cape TownSouth Africa

Personalised recommendations