On the Optimal Blacklisting Threshold for Link Selection in Wireless Sensor Networks

  • Flavio Fabbri
  • Marco Zuniga
  • Daniele Puccinelli
  • Pedro Marrón
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7158)

Abstract

Empirical studies on link blacklisting show that the delivery rate is sensitive to the calibration of the blacklisting threshold. If the calibration is too restrictive (the threshold is too high), all neighbors get blacklisted. On the other hand, if the calibration is too loose (the threshold is too low), unreliable links get selected. This paper investigates blacklisting analytically. We derive a model that accounts for the joint effect of the wireless channel (signal strength variance and coherence time) and the network (node density). The model, validated empirically with mote-class hardware, shows that blacklisting does not help if the wireless channel is stable or if the network is relatively sparse. In fact, blacklisting is most beneficial when the network is relatively dense and the channel is unstable with long coherence times.

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References

  1. 1.
    Karp, B., Kung, H.: Gpsr: greedy perimeter stateless routing for wireless networks. In: ACM Mobicom (2000)Google Scholar
  2. 2.
    Couto, D.D., Aguayo, D., Bicket, J., Morris, R.: A high-throughput path metric for multi-hop wireless routing. In: ACM Mobicom (2003)Google Scholar
  3. 3.
    Haenggi, M., Puccinelli, D.: Routing in ad hoc networks: a case for long hops. IEEE Communications Magazine 43(10), 93–101 (2005)CrossRefGoogle Scholar
  4. 4.
    Nardelli, P.H.J., de Abreu, G.T.F.: On Hopping Strategies for Autonomous Wireless Networks. In: IEEE GLOBECOM (2009)Google Scholar
  5. 5.
    Weber, S., Jindal, N., Ganti, R.K., Haenggi, M.: Longest edge routing on the spatial Aloha graph. In: IEEE Global Telecommunications Conference, pp. 1–5 (2008)Google Scholar
  6. 6.
    Woo, A., Tong, T., Culler, D.: Taming the underlying challenges of reliable multihop routing in sensor networks. In: ACM SenSys (2003)Google Scholar
  7. 7.
    Gnawali, O., Yarvis, M., Heidemann, J., Govindan, R.: Interaction of retransmission, blacklisting, and routing metrics for reliability in sensor network routing. In: IEEE SECON (2004)Google Scholar
  8. 8.
    Liu, T., Kamthe, A., Jiang, L., Cerpa, A.: Performance Evaluation of Link Quality Estimation Metrics for Static Multihop Wireless Sensor Networks. In: IEEE SECON (2009)Google Scholar
  9. 9.
    Zuniga, M., Seada, K., Krishnamachari, B., Helmy, A.: Efficient geographic routing over lossy links in wireless sensor networks. ACM TOSN, 12:1–12:33 (June 2008)Google Scholar
  10. 10.
    Srinivasan, K., Levis, P.: Rssi is under appreciated. In: Proceedings of the Third Workshop on Embedded Networked Sensors, EmNets (2006)Google Scholar
  11. 11.
    Hackmann, G., Chipara, O., Lu, C.: Robust topology control for indoor wireless sensor networks. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, SenSys 2008 (2008)Google Scholar
  12. 12.
    Bettstetter, C., Hartmann, C.: Connectivity of wireless multihop networks in a shadow fading environment. Wirel. Netw. 11, 571–579 (2005)CrossRefGoogle Scholar
  13. 13.
    Zuniga, M., Irzynska, I., Hauer, J., Voigt, T., Boano, C., Roemer, K.: Link quality ranking: Getting the best out of unreliable links. In: IEEE DCOSS (2011)Google Scholar
  14. 14.
    Sikora, M., Laneman, J., Haenggi, M., Costello, D., Fuja, T.: Bandwidth- and power-efficient routing in linear wireless networks. IEEE Transactions on Information Theory 52(6), 2624–2633 (2006)CrossRefMATHMathSciNetGoogle Scholar
  15. 15.
    Stamatiou, K., Rossetto, F., Haenggi, M., Javidi, T., Zeidler, J., Zorzi, M.: A delay-minimizing routing strategy for wireless multi-hop networks. In: WiOpt (2009)Google Scholar
  16. 16.
    Weber, S., Yang, X., Andrews, J.G., de Veciana, G.: Transmission capacity of wireless ad hoc networks with outage constraints. IEEE Transactions on Information Theory 51(12), 4091–4102 (2005)CrossRefMATHMathSciNetGoogle Scholar
  17. 17.
    Gnawali, O., Fonseca, R., Jamieson, K., Moss, D., Levis, P.: Collection Tree Protocol. In: ACM SenSys (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Flavio Fabbri
    • 1
  • Marco Zuniga
    • 1
  • Daniele Puccinelli
    • 2
  • Pedro Marrón
    • 1
  1. 1.Universität Duisburg-EssenGermany
  2. 2.University of Applied Sciences of Southern Switzerland (SUPSI)Switzerland

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