Skip to main content
SpringerLink
Log in

Link State Routing Based on Compressed Sensing

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In table routing protocols such as link state routing, every node in the network periodically broadcasts its link state and the state of its neighbors. These routing updates result in the transmission of a large number of packets. Some of these packets contain correlated or even redundant data which could be compressed if there is central management in the network. However, in autonomous networks, each node acts as a router, in which case central coordination is not possible. In this paper, compressed sensing is used to reduce routing traffic overhead. This can be done at nodes which have greater processing capabilities and no power consumption limitations such as backbone nodes in wireless mesh networks. A method is proposed to select a subset of nodes and thus a subset of links to probe their state. The sensed states are encoded to generate a low dimension sampled vector. This compressed link state vector is broadcast to the entire network. Nodes can then reconstruct link states from this vector using side information. Performance results are presented which demonstrate accurate anomaly detection while adapting to topology changes. Further, it is shown that a proper choice of weighting coefficients in the sampling process can improve detection performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Perkins, C., Belding-Royer, E., & Das, S. (2003). Ad hoc on-demand distance vector (AODV) routing. Internet Engineering Task Force, RFC 3561.

  2. Herberg, U., Cole, R., & Clausen, T. (2013). Definition of managed objects for the optimized link state routing protocol, version 2. draft-ietf-manet-olsrv2-mib-11.

  3. Nelakudit, S., et al. (2005). Blacklist-aided forwarding in static multihop wireless networks. In Proceedings of the IEEE communications society conference on sensor and ad hoc communications and networks (pp. 252–262).

  4. Triviño-Cabrera, A., & Cañadas-Hurtado, S. (2011). Survey on opportunistic routing in multihop wireless networks. International Journal of Communication Networks and Information Security, 3(2), 170–177.

    Google Scholar 

  5. Akyildiz, I. F., & Wang, X. (2005). A survey on wireless mesh networks. IEEE Communications Magazine, 43(9), S23–S30.

    Article  Google Scholar 

  6. Biswas, S., & Morris, R. (2004). Opportunistic routing in multi-hop wireless networks. ACM SIGCOMM Computer Communication Review, 34(1), 69–74.

    Article  Google Scholar 

  7. Candès, E. J. (2006). Compressive sampling. In Proceedings of the international congress of mathematicians, Madrid, Spain (pp 1433–1452).

  8. Candès, E. J., Romberg, J., & Tao, T. (2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2), 489–509.

    Article  MathSciNet  MATH  Google Scholar 

  9. Candès, E. J., Romberg, J. K., & Tao, T. (2006). Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 59(8), 1207–1223.

    Article  MathSciNet  MATH  Google Scholar 

  10. Candès, E. J., & Tao, T. (2006). Near optimal signal recovery from random projections: Universal encoding strategies? IEEE Transactions on Information Theory, 52(12), 5406–5425.

    Article  MathSciNet  MATH  Google Scholar 

  11. Candès, E. J., & Romberg, J. (2006). Quantitative robust uncertainty principles and optimally sparse decompositions. Foundations of Computational Mathematics, 6(2), 227–254.

    Article  MathSciNet  MATH  Google Scholar 

  12. Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289–1306.

    Article  MathSciNet  MATH  Google Scholar 

  13. Haupt, J., Bajwa, W. U., Rabbat, M., & Nowak, R. (2008). Compressed sensing for networked data. IEEE Signal Processing Magazine, 25(2), 92–101.

    Article  Google Scholar 

  14. Crovella, M., & Kolaczyk, E. (2003). Graph wavelets for spatial traffic analysis. In Proceedings of the IEEE INFOCOM, San Francisco, CA (pp. 1848–1857).

  15. Coifman, R. R., & Maggioni, M. (2006). Diffusion wavelets. Applied and Computational Harmonic Analysis, 21(1), 53–94.

    Article  MathSciNet  MATH  Google Scholar 

  16. Quer, G., et al. (2009). On the interplay between routing and signal representation for compressive sensing in wireless sensor networks. In Proceedings of the information theory and applications workshop, San Diego, CA (pp. 206–215).

  17. Bajwa, W., Haupt, J., Sayeed, A., & Nowak, R. (2006). Compressive wireless sensing. In Proceedings of the international conference on information processing in sensor networks, Nashville, TN (pp. 134–142).

  18. Fazel, F., Fazel, M., & Stojanovic, M. (2011). Random access compressed sensing for energy-efficient underwater sensor networks. IEEE Journal on Selected Areas in Communications, 29(8), 1660–1670.

    Article  Google Scholar 

  19. Srisooksaia, T., Keamarungsi, K., Lamsrichan, P., & Araki, K. (2012). Practical data compression in wireless sensor networks: A survey. Journal of Network and Computer Applications, 35(1), 37–59.

    Article  Google Scholar 

  20. Tian, H., Roughan, M., Sang, Y., & Shen, H. (2011). Diffusion wavelets-based analysis on traffic matrices. In Proceedings of the international conference on parallel and distributed computing, applications and technologies, Gwangju, South Korea (pp. 116–121).

  21. Coates, M., Pointurier, Y., & Rabbat, M. (2007). Compressed network monitoring. In Proceedings of the IEEE/SP workshop on statistical signal processing, Madison, WI (pp. 418–422).

  22. Coates, M., Pointurier, Y., & Rabbat, M. (2007). Compressed network monitoring for IP and all-optical networks. In Proceedings of the SIGCOMM conference on internet measurement, San Diego, CA (pp. 241–252).

  23. Lee, O., Kim, J. M., Bresler, Y., & Ye, J. C. (2011). Compressive diffuse optical tomography: Noniterative exact reconstruction using joint sparsity. IEEE Transactions on Medical Imaging, 30(5), 1129–1142.

    Article  Google Scholar 

  24. Bowden, R. A., Roughan, M., & Bean, N. (2011). Network link tomography and compressive sensing. ACM SIGMETRICS Performance Evaluation Review, 39(1), 351–352.

    Article  Google Scholar 

  25. Xu, W., Mallada, E., & Tang, A. (2011). Compressive sensing over graphs. In Proceedings of the IEEE INFOCOM, Shanghai, China (pp. 2087–2095).

  26. Baraniuk, R. G. (2007). Compressive sensing. IEEE Signal Processing Magazine, 24(4), 118–124.

    Article  MathSciNet  Google Scholar 

  27. Waxman, B. M. (1988). Routing of multipoint connections. IEEE Journal of Selected Areas in Communications, 6(9), 1617–1622.

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 83111-84156, Iran

    Samane Kargar & Faramarz Hendessi

  2. Department of Electrical and Computer Engineering, University of Victoria, PO Box 1700, STN CSC, Victoria, BC, V8W 2Y2, Canada

    T. Aaron Gulliver

Authors
  1. Samane Kargar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Faramarz Hendessi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T. Aaron Gulliver
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. Aaron Gulliver.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kargar, S., Hendessi, F. & Aaron Gulliver, T. Link State Routing Based on Compressed Sensing. Wireless Pers Commun 99, 253–271 (2018). https://doi.org/10.1007/s11277-017-5056-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-5056-8

Keywords

Search

Navigation