Spectrum-aware outage minimizing cooperative routing in cognitive radio sensor networks

  • Surajit Basak
  • Tamaghna AcharyaEmail author


This paper investigates the optimal path selection problem for end-to-end (e2e) outage probability minimization in clustered cognitive radio sensor networks. In order to improve outage performance of the optimal path, under a high node density regime, we consider feasibility of virtual multiple-input single-output (v-MISO) links in addition to conventional single-input single-output (SISO) links in the path. Since sensor nodes in such networks are allowed to access the spectrum of the primary network only in an opportunistic manner, the path selection problem is studied under the constraints of probabilistic interference to PU receivers and only single use of any PU channel along the path. The above problem is formulated as a joint hop-constrained routing, spectrum assignment and transmit power control problem. A convex optimization framework is used to find a closed form expression for the optimal transmit power of each transmitting node along the optimal route. Extension of the analytical result facilitates design of a novel routing algorithm, called spectrum aware-minimum outage intelligent cooperative routing (SA-MOICR) algorithm, which not only selects the minimum outage path for a given routing session, but also determines the number of nodes and the unique PU channel to be used for transmission in each hop along the path. Simulation results are found to corroborate our analytical results and quantify the significant improvement of the SA-MOICR scheme over only SISO or only v-MISO based routing solutions in terms of the achievable e2e outage probability.


Cognitive radio sensor networks Cross-layer approach Cooperative routing End-to-end (e2e) outage probability 


  1. 1.
    Song, M., Xin, C., Zhao, Y., & Cheng, X. (2012). Dynamic spectrum access: From cognitive radio to network radio. IEEE Wireless Communications, 19(1), 23.CrossRefGoogle Scholar
  2. 2.
    Song, L., & Hatzinakos, D. (2007). A cross-layer architecture of wireless sensor networks for target tracking. IEEE/ACM Transactions on Networking, 15(1), 145.CrossRefGoogle Scholar
  3. 3.
    Akyildiz, I. F., Melodia, T., & Chowdhury, K. R. (2007). A survey on wireless multimedia sensor networks. Computer Networks, 51(4), 921.CrossRefGoogle Scholar
  4. 4.
    Misra, S., Reisslein, M., & Xue, G. (2008). A survey of multimedia streaming in wireless sensor networks. IEEE Communications Surveys Tutorials, 10(4), 18.CrossRefGoogle Scholar
  5. 5.
    Chiwewe, T. M., Mbuya, C. F., & Hancke, G. P. (2015). Using cognitive radio for interference-resistant industrial wireless sensor networks: An overview. IEEE Transactions on Industrial Informatics, 11(6), 1466.CrossRefGoogle Scholar
  6. 6.
    Akan, O. B., Karli, O. B., & Ergul, O. (2009). Cognitive radio sensor networks. IEEE Network, 23(4), 34.CrossRefGoogle Scholar
  7. 7.
    Fadel, E., Faheem, M., Gungor, V., Nassef, L., Akkari, N., Malik, M., et al. (2017). Spectrum-aware bio-inspired routing in cognitive radio sensor networks for smart grid applications. Computer Communications, 101, 106.CrossRefGoogle Scholar
  8. 8.
    Felice, M. D., Doost-Mohammady, R., Chowdhury, K. R., & Bononi, L. (2012). Smart radios for smart vehicles: Cognitive vehicular networks. IEEE Vehicular Technology Magazine, 7(2), 26.CrossRefGoogle Scholar
  9. 9.
    Majumdar, C., Lee, D., Patel, A. A., Merchant, S. N., & Desai, U. B. (2017). Packet size optimization for cognitive radio sensor networks aided internet of things. IEEE Access, 5, 6325.CrossRefGoogle Scholar
  10. 10.
    Jakllari, G., Krishnamurthy, S. V., Faloutsos, M., Krishnamurthy, P. V., & Ercetin, O. (2007). A cross-layer framework for exploiting virtual MISO links in mobile ad hoc networks. IEEE Transaction on Mobile Computing, 6(6), 579.CrossRefGoogle Scholar
  11. 11.
    Aksu, A., & Ercetin, O. (2008). Reliable multi-hop routing with cooperative transmissions in energy-constrained networks. IEEE Transactions on Wireless Communications, 7(8), 2861.CrossRefGoogle Scholar
  12. 12.
    Pandana, C., Siriwongpairat, W. P., Himsoon, T., & Liu, K. J. R. (2006). Distributed cooperative routing algorithms for maximizing network lifetime. In Proceedings of the IEEE WCNC Las Vegas (USA) (Vol. 1, p. 451).Google Scholar
  13. 13.
    Lin, J., Jung, H., Chang, Y. J., Jung, J. W., & Weitnauer, M. A. (2015). On cooperative transmission range extension in multi-hop wireless ad-hoc and sensor networks: A review. Ad Hoc Networks, 29, 117.CrossRefGoogle Scholar
  14. 14.
    Xie, K., Wang, X., Wen, J., & Cao, J. (2016). Cooperative routing with relay assignment in multiradio multihop wireless networks. IEEE/ACM Transactions on Networking, 24(2), 859.CrossRefGoogle Scholar
  15. 15.
    Deng, Q., & Klein, A. G. (2012). Diversity of multi-hop cluster-based routing with arbitrary relay selection. IET Communications, 6(9), 1054.MathSciNetCrossRefGoogle Scholar
  16. 16.
    Ahmad, A., Ahmad, S., Rehmani, M. H., & Hassan, N. U. (2015). A survey on radio resource allocation in cognitive radio sensor networks. IEEE Communications Surveys Tutorials, 17(2), 888.CrossRefGoogle Scholar
  17. 17.
    Ren, J., Zhang, Y., Ye, Q., Yang, K., Zhang, K., & Shen, X. S. (2016). Exploiting secure and energy-efficient collaborative spectrum sensing for cognitive radio sensor networks. IEEE Transactions on Wireless Communications, 15(10), 6813.CrossRefGoogle Scholar
  18. 18.
    Liu, X., Evans, B. G., & Moessner, K. (2015). Energy-efficient sensor scheduling algorithm in cognitive radio networks employing heterogeneous sensors. IEEE Transactions on Vehicular Technology, 64(3), 1243.CrossRefGoogle Scholar
  19. 19.
    Usman, M., Har, D., & Koo, I. (2016). Energy-efficient infrastructure sensor network for ad hoc cognitive radio network. IEEE Sensors Journal, 16(8), 2775.CrossRefGoogle Scholar
  20. 20.
    Zhang, D., Chen, Z., Ren, J., Zhang, N., Awad, M. K., Zhou, H., et al. (2017). Energy-harvesting-aided spectrum sensing and data transmission in heterogeneous cognitive radio sensor network. IEEE Transactions on Vehicular Technology, 66(1), 831.CrossRefGoogle Scholar
  21. 21.
    Ren, J., Zhang, Y., Deng, R., Zhang, N., Zhang, D., & Shen, X. (2017). Joint channel access and sampling rate control in energy harvesting cognitive radio sensor networks. IEEE Transactions on Emerging Topics in Computing, PP(99), 1.Google Scholar
  22. 22.
    Ren, J., Hu, J., Zhang, D., Guo, H., Zhang, Y., & Shen, X. (2018). Rf energy harvesting and transfer in cognitive radio sensor networks: Opportunities and challenges. IEEE Communications Magazine, 56(1), 104.CrossRefGoogle Scholar
  23. 23.
    Ren, J., Zhang, Y., Zhang, N., Zhang, D., & Shen, X. (2016). Dynamic channel access to improve energy efficiency in cognitive radio sensor networks. IEEE Transactions on Wireless Communications, 15(5), 3143.CrossRefGoogle Scholar
  24. 24.
    Zhu, J., Song, Y., Jiang, D., & Song, H. (2016). Multi-armed bandit channel access scheme with cognitive radio technology in wireless sensor networks for the internet of things. IEEE Access, 4, 4609.CrossRefGoogle Scholar
  25. 25.
    Shah, G. A., & Akan, O. B. (2015). Cognitive adaptive medium access control in cognitive radio sensor networks. IEEE Transactions on Vehicular Technology, 64(2), 757.CrossRefGoogle Scholar
  26. 26.
    Du, M., Zheng, M., & Song, M. (2018). An adaptive preamble sampling based MAC protocol for cognitive radio sensor networks. IEEE Sensors Letters, 2(1), 1.CrossRefGoogle Scholar
  27. 27.
    Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-efficient routing protocols in wireless sensor networks: A survey. IEEE Communications Surveys Tutorials, 15(2), 551.CrossRefGoogle Scholar
  28. 28.
    Youssef, M., Ibrahim, M., Abdelatif, M., Chen, L., & Vasilakos, A. V. (2014). Routing metrics of cognitive radio networks: A survey. IEEE Communications Surveys Tutorials, 16(1), 92.CrossRefGoogle Scholar
  29. 29.
    Saleem, Y., Yau, K. L. A., Mohamad, H., Ramli, N., & Rehmani, M. H. (2015). SMART: A SpectruM-Aware clusteR-based rouTing scheme for distributed cognitive radio networks. Computer Networks, 91, 196.CrossRefGoogle Scholar
  30. 30.
    Pourpeighambar, B., Dehghan, M., & Sabaei, M. (2017). Non-cooperative reinforcement learning based routing in cognitive radio networks. Computer Communications, 106, 11.CrossRefGoogle Scholar
  31. 31.
    Shah, G. A., & Akan, O. B. (2013). Spectrum-aware cluster-based routing for cognitive radio sensor networks. In Proceedings of the IEEE ICC Budapest (Hungary) (pp. 2885–2889).Google Scholar
  32. 32.
    Shah, G. A., Alagoz, F., Fadel, E. A., & Akan, O. B. (2014). A spectrum-aware clustering for efficient multimedia routing in cognitive radio sensor networks. IEEE Transactions on Vehicular Technology, 63(7), 3369.CrossRefGoogle Scholar
  33. 33.
    Liang, Y. C., Zeng, Y., Peh, E. C. Y., & Hoang, A. T. (2008). Sensing-throughput tradeoff for cognitive radio networks. IEEE Transactions on Wireless Communications, 7(4), 1326.CrossRefGoogle Scholar
  34. 34.
    Basak, S., & Acharya, T. (2015). Joint power allocation and routing in outage constrained cognitive radio ad hoc networks. Mobile Networks and Applications, 20(5), 636.CrossRefGoogle Scholar
  35. 35.
    Basak, S., & Acharya, T. (2017). Cross layer optimization for outage minimizing routing in cognitive radio ad hoc networks with primary users outage protection. Journal of Network and Computer Applications, 98, 114.CrossRefGoogle Scholar
  36. 36.
    Basak, S., & Acharya, T. (2016). Route selection for interference minimization to primary users in cognitive radio ad hoc networks: A cross layer approach. Physical Communication, 19, 118.CrossRefGoogle Scholar
  37. 37.
    Lin, Y. E., Liu, K. H., & Hsieh, H. Y. (2013). On using interference-aware spectrum sensing for dynamic spectrum access in cognitive radio networks. IEEE Transactions on Mobile Computing, 12(3), 461.CrossRefGoogle Scholar
  38. 38.
    Hasna, M. O., & Alouini, M. S. (2004). Optimal power allocation for relayed transmissions over rayleigh-fading channels. IEEE Transactions on Wireless Communications, 3(6), 1999.CrossRefGoogle Scholar
  39. 39.
    Huang, S., Chen, H., Zhang, Y., & Zhao, F. (2012). Energy-efficient cooperative spectrum sensing with amplify-and-forward relaying. IEEE Communications Letters, 16(4), 450.CrossRefGoogle Scholar
  40. 40.
    Maham, B., & Popovski, P. (2015). Cognitive multiple-antenna network with outage and rate margins at the primary system. IEEE Transactions on Vehicular Technology, 64(6), 2409.CrossRefGoogle Scholar
  41. 41.
    Kang, X., Zhang, R., Liang, Y. C., & Garg, H. K. (2011). Optimal power allocation strategies for fading cognitive radio channels with primary user outage constraint. IEEE Journal on Selected Areas in Communications, 29(2), 374.CrossRefGoogle Scholar
  42. 42.
    Bertsekas, D., & Gallager, R. (1992). Data networks (2nd ed.). Englewood Cliffs: Prentice-Hall.zbMATHGoogle Scholar
  43. 43.
    Tse, D., & Viswanath, P. (2004). Fundamentals of wireless communications. Cambridge: Cambridge University Press.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics and Telecommunication EngineeringIndian Institute of Engineering Science and TechnologyShibpurIndia

Personalised recommendations