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CR-CEA: A collision- and energy-aware routing method for cognitive radio wireless sensor networks

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Abstract

Currently, working in the overcrowded shared unlicensed spectrum band, leads to a reduction in the quality of communications in wireless networks. This makes a considerable increase in packet loss caused by collisions that necessitates packets retransmissions. In the case of wireless sensor networks (WSN), a large amount of energy of sensor nodes will be wasted by these retransmissions. Cognitive radio technology makes it possible for sensor nodes, to opportunistically use licensed bands with better propagation characteristics and less congestion. In this paper a routing method for cognitive radio wireless sensor networks (CR-CEA) is presented, that is based on a cross-layer design that jointly considers route and spectrum selection. The CR-CEA method has two main phases: next hop selection and channel selection. The routing is performed hop-by-hop with local information and decisions, which are more compatible with sensor networks. Primary user activity and prevention from interference with them, is considered in all spectrum decisions. It uniformly distributes frequency channels between adjacent nodes, which lead to a local reduction in collision probability. This clearly affects energy consumption in all sensor nodes. In CR-CEA, route selection is energy-aware and a learning-based technique is used to reduce the packet delay in terms of hop-count. The simulation results reveal that by applying cognitive radio technology to WSNs and selecting a proper operating channel, we can consciously decrease collision probability. This saves energy of sensor nodes and improves the network lifetime.

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References

  1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  2. Yau, K. L., Komisarczuk, P., & Teal, P. (2009). Cognitive radio-based wireless sensor networks: Conceptual design and open issues. In IEEE 34th conference on local computer networks, (LCN 2009) (pp. 955–962).

  3. F.C. Commission. (November 2002). Spectrum policy task force, Technical report.

  4. Mitola, I. J., & Maguire, J. G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications Magazine, 6(4), 13–18.

    Article  Google Scholar 

  5. FCC. (December 2003). ET Docket No 03-222 Notice of proposed rulemaking and order.

  6. Akyildiz, I. F., Lee, W.-Y., Vuran, M. C., & Mohanty, S. (2008). A survey on spectrum management in cognitive radio networks. IEEE Communications Magazine, 46(4), 40–48.

    Article  Google Scholar 

  7. Akyildiz, I. F., et al. (2006). NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks: The International Journal of Computer and Telecommunications Networking, 50(13), 2127–2159.

    Article  MATH  Google Scholar 

  8. Fortuna, C., & Mohorcic, M. (2009). Trends in the development of communication networks: Cognitive networks. Computer Networks: The International Journal of Computer and Telecommunications Networking, 53(9), 1354–1376.

    Article  Google Scholar 

  9. Akan, O. B., Karli, O. B., & Ergul, O. (2009). Cognitive radio sensor networks. IEEE Network, 23(4), 34–40.

    Article  Google Scholar 

  10. Vijay, G., Bdira, E., & Ibnkahla, M. (2010). Cognition in wireless sensor networks: A perspective. IEEE Sensors Journal, 11(3), 582–592.

    Article  Google Scholar 

  11. Al-Karaki, Jamal. N., & Kamal, Ahmed. E. (2004). Routing techniques in wireless sensor networks: A survey. IEEE wireless communication, 11(6), 6–28.

    Article  Google Scholar 

  12. Singh, S. K., Singh, M. P., & Singh, D. K. (2010). Routing protocols in wireless sensor networks—A survey. International Journal of Computer Science & Engineering Survey (IJCSES), 1(2), 63–83.

    Article  Google Scholar 

  13. Cesana, M., Cuomo, F., & Ekici, E. (2010). Routing in cognitive radio networks: Challenges and solutions. Journal of Ad Hoc Networks, 10(5), 803–815.

    Google Scholar 

  14. Xin, C., Ma, L., & Shen, C. (2008). A path-centric channel assignment framework for cognitive radio wireless networks. Mobile Networks and Applications, 13(5), 463–476.

    Article  Google Scholar 

  15. Zhou, X., Lin, L., Wang, J., & Zhang, X. (2009). Cross-layer routing design in cognitive radio networks by colored multigraph model. Wireless Personal Communications, 49(1), 123–131.

    Article  Google Scholar 

  16. Wang, Q., & Zheng, H. (2006). Route and spectrum selection in dynamic spectrum networks. In 3rd IEEE consumer communications and networking conference CCNC 2006. doi:10.1109/CCNC.2006.1593099.

  17. Pyo, C. W., & Hasegawa, M. (2007). Minimum weight routing based on a common link control radio for cognitive wireless ad hoc networks. In IWCMC’07: Proceedings of the 2007 international conference on wireless communications and mobile computing (pp. 399–404).

  18. Xie, M., Zhang, W., & Wong, K. (2010). A geometric approach to improve spectrum efficiency for cognitive relay networks. IEEE Transactions on Wireless Communications, 9(1), 268–281.

    Article  Google Scholar 

  19. Ma, H., Zheng, L., Ma, X., & Luo, Y. (2008). Spectrum aware routing for multi-hop cognitive radio networks with a single transceiver. In 3rd international conference on cognitive radio oriented wireless networks and communications crowncom (pp. 1–6).

  20. Cheng, G., Liu, W., Li, Y., & Cheng, W. (2007). Spectrum aware on-demand routing in cognitive radio networks. In 2nd IEEE international symposium on new frontiers in dynamic spectrum access networks, DySPAN 2007 (pp. 571–574).

  21. Cheng, G., Liu, W., Li, Y. & Cheng, W. (2007). Joint on-demand routing and spectrum assignment in cognitive radio networks. In IEEE international conference on communications, ICC’07 (pp. 6499–6503).

  22. Yang, Z., et al. (2008). Local coordination based routing and spectrum assignment in multi-hop cognitive radio networks. Mobile Networks and Applications, 13(1), 67–81.

    Article  Google Scholar 

  23. Perkins, C., Belding-Royer, E., & Das, S. (2003). Ad hoc on-demand distance vector (AODV) routing. USA: RFC Editor.

    Google Scholar 

  24. Pefkianakis, I., Wong, S., & Lu, S. (2008). SAMER: Spectrum aware mesh routing in cognitive radio networks. In 3rd IEEE symposium on new frontiers in dynamic spectrum access networks (pp. 1–5).

  25. Chowdhury, K. R., & Akyildiz, I. F. (2011). CRP: A routing protocol for cognitive radio ad hoc networks. IEEE Journal on Selected Areas in Communications, 29(4), 794–804.

    Article  Google Scholar 

  26. Lakshmi Phani, G., & et al. (2010). ERFLA: Energy efficient combined routing, fusion, localization algorithm in cognitive WSN. In 7th international conference on wireless and optical communication networks (WOCN 2010) (pp. 1–5).

  27. Oey, C. H. W., Christian, I., & Moh, S. (2012). Energy-and cognitive-radio-aware routing in cognitive radio sensor networks. International Journal of Distributed Sensor Networks,. doi:10.1155/2012/636723.

    Google Scholar 

  28. Cavalcanti, D., Das, S., Wang, J., & Challapali, K. (2008). Cognitive radio based wireless sensor networks. In Proceedings of the 17th International Conference on Computer Communications and Networks (ICCCN’08) (pp. 491–496).

  29. Rajagopalan, R., & Varshney, P. K. (2006). Data-aggregation techniques in sensor networks: A survey. IEEE Communications Surveys & Tutorials, 8(4), 48–63.

    Article  Google Scholar 

  30. Pohl, I. (1970). Heuristic search viewed as path finding in a graph. Artificial Intelligence, 1, 193–204.

    Article  MathSciNet  MATH  Google Scholar 

  31. The Network Simulator (ns), version 2. http://www.isi.edu/nsnam/ns/.

  32. Calvo, R. A., & Campo, J. P. (2007). Adding multiple interface support in NS-2. http://personales.unican.es/aguerocr/files/ucMultiIfacesSupport.pdf.

  33. Zhong, J., & Li, J. (2009). Cognitive radio cognitive network simulator. Michigan Tech University. http://stuweb.ee.mtu.edu/~ljialian/.

  34. Dietrich, I., & Dressier, F. (2009). On the lifetime of wireless sensor networks. ACM Transactions on Sensor Networks, 5(1), 1–38.

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Engineering Department, Faculty of Electrical and Computer Engineering, Shahid Beheshti University G.C., Evin, Tehran, Iran

    Rojin Tizvar, Maghsoud Abbaspour & Mahdi Dehghani

Authors
  1. Rojin Tizvar
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  2. Maghsoud Abbaspour
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  3. Mahdi Dehghani
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Correspondence to Maghsoud Abbaspour.

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Tizvar, R., Abbaspour, M. & Dehghani, M. CR-CEA: A collision- and energy-aware routing method for cognitive radio wireless sensor networks. Wireless Netw 20, 2037–2052 (2014). https://doi.org/10.1007/s11276-014-0728-8

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  • DOI: https://doi.org/10.1007/s11276-014-0728-8

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