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Wireless Networks

, Volume 25, Issue 8, pp 4523–4539 | Cite as

Remaining idle time aware intelligent channel bonding schemes for cognitive radio sensor networks

  • Syed Hashim Raza BukhariEmail author
  • Mubashir Husain Rehmani
  • Sajid Siraj
Article
  • 112 Downloads

Abstract

Channel bonding (CB) is a technique used to provide larger bandwidth to users. It has been applied to various networks such as wireless local area networks, wireless sensor networks, cognitive radio networks, and cognitive radio sensor networks (CRSNs). The implementation of CB in CRSNs needs special attention as primary radio (PR) nodes traffic must be protected from any harmful interference by cognitive radio (CR) sensor nodes. On the other hand, CR sensor nodes need to communicate without interruption to meet their data rate requirements and conserve energy. If CR nodes perform frequent channel switching due to PR traffic then it will be difficult to meet their quality of service and data rate requirements. So, CR nodes need to select those channels which are stable. By stable, we mean those channels which having less PR activity or long remaining idle time and cause less harmful interference to PR nodes. In this paper, we propose two approaches remaining idle time aware intelligent channel bonding (RITCB) and remaining idle time aware intelligent channel bonding with interference prevention (RITCB-IP) for cognitive radio sensor networks which select stable channels for CB which have longest remaining idle time. We compare our approaches with four schemes such as primary radio user activity aware channel bonding scheme, sample width algorithm, cognitive radio network over white spaces and AGILE. Simulation results show that our proposed approaches RITCB and RITCB-IP decrease harmful interference and increases the life time of cognitive radio sensor nodes.

Keywords

Channel bonding Cognitive radio Dynamic spectrum access Wireless sensor networks Channel switching Primary user activity Remaining idle time 

References

  1. 1.
    Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.CrossRefGoogle Scholar
  2. 2.
    Wang, N., Zhang, N., & Wang, M. (2006). Wireless sensors in agriculture and food industry recent development and future perspective. Computers and Electronics in Agriculture, 50(1), 1–14.CrossRefGoogle Scholar
  3. 3.
    Qi, L., Xu, M., Fu, Z., Mira, T., & Zhang, X. (2014). C2SLDS: A WSN-based perishable food shelf-life prediction and LSFO strategy decision support system in cold chain logistics. Food Control, 38, 19–29.CrossRefGoogle Scholar
  4. 4.
    Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.CrossRefGoogle Scholar
  5. 5.
    Bukhari, S. H. R., Siraj, S., & Rehmani, M. H. (2018). Wireless sensor networks in smart cities: Applications of channel bonding to meet data communication requirements. Book Chapter for the Book, Transportation and Power Grid in Smart Cities: Communication Networks and Services. Wiley, UK (in print).Google Scholar
  6. 6.
    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), 888–917.CrossRefGoogle Scholar
  7. 7.
    Akhtar, F., Rehmani, M. H., & Reisslein, M. (2016). White space: Definitional perspectives and their role in exploiting spectrum opportunities. Telecommunications Policy, 40(4), 319–331.CrossRefGoogle Scholar
  8. 8.
    Gulbahar, B., & Akan, O. B. (2012). Information theoretical optimization gains in energy adaptive data gathering and relaying in cognitive radio sensor networks. IEEE Transactions on Wireless Communications, 11(5), 1788–1796.CrossRefGoogle Scholar
  9. 9.
    Oto, M. C., & Akan, O. B. (2012). Energy-efficient packet size optimization for cognitive radio sensor networks. IEEE Transactions on Wireless Communications, 11(4), 1544–1553.CrossRefGoogle Scholar
  10. 10.
    Akan, O. B., Karli, O. B., & Ergul, O. (2009). Cognitive radio sensor networks. IEEE Network, 23(4), 34–40.CrossRefGoogle Scholar
  11. 11.
    Sharma, M., & Sahoo, A. (2010). Opportunistic channel access scheme for cognitive radio system based on residual white space distribution. In 21st annual IEEE international symposium on personal, indoor and mobile radio communications (pp. 1842–1847). IEEE.Google Scholar
  12. 12.
    Song, Y., & Xie, J. (2010). Common hopping based proactive spectrum handoff in cognitive radio ad hoc networks. In Global telecommunications conference (GLOBECOM) (pp. 1–5). IEEE.Google Scholar
  13. 13.
    Wang, C.-W., Wang, L.-C., & Adachi, F. (2009). Modeling and analysis of multi-user spectrum selection schemes in cognitive radio networks. In IEEE 20th international symposium on personal, indoor and mobile radio communications (pp. 828–832). IEEE.Google Scholar
  14. 14.
    Li, X., & Zekavat, S. A. R. (2009). Traffic pattern prediction based spectrum sharing for cognitive radios. Den Haag: INTECH Open Access Publisher.CrossRefGoogle Scholar
  15. 15.
    Li, X., & Zekavat, S. A. (2008). Traffic pattern prediction and performance investigation for cognitive radio systems. In IEEE wireless communications and networking conference (pp. 894–899). IEEE.Google Scholar
  16. 16.
    Yang, L., Cao, L., & Zheng, H. (2008). Proactive channel access in dynamic spectrum networks. Physical Communications Journal, 1, 103–111.CrossRefGoogle Scholar
  17. 17.
    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), 326–1337.Google Scholar
  18. 18.
    Lin, Z., Ghosh, M., & Demir, A. (2013). A comparison of MAC aggregation vs. PHY bonding for WLANs in TV white spaces. In 24th international symposium on personal, indoor and mobile radio communications: MAC and cross-layer design track (pp. 1829–1834).Google Scholar
  19. 19.
    Bukhari, S. H. R., Rehmani, M. H., & Siraj, S. (2016). A survey of channel bonding for wireless networks and guidelines of channel bonding for futuristic cognitive radio sensor networks. IEEE Communications Surveys & Tutorials, 18(2), 924–948.CrossRefGoogle Scholar
  20. 20.
    Ramaboli, A. L., Falowo, O. E., & Chan, A. H. (2012). Bandwidth aggregation in heterogeneous wireless networks: A survey of current approaches and issues. Journal of Network and Computer Applications, 35(6), 1674–1690.CrossRefGoogle Scholar
  21. 21.
    Sharma, M., & Sahoo, A. (2010). Residual white space distribution-based opportunistic channel access for cognitive radio enabled devices. ACM SIGCOMM Computer Communication Review, 40(4), 427–428.CrossRefGoogle Scholar
  22. 22.
    Cordeiro, C., Challapali, K., Birru, D., & Shankar, S. (2006). IEEE 802.22 an introduction to the first wireless standard based on cognitive radios. Journal of Communications, 1(1), 38–47.CrossRefGoogle Scholar
  23. 23.
    Bukhari, S. H. R., Siraj, S., & Rehmani, M. H. (2016). PRACB: A novel channel bonding algorithm for cognitive radio sensor networks. IEEE Access, 4, 6950–6963.CrossRefGoogle Scholar
  24. 24.
    Yuan, G., Grammenos, R., Yang, Y., & Wang, W. (2010). Performance analysis of selective opportunistic spectrum access with traffic prediction. IEEE Transactions on Vehicular Technology, 59(4), 1949–1959.CrossRefGoogle Scholar
  25. 25.
    Min, A., & Shin, K. (2008). Exploiting multi channel diversity in spectrum agile networks. In INFOCOM.Google Scholar
  26. 26.
    Saleem, Y., & Rehmani, M. H. (2014). Primary radio user activity models for cognitive radio networks: A survey. Journal of Network and Computer Applications, 43, 1–16.CrossRefGoogle Scholar
  27. 27.
    Kim, H., & Shin, K. (2008). Fast discovery of spectrum opportunities in cognitive radio networks. In IEEE DySPAN.Google Scholar
  28. 28.
    Mehanna, O., Sultan, A., & Gamal, H. (2009). Cognitive MAC protocols for general primary network models, Technical Report. Cornell University.Google Scholar
  29. 29.
    Zahmati, A., Fernando, X., & Grami, A. (2010). Steady-state Markov Chain analysis for heterogeneous cognitive radio networks. In Sarnoff.Google Scholar
  30. 30.
    Adas, A. (1997). Traffic models in broadband networks. IEEE Communications Magazine, 35(7), 82–89.CrossRefGoogle Scholar
  31. 31.
    Vujicic, B., Cackov, N., Vujicic, S., & Trajkovic, L. (2005). Modeling and characterization of traffic in public safety wireless networks. In SPECTS.Google Scholar
  32. 32.
    Kim, H., & Shin, K. (2008). Efficient discovery of spectrum opportunities with mac layer sensing in cognitive radio networks. IEEE Transactions on Mobile Computing, 7, 533–545.CrossRefGoogle Scholar
  33. 33.
    Sriram, K., & Whitt, W. (1986). Characterizing superposition arrival processes in packet multiplexers for voice and data. IEEE Journal on Selected Areas in Communications, 4(6), 833–846.CrossRefGoogle Scholar
  34. 34.
    Rehmani, M. H., Viana, A. C., Khalife, H., & Fdida, S. (2013). SURF: A distributed channel selection strategy for data dissemination in multi-hop cognitive radio networks. Computer Communications, 36(10), 1172–1185.CrossRefGoogle Scholar
  35. 35.
    Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys & Tutorials, 11(1), 116–130.CrossRefGoogle Scholar
  36. 36.
    Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.CrossRefGoogle Scholar
  37. 37.
    Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.CrossRefGoogle Scholar
  38. 38.
    Chandra, R., Mahajan, R., Moscibroda, J., Raghavendra, R., & Bahl, P. (2008). A case for adapting channel width in wireless networks. ACM SIGCOMM Computer Communication Review, 38(4), 135–146.CrossRefGoogle Scholar
  39. 39.
    Yuan, Y., Bahl, P., Chandra, R., & Chou, P. (2007). KNOWS: Cognitive radio networks over white spaces. In International symposium on new frontiers in dynamic spectrum access networks (pp. 416–427).Google Scholar
  40. 40.
    Keranidis, S., Chounos, K., Korakis, T., Koutsopoulos, I., & Tassiulas, L. (2014). Demo: Enabling agile spectrum adaptation in commercial 802.11 WLAN deployments. In Proceedings of the 20th annual international conference on Mobile computing and networking (pp. 295–298).Google Scholar
  41. 41.
    Bukhari, S. H. R., Siraj, S., & Rehmani, M. H. (2018). NS-2 based simulation framework for cognitive radio sensor networks. Wireless Networks.  https://doi.org/10.1007/s11276-016-1418-5.CrossRefGoogle Scholar
  42. 42.
    Shah, G. A., & Akan, O. B. (2015). Cognitive adaptive medium access control in cognitive radio sensor networks. IEEE Transactions on Vehicular Technology, 64(2), 757–767.CrossRefGoogle Scholar
  43. 43.
    Li, X., Wang, D., McNair, J., & Chen, J. (2011). Residual energy aware channel assignment in cognitive radio sensor networks. In IEEE wireless communications and networking conference (pp. 398–403). IEEE.Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyWah CanttPakistan
  2. 2.COMSATS Institute of Information TechnologyAttockPakistan
  3. 3.Waterford Institute of Technology (WIT)WaterfordIreland
  4. 4.Leeds University Business SchoolUniversity of LeedsLeedsUK

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