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Current Research Trends on Cognitive Radio Based Internet of Things (IoT)

  • M. Rezwanul MahmoodEmail author
  • Mohammad Abdul Matin
Chapter
  • 17 Downloads
Part of the Internet of Things book series (ITTCC)

Abstract

The attractive features of Internet of Things (IoT) and the concept of cognitive radio have raised the opportunity of creating a smart world. The advancement of cost-effective technologies and protocols empower us to make practical implementation of IoT which impact on human lifestyle, business and industries. Research interest has thus been dragged into the IoT domain to exploit its potential. However, the increased number of devices have caused the spectrum crisis issue. To mitigate this crisis, Cognitive Radio (CR) technology is integrated with IoT that can search for the available spectrum and reuse it for communication. By using cognitive capabilities, cognitive radio can avoid collision among the network elements to ensure better connectivity, accessibility, scalability and reliability of the IoT system. Currently, the research on CR-IoT is at its early stage. This chapter attempts to focus on the recent research efforts related to spectrum sensing, sharing and allocation, cost-effective architectures, transmission parameter adaptation, energy efficient proposals and security provisioning problems for CR-IoT. Some design issues in CR-IoT system are also being discussed in this chapter.

References

  1. 1.
    Ashton, K.: That ‘Internet of Things’ thing. Hg. v. RFID J. 4986 (2009). http://www.rfidjournal.com/articles/pdf
  2. 2.
    Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17(4), 2347–2376 (2015)CrossRefGoogle Scholar
  3. 3.
    Rawat, P., Singh, K.D., Bonnin, J.M.: Cognitive radio for M2M and internet of things: a survey. Comput. Commun. 94, 1–29 (2016)CrossRefGoogle Scholar
  4. 4.
    Mitola, J., Maguire, G.Q., et al.: Cognitive radio: making software radios more personal. IEEE Pers. Commun. 6(4), 13–18 (1999)CrossRefGoogle Scholar
  5. 5.
    Iii, J.M.: Cognitive radio for flexible mobile multimedia communications. Mob. Netw. Appl. 6(5), 435–441 (2001)CrossRefGoogle Scholar
  6. 6.
    Haykin, S.: Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005)CrossRefGoogle Scholar
  7. 7.
    Matin, M.A.: Spectrum Access and Management for Cognitive Radio Networks. Springer (2017)Google Scholar
  8. 8.
    Afzal, A., Zaidi, S.A.R., Shakir, M.Z., Imran, M.A., Ghogho, M., Vasilakos, A.V., McLernon, D.C., Qaraqe, K.: The cognitive internet of things: a unified perspective. Mob. Netw. Appl. 20(1), 72–85 (2015)CrossRefGoogle Scholar
  9. 9.
    Wu, Q., Ding, G., Xu, Y., Feng, S., Du, Z., Wang, J., Long, K.: Cognitive internet of things: a new paradigm beyond connection. IEEE Internet Things J. 1(2), 129–143 (2014)CrossRefGoogle Scholar
  10. 10.
    Zhang, M., Qiu, Y., Zheng, R., Bai, X., Wei, W., Wu, Q.: A novel architecture for cognitive internet of things. Int. J. Secur. Appl 9, 235–252 (2015)Google Scholar
  11. 11.
    Khan, A.A., Rehmani, M.H., Rachedi, A.: Cognitive-radio-based internet of things: applications, architectures, spectrum related functionalities, and future research directions. IEEE Wirel. Commun. 24(3), 17–25 (2017)CrossRefGoogle Scholar
  12. 12.
    Alberti, A.M., Mazzer, D., Bontempo, M.M., de Oliveira, L.H., da Rosa Righi, R., Jr. Sodre, A.C.: Cognitive radio in the context of internet of things using a novel future internet architecture called NovaGenesis. Comput. Electr. Eng. 57, 147–161 (2017)Google Scholar
  13. 13.
    Kim, S.: Inspection game based cooperative spectrum sensing and sharing scheme for cognitive radio IoT system. Comput. Commun. 105, 116–123 (2017)CrossRefGoogle Scholar
  14. 14.
    Wu, Y.: Localization algorithm of energy efficient radio spectrum sensing in cognitive internet of things radio networks. Cogn. Syst. Res. 52, 21–26 (2018)CrossRefGoogle Scholar
  15. 15.
    Rajpoot, V., Tripathi, V.S.: A novel sensing and primary user protection algorithm for cognitive radio network using IoT. Phys. Commun. 29, 268–275 (2018)CrossRefGoogle Scholar
  16. 16.
    Zikria, Y.B., Ishmanov, F., Afzal, M.K., Kim, S.W., Nam, S.Y., Yu, H.: Opportunistic channel selection MAC protocol for cognitive radio ad hoc sensor networks in the internet of things. Sustain. Comput. Inform. Syst. 18, 112–120 (2018)Google Scholar
  17. 17.
    Bauwens, J., Jooris, B., Giannoulis, S., Jabandžić, I., Moerman, I., De Poorter, E.: Portability, compatibility and reuse of MAC protocols across different IoT radio platforms. Ad Hoc Netw. 86, 144–153 (2019)CrossRefGoogle Scholar
  18. 18.
    Mahmud, A., Lee, Y.-D., Koo, I.-S.: An efficient cooperative neighbor discovery framework of cognitive radio ad-hoc networks for future internet of things. Wirel. Pers. Commun. 91(4), 1603–1620 (2016)CrossRefGoogle Scholar
  19. 19.
    Sumathi, A.C., Vidhyapriya, R., Vivekanandan, C., Sangaiah, A.K.: Enhancing 4G co-existence with Wi-Fi/IoT using cognitive radio. Clust. Comput. 1–11 (2017)Google Scholar
  20. 20.
    Shigueta, R.F., Fonseca, M., Viana, A.C., Ziviani, A., Munaretto, A.: A strategy for opportunistic cognitive channel allocation in wireless Internet of Things. In: 2014 IFIP Wireless Days (WD), pp. 1–3. IEEE (2014)Google Scholar
  21. 21.
    Otermat, D.T., Otero, C.E., Kostanic, I.: Analysis of the FM radio spectrum for internet of things opportunistic access via cognitive radio. In: 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), pp. 166–171. IEEE (2015)Google Scholar
  22. 22.
    Zhu, J., Song, Y., Jiang, D., Song, H.: Multi-armed bandit channel access scheme with cognitive radio technology in wireless sensor networks for the internet of things. IEEE Access 4, 4609–4617 (2016)CrossRefGoogle Scholar
  23. 23.
    Jinyi, W., Qin, Y., Shrestha, A.P., Yoo, S.-J.: Optimization of cognitive radio secondary base station positioning and operating channel selection for IoT sensor networks. In: 2017 International Conference on Information and Communication Technology Convergence (ICTC), pp. 397–399. IEEE (2017)Google Scholar
  24. 24.
    Roncancio, G., Espinosa, M., Pérez, M.R., Trujillo, L.C.: Spectral sensing method in the radio cognitive context for IoT applications. In: 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 756–761. IEEE (2017)Google Scholar
  25. 25.
    Huang, P.-H., Chang, L.-H., Wu, J.-Y.: Normalized weighted energy detection for spectrum sensing under random primary signal arrival. In: 2017 International Symposium on Wireless Communication Systems (ISWCS), pp. 13–18. IEEE (2017)Google Scholar
  26. 26.
    Han, R., Gao, Y., Wu, C., Lu, D.: An effective multi-objective optimization algorithm for spectrum allocations in the cognitive-radio-based internet of things. IEEE Access 6, 12858–12867 (2018)CrossRefGoogle Scholar
  27. 27.
    Li, T., Yuan, J., Torlak, M.: Network throughput optimization for random access narrowband cognitive radio internet of things (NB-CR-IoT). IEEE Internet Things J. 5(3), 1436–1448 (2018)CrossRefGoogle Scholar
  28. 28.
    Eze, J., Zhang, S., Liu, E., Eze, E.: Cognitive radio-enabled internet of vehicles: a cooperative spectrum sensing and allocation for vehicular communication. IET Netw. 7(4), 190–199 (2018)CrossRefGoogle Scholar
  29. 29.
    Luo, X., He, Z., Wang, L., Wang, W., Ning, H., Wang, J.-H., Zhao, W.: An efficient Jaya algorithm for resource allocation in the cognitive-radio-networks-aided internet of things. In: 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 118–125. IEEE (2018)Google Scholar
  30. 30.
    Salameh, H.A.B., Al-Masri, S., Benkhelifa, E., Lloret, J.: Spectrum assignment in hardware-constrained cognitive radio IoT networks under varying channel-quality conditions. IEEE Access 7, 42816–42825 (2019)CrossRefGoogle Scholar
  31. 31.
    Paraskevopoulos, A., Dallas, P.I., Siakavara, K., Goudos, S.K.: Cognitive radio engine design for IoT using real-coded biogeography-based optimization and fuzzy decision making. Wirel. Pers. Commun. 97(2), 1813–1833 (2017)CrossRefGoogle Scholar
  32. 32.
    Kaur, A., Sharma, S., Mishra, A.: Performance optimization of cognitive decision engine for CR-based IoTs using various parameter-less meta-heuristic techniques. Arab. J. Sci. Eng. 1–17 (2019)Google Scholar
  33. 33.
    Kaur, A., Kaur, A., Sharma, S.: Cognitive decision engine design for CR based IoTs using differential evolution and bat algorithm. In: 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 130–135. IEEE (2018)Google Scholar
  34. 34.
    Kaur, A., Kaur, A., Sharma, S.: PSO based multiobjective optimization for parameter adaptation in CR based IoTs. In: 2018 4th International Conference on Computational Intelligence and Communication Technology (CICT), pp. 1–7. IEEE (2018)Google Scholar
  35. 35.
    Qureshi, F.F., Iqbal, R., Asghar, M.N.: Energy efficient wireless communication technique based on cognitive radio for internet of things. J. Netw. Comput. Appl. 89, 14–25 (2017)CrossRefGoogle Scholar
  36. 36.
    Aslam, S., Ejaz, W., Ibnkahla, M.: Energy and spectral efficient cognitive radio sensor networks for internet of things. IEEE Internet Things J. 5(4), 3220–3233 (2018)CrossRefGoogle Scholar
  37. 37.
    Shahini, A., Kiani, A., Ansari, N.: Energy efficient resource allocation in eh-enabled CR networks for IoT. IEEE Internet Things J. 6(2), 3186–3193 (2018)CrossRefGoogle Scholar
  38. 38.
    Ansere, J.A., Han, G., Wang, H., Choi, C., Wu, C.: A reliable energy efficient dynamic spectrum sensing for cognitive radio IoT networks. IEEE Internet Things J. (2019)Google Scholar
  39. 39.
    Das, A., Das, N., Barman, A.D., Dhar, S.: Energy incentive for packet relay using cognitive radio in IoT networks. IEEE Commun. Lett. (2019)Google Scholar
  40. 40.
    Jin, F., Varadharajan, V., Tupakula, U.: Improved detection of primary user emulation attacks in cognitive radio networks. In: 2015 International Telecommunication Networks and Applications Conference (ITNAC), pp. 274–279. IEEE (2015)Google Scholar
  41. 41.
    Salameh, H.A.B., Almajali, S., Ayyash, M., Elgala, H.: Security-aware channel assignment in IoT-based cognitive radio networks for time-critical applications. In: 2017 Fourth International Conference on Software Defined Systems (SDS), pp. 43–47. IEEE (2017)Google Scholar
  42. 42.
    Salameh, H.A.B., Almajali, S., Ayyash, M., Elgala, H.: Spectrum assignment in cognitive radio networks for internet-of-things delay-sensitive applications under jamming attacks. IEEE Internet Things J. 5(3), 1904–1913 (2018)CrossRefGoogle Scholar
  43. 43.
    Chaczko, Z., Slehar, S., Shnoudi, T.: Game-theory based cognitive radio policies for jamming and anti-jamming in the IoT. In: 2018 12th International Symposium on Medical Information and Communication Technology (ISMICT), pp. 1–6. IEEE (2018)Google Scholar
  44. 44.
    Farrukh, M., Krayani, A., Baydoun, M., Marcenaro, L., Gao, Y., Regazzoni, C.S.: Learning a switching Bayesian model for jammer detection in the cognitive-radio-based internet of things. In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), pp. 380–385. IEEE (2019)Google Scholar
  45. 45.
    Kim, S.: Cognitive radio anti-jamming scheme for security provisioning IoT communications. KSII Trans. Internet Inf. Syst. 9(10) (2015)Google Scholar
  46. 46.
    Awin, F.A., Alginahi, Y.M., Abdel-Raheem, E., Tepe, K.: Technical issues on cognitive radio-based internet of things systems: a survey. IEEE Access 7, 97887–97908 (2019)CrossRefGoogle Scholar
  47. 47.
    Nitti, M., Murroni, M., Fadda, M., Atzori, L.: Exploiting social internet of things features in cognitive radio. IEEE Access 4, 9204–9212 (2016)CrossRefGoogle Scholar
  48. 48.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  49. 49.
    Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)MathSciNetCrossRefGoogle Scholar
  50. 50.
    Yang, X.-S., Gandomi, A.H.: Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. 29(5), 464–483 (2012)CrossRefGoogle Scholar
  51. 51.
    Khan, A.A., Rehmani, M.H., Reisslein, M.: Cognitive radio for smart grids: survey of architectures, spectrum sensing mechanisms, and networking protocols. IEEE Commun. Surv. Tutor. 18(1), 860–898 (2015)CrossRefGoogle Scholar
  52. 52.
    Grover, K., Lim, A., Yang, Q.: Jamming and anti-jamming techniques in wireless networks: a survey. Int. J. Ad Hoc Ubiquitous Comput. 17(4), 197–215 (2014)CrossRefGoogle Scholar
  53. 53.
    Chatterjee, S., Mukherjee, R., Ghosh, S., Ghosh, D., Ghosh, S., Mukherjee, A.: Internet of things and cognitive radio–issues and challenges. In: 2017 4th International Conference on Opto-Electronics and Applied Optics (Optronix), pp. 1–4. IEEE (2017)Google Scholar
  54. 54.
    Etim, I.E., Lota, J.: Power control in cognitive radios, internet-of things (IoT) for factories and industrial automation. In: IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, pp. 4701–4705. IEEE (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.North South UniversityDhakaBangladesh

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