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Hybrid NN-based green cognitive radio sensor networks for next-generation IoT

  • S.I. : Deep Neuro-Fuzzy Analytics in Smart Ecosystems
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In any cognitive radio sensor networks (CRSNs), the secondary users (SUs) and primary users (PUs) share the opportunity to use the authorized frequency band. Here, the SU nodes can only transmit in the temporarily idle spectrum when it is not in use by any PU nodes. The proper estimation and detection of primary nodes are important for the energy-efficient spectrum access. The work basically presents a state of the art in implementing Bayesian-based convolutional neural network (CNN) for addressing the issue of energy constraints in next-generation Internet of things (Nx-IoT) using CRSN. Initially, we use blind source separation to extract the energy features and cyclic spectrum features of the signal and carry on the asymptotic autocorrelation calculation to the extracted signal. Finally, we construct the corresponding training set for CNN training and establish a suitable spectrum sensing model for Nx-IoT. Theoretical analysis and simulation results validate a suitable B-CNN spectrum sensing model along with energy-efficient cooperative communication between the SU nodes.

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References

  1. Mitola JI (2009) Cognitive radio architecture. Proc IEEE 97(4):626–641

    Article  Google Scholar 

  2. Haykin S (2005) Cognitive radio: brain-empowered wireless communications. IEEE J Selected Areas Commun 23(2):201–220

    Article  Google Scholar 

  3. Akan OB, Karli OB, Ergul O (2009) Cognitive radio sensor networks. IEEE Netw 23(4):34–40

    Article  Google Scholar 

  4. Mukherjee A, Maiti S, Datta A (2014) Spectrum sensing for cognitive radio using blind source separation and hidden markov model. In: Fourth international conference on advanced computing and communication technologies, pp 409–414, 2014.

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

    Article  Google Scholar 

  6. Jindal A, Aujla GS, Kumar N, Prodan R, Obaidat MS (2018) DRUMS: Demand Response Management in a Smart City Using Deep Learning and SVR. In: 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, 2018, pp 1–6. https://doi.org/10.1109/GLOCOM.2018.8647926

  7. Solyman AAA et al (2020) A low-complexity equalizer for video broadcasting in cyber-physical social systems through handheld mobile devices. IEEE Access 8:67591–67602. https://doi.org/10.1109/ACCESS.2020.2982001

    Article  Google Scholar 

  8. Zheng Y, Xie X, Yang L (2009) Cooperative spectrum sensing based on blind source separation for cognitive radio. In: 2009 First international conference on future information networks. IEEE, 2009.

  9. Mukherjee A et al (2019) Distributed artificial intelligence based cluster head power allocation in cognitive radio sensor networks. IEEE Sensor Lett. https://doi.org/10.1109/LSENS.2019.2933908,pp.1-4

    Article  Google Scholar 

  10. Arslan H (2007) Cognitive radio, software defined radio, and adaptive wireless systems. Signals Commun Technol, Springer, Cham

    Book  Google Scholar 

  11. Zhang L, Huang J, Tang C (2011) Novel energy detection scheme in cognitive radio. In: IEEE International Conference on Signal Processing,Communications and Computing (ICSPCC), pp 1–4

  12. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge, MA, USA

    MATH  Google Scholar 

  13. Ziwei Y et al (2019) Energy-efficient node positioning in optical wireless sensor networks. Optik 179(8):461–466

    Google Scholar 

  14. Kaneko S, Nomoto S, Ueda T et al. (2008) Prediction radio resource availability in cognitive radio-an experimental examinational. In: IEEE conference on cognitive radio oriented wireless networks and communications, Singapore, pp 1–6

  15. Mukherjee A et al (2019) A novel approach of power allocation for secondary users in cognitive radio networks. Comput Electr Eng 75:301–308

    Article  Google Scholar 

  16. Mukherjee A et al (2020) Green cooperative communication based cognitive radio sensor networks for IoT applications. In: 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland, pp 1–6. https://doi.org/10.1109/ICCWorkshops49005.2020.9145290.

  17. Gulati A, Aujla GS, Chaudhary R, Kumar N, Obaidat MS (2018) Deep learning-based content centric data dissemination scheme for internet of vehicles. In: 2018 IEEE international conference on communications (ICC), Kansas City, MO, 2018, pp 1–6. https://doi.org/10.1109/ICC.2018.8422427.

  18. Yu-Jie T, Qin-yu Z, Wei L (2010) Artificial neural network based spectrum sensing method for cognitive radio. In: IEEE conference in wireless communications networking and mobile computing (WiCOM), pp 1–4, 2010.

  19. He H, Jiang H (2019) Deep learning based energy efficiency optimization for distributed cooperative spectrum sensing. IEEE Wirel Commun 26(3):32–39

    Article  Google Scholar 

  20. Yang X, Sheng M, Sun H, Wang X, Li J (2016) Spatial throughput of energy harvesting cognitive radio networks. In: International symposium on personal, indoor, and mobile radio communications (PIMRC), Valencia (Spain), pp 1–6, 2016

  21. Yang G, Jan MA, Menon VG, Shynu PG, Aimal MM, Alshehri MD (2020) A centralized cluster-based hierarchical approach for green communication in a smart healthcare system. IEEE Access 8:101464–101475. https://doi.org/10.1109/ACCESS.2020.2998452

    Article  Google Scholar 

  22. Thilina KM, Choi KW, Saquib N et al (2013) Machine learning techniques for cooperative spectrum sensing in cognitive radio networks. IEEE J Selected Areas Commun 31(11):2209–2221

    Article  Google Scholar 

  23. Zhang M, Diao M (2017) Guo L (2017) Convolutional neural networks for automatic cognitive radio waveform recognition. IEEE Access 5:11074–11082

    Article  Google Scholar 

  24. He K, Zhang X, Ren S et al (2014) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):904–916

    Google Scholar 

  25. Chen Y, Zhang H, Hu H, et al. (2014) An efficient cooperative spectrum sensing algorithm based on BP neural network. In: International conference on wireless communication and sensor network. IEEE, 2014.

  26. Dai A, Zhang H, Sun H (2016) Automatic modulation classification using stacked sparse auto-encoders. In: IEEE 13th international conference on signal processing (ICSP). IEEE, 2016

  27. Neal RM (2012) Bayesian learning for neural networks. Lecture notes in statistics, vol 118. Springer, New York

  28. Bitar N, Muhammad S, Refai HH (2017) Wireless technology identification using deep convolutional neural networks. In: 2017 IEEE 28th annual international symposium on personal, indoor, and mobile radio communications (PIMRC). IEEE, 2017

  29. Lee W, Kim M, Cho DH (2019) Deep Cooperative Sensing: Cooperative Spectrum Sensing Based on Convolutional Neural Networks[J]. IEEE Trans Veh Technol 68(3):3005–3009

    Article  Google Scholar 

  30. Liu H, Zhu X, Fujii T (2019) Adversarial training for low-complexity convolutional neural networks using in spectrum sensing. In: 2019 international conference on artificial intelligence in information and communication (ICAIIC)

  31. Sobron I, Diniz PSR, Martins WA, Velez M (2015) Energy detection technique for adaptive spectrum sensing. IEEE Trans Commun 63(3):617–627

    Article  Google Scholar 

  32. Fang W, Liu F, Yang F, Shu L, Nishio S (2010) Energy-efficient cooperative communication for data transmission in wireless sensor networks. IEEE Trans Consum Electron 56(4):2185–2192

    Article  Google Scholar 

  33. Eduardo E, Olivo B, Pamela D, Osorio M, Alves H, Candido J, Filho SS, Latva-aho M (2016) An adaptive transmission scheme for cognitive decode-and-forward relaying networks: half duplex, full duplex, or no cooperation. IEEE Trans Wireless Commun 15(8):55865602

    Google Scholar 

  34. Mukherjee A, Goswami P, Yan Z, Yang L, Rodrigues JJPC (2019) ADAI and adaptive PSO-based resource allocation for wireless sensor networks. IEEE Access 7:131163–131171. https://doi.org/10.1109/ACCESS.2019.2940821

    Article  Google Scholar 

  35. Goswami P et al (2019) An energy efficient clustering using firefly and HML for optical wireless sensor network. Optik 182:181–185

    Article  Google Scholar 

  36. Deng Y, Member S, Kim KJ, Member S, Duong TQ, Elkashlan M, Karagiannidis GK (2016) Full-duplex spectrum sharing in cooperative single carrier systems. IEEE Trans Cogn Commun Netw 2(1):68–82

    Article  Google Scholar 

  37. Gulati A, Aujla GS, Chaudhary R, Kumar N, Obaidat M, Benslimane A (2019) DiLSe: lattice-based secure and dependable data dissemination scheme for social internet of vehicles. IEEE Trans Depend Secure Comput. https://doi.org/10.1109/TDSC.2019.2953841

    Article  Google Scholar 

  38. Aujla GS, Singh A, Singh M, Sharma S, Kumar N, Choo KR (2020) BloCkEd: blockchain-based secure data processing framework in edge envisioned V2X environment. IEEE Trans Vehicle Technol 69(6):5850–5863. https://doi.org/10.1109/TVT.2020.2972278

    Article  Google Scholar 

  39. Mukherjee A, Goswami P, Datta A (2016) HML-based smart positioning of fusion center for cooperative communication in cognitive radio networks. IEEE Commun Lett 20(11):2261–2263. https://doi.org/10.1109/LCOMM.2016.2602266

    Article  Google Scholar 

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Funding

This work was supported by Natural Science Foundation of Anhui (Grant no. 2008085MF186).

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Anhui University, Hefei, 230039, China

    Amrit Mukherjee & Lixia Yang

  2. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212013, China

    Manman Li & Pratik Goswami

  3. Electrical Engineering Department, École de Technologie Supérieure, Montréal, QC, H3C 1K3, Canada

    Sahil Garg

  4. Department of Computer Science and Engineering, Sejong University, Seoul, 05006, South Korea

    Md. Jalil Piran

Authors
  1. Amrit Mukherjee
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  2. Manman Li
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  3. Pratik Goswami
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  4. Lixia Yang
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  5. Sahil Garg
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  6. Md. Jalil Piran
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Corresponding authors

Correspondence to Lixia Yang or Md. Jalil Piran.

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Mukherjee, A., Li, M., Goswami, P. et al. Hybrid NN-based green cognitive radio sensor networks for next-generation IoT. Neural Comput & Applic 35, 23819–23827 (2023). https://doi.org/10.1007/s00521-021-05700-9

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  • DOI: https://doi.org/10.1007/s00521-021-05700-9

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