Skip to main content
SpringerLink
Log in

Deep Q-probabilistic algorithm based rock hyraxes swarm optimization for channel allocation in CRSN smart grids

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Due to intelligent communication, smart grids have been further developed compared to traditional power grids. In order to compensate for the growing demand for quality of service (QoS) requirements, the cognitive radio sensor network (CRSN) is being adopted. Most of the prevailing methods fail to meet the user’s spectrum demand, which causes the delay in data transmission. Hence to improve the spectrum allocation in CRSN, the deep Q-probabilistic algorithm based rock hyraxes swarm optimization (RHSO) is proposed in this work. The channel requested user’s id is stored in Q-table that is placed in the sink node. RHSO selects the high priority user from Q-table that search for the early requested user in the Q-table. The vacant channel is detected by a deep probabilistic neural network (DPNN) based on the user request. The DPNN search for vacant channel based on sleeping and active time. The proposed method improves channel allocation with reduced time by using DPNN. The proposed method is implemented in the Matlab platform. The proposed method offers higher throughput of 280 kbps for 25 idle channels by reducing the latency to 2 ms and retransmission probability to 0.01. These performance measures show the efficacy of the proposed method in channel allocation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data availability

No data availability.

Code availability

custom code.

References

  1. Kabalci, E., & Kabalci, Y. (2019). Introduction to smart grid architecture. Smart grids and their communication systems (pp. 3–45). Springer.

    Book  Google Scholar 

  2. Garau, M., et al. (2017). A 5G cellular technology for distributed monitoring and control in smart grid. 2017 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE.

  3. Al-Turjman, F., & Abujubbeh, M. (2019). IoT-enabled smart grid via SM: An overview”. Future Generation Computer Systems, 96, 579–590.

    Article  Google Scholar 

  4. Kuzlu, M., Pipattanasomporn, M., & Rahman, S. (2014). Communication network requirements for major smart grid applications in HAN, NAN and WAN. Computer Networks, 67, 74–88.

    Article  Google Scholar 

  5. Khan, M. W., et al. (2020). QoS-aware traffic scheduling framework in cognitive radio based smart grids using multi-objective optimization of latency and throughput. Ad Hoc Networks, 97, 102020.

    Article  Google Scholar 

  6. Balyan, V. (2021). Channel allocation with MIMO in cognitive radio network. Wireless Personal Communications, 116(1), 45–60.

    Article  Google Scholar 

  7. Ozger, M., Cetinkaya, O., & Akan, O. B. (2018). Energy harvesting cognitive adio networking for IoT-enabled smart grid. Mobile Networks and Applications, 23(4), 956–966.

    Article  Google Scholar 

  8. Kong, P. Y., & Song, Y. (2019). Joint consideration of communication network and power grid topology for communications in community smart grid. IEEE Transactions on Industrial Informatics, 16(5), 2895–2905.

    Article  MathSciNet  Google Scholar 

  9. Rathinam, D.D.K., et al. (2019). Modern agriculture using wireless sensor network (wsn).” 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS). IEEE.

  10. Sangam, S. V., Kulkarni, S. S., & Jambotkar, C. K. (2019). Smart grid communication protocols. International Journal of Trend in Scientific Research and Development, 3(2), 335–337.

    Article  Google Scholar 

  11. Ogbodo, E.U., Dorrell, D.G., and Abu-Mahfouz, A.M. (2017) Performance analysis of correlated multi-channels in cognitive radio sensor network based smart grid. 2017 IEEE AFRICON. IEEE.

  12. Kai, C., et al. (2020). An Amplify-and-Forward Full-duplex Cooperative Relay Scheme for Low-latency Downlink Transmission in CRAN. IEEE Communications Letters.

  13. Liu, Z., Zhao, M., Yuan, Y., & Guan, X. (2020). Subchannel and resource allocation in cognitive radio sensor network with wireless energy harvesting. Computer Networks, 167, 107028.

    Article  Google Scholar 

  14. Ogbodo, E. U., Dorrell, D., & Abu-Mahfouz, A. M. (2017). Cognitive radio based sensor network in smart grid: Architectures, applications and communication technologies. IEEE Access, 5, 19084–19098.

    Article  Google Scholar 

  15. Molokomme, D. N., Chabalala, C. S., & Bokoro, P. N. (2020). A review of cognitive radio smart grid communication infrastructure systems. Energies, 13(12), 3245.

    Article  Google Scholar 

  16. Ogbodo, E. U., Dorrell, D. G., & Abu-Mahfouz, A. M. (2019). Performance measurements of communication access technologies and improved cognitive radio model for smart grid communication. Transactions on Emerging Telecommunications Technologies, 30(10), 3653.

    Article  Google Scholar 

  17. Astaneh, A. A., & Gheisari, S. (2018). Review and comparison of routing metrics in cognitive radio networks. Emerging Science Journal, 2(4), 191–201.

    Article  Google Scholar 

  18. Alam, S., et al. (2017). Cognitive radio based smart grid communication network. Renewable and Sustainable Energy Reviews, 72, 535–548.

    Article  Google Scholar 

  19. Gupta, M. S., & Kumar, K. (2019). Progression on spectrum sensing for cognitive radio networks: A survey, classification, challenges and future research issues. Journal of Network and Computer Applications, 143, 47–76.

    Article  Google Scholar 

  20. Tarek, D., Benslimane, A., Darwish, M., & Kotb, A. M. (2020). Survey on spectrum sharing/allocation for cognitive radio networks internet of things. Egyptian Informatics Journal. https://doi.org/10.1016/j.eij.2020.02.003

    Article  Google Scholar 

  21. Ahmad, A., Ahmad, S., Rehmani, M. H., & Ul Hassan, N. (2015). A survey on radio resource allocation in cognitive radio sensor networks. IEEE Communications Surveys & Tutorials, 17(2), 888–917.

    Article  Google Scholar 

  22. Khan, M. W., & Zeeshan, M. (2019). QoS-based dynamic channel selection algorithm for cognitive radio based smart grid communication network. Ad Hoc Networks, 87, 61–75.

    Article  Google Scholar 

  23. Monisha, M., & Rajendran, V. (2020). SCAN-CogRSG: Secure channel allocation by dynamic cluster switching for cognitive radio enabled smart grid communications. IETE Journal of Research. https://doi.org/10.1080/03772063.2020.1729259

    Article  Google Scholar 

  24. Sultana, A., Bardalai, A., & Sarma, K. K. (2020). Wireless sensor network based smart grid supported by a cognitively driven load management decision making. Neural Processing Letters, 52, 663–678.

    Article  Google Scholar 

  25. Vishnu, J. B., & Bhagyaveni, M. A. (2020). Energy efficient cognitive radio sensor networks with team-based hybrid sensing. Wireless Personal Communications, 111(2), 929–945.

    Article  Google Scholar 

  26. Alam, S., Malik, A. N., Qureshi, I. M., Ghauri, S. A., & Sarfraz, M. (2018). Clustering-based channel allocation scheme for neighborhood area network in a cognitive radio based smart grid communication. IEEE Access, 6, 25773–25784.

    Article  Google Scholar 

  27. Alam, S., Aqdas, N., Qureshi, I. M., Ghauri, S. A., & Sarfraz, M. (2019). Joint power and channel allocation scheme for IEEE 802.11 af based smart grid communication network. Future Generation Computer Systems, 95, 694–712.

    Article  Google Scholar 

  28. Khan, M. W., & Zeeshan, M. (2019). Fuzzy inference based adaptive channel allocation for IEEE 802.22 compliant smart grid network. Telecommunication Systems, 72(3), 339–353.

    Article  Google Scholar 

  29. Stephan, T., Al-Turjman, F., & Balusamy, B. (2021). Energy and spectrum aware unequal clustering with deep learning based primary user classification in cognitive radio sensor networks. International Journal of Machine Learning and Cybernetics, 12(11), 3261–3294.

    Article  Google Scholar 

  30. Sangeetha, V. (2021). Fuzzy convolution neural network and convergence improved bat optimization for an energy efficient and secured spectrum access in cognitive radio network. International Journal of Modern Agriculture, 10(2), 1270–1286.

    Google Scholar 

  31. Devi, M., Sarma, N., & Deka, S. K. (2021). A centralized model enabling channel reuse for spectrum allocation in cognitive radio networks. Cybernetics and Information Technologies, 21(2), 183–200.

    Article  Google Scholar 

  32. Gatate, V., & Agarkhed, J. (2021). Energy preservation and network critic based channel scheduling (EPNCS) in cognitive radio sensor networks. International Journal of Information Technology, 13(1), 69–81.

    Article  Google Scholar 

  33. Agarkhed, J., & Gatate, V. (2020). ICBCA–Improved cluster based channel allocation in cognitive radio sensor networks. Journal of Telecommunications and Information Technology. https://doi.org/10.26636/jtit.2020.139820

    Article  Google Scholar 

  34. Jang, B., Kim, M., Harerimana, G., & Kim, J. W. (2019). Q-learning algorithms: A comprehensive classification and applications. IEEE Access, 7, 133653–133667.

    Article  Google Scholar 

  35. Al-Khateeb, B., Ahmed, K., Mahmood, M., & Le, D.-N. (2021). Rock hyraxes swarm optimization: A new nature-inspired metaheuristic optimization algorithm. CMC-Computers Materials & Continua, 68(1), 643–654.

    Article  Google Scholar 

  36. Zhao, G., Zhang, C., and Zheng, L. (2017). Intrusion detection using deep belief network and probabilistic neural network. 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) IEEE, 1.

  37. Webber, J., et al. (2017).Study on Idle Slot Availability Prediction for WLAN Using a Probabilistic Neural Network. 2017 23rd Asia-Pacific Conference on Communications (APCC) IEEE.

  38. Webber, J., et al. (2019).Optimized WLAN Channel Allocation Based on Gibbs Sampling with Busy Prediction using a Probabilistic Neural Network. 2019 International Conference on Communications, Signal Processing, and their Applications (ICCSPA) IEEE.

  39. Aggarwal, M., et al. (2019). Probability-based centralized device for spectrum handoff in cognitive radio networks. IEEE Access, 7, 26731–26739.

    Article  Google Scholar 

Download references

Funding

No funding is provided for the preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Electrical & Electronics Engineering, University College of Engineering, Kakatiya University, Kothagudem, 507115, India

    Korra Cheena

  2. Department of Computer Science & Engineering, Indian Institute of Technology (ISM), Dhanbad, 826004, India

    Tarachand Amgoth

  3. Department of Electrical Engineering, Indian Institute of Technology (ISM), Dhanbad, 826004, India

    Gauri Shankar

Authors
  1. Korra Cheena
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tarachand Amgoth
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gauri Shankar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have equal contributions in this work.

Corresponding author

Correspondence to Korra Cheena.

Ethics declarations

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by the authors.

Consent to participate

Two authors have equal contributions.

Consent to publish

Reviewers and Editors can publish this work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheena, K., Amgoth, T. & Shankar, G. Deep Q-probabilistic algorithm based rock hyraxes swarm optimization for channel allocation in CRSN smart grids. Wireless Netw 28, 2553–2565 (2022). https://doi.org/10.1007/s11276-022-02985-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-022-02985-z

Keywords

Search

Navigation