Spectrum Efficient Cognitive Radio Sensor Network for IoT with Low Energy Consumption

Main Article Content

Pravin Jaronde
Archana Vyas
Mahendra Gaikwad

Abstract

Cognitive Radio Sensor Networks (CRSNs) have emerged as a promising solution for efficient utilization of the limited frequency spectrum. One of the key challenges in CRSNs is achieving spectrum efficiency by avoiding interference and maximizing the use of the available spectrum. Particle Swarm Optimization (PSO) techniques have been widely used to optimize the spectrum allocation and improve the spectrum efficiency of CRSNs. In this paper the study provides an overview of the research on spectrum efficiency in CRSNs using PSO techniques and also discussed the key parameters that affect the spectrum efficiency, such as the swarm size, sensor's threshold and maximum number of iterations and highlights the importance of identifying the optimal combination of these parameters. This paper also emphasizes the need for further research and development in this area to improve the efficiency and effectiveness of PSO-based optimization techniques for CRSNs and to adapt them to various real-world scenarios. Achieving spectrum efficiency in CRSNs is critical for enabling effective wireless communication systems and improving the overall utilization of the available frequency spectrum.

Article Details

How to Cite
Jaronde, P. ., Vyas, A. ., & Gaikwad, M. . (2023). Spectrum Efficient Cognitive Radio Sensor Network for IoT with Low Energy Consumption. International Journal on Recent and Innovation Trends in Computing and Communication, 11(11s), 469–479. https://doi.org/10.17762/ijritcc.v11i11s.8176
Section
Articles

References

I. F. Akyildiz, W. y. Lee, M. C. Vuran, and S. Mohanty, “A survey on spectrum management in cognitive radio networks,” IEEE Communications Magazine, vol. 46, pp. 40–48, April 2008.

R. Chen, Y. Li, Y. Zhang, and K. Li, "A Survey of Spectrum Sensing Algorithms for Cognitive Radio Networks," IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2224-2257, Fourth Quarter 2020. DOI: 10.1109/COMST. 2020. 2999718.

P. W. Jaronde, A. Vyas and M. Gaikwad, "A Survey on Energy Aware Cognitive Radio Network," 2022 2nd International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), Mathura, India, 2022, pp. 1-6, doi: 10.1109/PARC52418.2022.9726654.

Z. Ding, J. Han, H. V. Poor, and Y. Liu, "A Decade of Research on Spectrum Sensing and Sharing in Cognitive Radio: A Survey," IEEE Communications Surveys & Tutorials, vol. 19, no. 2, pp. 1213-1267, Second Quarter 2017. DOI: 10.1109/COMST.2017.2655091.

Y. Li, Q. Liu, X. Li, and L. Yang, "Energy Detection of Unknown Signals over Fading Channels: A Survey," IEEE Access, vol. 8, pp. 123682-123695, 2020. DOI: 10.1109/ACCESS.2020.3008309.

A. N. Zahari, M. K. Hasan, and M. Ismail, "Energy Detection of Unknown Signals in Cognitive Radio Networks over Fading Channels," IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 5384-5397, May 2020. DOI: 10.1109/TVT.2020.2981860.

M. S. O. Alink, A. B. J. Kokkeler, E. A. M. Klumperink, G. J. M. Smit, and B. Nauta, “Lowering the SNR wall for energy detection using cross-correlation,” IEEE Transactions on Vehicular Technology, vol. 60, pp. 3748–3757, Oct 2011.

M. Hamid, N. Bjrsell, and S. B. Slimane, “Energy and eigenvalue based combined fully blind self-adapted spectrum sensing algorithm,” IEEE Transactions on Vehicular Technology, vol. 65, pp. 630–642, Feb 2016.

A. Gardner, "Cyclostationarity: Half a Century of Research," IEEE Signal Processing Magazine, vol. 30, no. 4, pp. 95-105, July 2013. DOI: 10.1109/MSP.2012.2237302.

T. E. Bogale and L. Vandendorpe, “Multi-cycle cyclostationary based spectrum sensing algorithm for OFDM signals with noise uncertainty in cognitive radio networks,” in MILCOM 2012 - IEEE Military Communications Conference, pp. 1–6, Oct 2012.

L. Yang, Z. Chen, and F. Yin, “Cyclo-energy detector for spectrum sensing in cognitive radio,” {AEU} - International Journal of Electronics and Communications, vol. 66, no. 1, pp. 89 – 92, 2012.

V. Chakravarthy, X. Li, Z. Wu, M. A. Temple, F. Garber, R. Kannan, and A. Vasilakos, “Novel overlay/underlay cognitive radio waveforms using {SD-SMSE} framework to enhance spectrum efficiency- Part I: Theoretical framework and analysis in AWGN channel,” IEEE Transactions on Communications, vol. 57, pp. 3794–3804, December 2009.

Mr. A. Kingsly Jabakumar. (2019). Enhanced QoS and QoE Support through Energy Efficient Handover Algorithm for UMTS Architectures. International Journal of New Practices in Management and Engineering, 8(01), 01 - 07. https://doi.org/10.17762/ijnpme.v8i01.73.

R. Zhou, X. Li, V. Chakravarthy, C. Bullmaster, B. Wang, R. Cooper, and Z. Wu, “Software defined radio implementation of SMSE based overlay cognitive radio,” in 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN), pp. 1–2, April 2010.

P. Rose, R. Zhou, Y. Qu, V. Chakarvarthy, Z. Wu, and Z. Zhang, “Demonstration of hybrid overlay/underlay waveform generator with spectrally compliant cognitive capability via SD-SMSE framework,” in 2016 13th IEEE Annual Consumer Communications Networking Conference (CCNC), pp. 258–259, Jan 2016.

S. Yin, Z. Qu, and S. Li, “Achievable throughput optimization in energy harvesting cognitive radio systems,” IEEE Journal on Selected Areas in Communications, vol. 33, pp. 407–422, March 2015.

X. Chen, Y. Liu, and Z. Ding, "Joint Resource Allocation for Information and Energy Transfer in Cognitive Radio Networks", IEEE Transactions on Communications, Volume: 69, Issue: 3, Pages: 1892-1904, Year: 2021, DOI: 10.1109/TCOMM.2020.3022307.

M. Naeem, M. F. Mumtaz, A. Kammoun, A. E. M. Khedr, and J. Rodriguez, "Spectrum Sensing Techniques in Cognitive Radio Networks: State-of-the-Art and Recent Advances," IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2460-2493, Fourth Quarter 2020. DOI: 10.1109/COMST.2020.2999696.

A. Singh, M. R. Bhatnagar, and R. K. Mallik, “Performance of an im- proved energy detector in multihop cognitive radio networks,” IEEE Transactions on Vehicular Technology, vol. 65, pp. 732–743, Feb 2016.

B. Li, M. Sun, X. Li, A. Nallanathan, and C. Zhao, “Energy detection based spectrum sensing for cognitive radios over time-frequency doubly selective fading channels,” IEEE Transactions on Signal Processing, vol. 63, pp. 402–417, Jan 2015.

Kalambe, K. D., A. R. Deshmukh, and S. S. Dorle. "Particle swarm optimization based routing protocol for vehicular ad hoc network." International Journal of Engineering Research and General Science 3.1 (2015): 1375-1382.

Shrivastava, Ms Prerana, S. B. Pokle, and S. S. Dorle. "An Energy Efficient Localization Strategy Using Particle Swarm Optimization in Wireless Sensor Network." International Journal of Advanced Engineering and Global Technology 2.19 (2014): 17-22.

Dehaq E. Mohsen, Ehsan M. Abbas, Maan M. Abdulwahid. (2023). Performance Analysis of OWC System based (S-2-S) Connection with Different Modulation Encoding. International Journal of Intelligent Systems and Applications in Engineering, 11(4s), 400–408. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2679.

Nathani, Neeta, G. C. Manna, and S. S. Dorle. "Network architecture model of infrastructure based mobile cognitive radio system in licensed band with blocking probability assessment." 2016 Future Technologies Conference (FTC). IEEE, 2016.

Verma, D. ., Reddy, A. ., & Thota, D. S. . (2021). Fungal and Bacteria Disease Detection Using Feature Extraction with Classification Based on Deep Learning Architectures. Research Journal of Computer Systems and Engineering, 2(2), 27:32. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/29.

Choudhary, Swapna, and Sanjay Dorle. "A quality of service?aware high?security architecture design for software?defined network powered vehicular ad?hoc network s using machine learning?based blockchain routing." Concurrency and Computation: Practice and Experience 34.17 (2022): e6993.

Dorge, Prabhakar D., and Sanjay S. Dorle. "Design of Vanet for improvement of Qos with different mobility patterns." 2013 6th International Conference on Emerging Trends in Engineering and Technology. IEEE, 2013.

Nathani, Neeta, G. C. Manna, and Sanjay S. Dorle. "Analysis of quality of service of cognitive radio systems." 2013 6th International Conference on Emerging Trends in Engineering and Technology. IEEE, 2013.

Bondre, Vipin, and Sanjay Dorle. "Energy Efficient Routing in Vehicular Adhoc Network for Emergency Services." 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE, 2018.

Nathani, Neeta, G. C. Manna, and S. S. Dorle. "Channelized blocking probability estimation model for infrastructure based mobile cognitive radio." Indian Journal of Science and Technology 9 (2016): 45.

Dorle, S. S., et al. "Design of base station's vehicular communication network for intelligent traffic control." 2009 IEEE Vehicle Power and Propulsion Conference. IEEE, 2009.

A. Bavistale, A. Dhokne, A. Kukade, A. Kumbhare, A. Talokar and P. Jaronde, "Energy and Spectrum Efficient Cognitive Radio Sensor Networks," 2023 11th International Conference on Emerging Trends in Engineering & Technology - Signal and Information Processing (ICETET - SIP), Nagpur, India, 2023, pp. 1-4, doi: 10.1109/ICETET-SIP58143.2023.10151496.

Anveshkumar, Nella, Abhay Suresh Gandhi, and Vigneswaran Dhasarathan. "Cognitive radio paradigm and recent trends of antenna systems in the UWB 3.1–10.6 GHz." Wireless Networks 26.5 (2020): 3257-3274.

Gupta, Sunil, et al. "Energy-efficient and reliable packet routing approach for wireless sensor networks." AIP Conference Proceedings. Vol. 2576. No. 1. AIP Publishing, 2022.

Penchala, Sathish Kumar, et al. "Energy efficient data transmission in wireless sensor network using cross site leaping algorithm." AIP Conference Proceedings. Vol. 2424. No. 1. AIP Publishing, 2022.