Enhancing Cognitive Radio WSN Communication through Cluster Head Selection Technique
Received: 23 December 2023 | Revised: 19 January 2024 | Accepted: 28 January 2024 | Online: 18 February 2024
Corresponding author: Brijesh Iyer
Abstract
The demand for frequency spectrum is increasing rapidly with the wide growth of wireless communications. Spectrum sensing issues present in Cognitive Radio Sensor Networks (CRSN) are detected dynamically using spectral sensing techniques, which also help to utilize frequency bands more effectively. The study proposes a novel Cosine Sand Cat Optimization (CSCO) protocol to address spectral sensing problems by selecting the optimal Cluster Head (CH) in a CRSN. The CRSN is simulated, and spectral allocation is performed using LeNet to extract signal components. Then, Primary User (PU) aware optimal CH selection is performed using the proposed CSCO by taking account of multi-objective fitness parameters. Finally, data communication is performed between nodes after CH selection using the CSCO protocol. The simulation results of CSCO were validated to determine its superiority concerning Secondary User (SU) density, and it attained residual energy, network lifetime, Packet Delivery Ratio (PDR), normalized throughput, and delay of 69.457 J, 77, 75.89%, 74.473, and 4.782ms, respectively.
Keywords:
cosine sand cat optimization, sine cosine algorithm, sand cat swarm optimization, LeNetDownloads
References
O. P. Awe, D. A. Babatunde, S. Lambotharan, and B. AsSadhan, "Second order Kalman filtering channel estimation and machine learning methods for spectrum sensing in cognitive radio networks," Wireless Networks, vol. 27, no. 5, pp. 3273–3286, Jul. 2021.
S. Haykin, "Cognitive radio: brain-empowered wireless communications," IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201–220, Feb. 2005.
H. Luo, Z. Huang, and T. Zhu, "A Survey on Spectrum Utilization in Wireless Sensor Networks," Journal of Sensors, vol. 2015, Mar. 2015, Art. no. e624610.
O. B. Akan, O. B. Karli, and O. Ergul, "Cognitive radio sensor networks," IEEE Network, vol. 23, no. 4, pp. 34–40, Aug. 2009.
Y. Cui, X. jun Jing, S. Sun, X. Wang, D. Cheng, and H. Huang, "Deep learning based primary user classification in Cognitive Radios," in 2015 15th International Symposium on Communications and Information Technologies (ISCIT), Jul. 2015, pp. 165–168.
M. Höyhtyä et al., "Spectrum Occupancy Measurements: A Survey and Use of Interference Maps," IEEE Communications Surveys & Tutorials, vol. 18, no. 4, pp. 2386–2414, 2016.
M. K. Giri and S. Majumder, "Eigenvalue-based cooperative spectrum sensing using kernel fuzzy c-means clustering," Digital Signal Processing, vol. 111, Apr. 2021, Art. no. 102996.
K. B. Vikhyath and N. A. Prasad, "Combined Osprey-Chimp Optimization for Cluster Based Routing in Wireless Sensor Networks: Improved DeepMaxout for Node Energy Prediction," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12314–12319, Dec. 2023.
A. Rajab, "Genetic Algorithm-Based Multi-Hop Routing to Improve the Lifetime of Wireless Sensor Networks," Engineering, Technology & Applied Science Research, vol. 11, no. 6, pp. 7770–7775, Dec. 2021.
T. Stephan, F. Al-Turjman, S. J. K, and B. Balusamy, "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, vol. 12, no. 11, pp. 3261–3294, Nov. 2021.
G. K. Walia, M. Kumar, and S. S. Gill, "AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges and Future Perspectives," IEEE Communications Surveys & Tutorials, 2023.
M. Zhang et al., "Exploiting Deep Learning for Secure Transmission in an Underlay Cognitive Radio Network," IEEE Transactions on Vehicular Technology, vol. 70, no. 1, pp. 726–741, Jan. 2021.
Z. Chen and W. Yue, "Differential Space-time Block Coding Based Cooperative Spectrum Sensing over Fading Environments in Cognitive Radio Sensor Networks," Journal of Information, vol. 9, no. 15, pp. 4599–4606, 2012.
G. Wei, G. Li, J. Zhao, and A. He, "Development of a LeNet-5 Gas Identification CNN Structure for Electronic Noses," Sensors, vol. 19, no. 1, Jan. 2019, Art. no. 217.
S. Mirjalili, "SCA: A Sine Cosine Algorithm for solving optimization problems," Knowledge-Based Systems, vol. 96, pp. 120–133, Mar. 2016.
A. Seyyedabbasi and F. Kiani, "Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems," Engineering with Computers, vol. 39, no. 4, pp. 2627–2651, Aug. 2023.
A. Patel, H. Ram, A. K. Jagannatham, and P. K. Varshney, "Robust Cooperative Spectrum Sensing for MIMO Cognitive Radio Networks Under CSI Uncertainty," IEEE Transactions on Signal Processing, vol. 66, no. 1, pp. 18–33, Jan. 2018.
P. Kumar, N. Chauhan, M. Kumar, and L. K. Awasthi, "Clustering based opportunistic traffic offloading technique for device-to-device communication," International Journal of System Assurance Engineering and Management, vol. 14, no. 3, pp. 827–839, Jul. 2023.
S. Panbude, B. Iyer, A. B. Nandgaonkar, and P. S. Deshpande, "DFPC: Dynamic Fuzzy-based Primary User Aware clustering for Cognitive Radio Wireless Sensor Networks," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12058–12067, Dec. 2023.
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Copyright (c) 2024 Shraddha Panbude, Prachi Deshpande, Brijesh Iyer, A. B. Nandgaonkar
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