Title: Intelligent cognitive internet of things-based spectrum sensing algorithm for future communication

Authors: Haewon Byeon; Mahmood Alsaadi; Aadam Quraishi; Ismail Keshta; Mukesh Soni; Pankaj Kumar; Mohit Bhadla; Muhammad Attique Khan; Robertas Damaševičius

Addresses: Department of Digital Anti-Aging Healthcare, Inje University, Gimhae, 50834, 4345-2459-6114, South Korea ' Department of Computer Science, Al-Maarif University College, Al Anbar, 31001, Iraq ' Intervention Treatment Institute, Houston Texas, USA ' Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia ' Division of Research and Development, Lovely Professional University, Phagwara, India; Dr. D.Y. Patil Vidyapeeth, Pune, Dr. D.Y. Patil School of Science and Technology, Tathawade, Pune, India ' Department of Computer Science and Engineering, G.L. Bajaj Institute of Technology and Management, Greater Noida – 201306, Uttar Pradesh, India ' Swarnim Startup and Innovation University, Gandhinagar, India ' Department of AI, Prince Mohammad Bin Fahd University, Saudi Arabia ' Kaunas University of Technology, Lithuania

Abstract: The emergence of fifth-generation (5G) mobile communication technologies has propelled the advancement of the internet of things (IoT). Nevertheless, the intricate nature of the IoT mobile communication environment and the fluctuating characteristics of the signal's present substantial obstacles to current spectrum detection techniques for future communication. Hence, an artificial intelligent spectrum sensing technique is introduced, which integrates artificial intelligent, IoT and denoising autoencoder (DAE) with an enhanced long-short-term memory (LSTM) neural network. The DAE utilises encoding and decoding to retrieve the fundamental structural characteristics of mobile signals, while the enhanced LSTM spectrum sensing classifier model incorporates previous moment information features to classify the time-series signal sequences. This method has demonstrated a 45% improvement in perception performance compared to SVM, RNN, LeNet5, LVQ, and Elman algorithm.

Keywords: future communication; internet of things; IoT; spectrum sensing; artificial intelligent; long-short-term memory; LSTM; denoising autoencoder; DAE.

DOI: 10.1504/IJWGS.2025.144970

International Journal of Web and Grid Services, 2025 Vol.21 No.1, pp.18 - 41

Received: 19 Aug 2024
Accepted: 11 Oct 2024
Published online: 14 Mar 2025 *

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