作者:
Geerthik SRamachandran AJegan JIshwarya M. VAssociate Professor
Department of Information Technology Agni College of Technology Chennai India Associate Professor
Department of Artificial Intelligence & Data Science Saveetha Engineering College Thandalam Chennai India Assistant Professor
Department of Computer Science and Engineering School of Computing SRM Institute Of Science And Technology Tiruchirappalli (Deemed To Be University) Trichy Tamilnadu India Associate Professor
Department of Artificial Intelligence & Data Science Agni College of Technology Chennai India
Wireless sensor networks (WSNs) is the essential component of wireless technology that provides effective solutions for various monitoring applications. WSN is vulnerable to several security risks as intrusions, attac...
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Wireless sensor networks (WSNs) is the essential component of wireless technology that provides effective solutions for various monitoring applications. WSN is vulnerable to several security risks as intrusions, attacks, and suspicious activities. Therefore, this paper proposes a Secure WSN in intrusion detection system using Central-Smoothing Hypergraph Neural Network Optimized with clouded leopard optimization algorithm (WSN-IDS-CSHGNN-CLOA). Here, the input data is taken from WSN-DS database. The gathered data is pre-processed by regularized bias-aware ensemble Kalman filter (RBAEKF) for data cleaning and normalization. The pre-processed data is given into feature selection using Memetic Salp Swarm optimizationalgorithm (MSSOA) to select optimal features. The selected features are given into CSHGNN for classifying the IDS as denial of service (DoS), black hole, gray hole, flooding, and scheduling attacks in WSN. The CLOA is implemented to optimize the hyperparameters of CSHGNN. The performance of the proposed WSN-IDS-CSHGNN-CLOA approach attains 24.39%, 35.71%, and 25.55% higher accuracy; 24.44%, 34.28%, and 14.44% higher precision when compared to the existing techniques.
In this manuscript, optimized wireless communication for 6G signals processing using memory-augmented deep unfolding network (WC-6G-SP-MADUN) is proposed. The aim of this work is to give a memory-augmented deep unfold...
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In this manuscript, optimized wireless communication for 6G signals processing using memory-augmented deep unfolding network (WC-6G-SP-MADUN) is proposed. The aim of this work is to give a memory-augmented deep unfolding network approach with a focus on belief propagation decoding of error correction codes and multiple-input multiple-output (MIMO) wireless systems. In contrast, compares and tabulates the performance of memory-augmented deep unfolding methodologies with classic principled procedures, which could potentially enable 6G. Here, the wireless communication for 6G signal processing methods was used for a variety of receiver functions, including self-interference cancelation, signal estimation and detection using memory-augmented deep unfolding Network (MADUN), and advanced error correction by clouded leopard optimization algorithm (CLOA). Furthermore, outline the general framework of memory-augmented deep unfolding method for any kind of signal processing application and talk about cutting edge research paths that will make it possible to build communication networks in the future. The proposed approach is implemented in MATLAB;the performance is analyzed with performance metrics such as reliability, complexity, SNR gain, convergence rate, hardware efficiency, and quantization bit width. The proposed approach attains higher SNR gain 17.96%, 24.75%, and 30.09%;lower complexity 17.92%, 23.41%, and 30.13%;and higher convergence 18.01%, 25.41%, and 31.39% when analyzed with existing techniques such as redefined wireless communication for 6G: signal processing meets deep learning and deep unfolding (RWC-6G-SP-DU), lower complication deep unfolded neural network receiver for MIMO under probability data association detector (LC-DUNN-MIMO-PDAD), and deep learning in physical layer communications: evolution with prospects on 5G and 6G networks (DL-PLC-6G), respectively. In this manuscript, optimized wireless communication for 6G signals processing using memory-augme
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