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- ItemSeCo2: Secure Cognitive Semantic Communication in 6G-IoT Networks Using Key-Policy Attribute-Based Encryption and Elliptic Curve Cryptography(Radioengineering Society, 2025-06) Sugitha, G.; Vasanthi, R.; Solairaj, A.; Kalpana, A. V.Secure and efficient data transmission is crucial for maintaining seamless system operations and user trust in the rapidly evolving Internet of Things (IoT) environments. However, IoT networks consistently suffer from data integrity breaches, security vulnerabilities at various network layers, and a high computational cost. Bridging the gap between IoT applications and network infrastructure is essential to addressing these issues. This paper introduces SeCo2, a secure cognitive semantic communication framework for 6G-IoT networks. The framework incorporates a blockchain-based system to provide a secure and privacy-preserving data transmission mechanism. Data preprocessing is conducted using the IoT-Sense dataset, and then encryption is done through a hybrid combination of Key-Policy Attribute-Based Encryption (KP-ABE) and Elliptic Curve Cryptography (ECC). Access control and data permissions are implemented via smart contracts to ensure secure transmission. Additionally, a blockchain security layer utilizing Proof of Stake with Fixed Staking Amounts (PoS-FSA) enhances network security and energy efficiency. For further protection of data integrity, tamper-proof provenance logging prevents unauthorized tampering. Experimental results demonstrate ultra-low latency data transmis¬sion (in the microsecond range), with a transmission delay as low as 0.003001 s for data sizes ranging from 1 GB to 50 GB, and a network security rate of 98%, ensuring more reliable and privacy-preserving IoT ecosystems.
- ItemA Feature Dynamic Enhancement and Global Collaboration Guidance Network for Remote Sensing Image Compression(Radioengineering Society, 2025-06) Fang, Q. Z.; Gu, S. B.; Wang, J. G.; Zhang, L. L.Deep learning-based remote sensing image compression methods show great potential, but traditional convolutional networks mainly focus on local feature extraction and show obvious limitations in dynamic feature learning and global context modeling. Remote sensing images contain multiscale local features and global low-frequency information, which are challenging to extract and fuse efficiently. To address this, we propose a Feature Dynamic Enhancement and Global Collaboration Guidance Network (FDEGCNet). First, we propose an Omni-Dimensional Attention Model (ODAM), which dynamically captures the key salient features in the image content by adaptively adjusting the feature extraction strategy to enhance the modelâ s sensitivity to key information. Second, a Hyperprior Efficient Attention Model (HEAM) is designed to combine multi-directional convolution and pooling operations to efficiently capture cross-dimensional contextual information and facilitate the interaction and fusion of multi-scale features. Finally, the Multi-Kernel Convolutional Attention Model (MCAM) integrates global branching to extract frequency domain context and enhance local feature representation through multi-scale convolutions. The experimental results show that FDEGCNet achieves significant improvement and maintains low computational complexity regarding image quality evaluation metrics (PSNR, MSSSIM, LPIPS, and VIFp) compared to the advanced compression models. Code is available at https://github.com/shiboGu12/FDEGCNet
- ItemPerformance Analysis of Relay Model-based Energy Harvesting in CR-WBAN(Radioengineering Society, 2025-06) Srinivasan, S. N.; Suresh Kumar, P.; Duraisamy, S.An emerging technique was introduced to extend the network lifetime of energy-limited relay nodes in wireless networks. In this paper, the spectral and energy efficiency of Wireless Body Area Networks (WBAN) is investigated. A novel Relay model-based WBAN with Energy Harvesting for enhancing spectrum utilization using Cognitive Radio (CR) technology. This approach involves the surrounding of RF signals, allowing the nodes to gather energy and process data within a WBAN, specifically for medical monitoring purposes enabling the coexistence of diverse implanted devices while maintaining their QoS. It facilitates the simultaneous operation of distinct sensor nodes for primary and secondary networks in on-body CR-WBAN, categorizing nodes based on medical and non-medical applications. The proposed protocols designed for energy harvesting notably Time Switching System (TSS) and Power-Splitting System (PSS) are utilized to enable the cooperation of secondary nodes with the primary network, allowing them to access the spectrum in exchange. The numerical analysis of proposed overlay CR-WBAN in aspects of outage probability, coverage analysis, throughput analysis, and energy efficiency performances considering a delay-limited scenario are examined. The numerical simulations confirm the validity of all the developed theoretical analyses and underscore the efficacy of the considered scheme by verifying using Monte Carlo simulations.
- ItemComparative Analysis of Input Image Characteristics in Convolutional Neural Network-based Signature Detection(Radioengineering Society, 2025-06) Adamec, M.; Turcanik, M.The detection of malware represents a primary concern in contemporary computer security and is therefore imperative for the protection of systems and data integrity. This research presents an innovative approach to comparing diverse input image formats with the objective of identifying the optimal methodology for detecting specific malware-related signatures using convolutional neural networks (CNN), which have been specifically developed by the authors for this purpose. Subsequently, machine code instructions are generated and then converted into four distinct image format options. The four image formats, namely 1xN fixed, 1xN scalable, NxN fixed, and NxN scalable, are subsequently employed for the training of the CNN. The study assesses the formats in question in terms of training time, accuracy, and computational complexity. The results demonstrate that the NxN scalable format exhibits the highest accuracy with accelerated training times in comparison to other formats. Furthermore, the scalable format necessitates only 25% of the original pixel count for a 96% classification success rate. The utilization of the NxN scalable format for machine code instruction representation results in enhanced accuracy, accelerated training, and a considerable reduction in pixel usage, indicating a promising avenue for optimizing the efficiency of malware detection.
- ItemEnhancing WSN Lifespan Based on Efficient-Energy Management Approach for Cluster Head Selection in IoT Application(Radioengineering Society, 2025-06) Mehra, B.; Datar, A.Wireless sensor networks (WSNs) are one of the most important components in the connected world i.e. Internet of Things (IoT). WSN is a network of distributed sensor nodes that communicate wirelessly to transmit and receive real-time data. These sensor nodes play a crucial role in monitoring various environments, enabling smarter decision-making and improving efficiency across numerous applications. This paper presents an energy-efficient protocol based on low energy adaptive clustering hierarchy (LEACH) for improving the lifetime of WSN. The proposed method modifies the basic LEACH protocol and incorporates the factors of residual energy of the network, number of neighbor nodes, average energy of the network, and threshold distance between the nodes and base station. The proposed work compares the result with the existing methods and has shown the improvement in the network performance parameter metrics. The simulation results show an improvement in the network lifetime due to better energy management, thus increasing the number of data packet transfers.The proposed method has shown improvement by 13% over the first dead (FD) node of EEBC-LEACH, 5% improvement over half dead (HD) node of PEGASIS, and 3% improvement over all dead (AD) nodes of FBCR-LEACH.
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