International Journal of Communication Systems最新文献

The increasing demand for stringent quality of service (QoS) guarantees and low latency in mission and time-critical 6G internet of things (IoT) applications necessitates advanced call admission control (CAC) mechanisms. Although 6G networks offer enhanced capabilities, resource limitations like bandwidth and processing power persist. Consequently, efficient resource allocation strategies are crucial to balancing the needs of diverse services, particularly for real-time streaming applications. This paper introduces a novel Analytics & Fuzzy Call Admission Control (AF-CAC) algorithm combined with an Adaptive Leaky Bucket (ALB) scheduling mechanism. The AF-CAC algorithm integrates predictive analytics and fuzzy logic to make informed, intelligent admission decisions, ensuring reliable communication and optimal resource utilization. It prioritizes critical data transmission and prevents network overloading by controlling the number of admitted calls. Concurrently, the ALB mechanism dynamically adapts to changing network conditions, user mobility, and traffic patterns, efficiently allocating resources to meet diverse QoS requirements. The proactive resource allocation is enhanced with a convolutional neural network (CNN) and reinforcement learning (RL) agents, enabling dynamic and efficient resource management to ensure high-quality multimedia streaming, support mission-critical applications, and maintain uninterrupted service during mobility. The average throughput for the AF-CAC technique is 1350 packets/slot, and the average delay over the range of 300 simulated devices is 840 ms. Hence, it exhibits significant enhancements in admitting connections and overall QoS compared to existing approaches for managing multimedia traffic in 6G networks.

The amalgamation of terrestrial and satellite networks for nonorthogonal multiple access services presents a viable approach to improve the energy effectiveness and decrease the latency in communication systems. However, existing approaches face drawbacks such as high computational complexity, limited scalability, and difficulties in handling multiagent coordination, especially in large-scale satellite–terrestrial networks. This research presents a hybrid optimization scheme that maximizes energy efficiency and minimizes delays based on vital considerations like base station (BS) satellite placement, caching strategy, user association, and transmission power control. A three-phase approach is suggested to tackle these issues, relying on collaboration Q-learning with convolutional neural network (C-QCNN) and binary meerkat–hippopotamus optimization for power-control enhancement, cache architecture, and user association. It is observed that the proposed C-QCNN achieves the highest resource utilization of 99.23% when compared with existing techniques. This indicates that C-QCNN effectively allocates and utilizes resources to the enhancement of overall network performance. Significant improvements in energy efficiency are found in integrated satellite–terrestrial networks, thereby enabling more reliable and efficient nonorthogonal multiple access services. The significance of the proposed approach lies in its emphasis on advanced techniques in resource allocation and network optimization for next-generation communication systems.

A significant interference challenge is presented by the upcoming sixth generation (6G) and beyond of wireless communication due to the increasing presence of ultra-scale intelligent factors, such as smart vehicles and mobile robots. Managing this interference can be difficult for detection algorithms in uplink massive multiple-input and multiple-output (MIMO) systems, particularly when dealing with higher-order quadrature amplitude modulation (QAM) signals. Differential spatial modulation (DSM) detection using deep learning (DL) has been a fundamental solution for effectively managing interference in 6G and enhancing the uplink performance of the MIMO system. In this paper, DSM detection in uplink multiuser massive MIMO systems using optimized shuffle attention convolutional neural network with MobileNet V1 (SMD-MIMO-SACNN-MNV1) is proposed. The proposed SACNN-MNV1 technique is used to detect the DSM in uplink multiuser massive multiple-input multiple-output (M-MIMO) systems. Artificial lizard search optimization algorithm (ALSOA) is utilized to enhance the SACNN-MNV1 detector that detects the DSM precisely. The proposed SMD-MIMO-SACNN-MNV1 method is executed in Python. The proposed method achieves 23.54%, 22.65%, and 23.18% higher spectral efficiency when compared with existing techniques respectively.

Wireless sensor networks (WSNs) suffer from the risk of security breaches because of limited node resources, dynamic topologies, and lack of security mechanisms. These limitations affect the integrity of the data and the reliability of the network. Therefore, this paper proposes an enhanced malicious node detection in wireless sensor networks using multimodal contrastive domain sharing generative adversarial networks and blockchain based distributed storage (MND-MCDSGAN-WSN). Firstly, the sensor data gathered from the wireless sensor networks dataset are used. The collected data are cleaned with the Multi-Observation Fusion Kalman Filter (MOFKF) to remove the noise and to make the data more consistent. The Multimodal Contrastive Domain Sharing Generative Adversarial Networks (MCDSGAN) framework gets preprocessed data as input and classifies the malicious activities as Normal, Grayhole, Blackhole, TDMA, and Flooding. Meanwhile, the Adaptive Marine Predator Optimization Algorithm (AMPOA) selects the MCDSGAN model parameters to improve the stability and the generalization of the model. The Interplanetary File System (IPFS) is used to keep the verified node data safe, and a fair proof-of-reputation (FPoR) consensus mechanism is employed to ensure trustful nodes' participation and updates that are resistant to tampering in the blockchain. The combined architecture enhances detection reliability, thus helping trust management and giving a scalable security solution to WSNs that are underresourced. The proposed MND-MCDSGAN-WSN method achieves higher accuracy of 99.07% when evaluated with other existing methods.

Lightweight image encryption for the Internet of Things (IoT) enables secure image data transmission between connected devices while accommodating the limited processing memory and power of IoT systems. A general drawback of lightweight image encryption is that it often sacrifices security strength for efficiency, making it more vulnerable to attacks compared to more robust encryption methods. In this manuscript, the development of a lightweight image encryption algorithm for ensuring confidentiality and privacy in Internet of Things devices (DLWIE-ECP-IoTD) is proposed. Firstly, the input image is gathered from the Caltech-101 dataset. Then, a chaotic pattern generation using a one-dimensional discrete chaos mapping system is applied to generate unpredictable patterns to secure image encryption, followed by a chaotic model of information encryption used for securing the image through unpredictable transformations. Then, a Uniform Physics Informed Neural Network (UPINN) is adapted to produce pseudo-random encryption keys from chaotic parameters, providing an efficient and secure key-generation mechanism. Finally, Fully Dynamic Advanced Encryption Standard (FDAES) is utilized for lightweight encryption of image data. The proposed DLWIE-ECP-IoTD approach is implemented in Python. The proposed DLWIE-ECP-IoTD approach achieves 1.120 s of computational time and 0.03% mean squared error (MSE) with existing methods, such as a lightweight multichaos-based image encryption scheme for IoT networks (LWMC-IES-IoTN), a lightweight image encryption scheme for IoT environments and machine learning-driven robust S-box selection (LWIE-IoTE-ML-DRSBS), and a lightweight image encryption scheme for the safe Internet of Things using a novel chaotic technique (LWIE-NCT-SIoT).

The Social Internet of Things (SIoT) allows smart devices to form social relationships for efficient service discovery and resource sharing. However, trust management is challenged by attacks like ballot stuffing and bad mouthing, which manipulate trust scores through biased recommendations. Existing approaches often evaluate recommenders based on their service provider role, overlooking their behavior as recommenders due to data sparsity. This paper presents a resilient trust management model using reinforcement learning. It combines multiple trust features—direct trust, service reliability, social ties, recommendation benevolence, and referred trust—to compute trust-based rewards. A filtering mechanism is also introduced to detect dishonest recommenders and reduce the impact of biased feedback. Experimental results demonstrate strong resilience against Bad mouthing attack (BMA), ballot stuffing attack (BSA), and Sybil attacks compared to state-of-the-art models, along with faster convergence on both SIoT/IoT network and Epinions datasets. These findings confirm the model's effectiveness in preserving trust and resisting attacks in SIoT environments.

A tunable terahertz (THz) multi-input, multi-output (MIMO) dielectric resonator (DR) antenna (DRA) with the self-diplexing ability is implemented. Two separated annular disc DRs with the top coated graphene material connected with two individual ports. And, these graphene coating can be connected to the separate DC power supplies to find the tunable response through individual port. The narrow-intensified resonance spectrum of annular disc DRs operating with the $��\mathrm{HE}�M_{�40�\delta �}�$ mode can provide the highly sensitive tunable MIMO response. This can also help in offering the tunable self-diplexing capability to antenna. The usage of DRs can provide the antenna with the high gain and efficiency in the THz frequency range. The antenna operation is validated using the circuit theory approach. Moreover, antenna operation is validated to work with the four ports and radiators, which can provide the operation with four port MIMO or self-multiplexing capability. The usage of array of the four radiators enhances the directivity significantly by reducing the minor lobe and confining the radiated power as in pencil beam. The multiport antenna operation is validated by calculating envelop correlation coefficient and diversity gain which remain around 0.06 and 10 dB in the passband, respectively. These prove the proposed antenna suitable for working in the multiport systems.

Big data are difficult to process because of its volume and frequent updates. Big data are used to predict network traffic, which allows for further analysis at the application level. Network traffic prediction is essential for effective network planning and management. Deep learning (DL) has emerged as an effective way of capturing complex spatiotemporal relationships, with graph neural network (GNN) models being especially popular in this area. Nonetheless, conventional GNN techniques have inefficiencies in long-term forecasting in network traffic prediction, resulting in suboptimal predictive performance. To overcome the difficulties in forecasting network traffic, an integrated deep graph neural network (DeepGNN) model is presented in this work. First, create an integrated learning module that takes advantage of spatial correlation. Furthermore, sequence convolutional neural networks (sequence convolutional neural network [CNN]) are used for nonlinear dependencies, whereas attention mechanism incorporation is designed for heterogeneous features. In this study, integrated DeepGNN is evaluated on two network traffic datasets, Milan and Trentino. Three services, SMS, call, and internet, are also included for evaluation services in the first dataset and cumulative services in the second. Integrated DeepGNN is compared with the various existing models considering mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). The proposed technique achieves a 4.923 MAE rate, which is lower than other techniques. The performance of the proposed technique is analyzed and compared with some related techniques to describe the superiority of the proposed model.

Traditional Mobile Wireless Sensor Networks (MWSNs) often use mobile sinks to mitigate issues like energy holes. However, mobile sinks introduce new challenges, such as significant delays and buffer overflows due to fixed trajectories and variable moving speeds. These issues are particularly problematic for delay-sensitive applications, as most existing researches either focus on delay-tolerant scenarios or rely on energy-intensive greedy data collection methods. This research proposes an improved deep learning model with STGRN for delay-sensitive, energy-efficient routing and mobile sink prediction in MWSNs. STGRN-HRFO addresses mobility challenges in MWSNs through optimized routing, energy-efficient transmission, adaptive resource allocation, predictive mobility models, dynamic topology adjustments, and machine learning, enhancing stability, reducing energy consumption, and improving performance. Based on the projections from STGRN, an effective cluster-based routing system is implemented. A Hybrid Red-billed Frilled lizard magpie Optimizer (HRFO) is introduced to optimize the selection of cluster heads and improve routing efficiency. Significant gains are made using the STGRN-HRFO framework, which reduces the end-to-end path's hop count to 0.2 s, minimizes network energy usage to 3.6 J, and boosts throughput to 90%. Additionally, the energy consumed per packet is minimized to 2.2 mJ. Comparative analysis demonstrates that the STGRN-HRFO protocol effectively enhances network performance, ensuring low latency, high packet delivery ratios, and efficient energy use, particularly in real-world scenarios with complex optimization needs.