International Journal of Communication Systems最新文献

Vehicular networks are central to intelligent transportation systems, yet their expanding attack surface yields complex multivulnerability paths that exceed traditional detection capabilities. This article presents a high-coverage attack-chain generation framework that models, clusters, and composes vulnerabilities with reduced redundancy. A five-tuple schema, Conditions, Tech, Tool, Target, and Results, unifies heterogeneous sources to expose underlying correlations. A semantically enhanced K-Modes method integrates semantic similarity with Hamming distance to improve categorization. A two-dimensional combinatorial engine then generates attack paths using a coverage matrix and greedy selection to balance tractability and coverage. Experiments on communication, system, and control-layer vulnerabilities achieve over 70% coverage and 85% accuracy, outperforming baselines. The framework supports vulnerability assessment and proactive defense planning, strengthening vehicular network resilience against complex attack chains.

Wireless sensor networks (WSNs) are generally used in environmental monitoring, data transfer, and object detection but are susceptible to intrusion attacks based on their integration with the Internet of things (IoT). Most conventional intrusion detection systems experience high false alarm rates and excessive computational overhead. For better performance in overcoming these limitations, a new intrusion detection framework called Multitask Multiattention Residual Shrinkage Convolutional Neural Network with optimization through the Fennec Fox Optimization Algorithm (MMRSCNN-FFOA-ID-WSN) is proposed. The framework combines preprocessing using an Ultrawideband Nanoplasmonic Bandpass Filter (UWNPBF) to eliminate redundant and biased entries, followed by feature selection through the Binary Waterwheel Plant Optimization Algorithm (BWWPOA). The MMRSCNN combines multitask learning with channel-wise attention and residual shrinkage operations to detect attack types from input data. To further improve detection accuracy, the weight parameters are optimized using the FFOA, chosen for its faster convergence in high-dimensional search spaces compared to other algorithms. Experiments were performed on the WSN-DS dataset having normal traffic and various attack types such as flooding, black hole, gray hole, and time division multiple access attacks (TDMA). The proposed method achieved an accuracy of 99.46%, specificity of 98.83%, and a false alarm rate of 1.12%, which correspond to relative improvements of up to 22.37%, 25.32%, and 32.40%, respectively, over the baseline model. These results show that MMRSCNN-FFOA-ID-WSN is an efficient and energy-saving solution for WSNs intrusion detection.

Cognitive radio vehicular ad hoc networks (CR-VANETs) demand secure and efficient routing to cope with high mobility, spectrum variability, and adversarial threats. To address these challenges, this paper introduces neural circle-driven finite element fusion with eel electro-dynamic optimization (NCir-FeF-E2dO). This integrated framework combines pre-processing, feature extraction, intelligent routing, and encryption. Smooth-Gauss histogram normalization (SGHNN) reduces noise and normalizes inputs, while the scale-calibrated transformer (SCT) extracts multi-scale features for robust representation. Routing predictions are achieved using a finite element neural fusion network (FE-NFN) enhanced by circle-driven optimization (CDO), and path selection is refined through eel electro-dynamic optimization (E2dO). For secure communication, the hyperchaotic system–Fibonacci Q-matrix encryption ensures confidentiality and resistance against statistical and differential attacks. Simulation results demonstrate significant performance gains: routing efficiency of 99.05%, packet delivery ratio of 97.22%, 30% lower end-to-end delay, 15% higher throughput, 10% better energy efficiency, 15% lower communication overhead, encryption speed of 142 Mbps, and system recovery time of 4.7 ms. Compared with OPBRP, OCSR, UGAVs-MDVF, DTE-RR, LSTM, UER, and RL-IoT, the proposed method consistently outperforms across all evaluated metrics. These findings establish NCir-FeF-E2dO as a reliable and scalable solution for enhancing secure routing in CR-VANETs.

Energy consumption is the most significant factor in Wireless Sensor Networks (WSNs) because it is completely impractical to recharge the sensor nodes' energy when they are deployed for monitoring in hostile environments. This energy utilization factor is highly indispensable as it has a direct influence on energy stability and network lifetime. The main intent of this research is to concentrate on energy-efficient CHs selection and sink node mobility, which helps in establishing single-hop communication amid chosen CHs and sink based on merits of meta-heuristic algorithms that are more ideal for determining optimal solutions to NP-hard problems. In this paper, Hybrid Puma and Arctic Puffin Multiobjective energy-efficient optimization clustering and sink node mobility routing (HPAPCSMP) Protocol is propounded for achieving better energy stability and network lifespan. This HPAPCSMP mechanism specifically used the Puma Optimization Algorithm (PUOA) for efficient selection of CHs using the fitness function depending on factors of network coverage, node degree, intracluster and intercluster distance and residual energy (RE). It used the Arctic Puffin Optimization Algorithm (APOA) to support sink node mobility that guarantees single-hop communication between chosen CHs and sink such that significant energy is sustained to obtain improved network lifetime. This APOA-based mobile sink path planning algorithm is a significant signal collection method for alleviating the limitations of the mobile sink path optimization process with the objective of shortening the trajectory of movement. The results of the HPAPCSMP mechanism confirmed maximized throughput of 21.38%, reduced energy consumptions of 25.42%, and extended network lifetime of 21.94% better than standard clustering approaches taken for comparison.

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.

Integrated sensing and communication (ISAC) is a key technology for next-generation cognitive radio (CR) systems that allows for the smooth coexistence of several wireless services and effective spectrum utilization. A comprehensive review of current developments in ISAC antenna configurations specifically designed for CR applications is provided in this article. The significant representations of CR architecture are dielectric resonator antenna (DRA)-based CR systems and microstrip patch antenna (MPA)-based CR systems. A communication and sensing antenna enables CR antennas to perform multiple purposes. The communication antenna, which is combined with a reconfigurable filter, offers narrowband operation for dependable data transmission, while the sensing antenna, which is usually constructed as an ultrawideband (UWB) structure, allows spectrum awareness by collecting a wide range of frequencies. The study covers key design approaches, reconfigurability strategies, and trade-offs related to ISAC antennas in CR systems, focusing on their use in adaptive wireless communication and dynamic spectrum access. The review paper also addresses the design, challenges, and performance parameters of CR.

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).