International Journal of Communication Systems最新文献

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.

Location-based services (LBS) offer useful information based on a user's location, but they raise privacy risks since sensitive data can be misused by third parties. Traditional peer-to-peer (P2P) systems try to protect privacy but struggle to balance anonymization with service accuracy, and the problem worsens as the number of users and queries increases. These issues are overcome by introducing a novel Xception Convolutional Scaling Wide Residual Network (XCovSWideR-Net) model to improve location privacy in P2P systems through anonymization. The anonymization process involves the user, an anonymization server, and the LBS server. The anonymizer first hides the user's personal and location details and then adds dummy locations to mask the real query. Privacy is further improved using the XCovSWideR-Net model, which combines Xception Convolutional Network (XCovNet) and Scaling Wide Residual Network (SwideRes-Net). The anonymized query is sent to the LBS server, which returns the requested information to the anonymization server, and finally to the user without revealing their actual location. The XCovWideR-Net model achieved maximum location privacy of 0.967, location preservation of 0.974, anonymous entropy of 8.268, and a minimum computation time of 2.480 s for 800 users in Scenario 3. These findings highlight the ability of the proposed method to effectively balance privacy, accuracy, and efficiency, providing a promising solution for secure and scalable LBS applications.

The industrial Internet of Things (IIoT) depends on wireless sensor networks (WSNs) to enable low-power, low-data-rate communication in resource-limited settings. While the IEEE 802.15.4 standard provides the communication foundation, its medium access control (MAC) protocols face challenges including energy consumption, latency, scalability, and adaptability. Traditional MAC protocols cannot keep up with the demands of IIoT networks as the number of connected devices continues to increase. Therefore, edge artificial intelligence (Edge AI) and tiny machine learning (TinyML) represent emerging approaches that show potential for improving the performance of traditional MAC protocols directly on IIoT devices. Edge AI and TinyML allow intelligent decision-making at the edge, which enables efficient data processing and adaptability to the environment without the need for cloud infrastructure, which may reduce latency and energy consumption. This paper systematically examines the emerging paradigm of combining Edge AI and TinyML to improve MAC protocols for WSNs in IIoT networks. We explore advanced machine learning (ML) methods applicable to resource-limited devices, and we investigate how these methods can improve key performance metrics for MAC protocols, including energy efficiency, throughput, and network lifetime. We also discuss the challenges and limitations of applying AI solutions in WSNs, including computational constraints, data scarcity, and model scalability. Finally, we propose potential future research directions to improve the application of AI and ML techniques to develop more efficient, adaptive, and intelligent MAC protocols for future IIoT networks.

The intelligent transportation system (ITS) is enabled by the vehicular ad hoc networks (VANETs), but the security threats, such as node impersonation, node tampering, and eavesdropping, are the greatest challenges and cause security concerns within the system. The large-scale vehicular environment is not effectively handled by the previous static and centralized security approaches, which can greatly increase the latency and data integrity problems within the network. Thus, this research proposes a deep learning–assisted blockchain approach for enabling the decentralized, reliable, and secure communication in the VANET. The main contribution of the research is to perform secure authentication and task offloading to enable secure task offloading within the VANET and to guarantee communication performance with minimum energy consumption and delays. First, the data confidentiality, privacy of the task offloading, authentication, and integrity are achieved by introducing blockchain technology. Second, the node authentication is performed using adaptive and attention-based hybrid serial learning (AAHSL), which is developed with the combination of a deep belief network (DBN) and temporal convolution network (TCN). After authenticating the data within the nodes, the adaptive deep reinforcement learning (ADRL)–based task offloading is proposed for reducing the task completion time within the network. In both models, the parameters are tuned using the pattern improvement parameter–based poor and rich optimization algorithm (PIP-PROA). The experimental results demonstrate that the proposed approach achieves an FNR of about 3.46% during the authentication process, and the reward score achieved by the designed model during the task offloading process is 9.22. Thus, the effectiveness of the suggested model is confirmed by the experimental analysis.

Energy efficiency is one of the main factors that determine the durability and dependability of WSNs. The performance and lifespan of a network are usually limited by the small battery capacity of the sensor nodes. Traditional cluster–based routing protocols enhance energy efficiency by creating clusters and sending data to the base station (BS) through the group-head nodes. Nevertheless, if a node that is between the BS and its cluster head sends data through the cluster head instead of directly to the BS, redundant reverse transmission can happen, which results in unnecessary energy dissipation. Hence, a Path Direction-Sensitive Routing Protocol (PDSRP) is introduced to resolve this problem. It uses signal strength indicators to locate the best transmission paths dynamically. The new protocol is instrumental in equalizing energy consumption, cutting down on reverse communication, and improving the general routing efficiency level. According to simulation results, PDSRP is able to considerably reduce power wastage and increase the longevity of the network; thus, it is a viable solution for IoT-based WSN applications that is scalable and energy-efficient.

Cognitive radio networks (CRNs) enable efficient spectrum utilization leveraging dynamic spectrum access to optimize bandwidth usage and improve overall network efficiency, but achieving energy efficiency remains a key challenge in CRNs. The proposed study presents a novel framework that integrates energy-saving and energy-harvesting strategies in CRNs using a semi-Markov process to model the dynamic behavior of secondary users (SUs) under an N-policy. Unlike Markov models, the semi-Markov models capture non-exponential sojourn times of energy saving states, providing a more accurate representation of CRN dynamics under varying energy and traffic conditions. The proposed model quantifies key performance metrics, including throughput, latency, and energy consumption. Numerical results demonstrate that our method significantly reduces energy usage while maintaining network performance, offering a sustainable solution for energy-constrained CRNs. This study highlights the potential of semi-Markov models to advance green wireless communication systems.

This paper presents an efficient dual-band rectenna for energy harvesting in IoT applications at the 5G (3.6 GHz) and Wi-Fi max (5.8 GHz) bands. The rectenna consists of a dual-band antenna, an impedance-matching network (IMN), and a rectifier circuit designed on the FR4 substrate to make the system economical. The antenna is optimized on the substrate of a compact area equal to $��28�.�5�\times �28�.�3��{�\text{mm}�}^{�2�}�$. A heptagonal patch with a heptagonal slot and tuning stubs provides proper impedance matching and bandwidth. The partial ground design yields a measured gain of 4.97 and 7.07 dBi at 3.6 and 5.8 GHz, respectively. The proposed antenna exhibits an omnidirectional radiational pattern with improved radiation efficiency. The received EM signal is then rectified using an HSMS-2860 Schottky diode-based rectifier circuit. The circuit is connected to the antenna via an IMN. The proposed IMN reduces the rectenna complexity by utilizing minimum transmission lines for dual-frequency operation. It shows the maximum measured power conversion efficiency (PCE) of 54% and 46% at 3.6 and 5.8 GHz, respectively. The output DC voltage of 1.64 and 1.25 V is measured for 3.6 and 5.8 GHz, respectively, at a 1-K$��\Omega �$load resistance.

This article introduces a metasurface-integrated CPW-fed dual-band 2 × 2 MIMO antenna designed for sub-6 GHz 5G and Wi-Fi 6E applications. The proposed 4 × 4 metasurface-inspired radiating structure enhances antenna performance and exhibits dual-polarization across two frequency bands. The slot-cutting technique is used to achieve the targeted frequency bands, thereby improving impedance matching at the resonant frequencies. The entire antenna occupies a compact size of only 40 × 40 × 1.6 mm³ (0.33λ1 × 0.33λ1 × 0.013λ1), where λ1 is the wavelength at the lowest cutoff frequency of the first operating band. The low-cost FR4 substrate (relative permittivity = 4.4, loss tangent = 0.02) is used in the antenna design, which resonates at 3.5 GHz for the sub-6 GHz NR 5G band and at 7.5 GHz for Wi-Fi 6E. Initially, diversity techniques ensure an average isolation of at least −20 dB across the bands. When the metasurface backplane is integrated, mutual coupling is significantly reduced. The mutual coupling is improved by 35 dB, reaching −55 dB in the first band. In the second band, mutual coupling is enhanced by 20 dB, reaching −48 dB. Along with the reduction in mutual coupling, the gain increases by 2.1 and 3 dB at the first and second resonant frequencies, respectively, achieving peak gains of 5 and 8.3 dBi. Additionally, the radiation efficiency improves by 34% and 40% at the respective bands. The simulated results are validated through measurements, demonstrating a diversity gain (DG) of 9.98 dB or higher, an envelope correlation coefficient (ECC) of 0.02 or lower, and excellent MIMO performance such as channel capacity loss (CCL) and the total active reflection coefficient (TARC), with CCL values below 0.4 bits/s/Hz and TARC under 10 dB in both the frequency bands. This antenna is suitable for use in portable devices supporting sub-6 GHz 5G and Wi-Fi 6E applications.