International Journal of Communication Systems最新文献

This study evaluates the performance of an Unmanned Aerial Vehicle (UAV)-assisted decode-and-forward (DF) relaying network. It investigates the use of Non-orthogonal multiple access (NOMA) to ensure fairness, low latency, and added security. The impact of the transmission antenna selection technique on the network's performance, specifically the outage probability of each group of users, is analyzed. The study derives closed-form expressions for the outage probability considering the Rayleigh fading channel and evaluates the significance of NOMA ordering, channel state information (CSI), and successive interference cancellation (SIC) on the performance. Monte Carlo simulations validate the accuracy of the analytical expressions and show that factors such as imperfect CSI, SIC methods, and NOMA ordering all significantly impact outage performance in the relaying networks.

Unmanned aerial vehicles (UAVs) have appeared as a remarkable solution for data gathering of huge-scale wireless sensor networks (WSNs). It is utilized as a sink node to accumulate data from sensor nodes. Also, it assists the network in extending its life span and eliminates the energy-hole issue experienced by sensor networks. The most important need is the timely collection of data from sensor nodes and to broadcast the data to the base station. The safest path is the basic line for efficient operations, and it is a significant and more complex thing to determine the effective route in an environment by compromising different hurdles and guaranteeing that the route can effectively achieve the target point. This research addresses the clustering problem and updates the trust level of nodes using the proposed Walrus Archimedes optimization algorithm (WaAOA)_DenseNet. More specifically, the trust updation is done utilizing DenseNet, and it is optimally tuned based on the proposed algorithm, which is an incorporation of the Walrus optimization algorithm (WOA) and Archimedes optimization algorithm (AOA).

To realize the predictable abilities of 5G networks, several technological progressions were deliberated. In wireless areas, there is an important surge in the extensive application of 5G technology because of multimedia applications. In the present cellular networks, multimedia content dissemination services have met with many challenges while achieving the desired performance. The worst channel condition with receiving user-support data rate and all multicast group members are served by the conventional multimedia transmission model. For all receiving users, the requirement of various different quality of experience (QoE) and a satisfied quality of service (QoS) was discussed recently. Nonetheless, several researchers have not concentrated on the enhancement of energy efficiency. Under reliable QoE constraint, this work proposed a new hybridized delayed velocity particle white shark swarm (hybrid DVPWS²) algorithm in a multicast device-to-device (D2D) network to optimize the energy efficiency as well as the better allocation of power. The D2D clustering model is achieved by using an agglomerative hierarchy clustering (AHC). To design a multimedia content dissemination model based on energy efficiency, the proposed model combines and is used to allocate inter and intra-cluster D2D multicast with the cellular network. MATLAB software handles the implementation part and reveals the superiority of the proposed method over previous works.

This study investigates the often-overlooked impact of random transmitter–receiver misalignment on energy-efficient simultaneous lightwave information and power transfer (SLIPT) in underwater wireless optical communication (UWOC) systems. Focusing on a two-node setup with angular misalignment over a turbulence-induced fading channel, we derive closed-form expressions for the average harvested energy (HE) and average achievable rate (AR). HE denotes the average energy collected at the sensor node, while AR represents the average rate at which data can be reliably transmitted over the UWOC link. Our analysis reveals a fundamental trade-off between HE and AR, showing that a higher bias ($��B�$) increases HE but reduces the AR, highlighting the HE-AR trade-off. As the randomness in transmitter–receiver orientation increases, the HE-AR region boundary shrinks and shifts leftward, indicating the detrimental impact of misalignment. Furthermore, we demonstrate that employing light-emitting diodes (LEDs) with larger half-power beamwidths can mitigate the performance degradation caused by misalignment. We also study the influence of horizontal link distance and different water types on the HE-AR region. The derived expressions are validated through computer simulations.

The oil and gas industry is encountering escalating challenges, including resource depletion, increasing extraction costs, and the growing need for more efficient utilization of resources. Addressing these challenges necessitates the optimization of critical processes across exploration, development, and production, where optimization algorithms play a pivotal role. These algorithms are fundamental for identifying optimal solutions by systematically exploring the solution space and optimizing key parameters, ultimately enhancing operational efficiency and resource management. This paper provides a comprehensive review of optimization methods and their applications within the oil and gas industry, with a focus on seismic exploration, well placement optimization, and reservoir management. The review highlights the importance of optimization algorithms in overcoming industry-specific challenges and emphasizes key studies on their application in upstream oil and gas operations. Moreover, this paper discusses the integration of machine learning techniques with optimization methods to enhance decision-making capabilities and outlines future research directions, particularly in improving algorithmic robustness and advancing multi-objective optimization approaches. The objective of this paper is to offer a clear framework for advancing optimization research and its transformative potential in the oil and gas industry to promote the development of intelligent energy.

Agriculture, a cornerstone of national prosperity, is evolving through innovative technologies like the Internet of Things (IoT). These advancements enhance farming productivity by enabling real-time monitoring, automation, and precise resource management. IoT applications in agriculture integrate sensors and connectivity to optimize processes such as soil analysis, crop health assessment, and irrigation management. Meanwhile, the emergence of 5G connectivity revolutionizes smart farming with ultralow latency, high-speed data transfer, and seamless communication among devices. These capabilities support the development of autonomous machinery, drones, and data-driven decision-making tools that streamline operations and improve yields. 5G technology enables rapid transmission of large datasets from IoT sensors, supporting applications such as machine learning for pest and disease detection, autonomous vehicle navigation, and environmental monitoring. Integration of 5G in precision farming facilitates intelligent resource allocation, minimizing waste while maximizing outputs. Challenges persist, including data security, interoperability, and infrastructure costs. However, the synergy between 5G and IoT holds transformative potential for sustainable agricultural practices. This paper explores the advancements in IoT and enabling 5G technologies within agriculture, focusing on their applications, benefits, and challenges. It highlights case studies and research insights, showcasing how these innovations contribute to more efficient, scalable, and sustainable farming. The findings underscore the critical role of digital technologies in addressing global food security and fostering resilience against climate change.

The rapid IoT device proliferation has greatly increased the attack exposure, making IoT networks highly susceptible to cyber threats. Conventional intrusion detection systems (IDSs) have trouble with the complexity of IoT networks due to their massive, heterogeneous, and real-time data streams. It is the immense amount of information produced by various IoT devices (e.g., sensors, cameras, and wearables) that send data streams continuously in real time. This impacts IDSs as it becomes difficult to process, analyze, and identify threats rapidly and precisely. The variety and velocity of data can overwhelm conventional IDSs, resulting in delays, ignored attacks, or false positives, particularly under limited computational resources common in IoT settings. Existing IDS methods frequently have poor scalability, large false-positive rates, and insufficient real-time threat detection, failing to ensure both data security and privacy. To address these issues, this paper introduces an optimized deep learning-driven model called dual sampling dilated pre-activation residual attention convolutional neural network optimized using greylag goose optimization (DSD-PRA-ConNet-GGO), which integrates blockchain technology for intrusion detection and prevention in IoT networks. The methodology starts with data acquisition from various IoT devices and sensors, such as smart cameras and environmental sensors, to capture traffic patterns and potential intrusions. The system shows superior performance, achieving 15.59% to 36.88% higher accuracy compared to existing methods such as LSTM-IDS, ML-XGBoost, RNN-IDS, and DL-DFCN, based on evaluations conducted using the BoT-IoT dataset, which includes a wide range of realistic attack scenarios and benign traffic commonly encountered in IoT environments.

The analysis for the fifth generation and the communication for the different wireless systems have been analyzed. The specification of the 6G is significantly related to the 5G networks. Some energy consumption-related problem based on green communication is done to process the data, and the energy consumption is done. Some of the solutions used for the management and the handling of efficient resource are done for providing the technique of the cancellation that can be proposed. The advanced technology used for wireless communication in this technique is the gradient boosting method, the method of hybrid microwave transmission, and so on. Then gradient boosting method is used for monitoring the patient's health in the hospitals using Internet of Things (IoT) devices. The method of hybrid microwave transmission used for analyzing the microwave transmission of the phone calls and the mobile application is evaluated. Effective analysis for data to transfer from one place to another is done. Then 45% of the data is removed based on the redundancy, and the low quality is formed. Furthermore, as seen by its high variance accounted for (VAF) score of 96% after 100 iterations, the suggested technique performs exceptionally well at capturing data variability. It also achieves a quite remarkable $��R�$-squared ($��{�R�}^{�2�}�$) score of 0.94, showcasing its strong predictive power and the ability to explain around 94% of the variance of the dependent variable. These figures hence establish the unmatched prediction accuracy exhibited by the proposed method of gradient boosting, explaining data variability and predictability. Trial Registration: We have not harmed any human person with our research data collection, which was gathered from an already published article.

The variability in estimating the noise variance can considerably diminish the effectiveness of the energy detection (ED). This study analyzes the performance of a newly introduced goodness-of-fit test called the modified Anderson–Darling (MAD) test, which shows improved statistical power when noise uncertainty is present. We derive and empirically validate the analytical formulations for the theoretical performance of the MAD regarding false alarm and detection probabilities. Additionally, we compare our developed method with existing techniques to assess its performance, including ED, generalized ED (GED), and a two-sample likelihood ratio statistic test. The MAD surpasses the investigated methods without the need for prior knowledge of a particular set of noise samples. Our findings indicate that the proposed spectrum sensing technique also results in reduced computational complexity. Moreover, we propose the idea of spectrum sensing based on channel bandwidth rather than detecting by frequency bin, which is more appropriate for enhancing the efficiency of tactical radio band detection in tactical radio communications.

Vehicular ad-hoc networks (VANETs) serve an important role in enabling intelligent mobility by allowing vehicle-to-vehicle (V2V) communication. However, due to their open and dynamic character, they are extremely vulnerable to a variety of internal and external threats, providing substantial hurdles to intrusion detection and data privacy. To address these problems, this study introduces a novel element-based intrusion detection model (EIDM) that combines the adaptive neuro-fuzzy inference system (ANFIS) with element perturbation computations and differential privacy approaches. The proposed system assures secure and privacy-preserving data sharing between cars, reduces security discrepancies between nodes, and allows for real-time alert broadcast to improve traffic safety and situational awareness. EIDM, unlike classic approaches, combines local training at vehicle nodes with collaborative detection, providing scalability and responsiveness while maintaining data confidentiality. The proposed system combines ANFIS with element perturbation and differential privacy in a unique way, allowing for precise and real-time intrusion detection while maintaining data confidentiality, in contrast to traditional IDS approaches that either compromise privacy or require a lot of computation. Because it tackles the crucial trade-off between scalability, privacy protection, and detection accuracy, this hybrid approach is ideal for dynamic VANET systems. The model is validated using large NS2 simulations and outperforms contemporary models such as deep belief networks (DBN) and neural networks (NN). It has a sensitivity of 96.3%, specificity of 93.8%, precision of 98.6%, detection accuracy of 94.1%, and packet delivery ratio of 93.9%. These findings confirm EIDM as a strong, scalable, and efficient approach for improving VANET security and dependability.