IET Communications最新文献

The emergence of novel AI technologies and increasingly portable wearable devices have introduced a wider range of more liberated avenues for communication and interaction between human and virtual environments. In this context, the expression of distinct emotions and movements by users may convey a variety of meanings. Consequently, an emerging challenge is how to automatically enhance the visual representation of such interactions. Here, a novel Generative Adversarial Network (GAN) based model, AACOGAN, is introduced to tackle this challenge effectively. AACOGAN model establishes a relationship between player interactions, object locations, and camera movements, subsequently generating camera shots that augment player immersion. Experimental results demonstrate that AACOGAN enhances the correlation between player interactions and camera trajectories by an average of 73%, and improves multi-focus scene quality up to 32.9%. Consequently, AACOGAN is established as an efficient and economical solution for generating camera shots appropriate for a wide range of interactive motions. Exemplary video footage can be found at https://youtu.be/Syrwbnpzgx8.

In next-generation wireless communications, reconfigurable intelligent surface (RIS) has emerged as a cost-effective technique for enhancing physical layer security (PLS) in millimeter-wave (mmWave) communications, especially under challenging scenarios with adversarial entities and obstructions. However, the primary studies for RIS incorporation in mmWave communication systems utilized static deployments, lacking adaptability and efficiency in complex environments. To address this problem, a dynamic RIS deployment design framework is introduced for PLS enhancement in mmWave systems against jamming and eavesdropping attacks. For the design, it is aimed to maximize the secrecy rate by jointly optimizing the RIS selection with the beamforming design. The resulting optimization problem is challenging to solve due to the coupling of the RIS control factor, joint beamforming design, and non-convex constraints. To tackle these issues, an efficient multi-RIS-aided PLS enhancement algorithm is proposed. It transforms the objective into a series of subproblems and employs the fractional programming technique and prox-linear block coordinate descent updating method to solve them alternatively and obtain the optimal solution. The simulations demonstrate the advantage of the dynamic deployment, which exhibits enhanced security performance with reduced complexity compared with benchmarks. Further examinations also provide insight into optimal RIS activation configurations, achieving optimal balance for securing mmWave communications against emerging threats while maintaining system efficiency.

Clustering algorithm is the primary technology used in target clustering and group status analysis which are key features of the Unmanned Aerial Vehicles (UAVs) control system. Due to variable application environment, the stability of the algorithm in the UAVs control system needs to be considered. K-means clustering is a widely used method in intelligent systems. However, K-means algorithm is susceptible to the local optimum due to the influence of the initial centroid. For this problem, the predecessors have proposed various effective solutions. These algorithms perform better on real and large-scale datasets, but they are unable to achieve optimum results with unbalanced datasets. Herein, a simpler and more effective algorithm for seed initialization is proposed, it has a better accuracy rate than the alternative algorithms.Moreover, after running tests multiple times with each algorithm independently, it has the highest stability and the lowest overall volatility. With unbalanced datasets, the proposed algorithm performs significantly better than several other algorithms and therefore can solve the problems that other algorithms have with unbalanced datasets.

Worker selection is critical to the success of federated learning, but issues such as inadequate incentives and poor-quality data can negatively impact the process. The existing studies have used the multi-weight subjective logic model, but it is vulnerable to malicious evaluation and unfair to newly added nodes. In this paper, the authors propose an improved reputation evaluation algorithm that allows evaluations from different sources to influence each other and reduce the impact of malicious comments. The authors’ approach effectively distinguishes between malicious and honest users and improves worker selection and collaboration in federated learning.

To solve the security problems of the moving robot system in the fog network of the Industrial Internet of Things (IIoT), this paper presents a privacy-preserving data integration scheme in the moving robot system. First, a novel data collection enhancement algorithm is proposed to enhance the image effects, and a k-anonymous location and data privacy protection protocol based on Ad hoc network (Ad hoc-based KLDPP protocol) is designed in secure data collection phase to protect the privacy of location and network data. Second, the secure multiparty computation with verifiable key sharing is introduced to realize the valid computation against share cheating in the robot system. Third, the ciphertext classification method in a neural network is considered in the secure data storage process to realize the special application. Finally, experiments and simulations are conducted on the robot system of fog computing in the IIoT. The results demonstrate that the proposed scheme can improve the security and efficiency of the said robot system.

Nowadays, the digital development of marine ranching requires a communication system with wide coverage, high transmission rate and stable communication links. It is known that fixed-wing unmanned aerial vehicles (UAVs) have great advantages in long-range applications. They have the potential to serve as low-altitude communication platforms for maritime communication. In this study, with a developmental perspective, considering the intense growth of marine terminals in the future, a new clustering algorithm applied to cluster nonorthogonal multiple access (C-NOMA) is proposed and its advantages are investigated. In addition, considering the limited energy of marine terminals, combining the wireless power communication (WPC) technology for the UAV to charge terminals, the charging and communication time are optimized with the Lagrange multiplier method and the bisection search method. After completing the above optimization content of charging and communication, combined with the optimization results, it is found the trajectory that maximizes the energy efficiency of the UAV with the convex optimization technique. Experimental results show that the proposed clustering algorithm has good throughput performance, better fairness and lower algorithm complexity, and the proposed trajectory optimization scheme has better energy efficiency.

Malicious domains provide malware with covert communication channels which poses a severe threat to cybersecurity. Despite the continuous progress in detecting malicious domains with various machine learning algorithms, maintaining up-to-date various samples with fine-labeled data for training is difficult. To handle these issues and improve the detection accuracy, a novel malicious domain detection method named MDND-SS-PO is proposed that combines semi-supervised learning and parameter optimization. The contributions of the study are as follows. First, the method extracts the statistical features of the IP address, TTL value, the NXDomain record, and the domain name query characteristics to discriminate Domain-Flux and Fast-Flux domain names simultaneously. Second, an improved DBSCAN based on the neighborhood division is designed to cluster labeled data and unlabeled data with low time consumption. Then, based on the clustering hypothesis, unlabeled data is tagged with pseudo-label according to the cluster results, which aims to train a supervised classifier effectively. Finally, Gaussian process regression is used to optimize parameter settings of the algorithm. And the Silhouette index and F1 score are introduced to evaluate the optimization results. Experimental results show that the proposed method achieved a precise detection performance of 0.885 when the ratio of labeled data is 5%.

The evolution of Artificial Intelligence of Things (AIoT) pushes connectivity from human-to-things and things-to-things, to AI-to-things, has resulted in more complex physical networks and logical associations. This has driven the demand for Internet of Things (IoT) devices with powerful edge data processing capabilities, leading to exponential growth in device quantity and data generation. However, conventional data preprocessing methods, such as data compression and encoding, often require edge devices to allocate computational resources for decoding. Additionally, some lossy compression methods, like JPEG, may result in the loss of important information, which has negative impact on the AI training. To address these challenges, this paper proposes a two-step attribute reduction approach, targeting devices and dimensions, to reduce the massive amount of data in the AIoT network while avoiding unnecessary utilization of edge device resources for decoding. The device-oriented and dimension-oriented attribute reductions identify important devices and dimensions, respectively, to mitigate the multimodal interference caused by the large-scale devices in the AIoT network and the curse of dimensionality associated with high-dimensional AIoT data. Numerical results and analysis show that this approach effectively eliminates redundant devices and numerous dimensions in the AIoT network while maintaining the basic data correlation.

Multi-user (MU) massive multiple input multiple output (mMIMO) is considered a potential technology for fifth generation (5G) and sixth-generation (6G) wireless systems. The presence of the antenna arrays at the base station level to communicate with the users or to serve tens of single antenna users leads to excessively high system costs and power consumption. The deployment 1-bit digital-to-analogue converters (DACs) in the base station can solve these problems. This paper starts by presenting an analytical study centered on the effects of 1-bit DACs on the system envisaged for a Rayleigh-type fading channel. Compact-form expressions are derived for the symbol error rate. Afterwards, an efficient end-to-end deep learning technique to compensate for the joint effect of 1-bit DAC and imperfect channel state information in downlink mMIMO systems. Moreover, to improve the performance of the considered system, a DAC mixed architecture is proposed, where a number of antennas use 1 bit DACs while the others do not. The simulations results showed the improvement in transmission quality of the downlink of the MU-mMIMO system in the presence of hardware imperfections using the considered end-to-end compensation technique.

Network function virtualization (NFV) technology deploys network functions as software functions on a generalised hardware platform and provides customised network services in the form of service function chain (SFC), which improves the flexibility and scalability of network services and reduces network service costs. However, irrational resource allocation during service function chain mapping will cause problems such as low resource utilisation, long service request processing time and low mapping rate. To address the unreasonable problem of service mapping resource allocation, an improved service function chain mapping resource allocation method (SA3C) based on the Asynchronous advantageous action evaluation algorithm (A3C) is proposed. This study proposes an SFC mapping model and a mathematical model for joint allocation, which modeled the minimization of processing time as a Markov process. The main network was trained and multiple sub-networks were generated in parallel using the ternary and deep reinforcement learning algorithm A3C, with the goal of identifying the optimal resource allocation strategy. The experimental simulation results show that compared with the Actor-Critic (AC) and Policy Gradient (PG) methods, SA3C algorithm can improve the resource utilisation by 9.85%, reduce the total processing time by 10.72%, and improve the mapping rate by 6.72%, by reasonably allocating node computational resources and link bandwidth communication resources.