The learning-based, passive, device-free wireless human activity recognition (WHAR) systems still face significant challenges, especially in real-world deployments. Environmental differences and domain diversities cause signals collected in the source domain to have a different distribution from those in the target domain, and this affects the accuracy. To achieve domain generalization (DG), a multi-scale linear attention vision transformer (ViT) based feature extractor and domain adversarial learning with Wasserstein distance are proposed. By aligning both marginal and conditional distributions across different source domains, the adversarial learning reduces the differences between trained and unseen domains. As a result, the extracted features become domain-invariant in the latent space, ensuring accuracy is preserved in new or unseen domains. Extensive evaluations using commercial IEEE 802.11ac routers with human activity data collected over different days, environments, human subjects, and obstacle configurations show that the proposed Wi-ViTAL achieves 97.57% average accuracy for five-label classification and more than 76% for eight-label classification in unseen domains. Wi-ViTAL also demonstrates an overall DG improvement compared to other recent benchmarks.

The advent of Cellular Vehicle-to-Everything (CV2X) technology has revolutionised intelligent transportation systems (ITS), but poses challenges for secure and efficient data sharing due to its dynamic nature. Traditional centralised systems are inadequate, prompting the need for decentralised solutions like blockchain. However, applying blockchain technologies in C-V2X always faces scalability issues. This paper proposes a scalable C-V2X blockchain network with a hierarchical consensus by integrating a dynamic load-balancing sharding mechanism and zero-knowledge proofs (ZKPs). Our scheme ensures scalability in the C-V2X environment through sharding while utilising ZKPs to enhance cross-shard validation efficiency, reducing its complexity to$O(1)$. Additionally, our approach reduces bandwidth consumption by 90.8% compared to Merkle tree-based solutions and its consensus time is lower than 360 ms.

In this paper, we propose a reconfigurable intelligent surface (RIS)-aided wireless powered anti-jamming communication network (WPAJCN), where the RIS is utilized to participate in downlink wireless power transfer (WPT), as well as uplink anti-jamming wireless information transfer (AJ-WIT). To evaluate the network anti-jamming performance, we maximize a sum anti-jamming throughput, with the constraints of downlink WPT and uplink AJ-WIT time scheduling, and unit-modulus RIS phase shifts. The formulated problem is not convex in terms of these two types of coupled variables, which cannot be directly solved. To address this problem, the Lagrange dual method and Karush-Kuhn-Tucker conditions are presented to transform its sum-of-logarithmic objective function into the logarithmically fractional counterpart, which reformulate the original problem into that with respect to RIS phase shift vectors and WPT time scheduling. Next, we propose to apply the Dinkelback algorithm to solve a non-linear fractional programming with respect to the downlink WPT and uplink AJ-WIT RIS phase shifts in an alternating fashion, each of which is derived into a semi-closed solution by utilizing theRiemannian Manifold Optimization(RMO). In addition, the optimal WPT time scheduling is obtained by numerical search. Finally, the numerical results are demonstrated to confirm the improved performance of the proposed approach compared to the benchmark counterparts, which highlights the that RIS can effectively enhance the uplink anti-jamming WIT capability as well as the downlink WPT efficiency.

With the rapid expansion of global logistics networks, container terminals, as critical nodes in the logistics chain, exert significant influence on the overall performance of supply chains. In terminal operations, container stacking strategies and equipment configuration are core factors determining operational efficiency. However, due to the complexity of terminal operations and the existence of multi-layered feedback mechanisms, there is currently a lack of systematic and quantitative evaluation methods to analyze the advantages and disadvantages of stacking strategies and equipment configurations. To address this gap, this study proposes a data-driven analytical framework. By processing real-world terminal operation data, the framework constructs a digital yard feature model, extracts key spatiotemporal operational features, and integrates the spatial feature extraction capability of 3D Convolutional Neural Networks (3DCNN) with the advantages of Boosting algorithms in handling non-linear relationships and feature importance. The study introduces a novel Deep-Tree Cascaded Regression (DTCR) algorithm to predict vessel handling efficiency, a highly uncertain and nonlinear indicator, to quantitatively assess the practical impact of different stacking strategies and equipment configurations. Experimental results demonstrate that the proposed model accurately captures the key correlations between container stacking and terminal operations, predicts vessel handling efficiency within a reasonable accuracy range, and realistically reflects the terminal’s operational processes. These findings provide a scientific basis for optimizing yard stacking strategies and equipment operation workflows, effectively improving overall terminal operational efficiency. Additionally, this research offers technical support and practical insights for the development of smart ports.

The ultra-dense network (UDN) is considered a vital technology for 5G mobile communications due to its ability to transmit high data rates in high-traffic environments. However, it also creates new challenges, such as increased interference and difficulty managing mobility. To ensure seamless base station connectivity and maintain a high quality of service, a reliable handover algorithm is necessary, especially in a UDN where the cell size is small. This paper proposes an optimisation method for handover parameters based on the Deep Deterministic Policy Gradient (DDPG) algorithm. It adjusts the handover margin (HOM) to determine the handover trigger points accurately and dynamically. Simulation results indicate that the system’s mobility performance has been greatly improved while maintaining high throughput and low latency at different speeds.

Nowadays, social media platforms play a significant role in real-world events, which can cause both positive and negative effects. The popularity of image-based content on social media has been dramatically increased, which brings the problem that the quality of content is rather spotty. Therefore, automated techniques of identifying fake images have drawn significant attention. The traditional detection methods focus on the elements’ consistency of the image, which requires massive computing resources and huge datasets for pairs of real and fake images. Many studies on detecting rumors on social media showed that there are propagation patterns for the spreading of fake content that can be used as clues of detection. Thus, the proposed approach attempts to characterize the propagation patterns of fake images on social media using several user features and tweet features. The detection model applies recurrent neural networks to capture the variation of suggested features along the propagation path over time. The results of the experiment on a Weibo dataset of image tweets show that the model can achieve 89% accuracy in classifying fake images from real ones. Moreover, the model already reaches high performance as the detection deadline is smaller than 24 h, which demonstrates the strong capability of early detection. The positive outcomes indicate that the proposed detection model has great potential to be further developed to an automated technique that can be used in classifying real images from fake images posted on social media.

Mobile edge caching is an emerging approach to manage high mobile data traffic in fifth‐generation wireless networks that reduces content access latency and offloading data traffic of backhaul links. This paper proposes a novel cooperative caching policy based on long short‐term memory (LSTM) neural networks considering the characteristics between the features of the heterogeneous layers and the user moving speed. Specifically, LSTM is applied to predict content popularity. Size‐weighted content popularity is utilised to balance the impact of the predicted content popularity and content size. We also consider the moving speeds of mobile users and introduce a two‐level caching architecture consisting of several small base stations (SBSs) and macro base stations (MBSs). To avoid content requests of fast‐moving users affecting the content popularity distribution of the SBS since fast‐moving users frequently handover among SBSs, fast‐moving users are served by MBSs no matter which SBS they are in. SBSs serve low‐speed users, and SBSs in the same cluster can communicate with one another. The simulation results show that compared to common cache methods, for example, the least frequently used and least recently used methods, our proposed policy is at least 8.9% lower and 6.8% higher in terms of the average content access latency and offloading ratio, respectively.

This paper proposes a DRL-based cache content update policy in the cache-enabled network to improve the cache hit ratio and reduce the average latency. In contrast to the existing policies, a more practical cache scenario is considered in this work, in which the content requests vary by both time and location. Considering the constraint of the limited cache capacity, the dynamic content update problem is modeled as a Markov decision process (MDP). Besides that, the deep Q-learning network (DQN) algorithm is utilised to solve the MDP problem. Specifically, the neural network is optimised to approximate the Q value where the training data are chosen from the experience replay memory. The DQN agent derives the optimal policy for the cache decision. Compared with the existing policies, the simulation results show that our proposed policy is 56%–64% improved in terms of the cache hit ratio and 56%–59% decreased in terms of the average latency.

Abstract To cope with the increasing demand for efficient data delivery, self‐organizing networks have been introduced in the Long Term Evolution (LTE) system to provide autonomous and flexible mobility management. The existing handover triggering scheme for LTE is not flexible enough to incorporate new performance metrics, and it introduces handover latency. There are studies on non‐conventional handoff algorithms for LTE applications, for instance, the fuzzy logic approach. However, the fuzzy logic approach needs regular manual tuning to constantly produce optimal output. In this paper, we address this issue by proposing an adaptive fuzzy logic‐based handoff decision algorithm, which can cope with environmental changes and improve efficiency by reducing human intervention. Performance results show that the proposed algorithm can reduce unnecessary handovers by about 20% compared with the fuzzy logic and conventional LTE handover triggering scheme, leading to reduced packet loss rates.

With the need for better mobility management strategy to manage increasing demand on efficient data delivery to the user, the Long Term Evolution (LTE) has introduced self-organizing networks (SONs) in order to provide autonomous control over the management of the network. It is important to have a "self-manage" element in the system to provide a "quick-fix" and thus reduce the need of constant human participation in the optimization process of the LTE's mobility management. The existing handover triggering scheme for LTE is not flexible enough to introduce new performance metrics such as user equipment (UE) speed, network jitter or even cell loading. Such requirements for flexibility can only be fulfilled by using flexible tools such as fuzzy logic schemes with adaptive capability to cope with the changes of the fast paced mobile environment. This paper will introduce the use of the adaptive neuro-fuzzy inference system (ANFIS) to provide not only flexibility to LTE for initial deployment, but also the adaptive capability to optimize the efficiency of the handover algorithm with minimal human interference.