In distribution networks, incipient faults often manifest as faint and transient electrical disturbances before fully developing. Incipient fault detection is challenging due to the weak and non-stationary characteristics of fault signatures. Moreover, fault feeder identification is more difficult, as residuals across feeders tend to appear highly similar. To address these challenges, we present FD-Mamba, a Mamba-based neural state-space model that integrates control-theoretic principles with signal-processing techniques. Specifically, we propose a Kalman-inspired neural correction mechanism that performs residual-driven state updates with learnable gain factors. In addition, we introduce a frequency-momentum updating mechanism that stabilizes frequency tracking under non-stationary perturbations. Experimental results on two datasets show that FD-Mamba outperforms existing methods. It achieves a root mean square error of 0.427 and fault feeder detection accuracy of 98.1% on real-world field dataset.

The stochastic and asymmetric characteristics of wireless communication can degrade the accuracy of average voltage observation and the optimal dispatch of active power in microgrids, resulting in reduced power quality and higher operational costs. To mitigate these issues, this paper investigates the limitations of conventional distributed average consensus and resource allocation algorithms under asymmetric communication and identifies the factors impeding their convergence. Building on this analysis, improved distributed average consensus and resource allocation algorithms are proposed, incorporating the designed deviation recording and transmission mechanism to counteract communication asymmetry. Leveraging these improved algorithms, a distributed secondary control strategy for microgrids is proposed, ensuring robustness against stochastic asymmetric communication. Subsequently, convergence criteria of distributed control tailored to stochastic asymmetric communication scenarios are then derived, providing a foundation for the design of control parameters. Finally, hardware-in-the-loop (HIL) experiments validate the proposed strategy’s ability to achieve precise average voltage regulation and economically optimal active power dispatch, outperforming existing approaches in stochastic asymmetric communication environments.

Gaussian splatting video has recently emerged as a promising representation for immersive 6-degree-of-freedom (6DoF) content due to its low-latency rendering, compact data structure, and high visual fidelity. In particular, 4D Gaussian splatting video-which models dynamic scenes as temporally evolving Gaussian splats in 3D space-offers an efficient solution for rendering photorealistic, interactive experiences. However, a systematic understanding of user behavior in such environments, especially head movement, remains largely unexplored due to the absence of dedicated datasets tailored to this format. This lack of data severely limits progress in viewpoint prediction, attention modeling, and video streaming optimization. To address this critical gap, we introduce ViewGauss-the first publicly available dataset that captures full 6DoF head movement during the viewing of 4D Gaussian splatting videos. Our dataset is collected from 35 participants using a high-precision Vive Focus Vision headset in a controlled environment, while they freely watched four reconstructed Gaussian splatting video sequences derived from the HiFi4G dataset. The data are recorded with high temporal resolution using position coordinates and unit quaternions, and organized into structured CSV files with precise timestamps for downstream synchronization and behavioral analysis. To demonstrate the practical value of ViewGauss, we conduct a preliminary viewpoint prediction experiment using the iTransformer model. The results show that head orientation patterns in 4D Gaussian splatting video scenes are not only temporally coherent but also learnable, highlighting the potential of ViewGauss as a benchmark for future behavioral modeling and predictive rendering systems. The dataset is publicly available at: https://github.com/Cedarleigh/ViewGauss-DataSet.

Point cloud is widely used in computer vision and augmented reality for representing 3D information of real-world scenes. However, challenges such as noise, incompleteness, and quality variations, particularly in dynamic environments, hinder effective processing and analysis. These issues are further complicated in human activity scenarios due to motion and changing lighting. To address these challenges, this paper introduces a point cloud video dataset PCVD captured with synchronized Azure Kinect cameras, designed to support tasks like denoising, segmentation, and motion recognition in single and multi-person scenes. It provides high-quality depth and color data from diverse real-world scenes with human actions. We compare it with existing datasets, and the results show its superiority in uniformity and completeness, making it ideal for dynamic environments. We also evaluate the state-of-the-art denoising schemes on this dataset to demonstrate the practicality and sophistication of the dataset. The dataset is publicly available at https://github.com/Atlantichan/PCVD-A-Dataset-of-Point-Cloud-Video-for-Dynamic-Human-Interaction.

In active distribution networks (ADNs), the rapid changes in photovoltaic (PV) generation can easily lead to short-term voltage stability issues. However, achieving real-time voltage control under limited communication resources is a major challenge. This paper addresses this issue by introducing a novel co-scheduling scheme for volt/var control and wireless resources allocation. We model the nonlinear dynamics between PV generation and communication delay into a co-scheduling optimization problem, targeting the minimization of system voltage deviations. To efficiently solve this problem, we propose a multi-agent reinforcement learning (MARL) algorithm, termed Meta-learning Equivalent model-based Hierarchical Reinforcement Learning (MEHRL). This algorithm employs a hierarchical reinforcement learning (HRL) framework to segment the complex action space and incorporates a meta-learning equivalent (ME) model to enhance adaptability during distributed training and decentralized execution (DTDE). Simulation results validate the efficacy of the proposed co-scheduling scheme in ADNs and underscore the advanced capabilities of the MEHRL algorithm in addressing the optimization challenge.

Cloud–edge collaboration and edge intelligence have greatly driven the growth of the Industrial Internet of Things (IIoT). However, the jittery network delay and limited computational resources of edge servers make it difficult to meet the stringent latency requirements in IIoT, and so far there is no good solution to solve this problem. To this end, we introduce scalable neurocomputing, which provides neural networks with different utilities and computation resource requirements, to be deployed on edge servers of cloud–edge IIoT systems. We then optimize such systems by formulating data scheduling and system computational resource allocation as an infinite horizon optimization problem, considering that the data collection from end devices is an infinite long-term process. To solve this hard problem, we design a rolling prediction-optimization framework that transforms the infinite horizon problem into a truncated finite horizon optimization that maximizes the average system utility while satisfying the stringent delay constraints. We have conducted extensive simulations and built a prototype system, which verify the feasibility and performance of our proposed scheme.

Sensor devices continuously generate large volumes of time series data in the Internet of Things (IoT) environment. These voluminous streams require models that scale to massive data while discerning the intricate, multi-scale patterns embedded in diverse temporal sequences. Transformer models have been widely used for IoT time series analysis due to their strong feature representation and global modeling capability. However, existing architectures struggle to explicitly capture temporal structures and adapt to non-stationary data, limiting classification performance. To address these issues, we propose a novel attention mechanism based on the autocorrelation function, named A2T, which leverages lag characteristics to unify temporal modeling and feature extraction. We further introduce a Parameterized Wavelet Transform Module that learns scale and bandwidth end-to-end and uses an attention gate to fuse multi-resolution coefficients. Building on this, we design a Dual-Channel Time-Frequency Feature Extraction module to improve adaptability to distribution shifts. Integrating these components, we develop A2Tformer for IoT time series classification. Experimental results on the UCR dataset demonstrate that A2Tformer achieves an average accuracy of 84.49% and ranks first on 26 out of all datasets, outperforming state-of-the-art Transformer-based models.

Volumetric video, also referred to as hologram video, is an emerging medium that represents 3D content in extended reality. As a next-generation video technology, it is poised to become a key application in 5G and future wireless communication networks. Because each user generally views only a specific portion of the volumetric video, known as the viewport, accurate prediction of the viewport is crucial for ensuring an optimal streaming performance. Despite its significance, research in this area is still in the early stages. To this end, this paper introduces a novel approach called Saliency and Trajectory-based Viewport Prediction (STVP), which enhances the accuracy of viewport prediction in volumetric video streaming by effectively leveraging both video saliency and viewport trajectory information. In particular, we first introduce a novel sampling method, Uniform Random Sampling (URS), which efficiently preserves video features while minimizing computational complexity. Next, we propose a saliency detection technique that integrates both spatial and temporal information to identify visually static and dynamic geometric and luminance-salient regions. Finally, we fuse saliency and trajectory information to achieve more accurate viewport prediction. Extensive experimental results validate the superiority of our method over existing state-of-the-art schemes. To the best of our knowledge, this is the first comprehensive study of viewport prediction in volumetric video streaming. We also make the source code of this work publicly available.

Unmanned aerial vehicles (UAV), due to their flexibility and extensive coverage, have gradually become essential for substation inspections. Wireless mesh networks (WMN) provide a scalable and resilient network environment for UAVs, where each node can serve as either an access point or a relay point, thereby enhancing the network’s fault tolerance and overall resilience. However, the UAV-WMN combined system is complex and dynamic, facing the challenge of dynamically adjusting node transmission power to minimize end-to-end (E2E) delay while ensuring channel utilization efficiency. Real-time topology changes, high-dimensional state spaces, and large solution spaces make it difficult for traditional algorithms to guarantee convergence and stability. Generic reinforcement learning (RL) methods also struggle with stable convergence. This paper introduces a new Lyapunov function-based proof to address these issues and provide a stable condition for dynamic control strategies. Then, we developed a specialized neural network power controller and combined it with the MATD3 algorithm, effectively enhancing the system’s convergence and E2E performance. Simulation experiments validate the effectiveness of this method and demonstrate its superior performance in complex scenarios compared to other algorithms.

With the continuous advancement of computer vision, image processing technologies, volumetric video, represented by point cloud videos, holds the potential for extensive applications in areas such as Virtual Reality (VR) and Augmented Reality (AR). Viewport prediction, also referred to as Field of View (FoV) prediction, is a crucial component in emerging VR and AR applications, playing a vital role in the transmission of point cloud videos. Currently, models for viewpoint prediction that integrate feature extraction and FoV information heavily rely on the spatial-temporal features extracted by convolutional neural networks. However, the drawback of 3D convolution lies in its inability to effectively capture long-term spatial-temporal dependencies within videos. Moreover, the temporal contrast layer used for time feature extraction only compares features within each block, leading to matching errors and inaccurate temporal feature extraction, consequently diminishing predictive performance. To address these limitations, we propose a Transformer-based Volumetric Point Cloud Video Viewport Prediction Network (VPFormer) that can efficiently extract spatial-temporal features from point cloud videos. VPFormer constitutes a viewport prediction framework that combines the spatial-temporal features of point cloud videos with user trajectory information. Specifically, we introduce a novel sampling method that effectively preserves spatial-temporal information while reducing computational complexity. Additionally, we incorporate context-aware dynamic positional encoding to capture inter-frame spatial-temporal context information. Subsequently, we introduce a voxel-based temporal contrast layer and partition the point cloud into smaller voxel blocks during feature matching, significantly reducing matching errors and enhancing the analysis and extraction of temporal features. Finally, by combining the spatial-temporal features of point cloud videos with user head trajectory information, we successfully predict future user viewpoints. Experimental results demonstrate that this approach outperforms other solutions in terms of performance.

In Mesh Wireless Networks (MWNs), the network coverage is extended by connecting Access Points (APs) in a mesh topology, where transmitting frames by multi-hop routing has to sustain the performances, such as end-to-end (E2E) delay and channel efficiency. Several recent studies have focused on minimizing E2E delay, but these methods are unable to adapt to the dynamic nature of MWNs. Meanwhile, reinforcement-learning-based methods offer better adaptability to dynamics but suffer from the problem of high-dimensional action spaces, leading to slower convergence. In this paper, we propose a multi-agent actor-critic reinforcement learning (MACRL) algorithm to optimize multiple objectives, specifically the minimization of E2E delay and the enhancement of channel efficiency. First, to reduce the action space and speed up the convergence in the dynamical optimization process, a centralized-critic-distributed-actor scheme is proposed. Then, a multi-objective reward balancing method is designed to dynamically balance the MWNs’ performances between the E2E delay and the channel efficiency. Finally, the trained MACRL algorithm is deployed in the QaulNet simulator to verify its effectiveness.

Efficient point cloud video streaming requires accurate viewport prediction, and research on this topic is still in its infancy. This paper demonstrates a high-precision scheme for viewport prediction in the point cloud video, named Landscape, exploring both video saliency information and viewport trajectory. Specifically, we first propose a novel point cloud video sampling method, which reduces computational load while preserving video features. Furthermore, we introduce a new saliency detection technique that integrates temporal and spatial information to detect dynamic, static geometric, and color salient regions. Finally, we intelligently fuse saliency and trajectory information to achieve more accurate viewport prediction. We verify the performance of our proposed viewport prediction methods over state-of-the-art wireless networks.

As a popular way of representing holographic video or volumetric video, point cloud video can provide users with a highly immersive viewing experience of 6 degrees of freedom (6DoF) and is expected to become the mainstream video format of the future. However, the real-time transmission of point cloud video faces many challenges due to the huge amount of data and the large search space of the optimization problem with constraints. To this end, we propose Horizon, a novel Dynamic Adaptive Streaming over HTTP (DASH) based real-time point cloud video streaming system, which aims to maximize the user's viewing experience by predicting the next several steps through a rolling framework and uses a Deep Reinforcement Learning (DRL) based algorithm to achieve a real-time solution to the rolling optimization problem. We have prototyped this system and demonstrated its performance on a state-of-the-art wireless network.

Volumetric video provides users with a good viewing experience of six degrees of freedom (DoF) and has wide applications in many fields such as teleconferencing and online games. However, the huge data volume and strict latency requirements of point cloud video, the most popular representative of volumetric video, pose a challenge to its transmission. Existing point cloud video transmission algorithms usually segment a long video by every one or several group of frames, predict network bandwidth and field of view (FoV) information, then perform adaptive transmission by solving the quality of experience (QoE) optimization problem. However, such segmentation neglects the impact of current optimization decisions on the subsequent video streaming process, as well as the accumulated prediction error across a long interval, severely degrading user’s QoE. Moreover, the complex constrained optimization problem makes the solution time too long to meet the real-time video streaming requirements. To this end, in this paper, we propose a rolling prediction-optimization-transmission (POT) framework, which makes predictions of network bandwidth and FoV in each short rolling window to reduce prediction error. And our framework takes into account the upper bounded QoE contribution of the subsequent point cloud video to improve the system performance. In addition, we design a deep reinforcement learning based real-time solver to make decisions for the fixed structure optimization problem in each roll, allowing our system to run in real-time. We have performed simulations and experiments, and the results show that our solution outperforms existing methods.

Field of view (FoV) prediction is critical in 360-degree video multicast, which is a key component of the emerging virtual reality and augmented reality applications. Most of the current prediction methods combining saliency detection and FoV information neither take into account that the distortion of projected 360-degree videos can invalidate the weight sharing of traditional convolutional networks nor do they adequately consider the difficulty of obtaining complete multi-user FoV information, which degrades the prediction performance. This article proposes a spherical convolution-empowered FoV prediction method, which is a multi-source prediction framework combining salient features extracted from 360-degree video with limited FoV feedback information. A spherical convolutional neural network is used instead of a traditional two-dimensional convolutional neural network to eliminate the problem of weight sharing failure caused by video projection distortion. Specifically, salient spatial-temporal features are extracted through a spherical convolution-based saliency detection model, after which the limited feedback FoV information is represented as a time-series model based on a spherical convolution-empowered gated recurrent unit network. Finally, the extracted salient video features are combined to predict future user FoVs. The experimental results show that the performance of the proposed method is better than other prediction methods.

Real-time smart grid monitoring is critical to enhancing resiliency and operational efficiency of power equipment. Cloud-based and edge-based fault detection systems integrating deep learning have been proposed recently to monitor the grid in real time. However, state-of-the-art cloud-based detection may require uploading a large amount of data and suffer from long network delay, while edge-based schemes do not adequately consider the detection requirement and thus cannot provide flexible and optimal performance. To solve these problems, we study a cloud-edge based hybrid smart grid fault detection system. Embedded devices are placed at the edge of the monitored equipment with several lightweight neural networks for fault detection. Considering limited communication resources, relatively low computation capabilities of edge devices, and different monitoring accuracies supported by these neural networks, we design an optimal communication and computational resource allocation method for this cloud-edge based smart grid fault detection system. Our method can maximize the processing throughput of the system and improve resource utilization while satisfying the data transmission and processing latency requirements. Extensive simulations are conducted and the results show the superiority of the proposed scheme over comparison schemes. We have also prototyped this system and verified its feasibility and performance in real-world scenarios.

Abstract The network sampling rate is important in the overhead transmission line monitoring system. A larger sampling rate can provide more available monitored data to be transmitted, which can effectively improve the response speed to emergency events of the overhead transmission line system. Considering the harsh environment of the overhead transmission line wireless network, quality‐of‐service (QoS) requirement becomes an important issue for multi‐hop transmission. Thus, in this paper, an end‐to‐end delay optimisation algorithm for string‐topology multi‐hop wireless network is proposed, by which the maximum packet arrival rate and the allowable maximum sampling rate of network can be obtained with desirable soft QoS guarantees. Based on the IEEE 802.11 standards and the basic probability theorem, a novel end‐to‐end delay performance analytical model is firstly proposed. Then, combined with the derived analytical model, an end‐to‐end delay optimisation algorithm by maximising the packet arrival rate is developed. Finally, a numerical study of a string‐topology multi‐hop network is presented to verify the effects of packet arrival rate, backoff contention window size, hop number, data packet size on the end‐to‐end delay performance.

Point cloud video is the most popular representation of hologram, which is the medium to precedent natural content in VR/AR/MR and is expected to be the next generation video. Point cloud video system provides users immersive viewing experience with six degrees of freedom (6DoF) and has wide applications in many fields such as online education and entertainment. To further enhance these applications, point cloud video streaming is in critical demand. The inherent challenges lie in the large size by the necessity of recording the three-dimensional coordinates besides color information, and the associated high computation complexity of encoding/decoding. To this end, this paper proposes a communication and computational resource allocation scheme for QoE-driven point cloud video streaming. In particular, with the goal to maximize the defined QoE by selecting proper quality levels (uncompressed tiles at different quality levels are also considered) for each partitioned point cloud video tile, we formulate this into an optimization problem under the limited communication and computational resources constraints and propose a scheme to solve it. Extensive simulations are conducted and the simulation results show the superior performance of the proposed scheme over the existing schemes.

Virtual Reality (VR) provides an immersive 360 viewing experience and has been widely used in vast areas such as education, entertainment and training. To further widen its applications, networked 360 VR video becomes essential. Quality of Experience (QoE), which objectively measures user experience, is vital for 360 VR video transmission mechanism design. However, to the best of our knowledge, there are few subjective QoE metric for 360 VR video transmission over wireless networks. In this paper, we aim to fill this gap by proposing a general QoE model based on subjective quality evaluation experiments. First, the state-of-the-art 360 VR video processing and wireless transmission schemes are used to conduct subjective experiments according to the international standard. Then, how user experience is affected by different factors, including users' viewing angle, tiling (how the 360 VR video is partitioned into smaller parts to facilitate transmission), stall and resolution switch, is analyzed mathematically. A general QoE model is finally proposed to facilitate the future 360 VR video streaming mechanism design.

With the success of wireless sensor networks (WSNs), traditional engineering and infrastructure industries are starting to develop solutions using WSN technologies. One of the main challenges of designing and developing WSNs for industrial monitoring and control is satisfying their strict reliability requirements. In this paper, we present a network-level reliability model, namely, end-to-end data delivery reliability (E2E-DDR), for estimating and optimizing the reliability performance of WSNs. In the E2E-DDR model, a framework is presented for capturing the mapping function between the packet reception ratio, background noise, and received signal strength (RSS). We use an alpha-stable distribution to accurately represent the background noise and a modified log-normal path loss model to more realistically describe the RSS. We also report a comprehensive performance evaluation performed by applying the E2E-DDR model in a real-world case study to estimate the network-level reliability and optimize the WSN deployment parameters.

Dynamic Adaptive Streaming over HTTP (DASH) is a fast growing video streaming platform which enables adaptive rate selection based on channel conditions. File Delivery over Unidirectional Transport (FLUTE) further enables multicasting of the DASH segments over LTE eMBMS systems. In this paper, an optimal DASH-multicasting solution is proposed to allow more DASH clients in an LTE network to receive better videos by optimizing the resource allocation, Forward Error Correction (FEC) code rate and modulation and coding scheme (MCS) of each multicasting group, which corresponds to a FLUTE session. Multiple FLUTE sessions are considered to deliver multiple videos and multiple video rates for enhancing the overall utility. We have applied the convex optimization method to find the optimal resource allocation in terms of utility for multiple FLUTE sessions. We also find the optimal FEC code rates to add redundancies to protect the video segments for each FLUTE session. Moreover, an efficient MCS selection is introduced to reduce the complexity of the algorithm. Simulation results, with realistic LTE parameters, are shown to prove the proposed scheme is optimal, with more DASH clients receiving better video representations within limited resources when compared to other existing algorithms.

Location estimation in Wi-Fi environment has gained considerable attention over the past years, and the Cramer-Rao Lower Bound (CRLB) can be used to evaluate the performance of the localization system. In this paper, we analyze the CRLB of Wi- Fi indoor localization using fingerprint and assistant nodes. This localization method combines received signal strength (RSS) and Time of Arrival (TOA) into together, and constructs a fixed spatial model with several assistant nodes to improve localization performance. There are two purposes of the CRLB analysis framework proposed in this paper. Firstly, the expression of lower bound on location estimation error can help in designing and refining efficient localization algorithm and parameters. Secondly, the error trends can provide suggestions for a positioning system design and deployment. Furthermore, detailed analysis as well as experimental results are both presented in this paper.

The costly explicit and implicit acknowledgements (ACKs) are issues that need to be addressed in the existing reliability aware flooding algorithms. This research focuses on energy efficiency on both data transmission and ACKs, while achieving target reliability and balancing the residual energy of sensor nodes. A correlation-based probabilistic flooding algorithm (CPFA) is proposed. It exploits the link correlation between neighbors and tracks aggregate ACKs to decide whether or not to retransmit a packet. Simulation is carried out to reveal that our proposed scheme saves more than 50% energy on explicit and implicit ACKs in most cases while balancing the residual energy of sensor nodes.