Visible light communication (VLC) is considered a promising technology for enabling Internet of Everything (IoE) applications in the sixth generation (6G), owing to its specific advantages over radio frequency (RF) communications. A comprehensive understanding of VLC channel characteristics and models is imperative for optimizing VLC technology, designing systems, and evaluating performance. This article presents an overview of ongoing research in channel characterization and modeling for VLC-IoE applications in the context of 6G. Recent advancements are systematically summarized, encompassing channel modeling methods, application scenarios, emerging combining technologies, such as reconfigurable intelligent surfaces (RISs) and integrated sensing and communication (ISAC), and distinctive channel characteristics. Additionally, future research directions in these domains are outlined to provide insights into forthcoming investigations for VLC-IoE applications in 6G.

Road pothole detection is significant for road maintenance which has been widely solved based on computer vision models in recent years. However, the accuracy of the pothole detection is still far from satisfactory and has the potential to be increased. One of the challenges is the diversity of the training samples. Another challenge is the extension of mature computer vision models. Therefore, in this paper, we created a pothole dataset with multiple road conditions and environments via samples of previous studies and developed an extended mask R-CNN model. Based on the created dataset, we compared the proposed model with the traditional model using the ResNet-50 and ResNet-101 networks. The experimental results show a$mAP_{0.5}$of 92.1% on the test dataset, surpassing the traditional backbone networks ResNet-50 and ResNet-101 by 3.6% and 4.5%, respectively. Furthermore, we compared the details of the different models. The experimental results show that the total area prediction error is only 3.19% on the test dataset. It suggests that our model can precisely extract geometric features and the area information of detected potholes.

Anomaly detection in multivariate time series is a critical research area, but it is also a challenging one due to its occurrence in various real-world scenarios, such as structural health monitoring and risk management. Traditional approaches for anomaly detection rely on deviating distribution and a static threshold that is set manually. However, static thresholds fail to detect contextual anomalies, leading to a high ratio of false anomalies. Therefore, a self-adaptive thresholding method is required to improve the accuracy of anomaly detection. In this study, we propose HCR-AdaAD, a multivariate anomaly detection framework that combines hierarchical context representation learning with deep learning methods. The core idea is to extract normal time-series patterns by transforming them into images, which can be used to extract spatial features and generate robust representations for normal time series. Next, we adopt Extreme Value Theory (EVT) to set self-adaptive thresholds in streaming time series, which can contribute to the ideal precision for anomaly detection and high interpretability with contextual information. We conducted evaluation experiments on three public datasets, and the results demonstrate the effectiveness and soundness of our proposed model. HCR-AdaAD offers a novel and effective approach to anomaly detection in multivariate time series that outperforms traditional methods, making it a promising solution for real-world applications in various domains.

Visible light positioning (VLP) systems provide higher accuracy and lower cost, more suitable for indoor positioning compared to their radio-frequency (RF) counterparts. And the VLP system based on received signal strength (RSS) is widely used due to the lowest hardware cost. However, existing RSS-VLP systems are usually sensitive to reflections and shadows. In this paper, we propose a VLP framework with an adaptive algorithm module, a diversity module, and a combination module. Simulations show that in the presence of reflections and shadows, this positioning framework can achieve an average error below 15 cm in a 5 m × 5 m area. Moreover, in case of severe blocking, its positioning error is even an order of magnitude lower than that of the typical RSS-VLP method.

Vehicle trajectory prediction is crucial and indispensable for ensuring the safe and efficient operation of autonomous vehicles in complex traffic environments. The application of Internet of Things technology in the collaborative automated driving system (CADS) has established a robust data foundation for vehicle trajectory prediction. Accurate prediction requires not only a substantial amount of high-quality data but also a deep understanding of the vehicle’s driving characteristics and interactions between neighboring vehicles. To enhance the study of vehicle trajectory prediction, this article proposes a novel Social Force-constrained Gated Recurrent Unit (SF-GRU) model, which integrates data-driven and physics-driven models. Specifically, the SF-GRU model is based on the gated recurrent unit encoder–decoder framework and incorporates social force constraints to enhance and supplement the model input based on vehicle time-series trajectory data, which describes the driving and interactive behaviors of vehicles during driving, as well as the interactions between neighboring vehicles and the surrounding environment. The model is trained and validated using the next generation simulation data set. Experimental results demonstrate that the SF-GRU model outperforms existing state-of-the-art models in both longitudinal and lateral motion, and that social force constraints are more effective than spatial variables in improving prediction accuracy. Furthermore, the SF-GRU model can intuitively and accurately consider the interactions between vehicles, and precisely describe the changes of relevant variables in the prediction process, thus enhancing the interpretability of the data-driven model. The SF-GRU model has great potential in vehicle trajectory prediction and can provide important support for the practical implementation of autonomous driving vehicles.

Machine learning models have recently demonstrated the ability to mine structural health monitoring data. While existing machine learning models can provide better performance than the previous univariate time-series model, it is still an open challenge of fully mining the spatial and temporal characteristics of the structural response to increase their accuracy. In addition, the heterogeneous correlation is always missed in traditional models. This article proposes a heterogeneous structural response prediction (HSRP) framework based on the deep learning model to improve the performance. The HSRP framework cannot only make full use of spatial and temporal correlations but also mine the correlation between heterogeneous responses. Motivated by recent studies in machine learning, an attention module is introduced to learn the correlation between different responses and initial the weights of sensors and past response. The convolutional neural network is also implemented to extract the spatial features and the long short-term memory network is used to extract the weekly, daily, and hourly patterns of structural response. A real-world data set collected from a bridge is used to evaluate the performance of the proposed model on single-step prediction and multistep prediction. The experimental results show that the proposed model outperforms several widely used benchmark models. Furthermore, additional experiments and evaluations are implemented to investigate the sensitivity and robustness of the proposed model.

Missing data are critical deficiency in the investigation of displacement measurement in urban Internet of Things system. In the insight of recovering missing displacement data, this article presents a data-driven and high-dimensional gap-imputation method, Tucker decomposition with L2 regularization. Results on the global navigation satellite system (GNSS) time series collected from an intelligent structural health monitoring system show that the recovery accuracy is improved compared with some popular benchmark methods. When the missing rate is 50%, compared with singular spectrum analysis, singular value decomposition, CP optimization algorithm,$k$-nearest neighbors, and Tucker decomposition via alternating least squares, Tucker decomposition with L2 regularization can improve the average mean absolute error by about 4.74, 4.95, 5.82, 2.29, and 5.67 mm for all locations. It can be concluded that the consideration of multiple temporal correlations is necessary for missing data imputation. Compared with matrix decomposition, tensor decomposition can improve the ability for high-dimensional correlations in the GNSS time series.

Bus passenger flow prediction is a critical component of advanced transportation information system for public traffic management, control, and dispatch. With the development of artificial intelligence, many previous studies attempted to apply machine learning models to extract comprehensive correlations from transit networks to improve passenger flow prediction accuracy, given that the variety and volume of traffic data have been easily obtained. The passenger flow on a station is highly affected by various factors such as the previous time step, peak hours or nonpeak hours, and extracting the key features from the data is essential for a passenger flow prediction model. Although the neural networks, k‐nearest neighbor, and some deep learning models have been adopted to mine the temporal correlations of the passenger flow data, the lack of interpretability of the influenced variables is still a big problem. Classical tree‐based models can mine the correlations between variables and rank the importance of each variable. In this study, we presented a method to extract passenger flow of different routes on the station and implemented a XGBoost model to find the contributions of variables to the prediction of passenger flow. Comparing to benchmark models, the proposed model can reach state‐of‐the‐art prediction accuracy and computational efficiency on the real‐world dataset. Moreover, the XGBoost model can interpret the predicted results. It can be seen that period is the most important variable for the passenger flow prediction, and so the management of buses during peak hours should be improved.

In reality, readings of sensors on highways are usually missing at various unexpected moments due to some sensor or communication errors. These missing values do not only influence the real-time traffic monitoring but also prevent further traffic data mining. In this paper, we propose a multi-view learning method to estimate the missing values for traffic-related time series data. The model combines data-driven algorithms (long-short term memory and support vector regression) and collaborative filtering techniques. It can consider the local and global variation in temporal and spatial views to capture more information from the existing data. The estimations of missing values from four views are aggregated to obtain a final value with a kernel function. Data from a highway network are used to evaluate the performance of the proposed model in terms of accuracy, precision, and agreement. The results indicate that our proposed model outperforms other baselines, especially for block missing pattern with a high missing ratio. Furthermore, the sensitivity of the parameters is analyzed. We can conclude that combining different views can improve the performance of the imputation.