Multidimensional time‐series prediction is significant in various fields, such as human production and life, weather forecasting, and artificial intelligence. However, a single model can only focus on specific features of time‐series data, making it unable to consider both linear and nonlinear components simultaneously. In this study, we propose a fusion network that combines the advantages of deep and broad networks for multidimensional time‐series prediction tasks. The complex multidimensional time‐series data are divided into nonlinear and time‐series data. Restricted Boltzmann machine and mapping functions are used for feature learning and generating mapping nodes at the mapping layer. The echo state network and gate recurrent unit are applied in the enhancement layer. The proposed model has been validated on PM2.5 and wind turbine power datasets, proving superior performance in multistep prediction tasks compared to the baseline models.

The performance of heterogeneous sensor networks is enhanced by high‐energy heterogeneous nodes. Determining the number and deployment of heterogeneous nodes is a significant research issue. A heterogeneous node configuration algorithm is presented in this paper, which can be used for overall network planning before the deployment of heterogeneous nodes. Subsequently, factors such as network performance and economic cost are comprehensively considered, and integrated into a single index using the entropy weighting method. The proportion of different indicators is then determined, and a formula for calculating the required number of heterogeneous nodes under various network conditions is derived by considering parameters such as network area size, node communication threshold distance, and the number of common nodes. Experimental results demonstrate that the proposed algorithm not only reduces networks costs but also enhances overall networks performance.

This article proposes an efficient hierarchical solution framework, null-space guidance vector field (NSGVF) with deep reinforcement learning (DRL)-based coefficient adjustment, to solve the problem of obstacle avoidance for unmanned surface vehicle (USV) with 3D-LiDAR in an unstructured environment. First, a simulation platform with high fidelity based on Virtual RobotX is developed, including the USV model, ocean environment, perception module, decision making and path planning, tracking controller, etc. With the modulated velocity commands, the NSGVF method enables the USV to navigate toward the target point and avoid obstacles, by defining the modulation matrix to quantify the influence of real-time sampling point, and introducing a null-space strategy to integrate the influence of point clouds. Because the key coefficients of NSGVF need to be set in advance with poor environmental adaptability, the DRL method with curriculum learning is then developed to find the optimum for the key coefficients. The experiment results show that the hierarchical DRL+NSGVF framework can be better applied to the task of obstacle avoidance in unstructured environments.

Abstract This paper proposes a novel approach for absolute velocity estimation of unmanned aerial vehicles in unknown and unmapped GNSS‐denied environments. The proposed method leverages the advantages of Fourier‐based image phase correlation and utilizes off‐the‐shelf onboard sensors, including a downward‐facing monocular camera, an inertial sensor, and a sonar. The non‐matching tracking approach is particularly appealing, offering accurate estimation while remaining robust against frequency‐dependent noise, significant intensity variations, and time‐varying illumination disturbances. In the proposed method, the first step involves computing global pixel motion from consecutive images using phase correlation, which utilizes the shift property of the Fourier transform. This pixel motion estimation serves as the basis for creating a closed‐loop solution for absolute velocity estimation. To further enhance accuracy, a Kalman filter is implemented to fuse all available data and provide a reliable velocity estimate. Validation of the proposed visual‐inertial technique is conducted through simulation experiments using AirSim and real‐world flight tests. The results demonstrate the practicality and effectiveness of the approach across a range of challenging scenarios.

Hybrid sensor networks, which contain mobile nodes and stationary nodes, are being used more and more widely. The second deployment of mobile nodes is a key problem to be solved, and the deployment performance of the network directly affects the monitoring effect of the network. Optimizing the configuration ratio of the two nodes can effectively reduce the network cost. In this paper, under the premise of knowing the coverage of the required monitoring area, the impact of sensor devices on node configuration is studied through parameter analysis, and the number and types of sensors that should be deployed in the hybrid sensor network are deduced, which can be conveniently and accurately used to design the actual hybrid sensor network. At the same time, for the secondary deployment of mobile nodes, this paper proposes a new mobile coverage method BS‐CCP (box search and concentric circle positioning) to improve the coverage of the hybrid sensor network and maximize the coverage of the target area with the specified sensor types and numbers. Compared with existing work, the method in this paper reduces the number of iterations and reduces the number of required nodes. Comparing BS‐CCP with the existing network mobile coverage algorithm, the experimental results show that the coverage obtained by this method is larger and more efficient.

Industrial Internet of Things (IIoTs) are significantly changing informative and manufacturing pattern in smart factories while it also brings security and trustworthiness issue. Concerning about trustworthiness issues and private preservation of tracking systems, a hierarchical framework with federated control theory is designed, which consists of a federated control center, network layer, and a federated control node. The framework combines a collaborative Cloud-Edge-End structure and machine learning-oriented localization, which further forms the EM-based federated scheme. On this basis, a trustworthy localization model is built with the untrustworthiness probability as a latent variable. By exploring expectation maximization (EM) of trustworthy localization, the local messages and aggression equations are derived in an iterative way of federated learning. The EM-based federated control scheme with machine learning-oriented localization is finally given. Experiments have been conducted to prove the localization accuracy and convergence of the proposed method with trustworthiness issue. The results show that trustworthy localization outperforms traditional methods without considering the security threats.

Prognostics and health management (PHM) technology has been widely accepted, and employed to evaluate system performance. In practice, system performance often varies continually rather than just being functional or failed, especially for a complex system. Profust reliability theory extends the traditional binary state space {0, 1} into a fuzzy state space [0, 1], which is therefore suitable to characterize a gradual physical degradation. Moreover, in profust reliability theory, fuzzy state transitions can also help to describe the health evolution of a component or a system. Accordingly, this paper proposes a profust reliability based PHM approach, where the profust reliability is employed as a health indicator to evaluate the real-time system performance. On the basis of the health estimation, the system remaining useful life (RUL) is further defined, and the mean RUL estimate is predicted by using a degraded Markov model. Finally, an experimental case study of Li-ion batteries is presented to demonstrate the effectiveness of the proposed approach.