Predicting traffic is the main duty of an intelligent transportation system (ITS). Precise traffic forecasts can significantly enhance the use of public funds. However, the dynamic and complex nature of spatio-temporal relationships presents significant challenges. Most current methods utilize static adjacency matrices, leading to reduced forecasting accuracy and precision. This approach fails to account for the complex spatio-temporal correlations that interact simultaneously. In order to show how different spatio-temporal correlations change over time in the traffic flow network, this study suggests a unified simultaneous Multi Fusion Graph Network (DMFGNet) model. The goal of the suggested DMFGNet model is to identify dynamic spatio-temporal linkages between various regions. Meanwhile, we propose a model, the Spatio-Temporal Attention Unit (STAU), to control the weights of neighbor aggregation. It is capable of meticulously combining spatio-temporal characteristics from different neighbors. We tested the model for both real-time and pre-processed predictions using a combination of edge and cloud infrastructure. This setup performs prediction tasks at the edge layer and conducts training in the remote cloud. This approach guarantees the use of only relevant data for model training and prediction-making, thereby boosting the system’s overall effectiveness. This approach not only optimizes resource allocation but also aids in reducing latency and enhancing the overall performance of cloud-based prediction models, potentially enhancing the capabilities of consumer technology and electronics solutions. We carefully tested and evaluated two large real-world traffic flow datasets to show that the proposed method works and is useful. The results of the tests show that the suggested model is better than the current best baseline methods. Additionally, the results demonstrate the effectiveness and usefulness of the recommended strategy.

The Fine Time Measurement (FTM) protocol introduced by IEEE 802.11 includes a new ranging method, named Wi-Fi Round Trip Time (Wi-Fi RTT), which can be used for indoor localization. Pedestrian Dead Reckoning (PDR) can provide accurate pedestrian tracking through inertial sensors in a short time. Information fusion of PDR and existing wireless technology is widely used in indoor localization to ensure the robustness and stability. In this paper, we propose a fusion indoor localization method of Wi-Fi RTT and PDR. Firstly, an adaptive filtering system consisting of multiple Extended Kalman Filter (EKF) and a new outlier detection method is proposed to reduce the localization error of Wi-Fi RTT. Secondly, the fusion algorithm based on the Federated Filter (FF) and observability is designed to combine Wi-Fi RTT with PDR. Finally, to further improve the localization performance of the fusion algorithm, a real-time smoothing method with fixed interval is used. We evaluate the proposed method in four different scenarios. The results show that the proposed indoor localization method has better stability and robustness, and the average localization error decreased by 37.4-67.6% compared with the classic EKF-based method.

The complex cyber layer challenges the accuracy of cooperative tracking motion for the networked multiple linear motors system. This article focuses on model predictive control (MPC) for networked control system with the denial-of-service (DoS) attack and network-induced time delay. In order to improve tracking accuracy and control performance of the system, several topologies for linear motion models are studied. The DoS attack and time delay are modeled as constraint models in the time domain. Combining dynamic prediction horizon with the buffer compensation in the controller to actuator (C-A) channel, the proposed networked control strategy based on MPC effectively solves the impact of DoS attacks and time delays on the tracking performance of multiple linear motors. In order to ensure the recursive feasibility of the MPC optimization problem and the uniform global asymptotic stability of system, the output terminal constraint and dynamic prediction horizon constraint are considered by combining four DoS attack cases of the networked control strategy, and a Lyapunov function is constructed. Finally, three linear motors with different topologies are built, and the simulation results verify the control effectiveness of the proposed control strategy on the motor tracking performance under cyber constraints.

Research on trajectory tracking is crucial for the development of autonomous vehicles. This paper presents a trajectory tracking scheme by utilizing model predictive control (MPC) and preview-follower theory (PFT), which includes a reference generation module and a MPC controller. The reference generation module could calculate reference lateral acceleration at the preview point by PFT to update state variables, and generate a reference yaw rate in each prediction point. Since the preview range is increased, PFT makes the calculation of yaw rate more accurate. Through physical constraints, the MPC controller can achieve the best tracking of the reference path. The MPC problem is formulated as a linear time-varying (LTV) MPC controller to achieve a predictive model from nonlinear vehicle dynamics to continuous online linearization. The MPC-PFT controller method performs well by increasing the effective length of the reference path. Compared with MPC and PFT controllers, the effectiveness and robustness of the proposed method are proved by simulations of two typical working conditions.