Ultra-wideband (UWB) positioning systems often operate in a non-line-of-sight (NLOS) environment. NLOS propagation has become the main source of ultra-wideband indoor positioning errors. As such, how to identify and correct NLOS errors has become a key problem that must be solved in high-accuracy indoor positioning technology. This paper firstly describes the influence of the NLOS propagation path on localization accuracy and the generation method of ultra-wideband signals, and secondly classifies and analyzes the currently available algorithms for ultra-wideband non-line-of-sight (NLOS) identification and error suppression. For the identification of NLOS, the residual analysis judgement method, statistical feature class identification method, machine learning method and geometric feature judgement method are discussed. For the suppression of NLOS propagation errors, weighting-based methods, filtering-based methods, line-of-sight reconstruction algorithms, neural network algorithms, optimization methods with constraints, and path tracing methods are discussed. Finally, we conclude the paper and point out the problems that need to be solved in NLOS indoor positioning.

With the widespread adoption of the Robot Operating System (ROS), technologies for autonomous navigation in mobile robots have advanced considerably. ROS provides a modular navigation stack that integrates essential components, such as SLAM, localisation, global path planning, and obstacle avoidance, forming the foundation for applications including service robotics and autonomous driving. Nonetheless, achieving safe and reliable navigation in complex and dynamic environments remains a formidable challenge, due to the need for real-time perception of moving obstacles, sensor fusion requirements, and the demand for robust and efficient algorithms. This study presents a systematic examination of the ROS-based navigation stack and obstacle-avoidance mechanisms. The architecture and implementation principles of the core modules are analysed, along with a comparison of the features and application suitability of common local planners such as the Dynamic Window Approach (DWA) and Timed Elastic Band (TEB). The key technical challenges in autonomous navigation are summarised, and recent advancements are reviewed to outline emerging trends in ROS-based systems, including integration with deep learning, multi-robot coordination, and real-time optimisation. The findings contribute to a deeper theoretical understanding of robotic navigation and offer practical guidance for the design and development of autonomous systems.

Non-line-of-sight (NLOS) errors significantly impact the accuracy of ultra-wideband (UWB) indoor positioning, posing a major barrier to its advancement. This study addresses the challenge of effectively distinguishing line-of-sight (LOS) from NLOS signals to enhance UWB positioning accuracy. Unlike existing research that focuses on optimizing deep learning network structures, our approach emphasizes the optimization of model parameters. We introduce a chaotic map for the initialization of the population and integrate a subtraction-average-based optimizer with a dynamic exploration probability to enhance the Snake Search Algorithm (SSA). This improved SSA optimizes the initial weights and thresholds of backpropagation (BP) neural networks for signal classification. Comparative evaluations with BP, Particle Swarm Optimizer–BP (PSO-BP), and Snake Optimizer–PB (SO-BP) models—performed using three performance metrics—demonstrate that our LTSSO-BP model achieves superior stability and accuracy, with classification accuracy, recall, and F1 score values of 90%, 91.41%, and 90.25%, respectively.

The Internet of Things, or IoT, has been widely recognized as a new perception paradigm for interacting between the digital world and the physical one. Acting as the interface and integral part of the Internet of Things, sensors embedded within the network are the principal components that collect the unprocessed data, and these sensors are usually deployed in unattended, hostile, or harsh areas, which inevitably makes the sensor readings prone to faults and even anomalies. Therefore, the quality of sensor readings will ultimately affect the quality of various data-oriented IoT services, and the sensor data are of vital importance affecting the performance of the system. However, the data anomaly detection is a nontrivial task for IoT because sensors are usually resource-constrained devices with limited computing, communication, and capacity. Therefore, an efficient and lightweight detecting method is needed to meet the requirements. In this study, we deal with the anomaly data by detecting the source sensor nodes through combination methods of the local outlier factor and time series. Simulations show that the proposed method can effectively detect the anomaly data and presents a better normal data rate.

The heterogeneous nodes in the Internet of Things (IoT) are relatively weak in the computing power and storage capacity. Therefore, traditional algorithms of network security are not suitable for the IoT. Once these nodes alternate between normal behavior and anomaly behavior, it is difficult to identify and isolate them by the network system in a short time, thus the data transmission accuracy and the integrity of the network function will be affected negatively. Based on the characteristics of IoT, a lightweight local outlier factor detection method is used for node detection. In order to further determine whether the nodes are an anomaly or not, the varying behavior of those nodes in terms of time is considered in this research, and a time series method is used to make the system respond to the randomness and selectiveness of anomaly behavior nodes effectively in a short period of time. Simulation results show that the proposed method can improve the accuracy of the data transmitted by the network and achieve better performance.