Bluetooth Low Energy (BLE) is one of the RF-based technologies that has been utilizing Received Signal Strength Indicators (RSSI) in indoor position location systems (IPS) for decades. Its recent signal stability and propagation distance improvement inspired us to conduct this project. Beacons and scanners used two Bluetooth specifications, BLE 5.0 and 4.2, for experimentations. The measurement paradigm consisted of three segments, RSSI–distance conversion, multi-beacon in-plane, and diverse directional measurement. The analysis methods applied to process the data for precise positioning included the Signal propagation model, Trilateration, Modification coefficient, and Kalman filter. As the experiment results showed, the positioning accuracy could reach 10 cm when the beacons and scanners were at the same horizontal plane in a less-noisy environment. Nevertheless, the positioning accuracy dropped to a meter-scale accuracy when the measurements were executed in a three-dimensional configuration and complex environment. According to the analysis results, the BLE wireless signal strength is susceptible to interference in the manufacturing environment but still workable on certain occasions. In addition, the Bluetooth 5.0 specifications seem more promising in bringing brightness to RTLS applications in the future, due to its higher signal stability and better performance in lower interference environments.

Recently, Autonomous Ground Vehicles (AGV) and mobile robots have been rapidly developed in various engineering applications, such as Industry 4.0 factory and smart manufacturing. Indoor navigation was one of the most important tasks for the said mobile systems as they were often designed to move from one location to another location autonomously without contacting the surrounding objects along the moving path in a usually dynamic and complex indoor environment. There were two key steps to achieve Simultaneous Localization and Mapping (SLAM). First, indoor positioning of the mobile system based on some measurements was done. The second step was to navigate itself inside the indoor map. This was a very challenging problem because there always existed uncertainties in the measurements. It was desired to estimate the positioning errors and determine a safe moving path with high reliability. This paper presented the methodologies for wireless indoor positioning and navigation of AGV with measurement uncertainties. Two kinds of AGV moving trajectories with various design parameters were simulated: a linear trajectory and a curved one. It was found that both greater number of sensors being used for wireless measurements and greater number of measurement trials for Multilateration could effectively improve the accuracy of AGV positioning. INDEX TERMSAutonomous Ground Vehicle (AGV), Indoor Positioning and Navigation (IPN), Monte Carlo Simulations (MCS), Multilateration, Wireless Distance Measurement.

Failure mode detection is essential for bearing life prediction to protect the shafts on the machinery. This work demonstrates the rolling bearing vibration measurement, signals converting and analysis, feature extraction, and machine learning with neural networks to achieve failure mode detection for a shaft bearing. Two self-designed bearing test platforms with two types of sensors conduct the bearing vibration collection in normal and abnormal states. The time-domain signals convert to the frequency domain for analysis to observe the dominant frequency between these two types of sensors. In feature extraction, principal components analysis (PCA) combines with wavelet packet decomposition (WPD) to form the two feature extraction methods: PCA-WPD and WPD-PCA for optimization. The features extracted by these two methods serve as input to the long short-term memory (LSTM) networks for classification and training to distinguish bearing states in normal, misaligned, unbalanced, and impact loads. The evaluation arguments include sensor types, vibration directions, failure modes, feature extraction methods, and neural networks. In conclusion, the developed methods with the typical lower-cost sensor can achieve 97% accuracy in bearing failure mode detection.

A 5G network can provide more comprehensive bandwidth connectivity for the industry 4.0 environment, which requires faster and tremendous data transmission. This study demonstrates the 5G network performance evaluation with MEC, without MEC, WiFi 6, and Ethernet networks. Usually, a 5G network engages with Multi-access Edge Computing, providing the computing functions dedicated to the users on edge nodes. The MEC network architecture presents significant facilities, a network schematic, and data transmission routers. The field test performs high-definition streaming video and heavy-traffic load testing to evaluate the performance based on different protocols by comparing throughput, latency, jitter, and packet loss rate. MEC network performance, streaming video performance, and load test evaluation results reveal that the 5G network working with MEC achieved better performance than when it was working without MEC. The MEC can improve data transmission efficiency by dedicated configuration but is only accessible with authentication from mobile network operators (MNOs). Therefore, MNOs should offer industrial private network users partial authentication for accessing MEC functionality to improve network feasibility and efficiency. In conclusion, this work illustrates the 5G network implementation and performance measurement for constructing a smart factory.

5G networks require dynamic network monitoring and advanced security solutions. This work performs the essential steps to implement a basic 5G digital twin (DT) in a warehouse scenario. This study provides a paradigm of end-to-end connection and encryption to internet of things (IoT) devices. Network function virtualization (NFV) technologies are crucial to connecting and encrypting IoT devices. Innovative logistical scenarios are undergoing constant changes in logistics, and higher deployment of IoT devices in logistic scenarios, such as warehouses, demands better communication capabilities. The simulation tools enable digital twin network implementation in planning. Altair Feko (WinProp) simulates the radio behavior of a typical warehouse framework. The radio behavior can be exported as a radio simulation dataset file. This dataset file represents the virtual network’s payload. GNS3, an open-source network simulator, performs data payload transmission among clients to servers using custom NFV components. By transmitting data from client to server, we achieved end-to-end communication. Additionally, custom NFV components enable advanced encryption standard (AES) adoption. In summary, this work analyzes the round-trip time (RTT) and throughput of the payload data packages, in which two data packages, encrypted and non-encrypted, are observed.