Measurement and Analysis of 4G/5G Mobile Signal Coverage in a Heavy Industry Environment.

In the evolving landscape of Industry 4.0, the integration of advanced wireless technologies into manufacturing processes holds the promise of unprecedented connectivity and efficiency. In particular, the data transmission in a heavy industry environment needs stable connectivity with mobile operators. This paper deals with the performance study of 4G and 5G mobile signal coverage within a complex factory environment. For this purpose, a cost-effective and portable measurement setup was realized and used to provide long-term measurement campaigns monitoring and recording several key parameter indicators (KPIs) in 4G/5G downlink and upload. To support the reproducibility of the provided study and other research activities, the measured dataset is publicly available for download. Among others findings, the obtained results show how the performance of 4G/5G is influenced by a heavy industry environment and of the time of day on the network load.

Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning.

The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. Typically based on signal strength measurement, a power level map is created first in the learning phase to align with measured values in the inference. Second, the location is determined by taking the point for which the recorded received power level is closest to the power level actually measured. The biggest limit of this technique is the reliability of power measurements, which may lack accuracy in many wireless systems. To this end, this work extends the power level measurement by using multiple anchors and multiple radio channels and, consequently, considers different approaches to aligning the actual measurements with the recorded values. The dataset is available online. This article focuses on the very popular radio technology Bluetooth Low Energy to explore the possible improvement of the system accuracy through different machine learning approaches. It shows how the accuracy-complexity trade-off influences the possible candidate algorithms on an example of three-channel Bluetooth received signal strength based fingerprinting in a one dimensional environment with four static anchors and in a two dimensional environment with the same set of anchors. We provide a literature survey to identify the machine learning algorithms applied in the literature to show that the studies available can not be compared directly. Then, we implement and analyze the performance of four most popular supervised learning techniques, namely k Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network. In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%.