Machine learning and 5G-based industrial IoT device positioning for location-aware power quality management

Abstract

The rapid development and spread of the Internet of Things (IoT) in conjunction with advanced machine learning algorithms over the last decade has gained the interest of academia and industry in the application of location-aware power quality management. A satellite-based device positioning system in the outdoor environment can provide people with convenient device positioning services to support applications such as cargo tracking, robotic navigation, and faulty area diagnosis. However, due to the high impact of multipath signal propagation and object obstruction on the smart power grid environment, the precision of satellite-based positioning technical solutions decreases and is insufficient to meet the application demands. Therefore, this chapter demonstrates how emerging 5G communication technologies (such as mmWave and massive MIMO) can be used for Industrial Internet of Things (IIoT) device positioning by machine learning algorithms. The majority of such innovative features (e.g., angle-of-departure) can be managed using traditional methods (e.g., triangulation), but the integration of these functionalities and the increased volume of data make machine learning strategies more manageable than traditional location-aware power quality management.