Small UAVs-supported Autonomous Generation of Fine-grained 3D Indoor Radio Environmental Maps

TL;DR

This paper presents an autonomous system using small UAVs to efficiently generate detailed 3D indoor radio environmental maps, leveraging machine learning for predicting signal quality at unvisited locations.

Contribution

It introduces a novel UAV-based system for autonomous 3D REM generation that is adaptable, interference-free, and capable of predicting signal quality using ML.

Findings

Successfully visited 72 waypoints and collected thousands of Wi-Fi samples.

Demonstrated ML-based prediction of Wi-Fi signal strength at unvisited locations.

System is deployable in new environments with minimal manual intervention.

Abstract

Radio Environmental Maps (REMs) are a powerful tool for enhancing the performance of various communication and networked agents. However, generating REMs is a laborious undertaking, especially in complex 3-Dimensional (3D) environments, such as indoors. To address this issue, we propose a system for autonomous generation of fine-grained REMs of indoor 3D spaces. In the system, multiple small indoor Unmanned Aerial Vehicles (UAVs) are sequentially used for 3D sampling of signal quality indicators. The collected readings are streamlined to a Machine Learning (ML) system for its training and, once trained, the system is able to predict the signal quality at unknown 3D locations. The system enables automated and autonomous REM generation, and can be straightforwardly deployed in new environments. In addition, the system supports REM sampling without self-interference and is technology-agnostic, as long as the REM-sampling receivers features suitable sizes and weights to be carried by the UAVs. In the demonstration, we instantiate the system design using two UAVs and show its capability of visiting 72 waypoints and gathering thousands of Wi-Fi data samples. Our results also include an instantiation of the ML system for predicting the Received Signal Strength (RSS) of known Wi-Fi Access Points (APs) at locations not visited by the UAVs.