LIDAUS: Localization of IoT Device via Anchor UAV SLAM

TL;DR

LIDAUS is a multi-stage UAV-based SLAM system that localizes IoT devices in 3D indoor environments without GPS, using RSSI signals, innovative clustering, anchor beacons, and optimized path planning.

Contribution

The paper introduces novel algorithms for RSSI observation selection, anchor beacon deployment, and path planning, enabling accurate indoor IoT device localization without infrastructure.

Findings

Achieves high localization accuracy with commodity IoT devices.

Effective in GPS-denied indoor environments like factories and disaster sites.

Demonstrates robustness through simulations and real experiments.

Abstract

We introduce LIDAUS Localization of IoT Device via Anchor UAV SLAM), an infrastructure-free, multi-stage SLAM system that utilizes an Unmanned Aerial Vehicle (UAV) to accurately localize IoT devices in a 3D indoor space where GPS signals are unavailable or weak, e.g., manufacturing factories, disaster sites, or smart buildings. The lack of GPS signals and infrastructure support makes most of the existing indoor localization systems not practical when localizing a large number of wireless IoT devices. In addition, safety concerns, access restriction, and simply the huge amount of IoT devices make it not practical for humans to manually localize and track IoT devices. To address these challenges, the UAV in our LIDAUS system conducts multi-stage 3D SLAM trips to localize devices based only on RSSIs, the most widely available measurement of the signals of almost all commodity IoT devices. The main novelties of the system include a weighted entropy-based clustering algorithm to select high quality RSSI observation locations, a 3D U-SLAM algorithm that is enhanced by deploying anchor beacons along the UAV's path, and the path planning based on Eulerian cycles on multi-layer grid graphs that model the space in exploring stage and Steiner tree paths in searching stages. Our simulations and experiments of Bluetooth IoT devices have demonstrated that the system can achieve high localization accuracy based only on RSSIs of commodity IoT devices.