RTK-LoRa: High-Precision, Long-Range, and Energy-Efficient Localization for Mobile IoT Devices

Open access
Date
2020Type
- Journal Article
ETH Bibliography
yes
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Abstract
High-precision global navigation satellite system (GNSS) is a crucial geolocalization feature enabling a wide range of applications, from mobile Internet-of-Things devices to autonomous drones and self-driving vehicles. Real-time kinematic (RTK) is a GNSS technology that attracting increased interest due to the centimeter precision achievable when wireless communication is present on the devices. On the other hand, sending continuously wireless data increases the energy consumption and the cost of the solution, especially when communication is carried over the 4G network. Due to those drawbacks, RTK is not much exploited in the localization of battery-operated devices. This work combines RTK with low-power long-range communication to achieve submeter precision in an energy-efficient RTK-based system. The proposed system exploits a state-of-the-art RTK-GNSS module combined with a long range (LoRa) to achieve geolocalization with minimal wireless radio infrastructure requirements. An energy-efficient algorithm is proposed and implemented in a microcontroller to have a quick startup and high accuracy. We evaluate three different GNSS modules and compare their performance in terms of power and accuracy. Experimental results, with in-field measurements, show that an average geolocalization precision of tens of centimeters is achievable on a battery-operated wireless end node connected to a single base station used as a geostationary reference anchor placed at kilometers of distance. The peak precision measured is below 10 cm. © 2020 IEEE. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000458469Publication status
publishedExternal links
Journal / series
IEEE Transactions on Instrumentation and MeasurementVolume
Pages / Article No.
Publisher
IEEESubject
Energy efficiency; Geolocalization; Long range (LoRa); Low-power sensors; Real-time kinematic (RTK)Organisational unit
03996 - Benini, Luca / Benini, Luca
01225 - D-ITET Zentr. f. projektbasiertes Lernen / D-ITET Center for Project-Based Learning
Funding
187087 - AeroSense: a novel MEMS‐based surface pressure and acoustic IoT measurement system for wind turbines (SNF)
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