A Primer on Real-world RSS-based Outdoor NB-IoT Localization

Abstract

Apart from long range communication, Low Power Wide Area Networks (LPWAN) are used to locate Internet of Things (IoT) devices. With hundreds of networks being deployed in a short amount of time, NarrowBand IoT (NB-IoT) is stealing the LPWAN market shares from its competitors. The deep indoor coverage of NB-IoT guarantees the energy-efficient communication link between a device and a nearby base station. We believe we are the first to perform signal strength-based outdoor localization experiments with this up-and-coming technology. Instead of simulating the experiments, we face the challenges of working with real measurements in a real-world city-scale environment. In contrast to satellite-based solutions, the position of a mobile device equipped with a small battery can be estimated in indoor and outdoor environments for several years. In this research, we investigate the accuracy of NB-IoT localization, using the Received Signal Strength (RSS) to a single NB-IoT base station antenna. We evaluate three outdoor RSS-based algorithms: proximity, ranging and optimized fingerprinting. The experiments result in a mean location estimation error of 340 m, 320 m and 204 m, respectively.