Ricerc@Sapienza

Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, thanks to its appealing features in terms of coverage, energy efficiency, and compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices. NB-IoT does not provide, however, reliable information on positioning. Time-based techniques inherited from Long Term Evolution (LTE), such as Observed Time Difference Of Arrival (OTDOA) , are not yet widely available in existing networks, and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth.
The goal of the project is to introduce a suite of algorithms for NB-IoT positioning based on fingerprinting, as very recently proposed for NB-IoT networks. The algorithms will combine network coverage, radio frequency (RF) signal parameters and Time of Arrival (ToA) information, collected from multiple cells. Multiple levels of hybridization between RF and ToA will be investigated, ranging from a low-level integration, with a single fingerprint including both RF and ToA data, to a high-level integration, with RF and ToA used to obtain two independent position estimates that are then averaged. The proposed strategies will be evaluated on experimental data for multiple NB-IoT operators, obtained in two large-scale urban measurement campaigns performed in Oslo, Norway, and Rome, Italy. Data will be preprocessed in order to combine RF and ToA information in each data measurement location, enabling the definition of hybrid fingerprints, and the resulting processed dataset will be released under an open-source license. Performance evaluation will also include a comparison between the proposed algorithms and OTDOA, carried out on both NB-IoT and LTE data (also part of the experimental data).