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NarrowBand-IoT (NB-IoT) is a radio-access technology standardized by 3GPP to support a large set of use cases associated with the rapid deployment of massive machine-type communications. NB-IoT facilitates the connection of devices in inaccessible areas, extends battery life, and reduces device complexity. Unfortunately, the opacity of the underlying schema (i.e., the way that these benefits are achieved) makes it very difficult for most users and developers to manage deployment scenarios. In this study, we built an embedded system comprising a Raspberry Pi with an NB module, referred to as NBPilot, which interacts with NB networks to identify essential signaling messages transmitted by a Qualcomm NB modem. This system gives researchers and developers an unprecedented understanding of network behavior as well as the ability to adjust them to their particular requirements. We employed the-state-of-the-art machine learning techniques for modeling and the analysis of NB performance. The efficacy of the proposed NBPilot system was established by applying it to a metropolitan NB-IoT network with over 2,000 NB sites for the collection and testing of data trace as well as the validation of a cellular station prior to going online. The developed machine learning techniques predict the actual performance (rate, delay, and power consumption) of the user equipment’s connection against the QoS advertised by the operator and pinpoint the affecting factors.

City-Wide NB-IoT Network Monitoring and Diagnosing