  Industrial Internet of Things (IoT) systems increasingly rely on wireless
communication standards. In a common industrial scenario, indoor wireless IoT
devices communicate with access points to deliver data collected from
industrial sensors, robots and factory machines. Due to static or quasi-static
locations of IoT devices and access points, historical observations of IoT
device channel conditions provide a possibility to precisely identify the
device without observing its traditional identifiers (e.g., MAC or IP address).
Such device identification methods based on wireless fingerprinting gained
increased attention lately as an additional cyber-security mechanism for
critical IoT infrastructures. In this paper, we perform a systematic study of a
large class of machine learning algorithms for device identification using
wireless fingerprints for the most popular cellular and Wi-Fi IoT technologies.
We design, implement, deploy, collect relevant data sets, train and test a
multitude of machine learning algorithms, as a part of the complete end-to-end
solution design for device identification via wireless fingerprinting. The
proposed solution is currently being deployed in a real-world industrial IoT
environment as part of H2020 project COLLABS.
