  The IoT vision of a trillion connected devices over the next decade requires
reliable end-to-end connectivity and automated device management platforms.
While we have seen successful efforts for maintaining small IoT testbeds, there
are multiple challenges for the efficient management of large-scale device
deployments. With Industrial IoT, incorporating millions of devices,
traditional management methods do not scale well. In this work, we address
these challenges by designing a set of novel machine learning techniques, which
form a foundation of a new tool, it IoTelligent, for IoT device management,
using traffic characteristics obtained at the network level. The design of our
tool is driven by the analysis of 1-year long networking data, collected from
350 companies with IoT deployments. The exploratory analysis of this data
reveals that IoT environments follow the famous Pareto principle, such as: (i)
10% of the companies in the dataset contribute to 90% of the entire traffic;
(ii) 7% of all the companies in the set own 90% of all the devices. We designed
and evaluated CNN, LSTM, and Convolutional LSTM models for demand forecasting,
with a conclusion of the Convolutional LSTM model being the best. However,
maintaining and updating individual company models is expensive. In this work,
we design a novel, scalable approach, where a general demand forecasting model
is built using the combined data of all the companies with a normalization
factor. Moreover, we introduce a novel technique for device management, based
on autoencoders. They automatically extract relevant device features to
identify device groups with similar behavior to flag anomalous devices.
