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The IoT vision of a trillion connected devices over the next decade requiresreliable end-to-end connectivity and automated device management platforms.While we have seen successful efforts for maintaining small IoT testbeds, thereare multiple challenges for the efficient management of large-scale devicedeployments. With Industrial IoT, incorporating millions of devices,traditional management methods do not scale well. In this work, we addressthese challenges by designing a set of novel machine learning techniques, whichform a foundation of a new tool, it IoTelligent, for IoT device management,using traffic characteristics obtained at the network level. The design of ourtool is driven by the analysis of 1-year long networking data, collected from350 companies with IoT deployments. The exploratory analysis of this datareveals 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 designedand 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 modelis built using the combined data of all the companies with a normalizationfactor. Moreover, we introduce a novel technique for device management, basedon autoencoders. They automatically extract relevant device features toidentify device groups with similar behavior to flag anomalous devices.

Combining Individual and Joint Networking Behavior for Intelligent IoT Analytics