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arXiv · 2203.03109

Combining Individual and Joint Networking Behavior for Intelligent IoT Analytics

Jeya Vikranth Jeyakumar·Ludmila Cherkasova·Saina Lajevardi·Moray Allan·Yue Zhao·John Fry·Mani Srivastava
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Abstract

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.

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Jeya Vikranth Jeyakumar, Ludmila Cherkasova, Saina Lajevardi, Moray Allan, Yue Zhao, John Fry, Mani Srivastava. 2022-03-07. Combining Individual and Joint Networking Behavior for Intelligent IoT Analytics. https://doi.org/10.1007/978-3-030-59615-6_4

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