Combining Individual and Joint Networking Behavior for Intelligent IoT Analytics

• URL: http://arxiv.org/abs/2203.03109v1
• Date: Mon, 7 Mar 2022 02:59:56 GMT
• Title: Combining Individual and Joint Networking Behavior for Intelligent IoT Analytics
• Authors: Jeya Vikranth Jeyakumar and Ludmila Cherkasova and Saina Lajevardi and Moray Allan and Yue Zhao and John Fry and Mani Srivastava
• Abstract summary: In 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. The design of our tool is driven by the analysis of 1-year long networking data, collected from 350 companies with IoT deployments.
• Score: 4.6503958614029415
• License: http://creativecommons.org/licenses/by/4.0/
• 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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