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Access Type

Open Access Thesis

Document Type


Degree Program

Electrical & Computer Engineering

Degree Type

Master of Science in Electrical and Computer Engineering (M.S.E.C.E.)

Year Degree Awarded


Month Degree Awarded



Low-cost network-connected smart outlets are now available for monitoring, controlling, and scheduling the energy usage of electrical devices. As a result, such smart outlets are being integrated into automated home management systems, which remotely control them by analyzing and interpreting their data. However, to effectively interpret data and control devices, the system must know the type of device that is plugged into each smart outlet. Existing systems require users to manually input and maintain the outlet metadata that associates a device type with a smart outlet. Such manual operation is time-consuming and error-prone: users must initially inventory all outlet-to-device mappings, enter them into the management system, and then update this metadata every time a new device is plugged in or moves to a new outlet. To address the problem, we propose AutoPlug, a system that automatically identifies and tracks the devices plugged into smart outlets in real time without user intervention. AutoPlug combines machine learning techniques with time-series analysis of device energy data in real time to accurately identify and track devices on startup, and as they move from outlet-to-outlet. We show that AutoPlug achieves ∼90% identification accuracy on real data collected from 13 distinct device types, while also detecting when a device changes outlets with an accuracy >90%. We implement an AutoPlug prototype on Raspberry Pi and deploy it live in a real home for a period of 20 days. We show that its performance enables it to monitor up to 25 outlets withlatency.


First Advisor

David E Irwin