Paper Info
Title
AutoPlug: An automated metadata service for smart outlets
Abstract
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. Inaccurate metadata may cause systems to misinterpret data or issue incorrect control actions. 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 a 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, while detecting new devices or changes in devices with <50s latency.
Year
DOI
Venue
2016
10.1109/IGCC.2016.7892604
2016 Seventh International Green and Sustainable Computing Conference (IGSC)
Keywords
Field
DocType
Raspberry Pi,time series analysis,machine learning,outlet-to-device mappings,error-prone operation,time-consuming operation,automated home management systems,electrical devices,energy usage,low-cost network-connected smart outlets,automated metadata service,AutoPlug
Metadata,Electrical devices,Scheduling (computing),Latency (engineering),Raspberry pi,Computer science,Real-time computing,Home management,Management system,Database,Cognitive neuroscience of visual object recognition
Conference
ISBN
Citations 
PageRank 
978-1-5090-5118-2
0
0.34
References 
Authors
6
2
Name
Order
Citations
PageRank
Lurdh Pradeep Reddy Ambati100.34
David E. Irwin289998.12