AutoPlug: An automated metadata service for smart outlets

Abstract

ACKNOWLEDGMENTSLow-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 iv 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.v

Efficient Online Classification and Tracking on Resource-constrained IoT Devices

“…A state transition may be characterized by multiple components 2 . The same state transition may also occur at several discrete levels of the input signals 3 . We aim to identify such state transition patterns in the continuous signals.…”

“…The algorithm aims to find a suitable solution to the problem given in Eqn. (3). It consists of two stages: the update stage and the merging stage.…”

“…A recent study that has implemented in embedded systems is presented in [3], called AutoPlug, which can perform timely appliance classification and identification of new appliances using time series analysis and machine-learning. To detect if an appliance is new or moved from one smart plug to another, AutoPlug matches the current active period with the previous one by comparing the Dynamic Time Warping distance between them or by fitting some common appliance models in each period and then comparing the fitted models to determine if device switch has occurred.…”

“…Few recent studies have implemented in embedded systems. For example, a system prototype is presented in [3], called AutoPlug, which can perform appliance classification and identification on a wireless gateway connecting to multiple power plugs. Unlike our smart plug prototype using resource-constrained Arduino platform, AutoPlug is based on more powerful Raspberry Pi 2 platform.…”

“…The heating element is responsible for heating the water for washing or heating the air for drying. Similarly, an air-conditioner contains both a compressor and a fan, among other basic functions 3. For instance, an electric iron is equipped with a temperature control dial, which allows a user to select the iron's operating temperature.…”