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A platform for ubiquitous sensor deployment in occupational and domestic environments

Published: 25 April 2007 Publication History

Abstract

In this paper, we introduce the "Plug" sensor network, a ubiquitous networked sensing platform ideally suited to broad deployment in environments where people work and live. The backbone of the Plug sensor network is a set of 35 sensor-, radio-, and computation-enabled power strips distributed throughout the third oor of the MIT Media Lab. A single Plug device fulfills all the functional requirements of a normal power strip (i.e., four 120V, 60Hz electrical outlets; surge protector circuit; standard electrical connector to a US-style wall socket), and can be used without special training. Additionally, each Plug has a wide range of sensing modalities (e.g., sound, light, electrical current and voltage, vibration, motion, and temperature) for gathering data about how it is being used and its nearby environment. To our knowledge, the Plug sensor network is the first to embody the idea of designing sensor nodes to seamlessly become a part of their environment, rather than play the role of alien, if unobtrusive, observers. We argue this design principle is essential for sensor networks to succeed in the realm of ubiquitous computing.
In this paper, we present an overview of the Plug hardware and software architectures, look at specific usage scenarios of a single Plug, and show example data taken across the entire Plug network to give a sense of the pulse of the building over a span of days. Finally, we present ongoing work interfacing heterogeneous devices with the Plug network for a variety of applications and discuss possible future work.

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      cover image ACM Conferences
      IPSN '07: Proceedings of the 6th international conference on Information processing in sensor networks
      April 2007
      592 pages
      ISBN:9781595936387
      DOI:10.1145/1236360
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      Published: 25 April 2007

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      • (2020)MAMAS: Supporting Parent--Child Mealtime Interactions Using Automated Tracking and Speech RecognitionProceedings of the ACM on Human-Computer Interaction10.1145/33928764:CSCW1(1-32)Online publication date: 29-May-2020
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