EasyLiving: Technologies for Intelligent Environments

By Barry Brumitt, Brian Meyers, J. Krumm, Amanda Kern, S. Shafer. HUC, 2000

1,035 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/30475fb381cd5a23fd2c8e1028b839a433d8e425

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

Citations in the corpus

Detecting Human Behavior Models From Multimodal Observation in a Smart Home (2009, background): “Some projects focus on supplying appropriate system services to the users [4], [ 10 ], [13], [30], while others focus on the correct classification of activities [7], [24], [25], [28].”
Practical robust localization over large-scale 802.11 wireless networks (2004, background): “…Localization over Large-Scale 802.11 Wireless Networks Andreas Haeberlen Rice University ahae@cs.rice.edu Algis Rudys Rice University arudys@cs.rice.edu ABSTRACT We demonstrate a system built using probabilistic techniques that allows for remarkably accurate localization across our entire…”
Evolution of Indoor Positioning Technologies: A Survey (2017, methodology): “An early system that uses WLAN technology is RADAR, developed by Microsoft Research [56] (website: http:// research.microsoft.com/en-us/projects/radar/).”
What we talk about when we talk about context (2004, background): “Examples of this sort of approach include Easy Living (Brumitt et al. 2000) and the Electronic Tourist Guide (Cheverst et al. 2000).”
Home automation in the wild: challenges and opportunities (2011, background): “Unfortunately existing smart home technologies do not consider this incremental growth or composability aspect [e.g., 4].”
Gaia: A Middleware Infrastructure to Enable Active Spaces1 (2002, background): “The Microsoft Easy Living project [32] focuses on home and work environments and states that computing must be as natural as lighting.”
Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors (2005, background): “Over the last several years much effort has been put into developing and employing a variety of sensors to solve key problems in the ubiquitous computing domain, including camera networks for people tracking [76, 13 , 66], as well as cameras and microphones for activity recognition [17, 50].”
People, Places, Things: Web Presence for the Real World (2000, background): “, the EasyLiving project [7], the Aware Home [32]).”
The location stack: a layered model for location in ubiquitous computing (2002, background): “The EasyLiving system [4] provides several behaviors which are dependent on the activities of occupants in the environment shown in Figure 3.”
Research Directions for the Internet of Things (2014, background): “VISION AND IOT SCOPE Many people [8], including myself [28], [29], hold the view that cities and the world itself will be overlaid with sensing and actuation, many embedded in " things " creating what is referred to as a smart world.”
Domestic Routines and Design for the Home (2004, background): “This agenda has largely been pursued through purpose built ‘living laboratories’ which enable researchers to explore ways in which inhabitants might experience the home of the future (Mozer, 1998; Kidd et al., 1999; Brumitt et al., 2000).”
A Review of Smart Homes—Past, Present, and Future (2012, background): “Human behavior modeling is another possible application of neural networks [25], [26].”