NDLI: Quality-of-inference (QoINF)-aware context determination in assisted living environments

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Quality-of-inference (QoINF)-aware context determination in assisted living environments

Content Provider	ACM Digital Library
Author	Julien, Christine Das, Sajal K. Roy, Nirmalya Misra, Archan
Abstract	Energy-efficient determination of an individual's context (both physiological and activity) is an important technical challenge for assisted living environments. Given the expected availability of multiple sensors, context determination may be viewed as an estimation problem over multiple sensor data streams. This paper develops a formal and practically applicable model to capture the tradeoff between the accuracy of context estimation and the communication overheads of sensing. In particular, we propose the use of tolerance ranges to reduce an individual sensor's reporting frequency, while ensuring acceptable accuracy of the derived context. In our vision, applications specify their minimally acceptable value for a Quality-of Inference (QoINF) metric. We introduce an optimization technique allowing a Context Service to compute both the best set of sensors, and their associated tolerance values, that satisfy the QoINF target at minimum communication cost. Early experimental results with SunSPOT sensors are presented to attest to the promise of this approach.
Starting Page	27
Ending Page	32
Page Count	6
File Format	PDF
ISBN	9781605585246
DOI	10.1145/1540373.1540381
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-05-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Health monitoring Sensor data streams Context-awareness
Content Type	Text
Resource Type	Article

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