Exploring Hardware Heterogeneity to Improve Pervasive Context Inferences

Context-aware inference apps have become pervasive as a result of the Internet of Things (IoT). However, most of these apps run continuously on a single device, resulting in limited sensor coverage and high energy consumption. Recent advances in IoT devices, specifically hardware heterogeneity, can...

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Bibliographic Details
Published inComputer (Long Beach, Calif.) Vol. 50; no. 6; pp. 19 - 26
Main Authors Chenguang Shen, Srivastava, Mani
Format Journal Article
LanguageEnglish
Published New York IEEE 2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Accelerometers
Ambient intelligence
Context awareness
context inferences
Devices
Digital signal processing
Energy consumption
Energy management
Feature extraction
Hardware
Heterogeneity
Inference
Internet of Things
IoT
Mobile communication
pervasive computing
Power efficiency
silicon
smartwatches
Very large scale integration
VLSI
wearables
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Summary:Context-aware inference apps have become pervasive as a result of the Internet of Things (IoT). However, most of these apps run continuously on a single device, resulting in limited sensor coverage and high energy consumption. Recent advances in IoT devices, specifically hardware heterogeneity, can be leveraged to improve the accuracy and energy efficiency of context inferences.
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ISSN:0018-9162
1558-0814
DOI:10.1109/MC.2017.174