Paper Info
Title
Energy-efficient data collection for context recognition.
Abstract
Detection of the user's context with mobile sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems is often making continuous detection impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings. It uses Markov chains to simulate the behaviour of the system in different configurations, and multi-objective genetic algorithm to find a set of good non-dominated configurations.
Year
DOI
Venue
2017
10.1145/3123024.3124430
UbiComp '17: The 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing Maui Hawaii September, 2017
Keywords
Field
DocType
Context recognition, energy efficiency, wearable sensors, smartphone, Markov chains
Data collection,Mobile sensing,Efficient energy use,Computer science,Markov chain,Ubiquitous computing,Battery (electricity),Genetic algorithm,Embedded system
Conference
ISBN
Citations 
PageRank 
978-1-4503-5190-4
1
0.40
References 
Authors
5
2
Name
Order
Citations
PageRank
Vito Janko1158.34
Mitja Luštrek241054.52