Open AccessDistributed inferencing with ambient and wearable sensors
Author(s) -
Atallah Louis,
McIlwraith Douglas,
Thiemjarus Surapa,
Lo Benny,
Yang GuangZhong
Publication year - 2012
Publication title -
wireless communications and mobile computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.42
H-Index - 64
eISSN - 1530-8677
pISSN - 1530-8669
DOI - 10.1002/wcm.893
Subject(s) - computer science , wearable computer , wireless sensor network , scalability , inference , ambient intelligence , distributed computing , focus (optics) , wireless , network topology , wearable technology , wireless network , graphical model , human–computer interaction , real time computing , embedded system , computer network , machine learning , artificial intelligence , telecommunications , database , physics , optics
Wireless sensor networks enable continuous and reliable data acquisition for real‐time monitoring in a variety of application areas. Due to the large amount of data collected and the potential complexity of emergent patterns, scalable and distributed reasoning is preferable when compared to centralised inference as this allows network wide decisions to be reached robustly without specific reliance on particular network components. In this paper, we provide an overview of distributed inference for both wearable and ambient sensing with specific focus on graphical models—illustrating their ability to be mapped to the topology of a physical network. Examples of research conducted by the authors in the use of ambient and wearable sensors are provided, demonstrating the possibility for distributed, real‐time activity monitoring within a home healthcare environment. Copyright © 2010 John Wiley & Sons, Ltd.