Distributed inferencing with ambient and wearable sensors

• Published in: Wireless communications and mobile computing Vol. 12; no. 1; pp. 117 - 131
• Main Authors: Atallah, Louis, McIlwraith, Douglas, Thiemjarus, Surapa, Lo, Benny, Yang, Guang-Zhong
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.01.2012
• Subjects: ambient sensing; Bayesian networks; body sensor networks; distributed inference; factor graphs; inference; wearable sensing
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1002/wcm.893

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. Scalable and distributed reasoning is preferable when compared to centralised inference in wireless sensor networks, due to the large amounts of data collected and the complexity of emergent patterns. 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 ‐ demonstrating a framework for distributed, real‐time activity monitoring within a home healthcare environment.