Abstract
The Internet-of-Things (IoT) connects devices with embedded sensors to the Internet and is expected to grow at a spectacular rate. IoT will drive increased quality of life for individual as well as spur business growth and efficiency gains in industry. Actions based on real-time information, better decision making and remote diagnostics are some key areas where IoT can make a difference. This paper presents a smart agent architecture for the Internet-of-Things based on Bayesian Network technology that can be used for automation, notification, diagnostics and troubleshooting use cases.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Pearson, London (2014)
Cisco: Fog Computing, Ecosystem, Architecture and Applications. http://www.cisco.com/web/about/ac50/ac207/crc_new/university/RFP/rfp13078.html
Finnegan, M.: Boeing 787 s to create half a terabyte of data per flight, says Virgin Atlantic, ComputerWorldUK (2013). http://www.computerworlduk.com/news/infrastructure/3433595/boeing-787s-create-half-terabyte-of-data-per-flight-says-virgin-atlantic/
Scoble, R., Israel, S.: Age of Context: Mobile, Sensors, Data and the Future of Privacy. CreateSpace Independent Publishing Platform, Seattle (2013)
Koller, D., Friedman, N.: Probabilistic Graphical Models Principles and Techniques. Massachusetts Institute of Technology, Cambridge (2009)
Ko, K.-E., Sim, K.-B.: Development of context aware system based on Bayesian network driven context reasoning method and ontology context modeling. In: International Conference on Control, Automation and Systems, 2008, ICCAS 2008, pp. 2309–2313, October 2008
Park, H.-S., Oh, K., Cho, S.-B.: Bayesian network-based high-level context recognition for mobile context sharing in cyber-physical system. Int. J. Distrib. Sens. Netw. 2011, 10 (2011)
Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Context aware computing for the internet of things: a survey. http://arxiv.org/pdf/1305.0982.pdf
Renninger, H., von Hasseln, H.: Object-Oriented Dynamic Bayesian Network-Templates for Modelling Mechatronic Systems. http://www.dbai.tuwien.ac.at/user/dx2002/proceedings/dx02final21.pdf
Carner, P.: Beyond home automation: designing more effective smart home systems. In: 9th IT & T Conference School of Computing, 1 October 2009
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Pizurica, V., Vandaele, P. (2015). A Cloud-Based Bayesian Smart Agent Architecture for Internet-of-Things Applications. In: Giaffreda, R., et al. Internet of Things. User-Centric IoT. IoT360 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-319-19656-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-19656-5_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19655-8
Online ISBN: 978-3-319-19656-5
eBook Packages: Computer ScienceComputer Science (R0)