Context-driven control algorithms for scalable simulation of human activities in smart homes

Lee, J.W. and Helal, Sumi and Sung, Y. and Cho, K. (2013) Context-driven control algorithms for scalable simulation of human activities in smart homes. In: Ubiquitous Intelligence and Computing, 2013 IEEE 10th International Conference on and 10th International Conference on Autonomic and Trusted Computing (UIC/ATC). IEEE, pp. 285-292. ISBN 9781479924820

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Abstract

Simulation of human activities in smart spaces is becoming a necessary toolset to enable telehealth research that promises smart, humanly scalable and cost-effective solutions for aging, disabilities and independence. Human activities are complicated and contain a large number of varieties. Most of the existing research on activity simulations has relied on sensor events processing. Even though event-driven simulation is well established and practiced, it suffers from lack of scalability when applied to human activities. Hence, a new approach for scalable human simulation is urgently needed. We recently introduced a new simulation approach, which introduced a novel context based approach that leads to what we call a context-driven simulation approach. Our new approach is macroscopic and drives a set of multi sensor events (a context) rather as contrasted to the microscopic event-driven approach which is a single sensor event driven. In this paper, we present the detailed algorithms that implement the context driven approach. We present important concepts and simulation loop rules, followed by the algorithms. To validate our approach and algorithms, we simulate an environment and a scenario to examine the algorithms and the scalability of our approach in general. © 2013 IEEE.

Item Type:
Contribution in Book/Report/Proceedings
Subjects:
?? CONTEXTCONTEXT-DRIVEN APPROACHEVENT-DRIVEN APPROACHHUMAN ACTIVITY SIMULATIONMODELINGSIMULATION ??
ID Code:
89911
Deposited By:
Deposited On:
31 Jan 2018 10:52
Refereed?:
Yes
Published?:
Published
Last Modified:
20 Sep 2023 02:24