Abstract—Pervasive systems refer to context-aware systems that can sense their context, and adapt their behavior accordingly to provide adaptable services. Proactive adaptation of such systems allows changing the service and the context-based on prediction. However, the definition of the context is still vague and not suitable to prediction. In this paper, we discuss and classify previous definitions of context. Then, we present a new definition which allows pervasive systems to understand and predict their contexts. We analyze the essential lines that fall within the context definition and propose some scenarios to make it clear our approach.
A spatiotemporal context definition for service adaptation prediction in a pervasive computing environment.
Authors: Darine Ameyed|
Source: International Journal of Scientific & Engineering Research
About the Author: Darine Ameyed
Darine Ameyed, Ph.D. in engineering, Software and Information Technologies (ÉTS-2017). She received an M.Msc applied IA ( Columbia university US-2019). She obtained her M.Sc. in Digital Art and Technology from the University Rennes 2 (U.R.2) (University of Upper Brittany, France) in 2010. She earned her M.Sc. Multimedia Engineering from the University Paris Est Marne la Vallée (U.P.E.M) (University of Paris 11, France) in 2008. She received a B.Sc. in computer science and Management from the Institut Supérieur de Gestion (I.S.G) (University of Tunis, Tunisia) in 2005. She is currently a research associate at Synchromédia lab, École Technologie Supérieure (ÉTS), Montreal. Also, an expert in United for smart and sustainable cities (U4SSC)-International Telecommunication Union- ITU-UN. She has been a scientific program and project manager at CIRODD (2015-2019), Co-founder and CEO of NyX-R (2015-2018), a tech advisor for tech and science-based startups. On the other hand, she has a long multidisciplinary academic and industrial career in Canada, Europe, and Africa in the areas of software engineering, mobile computing, ERP system management, art, and entrepreneurship. Her research interests include Predictive Modeling, Context-Aware System, Ambient Intelligence (AmI), C-IoT, Human-Centered Computing, Machine Learning, Activity Recognition, Human-Machine Interaction (HMI), Data security and privacy in IoT platforms and computational sustainability.