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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Modeling and Reasoning of Contexts in Smart Spaces
AU - Lee, J.W.
AU - Helal, S.
N1 - ©2020 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2020/8/4
Y1 - 2020/8/4
N2 - Context can be defined to be a meaningful and descriptive state of a physical space, in which relevant activities of the humans and movements of objects in the same space are consecutively performed. Context aware computing thus requires the instantiation and use of a suitable context model and algorithms capable of discerning a most relevant context at the present time. In our previous research, we developed a method based on stochastic analysis, for inferring contexts from sensor datasets collected from a given smart space. By utilizing three statistical techniques including conditional probability table (CPT), K-means clustering, and Principal Component Analysis (PCA), our inference method is able to predict and generate contexts which can potentially happen. Generated contexts are then used as references when the present context needs to be found. In this paper, we build on our prior work and propose a method that analyzes the current state space and determines the present context guided by the collection of generated potential contexts. We first reconsolidate our context models and context graph in which contexts are structured, and then introduce reasoning methods which apply Euclidean distance and Cosine Similarity. PCA is also used to statistically analyze the state space, which helps to achieve better performance. Using experimental evaluation, we validated the accuracy of our proposed approach in identifying the present context.
AB - Context can be defined to be a meaningful and descriptive state of a physical space, in which relevant activities of the humans and movements of objects in the same space are consecutively performed. Context aware computing thus requires the instantiation and use of a suitable context model and algorithms capable of discerning a most relevant context at the present time. In our previous research, we developed a method based on stochastic analysis, for inferring contexts from sensor datasets collected from a given smart space. By utilizing three statistical techniques including conditional probability table (CPT), K-means clustering, and Principal Component Analysis (PCA), our inference method is able to predict and generate contexts which can potentially happen. Generated contexts are then used as references when the present context needs to be found. In this paper, we build on our prior work and propose a method that analyzes the current state space and determines the present context guided by the collection of generated potential contexts. We first reconsolidate our context models and context graph in which contexts are structured, and then introduce reasoning methods which apply Euclidean distance and Cosine Similarity. PCA is also used to statistically analyze the state space, which helps to achieve better performance. Using experimental evaluation, we validated the accuracy of our proposed approach in identifying the present context.
KW - context awareness computing
KW - context graph
KW - context modeling and reasoning
KW - IoT
KW - principal components analysis
KW - sensors
KW - smart spaces/cities
KW - Stochastic systems
KW - Ubiquitous computing
KW - Conditional probability tables
KW - Context-aware computing
KW - Euclidean distance
KW - Experimental evaluation
KW - Inference methods
KW - Reasoning methods
KW - Statistical techniques
KW - Stochastic analysis
KW - K-means clustering
U2 - 10.1109/PerComWorkshops48775.2020.9156213
DO - 10.1109/PerComWorkshops48775.2020.9156213
M3 - Conference contribution/Paper
SN - 9781728147178
BT - 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020
PB - IEEE
ER -