Abstract
In the last decade, smart spaces and automatic systems have gained significant popularity and importance. Moreover, as the COVID-19 pandemic continues, the world is seeking remote intervention applications with autonomous and intelligent capabilities. Context-aware computing (CAC) is a key paradigm that can satisfy this need. A CAC-enabled system recognizes humans’ status and situation and provides proper services without requiring manual participation or extra control by humans. However, CAC is insufficient to achieve full automaticity since it needs manual modeling and configuration of context. To achieve full automation, a method is needed to automate the modeling and reasoning of contexts in smart spaces. In this paper, we propose a method that consists of two phases: the first is to instantiate and generate a context model based on data that were previously observed in the smart space, and the second is to discern a present context and predict the next context based on dynamic changes (e.g., user behavior and interaction with the smart space). In our previous work, we defined “context” as a meaningful and descriptive state of a smart space, in which relevant activities and movements of human residents are consecutively performed. The methods proposed in this paper, which is based on stochastic analysis, utilize the same definition, and enable us to infer context from sensor datasets collected from a smart space. By utilizing three statistical techniques, including a conditional probability table (CPT), K-means clustering, and principal component analysis (PCA), we are able to automatically infer the sequence of context transitions that matches the space–state changes (the dynamic changes) in the smart space. Once the contexts are obtained, they are used as references when the present context needs to discover the next context. This will provide the piece missing in traditional CAC, which will enable the creation of fully automated smart-space applications. To this end, we developed a method to reason the current state space by applying Euclidean distance and cosine similarity. In this paper, we first reconsolidate our context models, and then we introduce the proposed modeling and reasoning methods. Through experimental validation in a real-world smart space, we show how consistently the approach can correctly reason contexts.
Highlights
For the last decade, we have been experiencing speedy and dramatic changes in our lives due to smart technology
This is surely appropriate and useful during the COVID-19 pandemic, which requires people to maintain social distancing and avoid direct contact. As this worldwide pandemic continues without signs of ending, the need for such smart spaces is increasing, which will accelerate the development of advanced intelligent systems for smart spaces [6–10]
In our prior study [13], we proposed a context model which works in the simulation of smart spaces
Summary
We have been experiencing speedy and dramatic changes in our lives due to smart technology. Since smart spaces are operated by sensing devices and intelligent systems, human assistance, in which services are physically contacted, is no longer needed This is surely appropriate and useful during the COVID-19 pandemic, which requires people to maintain social distancing and avoid direct contact. Once context models are fed the status data of entities, the present context is reasoned by analyzing the obtained status data. We developed a method in which a well-organized context model is built by analyzing sensory data that describe the status of contextual entities. To model this context structure, we considered two key ideas.
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