Connecting Cloud Computing and Machine Learning Through Functional Situation-Awareness: A User-Centric Smart Monitoring Application

For many parents, the time between their children leaving home and the ensuing arrival at school is a grey area with uncertainty. The need of being aware of the highly critical safetyness situations there- fore arises. Situation-aware computing serves as a promising technique, both theoretically and pragmatically, to bring smart monitoring to the field. It seamlessly integrates the benefits offered by machine learning techniques and cloud computing technologies with an emphasis on user centric situations. In this paper we present a real world smart monitoring application: its underlying model, its human computer interactive design and design principles, its support from the theory and practice of situation-awareness computing, as well as its implementation details and viability test. This application also serves the purpose of elaborating important attributes of safety-aware situation computing, with a strong linkage to safety critical human computer interaction. The goal of this application is to accurately recognize and identify the faces of children, under relevant environmental contexts, when they enter or exit a bus, and then promptly alert their parents. Two main components, one being a distributed bus subsystem and the other being a central cloud-based subsystem, are built into this application highlighting lightweight to effectively serve the users. The novelty of our approach lies in the fact that we deeply embrace functional programming paradigm to tackle the specification of the two subsystems at the design level with rigorous computational semantics, which in turn contributes to the quality implementation of the lower level details.