A discrete event approach for model-based location tracking of inhabitants in smart homes

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Life expectancy has steadily increased in many industrialized nations, presenting new challenges for the autonomy and independence of the elderly. Smart Homes are being developed to address these challenges, allowing individuals to live longer in safe and comfortable environments. This involves integrating sensors, actuators, and controllers to monitor inhabitants' behavior and adjust their surroundings to enhance safety, health, and comfort. A key component of these systems is real-time indoor Location Tracking. This thesis introduces a novel model-based approach for tracking an unknown number of inhabitants, utilizing Discrete Event Systems paradigms, theory, and tools. It employs Finite Automata (FA) to model the detectable movements of individuals, along with various methods for creating these FA models. Algorithms for efficient Location Tracking are defined based on these models. Additionally, the thesis proposes several methods to evaluate the effectiveness of Smart Home instrumentation for Location Tracking purposes. The contributions are illustrated through case studies, and the approach has been fully implemented and tested, demonstrating its practical applicability.