Exploration of Human Activities Using Message Streaming Brokers and Automated Logical Reasoning for Ambient-Assisted Services

Abstract

Intelligent environments combine physical spaces with pervasive computing technologies to provide context-aware, people-centred, and ambient-assisted strengthening of the activities of inhabitants in their daily lives. We propose a system to support mountain rescuers in their daily tasks. The system explores the activities of the mountain hikers, by analyzing data gathered from wireless sensor and mobile networks which cooperatively monitor an environment. The system utilizes message streaming brokers to transport data within the system. Massive amounts of data are pre-processed into formats, to allow analysis by logical SAT solvers. Pairing brokers and solvers as advanced technologies is challenging. The processed data contains valuable information about human activities and context situations, providing a basis for context reasoning and prediction. The resulting pro-active, hierarchical, and real-time smart decisions provide warnings about threatening situations, making tourists' stays safer. This combination of acting on predicted context, data streaming platforms, and logical solvers is a novel and innovative aspect of this approach. This strictly modeled system, intensively experimented, allows us to bridge the gap between the low-level observations produced by mobile and sensor networks and the high level smart services which support human activities.