Abstract-The Ambient intelligence (AmI) paradigm refers to electronic environments which are sensitive and responsive to the presence of people. Queue systems are practically used in various institutions and commercial enterprises constituting a challenge for the intelligent environments in smart cities. The management of the customer flows guarantees elimination or reduction of the queues as well as the economic benefits which follow the clients' satisfaction of the better service quality. There has been proposed the intelligent queue management system designed as the pro-active and context-aware system basing on multiple lowlevel sensors and devices constituting the IoT (Internet of Things) network. The designed context-driven system is characterized by user friendliness, as well as the client behavior understanding to generate actions that support clients. There has been proposed the conceptual version of the system. The selected aspects of the prototype version has been simulated. This prototype can be used as the necessary experience for building the target system meeting the precise needs and assumptions typical for context-aware and pro-active system basing on IoT networks.
The aim of this study was to assess air quality by using medium-cost sensors in recreational areas that are not covered by permanent monitoring. Concentrations of air pollutants PM2.5, PM10, PM1, CO, O 3 , NO 2 in the Niedzica recreational area in southern Poland were obtained. The research revealed that in cold weather, particulate matter concentrations significantly exceeded acceptable levels determined for PM2.5 and PM10. The most important factor that affects air quality within the studied area seems to be the combustion of poor quality fuels for heating purposes. The information obtained by the research presented could be a useful tool for local authorities to make environmental decisions, based on the potential health impacts of poor air quality levels on the population.
Abstract. Providing accurate/suitable information on behaviors in smart environments is a challenging and crucial task in pervasive computing where context-awareness and pro-activity are of fundamental importance. Behavioral identifications enable to abstract higher-level concepts that are interesting to applications. This work proposes the unified logical-based framework to recognize and analyze behavioral specifications understood as a formal logic language that avoids ambiguity typical for natural languages. Automatically discovering behaviors from sensory data streams as formal specifications is of fundamental importance to build seamless human-computer interactions. Thus, the knowledge about environment behaviors expressed in terms of temporal logic formulas constitutes a base for the reactive and precise reasoning processes to support trustworthy, unambiguous and pro-active decisions for applications that are smart and context-aware.
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