Abstract
This article presents research results on a smart building prediction, navigation and asset management system. The main goal of this work was to combine all comfort subsystems, such as lighting, heating or air conditioning control, into one coherent management system supported by navigation using radio tomographic imaging techniques and computational intelligence in order to improve the building’s ability to track users and then maximize the energy efficiency of the building by analyzing their behavior. In addition, the data obtained in this way were used to increase the quality of navigation services, improve the safety and ergonomics of using the room access control system and create a centralized control panel enriched with records of the working time of individual people. The quality of the building’s user habit learning is ensured by a network of sensors collecting environmental data and thus the setting values of the comfort modules. The advantage of such a complex solution is an increase in the accuracy of navigation services provided, an improvement in the energy balance, an improvement in the level of safety and faster facility diagnostics. The solution uses proprietary small device assemblies with implementation of popular wireless transmission standards such as Bluetooth, Wi-Fi, ZigBee or Z-Wave. These PANs (personal area networks) are used to update and transmit environmental and navigation data (Bluetooth), to maintain the connection of other PANs to the master server (Wi-Fi) and to communicate with specific end devices (ZigBee and Z-Wave).
Highlights
Introduction iationsThis article presents the research results on a prediction, navigation and asset management system for smart building
In case of lamps—set ported by the radio tomographic imaging technique and computational intelligence
The complexity of the measurement model, as well as the very nature of the electromagnetic wave, means that many tests must be carried out based on experimental methods, which in the case of radio tomography, are extremely difficult to simulate, all the more so because the transceiver systems used do not have internal synchronization, timestamp or even mutual liberation
Summary
Introduction iationsThis article presents the research results on a prediction, navigation and asset management system for smart building. The most important two elements providing these features are radio tomography and computational intelligence. Radio tomographic imaging (RTI) is a field of knowledge dealing with the creation of non-invasive cross-sections of areas (less frequently objects) subjected to electromagnetic waves at a specific frequency [1,2,3]. It is due to the increased tendency of some ranges of radio waves to be scattered and reflected. This phenomenon is especially noticeable in the example of water, the high percentage of which in living organisms makes them perfect for detecting people and animals in closed rooms [4,5].
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