suggested using IoT for monitoring of discrete manufacturing process based on IoT. used IoT in order to construct a real-time system monitoring system for micro-environment parameters such as temperature, humidity, PM10 and PM2.5. This growth could be the result of a very wide range of applications, ranging from basic home appliances and security systems to more sophisticated applications. The number of connected IoT devices is rapidly growing. In simple words, the IoT allows objects other than computers or smartphones to use the Internet for sending and receiving information. The Internet of Things, shortly known as IoT, can be assumed as an integration layer which creates an interconnection of several physical devices, sensors, actuators, and controllers. The current trend for processing large volumes of measured quantities using Soft Computing Methods (SC) is to use the available Big Data Analysis tools within the IoT platform. On this occasion, visualization is used to display relevant life information in this intelligent environment, which includes the evaluation of the seniors’ position and recognition of life activities. described a system based on intelligent cameras for 24-h monitoring and supervision of senior citizens. The visualization includes sensor data from the building to capture the duration of the activities. used miniature wireless sensors in the wireless network to track and recognize the behavior of persons in the house. explored machine-machine communication (M2M) to address the need for autonomous control of remote and distributed mobile systems in intelligent buildings. This work comprises of a visualization for displaying health data and a proposal for improving the health and wellbeing of the users. described the use of computational and sensor technology in intelligent buildings with a focus on health monitoring in the households. The properly devised visualization complements the final correct functionality of the intelligent building. In order to provide a user-friendly environment for the management of the operational and technical functions along with providing support for the independent housing of senior citizens and disabled persons in buildings indicated as Smart House Care (SHC), it is necessary to make appropriate visualization of the technological process as required by the users with the possibility of the indirect monitoring of the seniors’ life activities based on the information obtained from the sensors used for the common management of the operational and technical functions in SHC.
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Privacy, reliability and false alarms are the main challenges to be considered for the development of efficient systems to detect and classify the Activities of Daily Living (ADL) and Falls. The system monitors actions taken by the residents and looks for patterns in the environment which reliably predict these actions, where a neural network learns these patterns and the system then performs the learned actions automatically for improving the Quality of Life (QoL). The researchers point out that the Adaptive House (the concept of a home which programs itself), Learning Homes and Attentive Homes must be programmed for a particular family and home and updated in line with changes in their lifestyle. Intelligent buildings respond to the needs of occupants and society, promoting the well-being of those living and working in them. An intelligent building requires real-time information about its occupants so that it can continually adapt and respond. The prediction accuracy achieved in the selected experiments was greater than 95%.Īn intelligent building is one that is responsive to the requirements of occupants, organizations, and society. To increase the accuracy of CO 2 predictions, a wavelet transform was applied to remove additive noise from the predicted signal. The most accurately predicted results were obtained from data processed at a daily interval. The Radial Basis Function (RBF) method was applied to predict CO 2 levels from the measured indoor and outdoor temperatures and relative humidity. The processed data were compared at daily, weekly and monthly intervals for the spring and autumn periods.
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Spss modeler 18 r essentials software#
The paper examines the possibilities of increasing the accuracy of CO 2 predictions in Smart Home Care (SHC) using the IBM SPSS software tools in the IoT to determine the occupancy times of a monitored SHC room. These solutions optimize the operational and technical functions managing the quality of the indoor environment and factor in the real needs of residents. Standard solutions for handling a large amount of measured data obtained from intelligent buildings are currently available as software tools in IoT platforms.