Data visualization and analytics platform design for occupancy sensing system

• Main Author: Zhang, Shujie
• Other Authors: Xie Lihua
• Format: Final Year Project
• Language: English
• Published: 2017
• Subjects: DRNTU::Engineering::Computer science and engineering::Information systems; DRNTU::Engineering::Computer science and engineering::Information systems::Information interfaces and presentation
• Online Access: http://hdl.handle.net/10356/71899
• Institution: Nanyang Technological University

Wi-Fi based indoor positioning system was built in NTU Internet of Things laboratory. The system could detect Wi-Fi enabled devices and transfer the targets’ data using through a web API. The system could be used in occupancy sensing, tracking, security, human activity pattern analysis and energy consumption analysis. Data are basic to scientific researches, however data need to be manipulated and presented in certain ways so that it could be easier for people to understand. Therefore, Data visualization is used in this project to map the data into charts and graphs to enable people view the data in a different way. This report documented the process of designing a website as a data analytics platform for this occupancy sensing system. The process includes acquiring data, processing data, drawing graphs using data, and adding interactions to graphs. A column chart with pie chart is made to display the distribution and composition of population in IoT regarding time and space. A multiple lines represents the density of population and trends could be observed. Lastly, a bar chart is used to present the length of hours people spent in IoT. In the latter part of the report the author demonstrated how to use this data analytic platform to navigate the data and phenomenon observed from the graphs. Moreover, this report compared different methods used in the process and summarized the techniques used to improve effectiveness of visualization from literature and experience in the project. Lastly, the report proposed several applications in energy consumption studies and human activity studies using the data analytics platform designed in this project.