| dc.description.abstract | With the widespread use of smartphone devices, a surge in mobile sensing, progress in wireless communication and networking techniques, as well as the development of the Internet of Things (IoT) and cloud computing, mobile-based community sensing has turned into a leading paradigm for pervasive sensing. Smartphones with embedded sensors have become ubiquitous devices carried by millions of people. Community sensing empowers individuals to collectively sense, analyze and share local observations and mine data in order to determine and map phenomena relating to real world conditions by using mobile devices across many applications, including transportation and healthcare. While there are currently many tools and frameworks that allow researchers and developers to collect and analyze data at the individual user level, a parallel framework for data collection and analysis at the community level does not yet exist. Such a framework would provide the functionality to create various models for building smart city applications for urban planning, sustainable communities, transportation, public health, and public security.
This thesis presents a review of current smart city network architectures, along with their associated technologies, and proposes an architecture for the smart city and its services while considering communities as the main part of the design. Of the different components of the proposed architecture, two are vital for enabling a community structure for the smart city. These two components are community detection and data aggregation. This thesis proposes new methods for community detection and analysis using graphs and clustering algorithms based on the sensor data collected from individuals’ smartphones and IoT sensors. As far as can be ascertained, the proposed method is the first to transform the time series data collected from individuals’ smartphones to correlation networks for community detection. The proposed methods leverage not only the individuals’ groups but effectively discover communities of common interest. Two different case studies were conducted in this thesis in order to show the performance of the proposed methods. In these case studies, the data collected from individuals’ smartphones and vehicles are used and communities of individuals, based on their movement patterns and similarities, are detected. The performance evaluation shows that the proposed methods effectively identify the individuals’ communities with good accuracy. | en |
