Hotspots Analysis using Cyber-Physical-Social system for a Smart City

Internet of things plays a vital role in providing various services to users. Significant volumes of data are generated from the communication between a large numbers of heterogeneous devices over the Internet. Big data technology is generally used to handle the large volume of data. Complex networks are graphs (networks) having non-trivial topological features, such as random graphs and lattices. Big data of complex networks concerns big data methods that can be used to analyze massive structural data sets, including considerably large networks and sets of graphs. This study is based on the critical phenomenon arising in complex networks that enable us to analytically predict the hotspots in smart cities. Hotspots are places with significantly high communication traffic relative to others. In this study, we propose a cyber-physical-social system for the analysis of high communication traffic hotspots using telecom data. The proposed model constructs a graph, and perform social network analysis on it. The process of hotspot extraction is performed, followed by social network analysis, which is conducted by quantifying the importance of each hotspot based on network metrics. These metrics aid in determining the importance of each hotspot in a telecom data network. Our objective is to prioritize different areas and detect hotspots quickly. Our results indicate that the proposed model has an efficiency comparable with that of state of the art methods. This research study will be helpful for urban planning and development, as well as in upgrading telecommunication infrastructure.