| Abstract |
Recently, the concept of smart cities has become popular and got researchers' attention because it helps to improve citizens' lives by providing valuable services, for instance, smart transportation, smart homes, telecommunication, infrastructure, etc. Hotspot analysis is a classic problem concerned with spatial analysis. Telecommunication operators and companies always care to identify the Hotspots in the city. The hotspots are the places with very high communication strength relative to others. It is evident from the current literature that cyber physics social systems (CPSS) are useful in the identification of hotspots in a smart city. However, big data storage, analysis, processing, accuracy, and robustness are the key concerns. Thus herein, we propose a smart cyber-physical-social system for the analysis of hotspots using telecom data. Herein, our proposed CPS model is comprised of three layers and each layer has different functionality. In our proposed model, initially, raw Call Detail Data (CDR) data is collected at the data collection layer. Then smart CPSS passed it to the next layer. In the Data processing layer, CPSS performs pre-processing, data storage, and analysis. Then, it constructs a graph and performs a social network analysis (SNA). Herein, different from traditional centrality measures, we suggest Eigenvector and k-shell as social network similarity and Jaccard, cosine, as social behavioral measures. Herein, the process of city hotspot identification is performed, followed by SNA, which is conducted by quantifying the importance of each hotspot based on metrics. Finally, our proposed smart CPSS model accurately identifies Top-Ten hotspots. In this study, we use five-day data and compare the changes in the hotspot patterns. We validate our findings of hotspots with the original dataset and confirm the robustness and accuracy using autocorrelation and cross-correlation functions. |