title: Semantic Framework of Internet of Things for Smart Cities: Case Studies.
abstract: In recent years, the advancement of sensor technology has led to the generation of heterogeneous Internet-of-Things (IoT) data by smart cities. Thus, the development and deployment of various aspects of IoT-based applications are necessary to mine the potential value of data to the benefit of people and their lives. However, the variety, volume, heterogeneity, and real-time nature of data obtained from smart cities pose considerable challenges. In this paper, we propose a semantic framework that integrates the IoT with machine learning for smart cities. The proposed framework retrieves and models urban data for certain kinds of IoT applications based on semantic and machine-learning technologies. Moreover, we propose two case studies: pollution detection from vehicles and traffic pattern detection. The experimental results show that our system is scalable and capable of accommodating a large number of urban regions with different types of IoT applications.
authors.affiliation: Computer Science and Technology Institute, Zhejiang University, Hangzhou 310058, China. zhangningyu@zju.edu.cn.
authors.forename: Ningyu
authors.initials: N
authors.lastname: Zhang
authors.affiliation: Computer Science and Technology Institute, Zhejiang University, Hangzhou 310058, China. huajunsir@zju.edu.cn.
authors.forename: Huajun
authors.initials: H
authors.lastname: Chen
authors.affiliation: Computer Science and Technology Institute, Zhejiang University, Hangzhou 310058, China. xichen@zju.edu.cn.
authors.forename: Xi
authors.initials: X
authors.lastname: Chen
authors.affiliation: Computer Science and Technology Institute, Zhejiang University, Hangzhou 310058, China. jiaoyanchen@zju.edu.cn.
authors.forename: Jiaoyan
authors.initials: J
authors.lastname: Chen
keywords: Internet of Things; energy management; smart city; traffic pattern