Semantic Framework of Internet of Things for Smart Cities: Case Studies
AbstractIn 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. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Zhang, N.; Chen, H.; Chen, X.; Chen, J. Semantic Framework of Internet of Things for Smart Cities: Case Studies. Sensors 2016, 16, 1501.
Zhang N, Chen H, Chen X, Chen J. Semantic Framework of Internet of Things for Smart Cities: Case Studies. Sensors. 2016; 16(9):1501.Chicago/Turabian Style
Zhang, Ningyu; Chen, Huajun; Chen, Xi; Chen, Jiaoyan. 2016. "Semantic Framework of Internet of Things for Smart Cities: Case Studies." Sensors 16, no. 9: 1501.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.