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Sensors 2016, 16(9), 1501;

Semantic Framework of Internet of Things for Smart Cities: Case Studies

Computer Science and Technology Institute, Zhejiang University, Hangzhou 310058, China
Author to whom correspondence should be addressed.
Academic Editors: Andrea Zanella and Toktam Mahmoodi
Received: 19 July 2016 / Revised: 6 September 2016 / Accepted: 8 September 2016 / Published: 14 September 2016
(This article belongs to the Special Issue Smart City: Vision and Reality)
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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. View Full-Text
Keywords: Internet of Things; smart city; energy management; traffic pattern Internet of Things; smart city; energy management; traffic pattern

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Zhang, N.; Chen, H.; Chen, X.; Chen, J. Semantic Framework of Internet of Things for Smart Cities: Case Studies. Sensors 2016, 16, 1501.

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