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副研究員  |  陳伶志  
 
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Research Descriptions
 

My research interests lie in the area of networked sensing systems and applications. The primary objectives of my research are 1) to investigate theoretical models of real world systems; 2) to conduct real world deployment to verify theoretical models; and 3) to combine theoretical models and system deployment to solve real world problems.

Specifically, we investigate participatory sensing systems that combine networked sensing and crowdsourcing techniques to collect streams of data of the surroundings at personal, society, and urban levels. We have carried out a comfort measuring system, called TPE-CMS, for public transportation systems in the greater Taipei area. Moreover, we have conducted data-driven research to discover knowledge and information from the collected data, and the results demonstrate that such system is feasible and scalable in yielding detailed information of transportation service and traffic conditions at the city level.

In addition, we research Internet of Things systems and develop a large-scale system, called AirBox, for PM2.5 monitoring system. The project engages citizens to participate in environmental sensing and enables them to make low-cost PM2.5 sensing devices on their own. It also facilitates PM2.5 monitoring at a finer spatio-temporal granularity and enriches environmental data analysis by making all measurement data freely available to everyone. Till 2017, we have deployed more than 2,000 devices in more than 30 countries, and we have developed a set of algorithms for device ranking, emission source detection, and anomaly detection. Based on our research results, we have been collaborating with the government agencies on smart governance, smart inspection, and the related issues.

My ongoing research focuses on spatio-temporal data analysis of IoT systems and its applications. We intend to apply our research results to other real-world networked sensing systems, such as participatory sensing for urban profiling, environmental monitoring, and wearable sensing and computing. Moreover, we wish to employ advanced artificial intelligence techniques to increase the smartness of networked sensing systems, and we will incorporate our research results with emerging social computing systems as a whole to facilitate cyber-physical socially networked systems in the future.

 
 
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