Smart Campus Construction in AI, Big Data, IoT, and Cloud Computing Era: A Case of Smart Power Saving Service
With the flourishing of Internet of Things (IoT) technology, ubiquitous power data can be linked to the Internet and be analyzed for real-time monitoring requirement. Numerous power data would be accumulated to even Terabyte level as the time goes. To approach a real-time power monitoring platform on them, an efficient and novel implementation techniques has been developed and formed to be the kernel material of this talk. How to reduce expenses by reducing electricity consumption, and effective energy conservation in the huge campus of electricity, reduce electricity costs and unnecessary consumption is very important. The campus building electricity information through smart meters is collected and processed a huge amount of data by Big Data processing techniques. The proposed system adopts the big data and cloud computing technologies in Hadoop because of the big data collection, data storage, and the computational efficiency of data analysis. The data collection and storage are handled by the Hadoop subsystem and the data ingestion to Hive data warehouse is conducted by the Spark unit. We proposed an architecture to import existing power data storage system of our campus into Big data platform with Data Lake. We use Apache Sqoop to transfer historical data from existing system to Apache Hive for data storage. Apache Kafka is used for making sure the integrity of streaming data and as the input source for Spark streaming that writing data to Apache HBase. To integrate the data, we use the concept of Data Lake which is based on Hive and HBase. Apache Impala and Apache Phoenix are individually used as search engines for Hive and HBase. We used Apache Spark to analyze the power consumption of the campus using analysis module of power anomaly, power consumption forecasting, and power failure analysis. The kernel contributions of this research work can be highlighted by two parts: (1) Multi- layer software modules are adopted to design and implement the real-time power-monitoring platform embedded with some excellent characteristics of high efficiency, high feasibility and low cost. (2) The rudimental experiments are conducted to verify the query-response efficiency, and performance evaluations for the proposed real-time power monitoring platform, which reveals the high feasibility for the target research goals.Biography
Dr. Chao-Tung Yang is a Distinguished Professor of Computer Science at Tunghai University in Taichung, Taiwan. He received a B.Sc. degree in Computer Science from Tunghai University, Taichung, Taiwan, in 1990, and the M.Sc. degree in Computer Science from National Chiao Tung University, Hsinchu, Taiwan, in 1992. He received the Ph.D. degree in Computer Science from National Chiao Tung University in July 1996. He won the 10th 1996 Acer Dragon Award for an outstanding Ph.D. dissertation. He has worked as an Associate Researcher for ground operations in the Ground System Section of the National Space Organization (NSPO) in Hsinchu Science-based Industrial Park since Oct. 1996. In August 2001, he joined the faculty of the Department of Computer Science at Tunghai University as an Associate Professor. He is a full Professor started in August 2007 and as a Distinguished Professor in August 2015. He got three times Outstanding Industry-Academic Cooperation Awards in 2012, 2014, 2015 and twice Excellent Industry-Academic Cooperation Awards in 2013, 2016, respectively, from Ministry of Science and Technology (MOST). Also, he got three times Outstanding Research Paper Awards and three times Excellent Industry-Academic Cooperation Awards from Tunghai University in 2007, 2011, 2014, and 2014, 2015 and 2017, respectively. Dr. Yang was awarded eight times (2010~2017) Talent Awards from Ministry of Science and Technology (MOST). He is serving in a number of journal editorial boards, including Future Generation Computer Systems (SCIE), International Journal of Communication Systems (SCIE), KSII Transactions on Internet and Information Systems (SCIE), Journal of Cloud Computing, IJ-CLOSER, International Journal of Next-Generation Computing (IJNGC), “Next Generation Internet of Things (IoT) and Cloud Security Solutions” Special Issue of International Journal of Distributed Sensor Networks (SCIE), “Grid Computing, Applications and Technology” Special Issue of Journal of Supercomputing (SCIE), and “Grid and Cloud Computing” Special Issue of International Journal of Ad Hoc and Ubiquitous Computing (SCIE). Dr. Yang has published more than 300 papers in journals, book chapters and conference proceedings. His present research interests are in Cloud computing and Big data, Parallel and multicore computing, and Web-based applications. He is both a member of the IEEE Computer Society and ACM. He is also both a member of IICM and TACC in Taiwan.