Challenges in Creating Smart City Services with IoT/CPS platforms
Hideyuki Tokuda, Ph.D.
Professor, Graduate School of Media and Governance/
Faculty of Environment and Information Studies
Convergence between cyber and physical spaces is accelerating due to the penetration of various hard, soft, and social sensors, smart phones, wearable devices and actuators. Innovative smart city services are being created by connecting many things to the Internet through such personal as well as global enablers.
However, many smart city services are created in a vertical fashion and it is not easy to create a new service based on heterogeneous data streams or services. Similarly, there are many interesting city data such as weather, air quality index values, traffic conditions and car parking occupancy published in the Web space, however, those data are not easy to be used by smart city apps due to a lack of APIs.
In this talk, we discuss challenges in creating smart city services with IoT/CPS platforms. We first introduce the two types of smart city development efforts and then discuss the various types of smart city services and apps.
We then discuss on-going projects, namely the ClouT project, SODA project and MEXT project that are aiming to empowering citizens and improving QoL and resiliency of the cities. We also introduce, so called Sensorizer for sensorizing passive data from web pages without any modifications. We summarize with the discussion of the challenges in creating sustainable smart city services and platforms.
Hideyuki Tokuda obtained his B.S. (1975), M.S. (1977) from Keio University and Ph.D. (Computer Science) (1983) from University of Waterloo, Canada, respectively. He is currently Director of Ubiquitous Computing and Communication Laboratory and a Professor of the Faculty of Environment and Information Studies, Keio University, Japan. In 1983, he joined School of Computer Science, Carnegie Mellon University and Senior Research Computer Scientist in 1991. Since 1990, he joined Keio University. He was Associate Professor (1990-1996), Executive Vice President (1997-2001), Dean of the Graduate School of Media and Governance (2001-2007), Dean of the Faculty of Environment and Information Studies (2007-2009), and Dean of the Graduate School of Media and Governance (2009-2015) in Keio.
After he completed Ph.D., he joined School of Computer Science, Carnegie Mellon University and worked on distributed real-time operating systems such as Real-Time Mach, the ARTS Kernel. In 1990, he came back to Keio University. His research and teaching interests include ubiquitous computing systems, operating systems, decentralized autonomous systems, sensor networks, IoT/CPS and smart cities. He has created many ubiquitous computing platforms such as Smart Space Lab., Smart Furniture, uPhoto, uTexture and uPlatea. Because of his research contribution, he was awarded Motorola Foundation Award (89), IBM Faculty Award (02), Ministry of Economy, Trade and Industry Award (04) and Ministry of Internal Affairs and Communication Award (05), KEIO-Gijyuku Award (06), IPSJ Achievement Award (2011), Information Security Cultural Award (15) .
He is a member of Science Council of Japan, a vice president of IPSJ (Information Processing Society of Japan), IPSJ Fellow, JSSST (Japan Society for Software Science and Technology) Fellow, and a member of ACM, IEEE IEICE and JSSST.
Mining Demographic and Personality Attributes from Human Mobility Data
Xing Xie, Ph.D.
Senior Researcher, Microsoft Research Asia
With the rapid development of positioning, sensor and smart device technologies, large quantities of human behavioral data are now readily available. They reflect various aspects of human mobility and activities in the physical world. The availability of this data presents an unprecedented opportunity to gain a more in depth understanding of users. In this talk, I will first introduce the predictive power of human mobility data for inferring users' demographics and propose a simple yet general location to profile (L2P) framework. Then I will present our work on understanding individual novelty-seeking trait embodied at different levels and across heterogeneous domains. Finally I will describe a computational framework, termed Consumer Impulsivity Model (CIM), for exploring a consumer's impulsivity in both offline and online context.
Dr. Xing Xie is currently a senior researcher in Microsoft Research Asia, and a guest Ph.D. advisor for the University of Science and Technology of China. He received his B.S. and Ph.D. degrees in Computer Science from the University of Science and Technology of China in 1996 and 2001, respectively. He joined Microsoft Research Asia in July 2001, working on spatial data mining, location based services, social networks and ubiquitous computing. During the past years, he has published over 160 referred journal and conference papers. He has more than 50 patents filed or granted. He has been invited to give keynote speeches at Socialinformatics 2015, GbR 2015, W2GIS 2011, HotDB 2012, SRSM 2012, etc. He currently serves on the editorial boards of ACM Transactions on Intelligent Systems and Technology (TIST), Springer GeoInformatica, Elsevier Pervasive and Mobile Computing, Journal of Location Based Services, and Communications of the China Computer Federation (CCCF). In recent years, he was involved in the program or organizing committees of over 70 conferences and workshops. Especially, he initiated the LBSN workshop series and served as program co-chair of ACM UbiComp 2011, the 8th Chinese Pervasive Computing Conference (PCC 2012) and the 12th International Conference on Ubiquitous Intelligence and Computing (UIC 2015). In Oct. 2009, he founded the SIGSPATIAL China chapter which was the first regional chapter of ACM SIGSPATIAL. He is a member of Joint Steering Committee of the UbiComp and Pervasive Conference Series. He is a senior member of ACM and the IEEE, and a distinguished member of China Computer Federation (CCF).