The 3rd International Workshop On Self-Aware Internet of Things 2015 iKaaS Data Modeling: A Data Model for Community Services and Environment Monitoring in Smart City Prof. . Kazuo Hashimoto 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 1
Table of Contents 1. Introduction 1. iKaaS project 2. Context aware services 2. Related works A) Standardization B) Existing projects C) Fundamental technology for context-aware information processing 3. iKaaS Data Model A) Field of Smart City Experiment B) Community Services C) Overview of Data Model D) Design of 3D Geospatial City Data Model 4. Indicative use of iKaaS Application A) Ubiquitous assistant B) Town management 5. Conclusion 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 2
iKaaS project • I ntelligent K nowledge a s a S ervice ( iKaaS ) • A part of EU-JP research collaboration • Featuring IoT, Cloud, Big data analysis and privacy management – Dr. Yutaka Miyake (KDDI R&D Labs) will make an official introduction of the project at the panel session – Dr. Yuichi Hashi (Hitachi Solutions East Japan) Design and Implementation of Data Management Scheme to Enable Efficient Analysis of Sensing Data 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 3
iKaaS project partners 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 4
1. Introduction • Preference modeling Traditional customer service like Amazon.com stores a purchase history for each customer, and classifies customers with the history. • Context monitoring Automobile companies are planning to provide driving context information by monitoring with various sensors. • Enhancing existing personalizing services with contextual information Context aware services are regarded as a promising next step from the existing personalized services. Contextual preference modeling is actively studied, however, further evaluation will be needed to apply for practical services. 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 5
1. Introduction Context awareness vs ubiquity: With IoT technology, internet services can obtain sufficient information for context awareness. 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 6
1. Introduction • Context information (Example in iKaaS use case) – Town management service Geospatial Representation of (mainly) outdoor space in town • Temperature, humidity, pollen sensors Temporal • Energy consumption at each household – Health support service Geospatial Representation of indoor/outdoor space in town Temporal Wearable sensors for activity monitoring 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 7
2. Related works: Standardization • Open Geospatial Consortium (OGC) City GML • Information model for the storage and object exchange of 3D city model • Technical committee 211(TC211), ISO OWL • Ontology for geographic information ontology Conceptual framework Implementation rules on OWL • TC211, ISO Place Identifier • A general mechanism to link location to Integration of information other types of information OWL: Web Ontology Language 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 8
2. Related works: Existing Projects using CityGML Berlin Economic a) Purpose of project : Berlin Business Location Center Atlas (Germany) (BBLC) provides an online 3D data map service . b) Maintenance area: 500,000 buildings in about 890 km2, the central part in Berlin city. c) Data provision: Paid service (for non-business use only ） d) Providing method: Not open in public EU i-Scope Solar a) Purpose of project : i-Scope project provides Personal City Project mobility , Solar light potential and Monitoring for (United Kingdom) noise and environment . The 3D city data model in Newcastle city has been created for the city management by Newcastle local government. b) Maintenance area: The whole area of Newcastle city c) Data provision: Not applicable d) Providing method: IMGeo or CityGML format 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 9
2. Related works: Fundamental technology for context-aware information processing (1) Geospatial reasoning W3C Geospatial Incubator Group, http://www.w3.org/2005/Incubator/geo/ Gutierrez, C., Hurtado, C., and Vaisman, A. Temporal (2) Temporal reasoning RDF . In European Conference on the Semantic Web (ECSW’05) (Best paper award), pages 93 – 107, 2005, http://www.dcc.uchile.cl/~cgutierr/papers/temporalRD F.pdf (3) Event ontology Raimond, Y. Abdallay, S., Event Ontology , 2007, http://motools.sourceforge.net/event/event.html Jan Aasman, Unification of Geospatial Reasoning, (4) Hybrid system to process Temporal Logic & Social Network Analysis in a arbitrary combination of Semantic Web 3.0 Database , Semantic Graph reasoning in (1), (2) and (3) Tehnologies, Franz Inc., http://franz.com/agraph/cresources/white_papers/ 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 10
3. iKaaS Data Model: : Field of Smart City Experiment • Tago-Nishi is a new housing area under construction in Sendai, intended primarily for the citizens who lost their homes in the great Japan earthquake and tsunami in 2011. Tago-Nishi has been built as a smart city, including town management services and health services. • Sendai is the largest city in the north east of Japan with a population of one million. On March 11, 2011, the Great East Japan earthquake took place with a magnitude 9.0 or more, and the huge tsunami hit a large coastal area of Tohoku Region. Sendai is one of those areas that suffered catastrophic damage from the destructive tsunami. 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 11
3. iKaaS Data Model: Field of Smart City Experiment Environmental sensor Buildings Utility pole Road, pavement Underground object 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 12
3. iKaaS Data Model: Community Services Smart city is understood as a social system which is characterized by • efficient management of social infrastructure • resiliency of society and its infrastructure in disastrous situation using the advanced technology such as ICT, environmental science. Sendai has the following concept for Tago-Nishi smart city. 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 13
3. iKaaS Data Model: Overview of Data Model Static City Data Data Model Model CO 2 Sensor Data Temperature Environment Sensor Data (non-mobile) Sensor Data Humidity Model Sensor Data Electricity/Water/Gas Consumption Data Dynamic Environmental Data Model Data model Mobile Human Sensor Data Activity Data Model EV Car Sensor Data iKaaS Data Sensing Data Model CESP Data Data Source Model Event Service Data Model Virtual Entity Madrid Data Model Event of Mobility 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 14
3. iKaaS Data Model: Design of 3D Geospatial City Data Model Reusable object definition defined by other projects or the standard Object definition to be newly developed in iKaaS project 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 15
3. iKaaS Data Model: Design of 3D Geospatial City Data Model • Source information Dataset usage Urban planning map Defines legal land zoning. Zoning directory affects legal constraint and cost estimation for PV. Road cross section Road cross section data used for engineering work which can map be geo-referenced. Can be used for detailed 3D shape generation. Drainage work Construction engineering data. Can be used for detailed 3D planning map shape generation. Utility access map Pipeline network with depth, size, slope angle, material information. Can be used for detailed 3D shape generation. Power line Wire network with electricity information. Can be used for detailed 3D shape generation. 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 16
3. iKaaS Data Model: Design of 3D Geospatial City Data Model • Source information Process content • Measurement Airborne vertical photography • Airborne laser scanner point cloud • MMS laser scanner point cloud, spherical imagery • Processing of Generate TIN textured surface model • measured data 3D topographic mapping MMS: Mobile Mapping System 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 17
4. Indicative use of iKaaS Application (Ubiquitous Assistant) Flow diagram for home automation and smart mobility service 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 18
4. Indicative use of iKaaS Application Town management and health support service in the Tago-Nishi use case 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 19
4. Indicative use of iKaaS Application Information sharing between Town management and health support service in the Tago-Nishi use case 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 20
5. Conclusion • IoT is best fit to the monitoring of environment • Context aware service is one of the practical applications to use environmental data – Contextual preference model might be the key to the higher personalization – iKaaS project is developing a prototype application of context aware service to prove the feasibility • Obstacle is the cost of data gathering – iKaaS project is attempting to prove that community services in smart city will overcome this obstacle by data/knowledge sharing among services. 7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 21
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