[PPT] - Prof. . Kazuo Hashimoto 7/7/2015 Self-IoT 2015, July 7 2015, PowerPoint Presentation

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The 3rd International Workshop On Self-Aware Internet of Things 2015

Self-IoT 2015, July 7 2015, Grenoble, France

iKaaS Data Modeling: A Data Model for Community Services and Environment Monitoring in Smart City

Prof. . Kazuo Hashimoto

7/7/2015

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iKaaS project

Intelligent Knowledge as a Service (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

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iKaaS project partners

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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.

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1. Introduction

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Context awareness vs ubiquity: With IoT technology, internet services can obtain sufficient information for context awareness.

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1. Introduction
Context information (Example in iKaaS use case)

– Town management service – Health support service

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Geospatial Representation of (mainly) outdoor space in town Temporal

Temperature, humidity, pollen sensors
Energy consumption at each household

Geospatial Representation of indoor/outdoor space in town Temporal Wearable sensors for activity monitoring

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2. Related works: Standardization

City GML

bject
Open Geospatial Consortium (OGC)
Information model for the storage and

exchange of 3D city model OWL

ntology
Technical committee 211(TC211), ISO
Ontology for geographic information
Conceptual framework
Implementation rules on OWL

Place Identifier Integration of information

TC211, ISO
A general mechanism to link location to
ther types of information

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OWL: Web Ontology Language

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2. Related works: Existing Projects using CityGML

Berlin Economic Atlas (Germany) a) Purpose of project : Berlin Business Location Center (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 City Project (United Kingdom)

a) Purpose of project : i-Scope project provides Personal mobility, Solar light potential and Monitoring for 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

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2. Related works: Fundamental technology for context-aware

information processing

(1) Geospatial reasoning W3C Geospatial Incubator Group, http://www.w3.org/2005/Incubator/geo/ (2) Temporal reasoning

Gutierrez, C., Hurtado, C., and Vaisman, A. Temporal

RDF. In European Conference on the Semantic Web

(ECSW’05) (Best paper award), pages93–107, 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

(4) Hybrid system to process arbitrary combination of reasoning in (1), (2) and (3)

Jan Aasman, Unification of Geospatial Reasoning, Temporal Logic & Social Network Analysis in a Semantic Web 3.0 Database, Semantic Graph Tehnologies, Franz Inc., http://franz.com/agraph/cresources/white_papers/

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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.

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3. iKaaS Data Model: Field of Smart City Experiment

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Buildings

Road, pavement

Underground object

Utility pole

Environmental sensor

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3. iKaaS Data Model: Community Services

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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.

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3. iKaaS Data Model: Overview of Data Model

7/7/2015 Self-IoT 2015, July 7 2015, Grenoble, France 14 iKaaS Data Model Environmental Data model Dynamic Data Model Environment (non-mobile) Sensor Data Model CO2 Sensor Data Temperature Sensor Data Humidity Sensor Data Electricity/Water/Gas Consumption Data Mobile Sensor Data Model EV Car Sensor Data Human Activity Data Static Data Model City Data Model Service Data Model CESP Data Model Sensing Data Data Source Event Madrid Data Model Virtual Entity Event of Mobility

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3. iKaaS Data Model: Design of 3D Geospatial City Data Model

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Reusable object definition defined by

ther projects or the

standard Object definition to be newly developed in iKaaS project

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3. iKaaS Data Model: Design of 3D Geospatial City Data Model
Source information

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Dataset usage Urban planning map Defines legal land zoning. Zoning directory affects legal constraint and cost estimation for PV. Road cross section map Road cross section data used for engineering work which can be geo-referenced. Can be used for detailed 3D shape generation. Drainage work planning map Construction engineering data. Can be used for detailed 3D 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.

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3. iKaaS Data Model: Design of 3D Geospatial City Data Model

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Source information

Process content Measurement

Airborne vertical photography
Airborne laser scanner point cloud
MMS laser scanner point cloud, spherical imagery

Processing of measured data

Generate TIN textured surface model
3D topographic mapping

MMS: Mobile Mapping System

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4. Indicative use of iKaaS Application

(Ubiquitous Assistant)

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Flow diagram for home automation and smart mobility service

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4. Indicative use of iKaaS Application

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Town management and health support service in the Tago-Nishi use case

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4. Indicative use of iKaaS Application

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Information sharing between Town management and health support service in the Tago-Nishi use case

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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.

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Acknowledgement

This research is supported by the collaboration of the

European Union and the Ministry of Internal Affairs and Communications, Japan, Research and Innovation action: iKaaS. EU Grant number 643262.

Thank you very much

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