Smart City Saitama Model: Information Bank and Data Services
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This slide describes the smart city project in Saitama, Japan mainly focusing on information bank and smart city data services. The data infrastructures and data services are given as typical examples. This slide is shared at GCTC Data SuperCluster City Platform Review Meeting.
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Smart City Saitama Model: Information Bank and Data Services
- 1. Smart City Saitama Model: Information Bank and Data Services Prof. of Keio University Chair person of Misono Town Management Consortium President of Omotenashi ICT Consortium Director of Edge Platform Consortium Hiroaki Nishi
- 2. Saitama Urawa-Misono Smart Town Smart Town Project abound Urawa-Misono Station - Area: 320ha - Population: 32,000 (Current 12,000) Urban Design Center Misono (Center office) Members Saitama City Keio Univ., Kougakuin Univ., Shibaura Tech Univ., Tokyo Denki Univ. AEON Retailing Group, Softbank, Tokyo Gus, IBM Japan, Felica Pocket Marketing, TANITA, Panaso nic, Mitsubishi Local house building companies, Local banks (40> companies and organizations)
- 3. UDCMi Smart Data Infrastructure Smart city data services Data services based on secondary-use of data Data anonymization Accumulating use cases and penetration into the whole city IoT / Sensors / Shrives / Infrastructures Smart city information platform Flexible service application provisioning Data and IoT device are encapsulated by using data anonymization and traffic management at edge Vender Consumer Relationship Management Venders manage consumers for providing services Consumers manage service vendors for controlling private data distribution D P M S
- 4. Services in UDCMi 4 Source: http://www.misono-tm.org/udcmi/ Health Monitoring Smart Home town blocks e-money card BLE Local Positioning BLE antenna Infrastructure Workshops for “Designing My Own Smart Town” Rental electric bicycle STB UDCMi Channel EV garbage truck Hydrogen Station for FCV WiFi Sopt Urawa-Misono Smart Town Automated Driving EV Bus Rental electric kickboard Child-rearing support BAMBI Body composition meter Activity meter
- 6. Keio Univ. UDCMi Edge Server HGW Smart house with Home energy management system EMS (TOSHIBA) EMS (Panasonic) Residential Area BLE BLE positioning system Town Area Distributed PV management e-money card (AECON) Health information (TANITA) Smart Meter Reader (IIJ) Smart Meter Bicycle (AEON) Pet (AEON) Traffic collision avoidance (HONDA) watching children go to school UDCMi Infrastructures 6 Commercial Area Town security (SECOM) Service Providers / Communication Platform • Operation log of provided applications • Questionnaires by service providers • Access log and transaction log at edge
- 7. InformationBankService Managing Opt-In, Opt-Out, Data anonymization method and level, service providing method, incentives, service providing position (Cloud, Fog, Edge)
- 8. Why does Saitama City manage Info-Bank? • To carry out their roles • One of city’s roles is to provide basic local services for citizens by using tax, such as load, water, security, gas, electricity, etc. • However, information is not managed at this moment, and it causes cyber crimes and deprive of wealth. Information should be same with other infrastructures. • Future taxing • VS GAFA: GAFA and other dot-com companies earn money by using private information of citizens and local resources; however, they does not pay tax. cf. online purchasing • Developing local economics • Using new smart city data services • Branding • Get feedbacks from citizens as data and use the data to improve city services • Making rewards as monetary benefit to citizens • Providing new services of data security, preservation of privacy, etc.
- 9. Data Privacy/Security in Smart Cities 9 Single Point of Failure Problem Possibility of Advanced Persistent Threat Data creators in a smart city must take measures against cyber attacks as well as cloud services Analyze and store at remote Create at local Use at local Smart City Data Data Private Data Local Data Data Cloud
- 10. Smart Community Data Privacy/Security 10 Cloud Share appropriately anonymized data only Private data is encapsulated in a local area Locally provide private services and globally provide common services Cyber attack cost becomes high Create at local Store at local Analyze at local Use at local Anonymize at local Globally analyze and store at remote Data Data Share data for global service Local services Data Smart City Private Data Local Data
- 11. Data Anonymization with Watermarking • Embed metadata into anonymized data using diversity of anonymization • When replacing word in anonymization process, it selects appropriate word considering the words distance of Skip-Gram model. • Metadata: who, when, to whom, what object, contract type • The metadata works as a deterrent for illegal data leakage and helps taking legal actions. 11 AES encryption Error correction code D Watermarking Anonymized Data Watermarked Anonymized data Detecting watermarked bit-string Gray code reverse transformationECC decryption 010 ⋯ 101Metadata AES decryption 010⋯ 101 011⋯ 100 ＋ 識別情報 Metadata 128bits 010 ⋯ 101 256bits Parity 010⋯ 101 011⋯ 100 Gray code translation …
- 12. Purchasing History using e-money card Body composition meter Deposit Personal Data Provide Personal Data Reward / Services Information Bank
- 13. Smart Community Information Platform 13 CloudEdge / Fog Home Gateway IoT Devices Small Terminal Sensors Smart Meter Service Application Anonymization Watermark Printing Use of Cloud Service Service Migration to Fog No need to change Dest IP as well as protocols
- 14. Demonstration of Service Provisioning 14
- 15. Demonstration of Smart Service 15
- 16. ECO-Comfort Report Service • ECO-Comfort nudge service using AI • Automatically generate the ECO-Comfort life report • Behavior change is automatically detected using sensors and the report is generated according to the behavior change 16 Providing nudge service Behavior change Behavior change detection
- 17. Results of ECO-Comfort Service • 60% in ECO activity and 85% in Comfort activity reports are satisfied by users according to questionnaire queries. • Power consumption was reduced by 20% in total. • Comfort level was improved especially in risky area for health. Power consumption Effective temperature EffectiveTemp.[℃] Ave. before:15.2 Ave. after:12.1 Before After Before After Comfort Area Humidity Before After kWh/day
- 18. Housing performance evaluation for GND • Saitama has started GND following US and EN model. • City or investment company achieves improvement work of housing performance firstly and get return by the reduction of HVAC cost. • The problem is “how to select houses that should join the program” • Simple method was proposed using limited sensors (AC power consumption and temperature sensor) and ave. data (anonymized). 18
- 19. Positioning Service • Original services • Bicycle location and school children commuting management • Integrated services • Walking mileage program • Trajectory analysis around station for designing the optimal flows of bicycles and pedestrians • Sudden breaking point detection and improvements 19 BLE tag for bicycle BLE Badge Application Photo of antenna BLE Antenna
- 20. Data-Service Matching (Future work) • Realtime Data Showcase: Stream data is available on the platform • Easy Service Deployment: The system recommend the appropriate dataset for providing a service by finding “similar service and dataset” from using history. • Anywhere feature: capture data from IoT devices, gateways, edge and fog nodes. Personal Area Home Area Town Area City Area CloudEdge / FogIoT Devices Small Terminals Home Gateway Data Recommendation Smart Community Information Platform Smart Community Data Platform Information BankAnonymization Easy service provisioning Service Provider Automated Selection of Community Data Idea of services to provide ・Realtime Data Showcase ・Easy Service Deployment Data Capturing from network Encapsulating Private Information ・Capture Community data from anywhere ・Deploy Smart Community Services to anywhere
- 21. To solve urbanization problems by city data cycle New PCDA Cycle for improving Quality of Life 21 Community Big Data Community Massive data Providing Services Community Sensing Action by Residents New community Data sensing Smart Community / City Service Platform Feedback for improving QoL IoT M2M New Hardware Devices Machine Learning Data Mining Incentives Open API xEMS ASCA Sensors Localizing and personalizing services Terminal Smart Phone Signage Visualization Machine Control
Editor's Notes
- Thank you for kind introduction. It is my pleasure to explain about one of our smart city project; Urawa-Misono smart town from the perspective of smart city data services. I’m Hiroaki Nishi, the chair of UDCMi, which is the abbrebiation of Urban Design Center Misono.
- We are conducting smart town project as a part of saitama smart city project. Urawa-Misono town in Saitama city is located at about 30km north away from the center of Tokyo. The Urawa-Misono town has 320ha, and the planed population is 32,000. Currently, it is about 12,000, and is increasing. UDCMi is the center office of this smart town project, established by Saitama city.
- UDCMi provides various smart city services, and now we are focusing on smart data services. To provide smart data services, secondary-use of residents’ data is indispensable. For providing safe data services, data anonymization, which is the process to generalize private information. To provide the data services, smart city information platform was developed. It enables flexible service application provisioning and private information is encapsulated into the dedicated town by using the special data and network infrastructure. For connecting service vender and consumer, vender consumer relationship management was designed and used in UDCMi.
- UDCMi provides many services as shown here. We corrects purchasing history data from e-money card, health data from these devices, living environment and power consumption data from smart house, location data service using smartphone and BLE-based local positioning system or these mobilities.
- By using proposed smart community information platform, we provide several smart city services as shown in here. Location services for bicycles, pets, and watching children as well as security service are provided. We also have a plan to reduce collision accidents by exchanging these information with vehicles. In the residential area, smart houses are introduced including sensor of electricity usage, indoor and outdoor environment, and residents’ behaviors. In commercial area, AEON, one of the biggest retailing company, recommendation and incentive services using points of e-money card, and TANITA a famous company at health or body meter and care service, are managing residents’ health conditions. These information are gathered into our server and cloud by using the SCIP.
- From the technological perspectives, both consumer and provider can control data flow and service provisioning. Consumers, they are also data providers at the same time, can control anonymization level and data filtering. Providers can control service level. These control is managed by the relationships as a contract and agreement between them. Namely, the user can control the service level and anonymization level.
- Advanced Persistent Threat Jizoku-teki Hyouteki-gata Kougeki We cannot compromise the cyber attack measures Dakyo
- As given in this movie, Saitama city settled Information bank and manages smart town data including private information.
- Finally, I would like to explain where we are heading. The problems that we have to solve are diverse. We do not have clear answer for solving urbanization problems. However, we believe the data cycle in this figure will give some solutions. Many sensors samples the behavior of city. It generates big data as including many useless information, and it will be changed to a massive data of useful information. Machine learning technology will give the way to give directions to systems and us to improve the current status. And our behavior change will monitored again by using sensors. This data cycles will continuously tackle to give solutions to the urbanization problems. We are now on the way to establish this data system. This is the end of my presentation. Thank you for your kind listening.