Reusable, Sharable, and Transferable Smart Data Platform for Collaborative
Development of Data-Driven Smart Cities
Background :- UN's Sustainable Development Goals (https://sdgs.un.org/goals)- Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable- Goal 13: Take urgent action to combat climate change and its impacts- Goal 17: Strengthen the means of implementation and revitalize the global partnershipfor sustainable development- Society 5.0 (https://www8.cao.go.jp/cstp/english/society5_0/index.html)- "A human-centered society that balances economic advancement with the resolutionof social problems by a system that highly integrates cyberspace and physical space.”
Targets:- Disaster risk reduction and sustainable transport- Smart environmental Tourism and Sustainable Mobility (Dalat city, Vietnam)- Smart Dengue Early Warning System (Cauayan City, Philippines)- Smart Outdoor Activities (Singapore)- Atmosphere and Climate Change- Transboundary Air-Pollution Forecasting (Brunei)- Multi-stakeholder partnerships and voluntary commitments- xData Platform and Event Data Sharing: A decentralized and collaborative approach tofast, economically, and sustainably develop user-centered applications.
Speaker: Minh-Son Dao
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Reusable, Sharable, and Transferable Smart Data Platform for Collaborative
Development of Data-Driven Smart Cities
Project Members :- Minh-Son Dao, Senior Researcher, NICT, Japan (PI)- Asem Kasem, Assistant Professor, Universiti Teknologi Brunei (UTB)- Dang Thanh Hai, Lecturer, Dalat University, Vietnam (DLU)- Filip Biljecki, Assistant Professor, National University of Singapore (NUS)- Betchie Aguinaldo, Associate Professor, Isabela State University, Philipplines (ISU)Project Duration :- 24 months (2019/04/01 – 2022/03/31)

Project Budget:- 40,000 USD/year
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Project Activities: Scientific and Technological Development
1. Scientific- Values and Complex Events Prediction- Insights from cross-data- Decentralized and collaborative development- Data Visualization and Geo-based Navigation
2. Technological development- CRNN-based models for (Transboundary) Air
Pollution Prediction using peripheral and weather
from stations and open data.
- 3D-CNN models for complex event prediction anddetection using spatiotemporal cross-data.
- Partial Periodic Spatial Patterns Discovery in Spatiotemporal Databases- ETL APIs (e.g., data loader) for supporting data collection and sharing/exchanging amongdevices (e.g., mobile sensor boxes, lifelog cameras, peripheral stations), xData Edge (i.e.,local server) and xData Platform (i.e., basement infrastructure)- Federated Learning and Transferred learning versions of CRNN-based models fordecentralized and collaborative development different instances of original models.
- Data collection system (e.g., sensor networks, crowdsourcing)- A risk-avoidable navigation system based on cross-data
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Project Activities: Experiments including field testing- Sensor Networks designation and deployment- Environment-mobility sensor networks in Dalat city (Vietnam)- Dengue sensor networks in Cauayan city (Philippines)- Data structure and database designation based on 3D-GIS data format to harmonize dataexchanged among partners and to further adapt to smart cities.
- Data collection:- Collect haze trans-border-related datasets (air pollution, fire forest, weather) fromASEAN countries- Collect Dengue-related dataset in Cauayan city (Philippines)- Organize Tokyo Olympic (Japan) and Dalat city (Vietnam) data collection campaigns
to collect peripheral sensing data, lifelog data (images and questionnaire), and open
data. The campaign has happened for three months (September-November 2020)- Transfer knowledge:- Transfer prediction models and association rules discovery to partners for reusing on
local data from NICT to UTB and DLU- Models Evaluation:- Evaluate the adaptability and accuracy of transferred models on local data (UTB, NICT,DLU, NUS)
- System Manual Instruction:- Construct the website to give manual instructions for reusing and sharing productsdeveloped by partners (https://www.xdata.nict.jp/xDataPFDocs/WebAPI/1.0/en/)
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Project Activities: Budget plan in detail
1. 20,000 USD for renting GPU cloud (for high-performance andcollaborative training deep-learning models) and GIS cloud (forflexible and instant crowdsourcing). These resources are shared
among partners.
2. 20,000 USD divided equally for four participants for supporting
local sensor networks setup (Dalat city - Vietnam, Cauayan city –Philippines, Brunei), data acquisition (all partners), andmiscellaneous things facility (all partners) towards enhancing theproject activities.
3. All traveling and on-site events organizing, and attending arecanceled due to the Corona pandemic. Hence, no budget islocated for this category.

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R&D results: Scientific and technological
1. Values and Complex Events Prediction- MASTGN: Predict air pollution using Multi-Attention Spatio-Temporal Graph Networks
on Tokyo peripheral dataset (air pollution, weather)- Image-2-AQI: Predict environmental quality using image retrieval and deep learningmodels on lifelog images, times, and locations.
- 3DCNN: Predict events using CNN, raster images, and concurrent fusion on cross-data(weather, congestion, accident, and tweets).
2. Insights from cross-data- Mining of Spatial High Utility Itemsets in Very Large Spatiotemporal Databases,applied for Tokyo and Kobe dataset. The patterns can help to predict the length ofcongestion in future.
3. Decentralized and collaborative development- Transferred Learning: The CRNN AQI prediction model is re-trained on Brunei dataset
to predict PM10.
- MM sensing system: the integration system to connect personal multimedia devices,xData Edge (local servers) and xData PF for predicting events (e.g., AQI, congestion)- Data loader APIs: many data sources are uploaded and shared by different dataloader functions designed according to related data types.
4. Data Visualization and Geo-based Navigation- A risk-avoidance navigation system based on localized torrential rain data to plan theoptimal route for travelers in real-time mode.
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5

R&D results: Application (or system) development
xData platform
EvWH
xData EdgeImage-2-AQI, dataloader, personal
data protection（ Deployed at data

High-semantic level
event database

MobileApps forcapturing
Images,Anket,Time,Locationcollection base ）MM sensing system
Dalat navigation risk map

APIs library and manual instruction website(included all developed predcition models,association discovery, data loader, and risk map)
https://www.xdata.nict.jp/xDataPFDocs/WebAPI/1.0/en/

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Dengue Early Warning System Architecture

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R&D results: Experiments including field testing
Transferred CRNN
model predictsvalues of PM10
concentrations atMumong station,
Belait district,Brunei, for (a)testing sets (20182019), and (b)training sets from2005 to 2017

Prediction accuracy in evaluation experiments
using data collected by Tokyo Dataset.

50

MAE

4030

IDW

20

DTL-CRNN

100
Route1

Route2

Route3

Route4

Route5

CRNN model: 15-37% improvement in prediction
error compared to spatial completion method (IDWmethod) of measurement station data (Tokyo)
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Environment- mobility risk map simulation running onarchive dataset of Dala city, Vietnam
ASEAN IVO Forum 20207Scientific Contribution: Presentations at International Conferences
The date oftheconference

The venueof theconference2629/10/2020

Dublin,Ireland(VirtualConference)*NICT,**NUS

Intelligent CrossData Analysis andRetrieval Workshop,(ICMR 2020)

2629/10/2020

Dublin,Ireland(VirtualConference)*NICT

Database andExpert Systems
Applications Int.
Conference(DEXA2020)

1417/09/2020

Bratislava,
Slovakia(Virtualconference)

IEEE Big Data 2020

1013/12/2020

VirtualConference

1-3/9/2020

Shanghai,
China (virtualconference)

1013/12/2020

VirtualConference

Affiliation

Conference
name:

*DLU

Intelligent CrossData Analysis andRetrieval Workshop,(ICMR 2020)

No

Paper title:Author names

1

MNR-HCM Data: A Personal Lifelog
and Surrounding Environment
Dataset in Ho-Chi-Minh City, VietNam

T.L. Nguyen-Tai, D.H.
Nguyen, M.T.
Nguyen, T.D. Nguyen,
T.H. Dang*

2

ICDAR'20: Intelligent Cross-Data
Analysis and Retrieval

M.S. Dao*, M. Fjeld,
F. Biljecki**, U.
Yavanoglu, M.X.
Dong

3

Leveraging 3D-Raster-Images andDeepCNN with Multi-source Urban
Sensing Data for Traffic Congestion
Prediction

N.T. Nguyen, M.S.
Dao*, K. Zettsu

4MASTGN: Multi-Attention SpatioTemporal Graph Networks for Air
Pollution Prediction

P.J. Zhapo*, K. Zettsu

5Convolution Recurrent Neural
Network for Daily Forecast of PM10Concentrations in Brunei Darussalam

E. N. Aziz, A. Kasem*,W. S. H. Suhaili and P.
Zhao***UTB,**NICTThe 6th International
Conference on Low
Carbon Asia &Beyond

6Distributed Mining of Spatial High
Utility Itemsets in Very Large
Spatiotemporal Databases using Spark
In-Memory Computing Architecture

U.K. Rage, S. Ito, M.S.
Dao*, K. Zettsu, C.W.
Wu, Y. Watanobe, I.
Paik, C.T. Truong*NICTIEEE Big Data 2020

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Scientific Contribution: Social Impacts
1. Open public dataset:- SEPHLA: Challenges and Opportunities within Environment-Personal HealthArchives- 15 publications that use SEPHLA as the benchmark dataset (i.e., citations)
2. Challenges:- MediaEval 2020 Insight for Wellbeing: Multimodal personal health lifelog dataanalysis (https://multimediaeval.github.io/editions/2020/tasks/lifelogging/)- 11 teams attended (Vietnam, UAE, India, Pakistan, Rumania, China,Brunei)
3. Conference special session and workshops:- ICMR2020 workshop on Intelligent cross-data analytics and retrieval(https://www2.nict.go.jp/bidal/icdar_icmr2020/index.html)- 17 submitted papers, 6 accepted papers for oral presentation- ICME2021 Special session on Insights for wellbeing: Understand thesurrounding environment's impact on human lives(https://2021.ieeeicme.org/conf_sessions)- 5 confirm-to-submit papers and more papers are coming (deadline29/11/2020)
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9Conclusion:
1. Scientific and technological• Researched and developed several topics and methods that contribute to the success ofthe project including air pollution CRNN-based prediction models, 3DCNN event prediction
models, periodic frequent patterns mining, predict AQI from lifelog images, safe routenavigation planning.
2. Application (or system) development• MM sensing system• A risk-avoidance navigation system• Dengue Early Warning System architecture• APIs library and manual instruction website
3. Experiments including field testing• Evaluated (transferred) CRNN-based prediction models in Tokyo and Brunei dataset• Evaluated 3DCNN event prediction models in Kobe dataset• Evaluated risk map in Dalat city dataset• Evaluated Image-2-AQI in Tokyo and Dalat city dataset.
4. Datasets• Tokyo environment-lifelog dataset• Dalat city environment-lifelog-tourism-CCTV dataset• Cauayan city Dengue dataset• ASEAN air pollution dataset• SEPHLA: open dataset for Challenges and Opportunities within Environment-Personal
Health Archives
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Future works:
1. Scientific and technological• Continue ongoing scientific and technological purposes mentioned above• Research and Develop more topics and methods focusing on improving the smart ofdata, the flexible ability of Reusable, Sharable, and Transferable of the system.
2. Application (or system) development• Smart environmental Tourism and Sustainable Mobility for Dalat city, Vietnam• Smart Dengue Early Warning System for Cauayan City, Philippines• Smart Outdoor Activities for Singapore• Transboundary Air-Pollution Forecasting for Brunei• Open dataset for open science data community• Completed xDataPF – xDataEdge and MM sensing system with high privacy protection
3. Experiments including field testinga) Field experiment of environmental quality data collection by residents using MM
sensing and customization of short-term prediction of environmental quality of
tourist spots (Dalat City, Vietnam) and environmental health (Cauayan City,Philippines) using collected data.
b) Field experiment of environmental walking (Tamagawa Walking Society, Tokyo)
evaluated the effectiveness of environmental quality data collection in various
environments such as highways, rivers, and residential areas.
c) Hackathon to attract the attention from industry-academy-government onenvironment-human topics, expected to be organized in NUS
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