CPaaS.io: An EUJapan Collaboration on Open
Smart City Platforms
Noboru Koshizuka (The University of Tokyo)
Stephan Haller (Bern University of Applied Sciences)
Ken Sakamura (Toyo University)
Abstract
Data-driven cities and governments rely significantly on data collection, management, and distribution platforms. In this article, we introduce CPaaS.io, a collaborative project between Japan and the European
Union with the goal of establishing common smart city platforms for deployment in real smart city use cases.

1

INTRODUCTION

Today, data are crucial to the functioning of society. In fact, it is sometimes said
that the most competitive area in information and communications technology(ICT) is not algorithms but data. The ICT National Strategy of Japan known
as Society 5.0 proposes a data-driven society in which data help solve problems
in the fields of mobility, supply chains, healthcare, and lifestyle to name a few.
This will generate further economic growth and increase quality of life. Consequently, both in Japan and the European Union (EU), data have been termed
the oil of the 21st century. These data come from a variety of sources: theInternet of Things (IoT) and sensors, open government resources, social media,
and industry and business repositories, not to mention the wealth of personal
information from individual users. These can be obtained, linked, and analyzed
to extract valuable intelligence and transform our society for a better future. In
the deployment of smart city services, providing a platform for data collection,
management, and distribution is crucial.

This manuscript has been published in Computer (Volume: 51 , Issue: 12 , Dec. 2018).
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1

2

CPaaS.io: CITY PLATFORM AS A SERVICE―INTEGRATED AND OPEN

To reach these goals, we have started a joint smart city platform project,
CPaaS.io: City Platform as a Service― Integrated and Open (www.cpaas.io),
funded by the European Commission under the Horizon 2020 program as wellas the Japanese government. It was started in July 2016 and completed in
December 2018. The project is a collab.oration among organizations in Japanand Europe. The project is coordinated by the Yokosuka Telecom Research
Park, Inc. Ubiquitous Networking Laboratory (YRP UNL) in Japan and by
the Bern University of Applied Sciences in Europe. On the Japanese side, theproject partners include Microsoft Japan, Access Co. Ltd., Ubiquitous Computing Technology, Inc., and The University of Tokyo; on the European side,
partners include AGT Group (R&D), NEC Laboratories Europe Ltd., Odin
Solutions, The Things Industries, and the University of Surrey.
CPaaS.io is a project to develop a data-driven smart city platform for solving
urban issues and supporting regional or even global applications that cities inJapan and the EU face today. Smart city is one of the hottest topics in the ICTarea.
The are many smart city projects and platforms around the world[1, 2], alldiﬀerent from each other and each exhibiting unique points and novel features.
Our smart city approach is data-driven and forms the basis for a smart city data
infrastructure. The CPaaS.io platform we are developing assumes the role ofsuch a smart city data infrastructure and, technically speaking, is a full-fledged
ICT-based platform with functions for collecting, integrating, distributing, andsharing data. It is also equipped with the necessary access control and security
functions for proper data governance.
The integration of IoT data enables city authorities to see in real time what
happens in their city, an important capability for better managing the smart city.
In addition, publishing open data helps improve transparency and enables third
parties to build value-added services on top of the infrastructure, thus fostering a
dynamic economic environment, particularly for startups. Technical challenges
being addressed include data provenance, data quality, adaptive privacy levels,
policies, and adaptive processes for distributing and deploying intelligence tothe cloud or edge.
The CPaaS.io platform is designed using a common functional architecture,but individual platform instances are implemented based on two diﬀerent sets oftechnologies. In Japan, CPaaS.io is implemented based on the Ubiquitous ID 2.0(u2) architecture[2, 4] while the European instance is based on FIWARE. The
u2 architecture is an architecture for IoT platforms coming out of the TRON
Project of Japan (www.tron.org); FIWARE is an infrastructure framework forcloud and IoT solutions widely deployed in the EU (fiware.org). As shownin Figure 1, a loosely coupled federation between these platforms is possibleusing linked data and open application programming interfaces (APIs). We
will describe these two architectures more precisely later in the article. Before2Smart City Service

Smart City Service

Smart City Service

Smart City Service

u2-based Instance
SPARQL

Open API

Federation using
Linked Data andOpen API

Information Service Layer
IoT Translation Engine

KokosilSemantic Integration Layerucode Manager-

ucR Manager

ucode RPID Resolution
IoT Service Finding

Open Data
Distribution
Platform-ucR Query Protocol- SPARQL-based- REST-based

IoT Aggregator- Tunneling
Access contron

eTRON Security Architecture

u2 Open Data Catalog

Smart City Service

FIWARE-based InstanceOpen API-

Smart City Service

SPARQL

Security Layer
KeyRock

XACML

CapManager

PEP ProxyContext Management
IoT
Knowledge
ServerIoT
Discovery

CP ABE

Process
Management
IoT
BrokerData
Context
BrokerFogFlow
Service
Orchestration

IoT Devices
IoT Agent

SPARQL AgentOPaaS.io- Personal Data
Store

ProtocolAdapter

DevicesProtocolAdapter

Data
Handling

Devices

IoT Engine(uT-Kernel 2.0)

ucode BLE

ucode NFC

ucode QR

DeviceDeviceNGSI Device

NGSI Device

Figure 1: The CPaaS.io platform architecture showing the components andthe federation between a u2-based instance and a FIWARE-based instance.
CAP: capability; PEP: policy enforcement point; CP ABE: ciphertext-policy
attribute-based encryption; NFC: near-field communication.
explaining the individual technical components, we first consider some use cases
for the platform.

3

SMART CITY USE CASES

Practical use cases are being implemented in cities in Japan and Europe. The
use cases in Japan have been selected with the upcoming 2020 Tokyo Olympics
in mind, targeting issues relevant to this major event and allowing the incorporation of lessons learned from the project’s experimental implementations to
be incorporated into other cities’ planning. Thus, the major use cases in Japanare public transportation in Tokyo, tourism support (tested at Sapporo’s yearly
Snow Festival), and emergency medical service in Yokosuka City. In Europe, on
the other hand, the following use cases are being implemented: water control toavoid floods after heavy rain in Amsterdam, management of public events likethe Color Run (thecolor.run.com), and smart parking in Murcia, Spain.

3.1

Open data of public transportation, Tokyo

Public transportation is one of the most important and complex infrastructures
for the basic functioning of large cities. This is especially the case for the public
transportation network in Tokyo―one of the most complex in the world with3

Figure 2: The Public Transportation Open Data Center providing both static
and real-time location data of trains and buses in Tokyo (red dots show train
positions and blue dots show bus positions).
hundreds of railway stations and thousands of bus stops and routes. Moreover, these are operated by many private companies: approximately 50 railway
companies, more than 100 bus companies, and over 1,000 taxi companies.
Today, advanced public transportation operators around the world provide
information services, such as transfer guidance based on timetables, and operational information services, such as delays and route troubles. In the case of amegacity with advanced public transportation networks like Tokyo, open data
oﬀers the best approach for providing integrated information.
The Association for Open Data of Public Transportation (ODPT) is currently making eﬀorts to build a Public Transportation Open Data Center oﬀering information on railways, buses, airlines, and all other means of transportation in Tokyo (Figure 2). The demand for open data of public transportation
has become very high, and the number of association members has increased to
56 corporations (as of 23 April 2018) and nine observers.
From the standpoint of technology, ODPT uses IoT and open data for thisactivity. Static data, such as timetable and station map data, can easily be
distributed using only open data technologies, but dynamic, real-time data, suchas train/bus location and their real-time operation status data, must be dealt
with using IoT technology such as sensor networks and geolocation systems.

3.2

Location-aware city guide services based on open data,
SapporoInternational tourism is a big industry both in Japan and Europe. For example,
in 2016, the number of international tourists arriving in Japan increased toover 24 million; in the EU, close to 500 million arrivals were counted. Clearly,

4tourism support is a relevant application for the smart city. Sapporo is one
of Japan’s most popular tourist cities, oﬀering nearby ski resorts, hot springs,
and many other attractions. Promoting tourism using digital technologies anda smart city platform is very much in the interest of Sapporo.
In a joint project with the city, we first established the Sapporo Open Data
Association in 2016 with 22 organizations (sapporo.odcity.org). This association conducts research, such as studying open data provisioning and its uses for
Sapporo tourism and public transportation, and holds events like application
contests, hackathons, and ideathons; promotes open data usage; and encourages application usage on smart phones during feasibility study experiments.
We have collected, integrated, and published many data sets related to tourism,
e.g., sightseeing, hotel, restaurant, and public transportation information. For
the latter, we use the Public Transportation Open Data Center, as mentioned
previously.
Next, we developed a mobile application, Kokosil Sapporo (home.sapporo.
kokosil.net/en/), which provides information based on the open data for Sapporo tourism and using location-aware technologies. With a geospatial position recognition system using GPS and Bluetooth Low-Energy (BLE) beacons,tourists can get information they need in a timely manner.
The approach of combining open data and IoT technology on the CPaaS.io
platform has proven to be eﬃcient for tourism information development, inparticular regarding the governance of data and systems as well as the sharingof responsibilities among government and industries. In the case of Sapporo, thelocal government provides open data for tourism and an IoT infrastructure, such
as BLE beacons, and the tourism industry provides services and applications
for tourists. We have already deployed such tourism information services for big
events like the Sapporo Snow Festival in 2016 and 2017 and the Asian WinterGames in 2017 [Figure 3-(a)].

3.3

IoT emergency medical services, Yokosuka

IoT emergency medical services are made possible by teamwork among rescue
crews in ambulances and staﬀ at hospitals and dispatching headquarters. Information sharing is very crucial to provide smooth emergency medical care.
Yokosuka City and YRP UNL in Japan have jointly been developing the IoT
Emergency Medical Support System on the CPaaS.io platform, which collects
information from emergency hospitals and ambulances to share among the involved groups.
Our system is very simple technically, as it transmits the video images ofpatients in ambulances, facilitating information sharing between the rescue crew
and the doctors at the hospitals in real time using tablet devices and Internet
Protocol cameras placed in ambulances [Figure 3-(b) and (c)]. The system alsotransmits real-time location information of ambulances to emergency hospitals
so that the staﬀ knows when the ambulance will arrive. Thus, they can prepare
for receiving patients smoothly and facilitating handover. This system has been
adopted for all of the emergency medical care crew units since 2014 in Yokosuka
5

Figure 3: The Sapporo open data for tourism and Yokosuka smart ambulanceusing IoT technologies. (a) A location-aware information service showing tourist
information from open data using Kokosil Sapporo and BLE beacon. (b) The
inside of a smart Yokosuka ambulance. (c) A doctors’ screen showing vital signs
monitoring (bottom) and the position of the ambulance and a patient image(top). (Photos courtesy of YRP Ubiquitous Networking Laboratory, Japan.)6and since 2017 in neighboring Miura.

3.4

Waterproof Amsterdam

Disaster prevention is an important issue in cities and is also a good applicationof smart city platforms, such as the CPaaS.io platform. Flooding is a major
hazard in The Netherlands because large parts of the country are below sea
level, and the problem has increased with global warming.
Hence, The Netherlands has, over the years, invested heavily in infrastructure, dikes, and smart technology to keep its citizens safe and dry. Because
Amsterdam also aspires to be waterproof, several pilot projects are examining solutions for this issue.
As part of the CPaaS.io project, we are deploying smart water buﬀers on
rooftops, controlled through an IoT LoRaWAN. Data from the water buﬀers
and the sewage system are combined with additional data like weather forecasts
in a back-end application for analysis and pattern detection, e.g., when heavy
rain is forecast, the valves of the buﬀers are closed, keeping rain water in the
buﬀers and preventing flooding of the sewage system. Once the sewage system
has regained enough capacity, the valves can be opened again [Figure 4-(a)].

3.5

Color Run: Managing public events

International tourism is a big industry in Europe. The focus here is not justto provide information but to enhance the visitor experience in participatory
events, such as the Color Run, a series of events held at diﬀerent locations
around the world. In the CPaaS.io project, we have used one of these events
to demonstrate what IoT sensors and analytics can add to such an event. By
providing runners with wristbands, deploying video cameras along the course,
and linking the data with geospatial information provided by the city, we areable to create personalized experiences. For instance, wristband data can beused to extract trajectory, eﬀort (heartbeat), and excitement levels during theevents. Video analytics solutions can be used to create dynamic, geolinked color
maps that can be linked to runners’ sensor trails [Figure 4-(b)].

4

EU AND JAPAN INSTANCES OF THE CPaaS.io
SMART CITY PLATFORM

In the CPaaS.io project, we have defined a layered functional architecture fora smart city platform. Based on this architecture, concrete platform instances
have been developed and set up both in Japan and in Europe, building on topof existing platforms and frameworks that are commonly used in the respective
regions, as shown in Figure 1. The smart city platform instance in Japan isbased on the u2 architecture, while the smart city platform in Europe is basedon the FIWARE architecture.

7

Figure 4: Waterproof in Amsterdam and a Color Run event. (a) A pilot system
hardware and screen of the dashboard system for the Amsterdam waterproof
use case. (Photo courtesy of Dakdokters.) (b) A running tracking system viewof the Color Run use case in Utrecht, The Netherlands. (Photo courtesy of
AGT.) AI: artificial intelligence; UI: user interface.

8

While these implementation architectures are quite diﬀerent regarding existing APIs and data format specifications, both provide common functions
from a service-level point of view. For example, both provide a semantic data
platform based on semantic web technology, an open data platform, personal
data management, IoT device management, virtualization functions of IoT devices, security functions, service finding functions, and so on. Because of thesecommonalities, the federation of platform instances becomes possible on the semantic level, as we will show after first discussing how the two implementationarchitectures look.

4.1

u2-based architecture

The left side of Figure 1 shows the u2-based smart city platform architecture.
The u2 architecture has been developed as part of the TRON Project, an ongoing eﬀort that originally started in 1984 as probably the world’s first projecton the IoT. So far, more than 1,000 companies and organizations worldwide
have contributed to it and to publishing the TRON standards, industrial open
standard specifications for IoT systems.
4.1.1

ucode and ucR

In developing an IoT-based architecture standard, we need to explore the essenceof IoT or ubiquitous computing: context awareness.
So the central point of the u2 architecture is a context-aware computing
mechanism: either a standard for context representation or a standard for acquiring context information management. In the u2-based architecture, theucode and ucR standards deal with these issues[4].
ucode: In terms of the context representation, we need a consistent systemfor determining the identity, location, and status (who, what, where, andwhen) of real-world entities. In the u2 architecture, every real-world entity is assigned a ucode, a 128-b unique identifier that has been adopted
as International Telecommunication Union-Telecommunication Standardization Sector Recommendation H.621.1 (2012).
ucode Relation (ucR): The u2 architecture represents the real-world context
by modeling the relationships of real-world entities. The ucR model defines
the relation of an entity identified by a ucode with another entity also
identified by a ucode or with a literal value, such as a string, number, ordate. The relation itself is also repre.sented by a (relation) ucode. These
three ucodes are the basic unit of the ucR model and are hence also calleda ucR unit. In a sense, the ucR model is similar to triples used in the
Semantic Web [e.g., Resource Description Framework (RDF)]; but, while
the Semantic Web uses uniform resource identifiers, the ucR model relies
on ucodes.

94.1.2

ucode manager and ucR manager

The wide-area distributed database that manages ucR is called a ucR database.
The ucR database comprehensively manages information on the relations among
multiple ucodes, in addition to the content such as information services associated with individual entities to which ucodes are assigned. The ucR databaseis basically an open database that anyone can use to reference or register information; if needed, however, access control can also implemented.
When a user physically accesses an entity in the real world, the u2 architecture identifies the appropriate information for the situation from the ucR
database, based on the ucode assigned to the entity. This process is calleducode resolution. Moreover, the information associated with ucodes, i.e., theucR graph, is registered in the ucR database. The protocol for accessing theucR database in this manner is called ucode Resolution Protocol and is implemented in the ucode Manager. The ucR graphs, on the other hand, can bequeried using SPARQL or RESTful APIs via the ucR Manager.
4.1.3

IoT aggregator

We have been investigating the concept of aggregate computing1 and its realization framework, called IoT Aggregator[3, 5]. As shown in Figure 5, aggregatecomputing lets the devices, services, and systems connected to the network cooperate with each other to achieve an optimal environment.
The IoT Aggregator mainly provides three functions in IoT cloud computing. First, it oﬀers standardized access to IoT edge nodes, abstracting from thediﬀerent native interfaces of each node. Via the IoT Aggregator, IoT applications can communicate and collaborate with IoT edge nodes much more easily.
Second, it provides light-weight IoT security functions based on the eTRON
security framework by establishing virtual encrypted tunneling links between
virtual objects in cloud servers and real objects in edge node devices. Third, it
provides a flexible access control function in the IoT Aggregator cloud services.
In IoT applications and services, access control to the devices and their functions is essential and usually requires heavy computation power because flexible
access control must manage a large number of governance policies, which canchange dynamically according to the real-time smart city context.
Figure 5 shows that access control is implemented on the virtual object in
the IoT Aggregator cloud system based on access control policy.
4.1.4

Omotenashi platform (OPaaS.io)

The IoT environment and smart city services need to learn and collect the attributes of each individual user, such as language spoken, age, pertinent physicalcharacteristics, preferences, and so on. Such knowledge must be reflected in thecreation of an optimized environment. For this purpose, the u2-based smart
city platform includes a general-purpose personal data store. The platform istargeted to manage personal information of visitors to the 2020 Tokyo Olympic
Games, so we call this OPaaS.io or Omotenashi (hospitality) cloud.
10

Figure 5: An IoT aggregator.
4.1.5

T-Kernel 2.0

T-Kernel 2.0 (IEEE Std. 2050-2018) is a lightweight real-time operating systemwith small footprint; it is open source software and adequate for the IoT edge
node devices. The standard edge nodes of the u2-based architecture use TKernel 2.0.

4.2

FIWARE-based architecture

FIWARE is a smart-solution platform consisting of a number of so-called genericenablers, with which cloud-based service platforms can be built. Open source
reference implementations are available for these enablers as well as public androyalty-free APIs that support the development of smart applications in multiple
vertical sectors. FIWARE has grown out of a significant eﬀort of the European
Commission involving many partners mainly from industry but also academia.
It is used in many European projects as well as many smart city deployments(e.g., Vienna), and its use is encouraged by the Open and Agile Smart Cities
initiative (oascities.org).
The components of the FIWARE-based CPaaS.io instantiation are shown on
the right side of Figure 1. We explain the most relevant ones in the following:• IoT Broker is specified as a lightweight and scalable middleware component that separates IoT applications from the underlying device installations.
• IoT Discovery is responsible for discovering the availability of context. In
the CPaaS.io project, National Electric Code Configuration Management
is used and enhanced as an implementation of the FIWARE IoT Discovery.
• IoT Knowledge Server adds semantic information into Next Generation
Service Interfaces (NGSI) messages and enhances these NGSI messages
with semantic reasoning. The IoT Knowledge Server component has aninternal triple-store where NGSI (or other) ontologies are kept. It serves
11semantic knowledge such as entity subtypes or supertypes and provides
high-level access to the semantic ontologies via query/subscription functionalities.
• Data Context Broker is a specific middleware for brokering the communication between IoT data providers and IoT applications based on the
NGSI standard as an open source generic enabler. It can provide various functionalities, such as queries pertaining to historical infor.mation,federation, or entity compositions.
• FogFlow Service Orchestrator[6] is a new component that has partiallybeen developed within the CPaaS.io project. It is mainly used to dynamically generate, configure, and deploy data processing flows over clouds andedges in an optimized manner. The data processing flows are generated
from a service topology based on a service requirement. This service requirement defines which service topology to trigger, which types of output
data are expected, and which type of scheduling algorithm is preferred.

4.3

Federation of EU and Japan smart city platforms into
CPaaS.io platform

As discussed, the two platform instantiations provide many common functions
for smart cities. Also, some technologies are common to both platforms, including linked open data technologies such as RDF and SPARQL for semantic
data management; RESTful APIs and the JavaScript Object Notation-LD data
format for standard APIs; Open Identification Connect for Single-Sign-On; andFederation of Personal Authorization and Authentication.
But, because the u2 architecture and FIWARE are technologically quite different, a tightly coupled integration of implemented modules is not a realistic
approach. Nor is such an approach desirable. While closely integrated systems
may facilitate the development of smart-city applications, a smart city platform
like CPaaS.io must be flexible and constantly adaptable to changing contexts;
this can be achieved only with loosely coupled systems. The case holds particularly true when we target wide-area cooperation between smart cities in the
EU and Japan. Thus, federation needs to be loosely coupled and will happen
mostly at the semantic level (plus API-based data exchanges).
We are currently implementing realistic prototypes to show such a federation
across distributed smart city platform instances. With this achieved, CPaaS.io
will provide a solid platform on top of which cities can implement their smart
city strategy, bringing innovation and quality of life to residents.

5

AIMING FOR FUTURE SMART CITIES

Today, there are many smart city activities in the world. Smart city services arevery useful for improving the quality of residents’ lives. But the most serious
problem of these smart city service platforms is that they are mostly custom
12made, developed one by one for each city. This is because there is no generaland practical smart city platform.
CPaaS.io platform is our answer for these general and practical smart city
platforms which can support smart city services improving the residences’ life
quality. CPaaS.io is developing a technology platform, but for the deploymentof smart city services, social and business platforms are also very important. In
the future, the co-design of both technology platform and social platform will
be necessary for smart cities.

ACKNOWLEDGEMENTS
The research results have been achieved with the support of the European Commission and the National Institute of Information and Communications Technology (NICT), Japan. CPaaS.io is a Horizon 2020 Research and Innovation Actionin the call ”EUJ-02-2016: IoT/Cloud/Big Data platforms in social application
contexts”; EU Grant number 723076, NICT management number 18302.

AUTHORSNOBORU KOSHIZUKA is a professor and director of the Department ofApplied Computer Science, The University of Tokyo, Japan. His researchinterests include ubiquitous computing, the Internet of Things, embedded
systems, and open data. Koshizuka received a Ph.D. from The Universityof Tokyo, Japan, in 1994. He is a Member of the IEEE and ACM. Contact
him at noboru@koshizuka-lab.org.
STEPHAN HALLER is a professor at the e-Government Institute of Bern
University of Applied Sciences, teaching about e-business, enterprise architecture, and project management. His main research interests include
the Internet of Things, linked open data, and the smart city. He has been
involved in European research activities in this area for many years, serving in several expert groups of the European Commission and on scientificreview boards as well as acquiring and successfully managing publicly
funded research projects like CPaaS.io. He is a Member of the IEEE.
Contact him at stephan.haller@bfh.ch.
KEN SAKAMURA is director of the Yokosuka Telecom Research Park Ubiquitous Networking Laboratory and a professor and dean of Faculty ofInformation Networking for Innovation and Design at Toyo University,
Japan. He is the creator of the real-time operating system architecture
TRON. For his achievements, Sakamura is the recipient of Japan’s Prime
Minister Award, the Japan Academy Prize, and the prestigious ITU150
award, among others. He was editor in chief of IEEE Micro and is a Fellow
and Golden Core member of the IEEE Computer Society. Contact him at
ken@sakamura-lab.org.

13

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