H2020-EUJ-02-2016
H2020 Grant Agreement Number 723076
NICT Management Number 18302

Deliverable D2.6

Feedback and validation
reportVersion Draft V1.5
October 3, 2018

ABSTRACTThis document contains a report on the pilot execution and the validation reportof the use cases and platform. It will summarize the main aspects of the weekly
feedback from the pilot execution and provide a detailed report of the use caseand platform validation process and results

This work is licensed under the Creative Commons Attribution 4.0 International License.
To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Deliverable D2.6

Feedback and validation report

Disclaimer
This document has been produced in the context of the CPaaS.io project which is jointly funded by theEuropean Commission (grant agreement n° 723076) and NICT from Japan (management number 18302).
All information provided in this document is provided "as is" and no guarantee or warranty is given thatthe information is fit for any particular purpose. The user thereof uses the information at its sole risk andliability. For the avoidance of all doubts, the European Commission and NICT have no liability in respectof this document, which is merely representing the view of the project consortium. This document issubject to change without notice.

Document Information
Editors
Authors
Reviewers
Dissemination
Level
Contractual
Delivery Date
ActualDelivery Date
Keywords

H2020 EUJ-02-2016

Sylvain Prost (TTN), Hiroshi Amano (MSJ)
Sylvain Prost (TTN), Hiroshi Amano (MSJ), Martin Strohbach (AGT)Stephan Haller (BFH), Chiaki Ishikawa (YRP)
R
PU
January 31 2017
January 30 2017

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Revision History
Rev.
0.1
0.2
0.3
0.4

Date
04/12/17
02/01/18
08/01/18
09/01/18

0.5
0.6
0.7
0.8
1.0
1.1
1.2
1.3

09/01/18
14/01/18
22/01/18
25/01/2018
30/01/2018
25/09/2018
27/09/2018
29/09/2018

1.4

01/10/2018

1.5

03/10/2018

H2020 EUJ-02-2016

Description
TOC
Smart Parking
WaterProof Amsterdam
Tokyo Management of Service
vehicleEnhanced User Experience
Yokosuka Emergency Medical Care
Review
Include review
Submission
Resubmission table template
Contribution to Smart Parking
Yokosuka Emergency Medical caremodification
Waterproof contribution anddocument review
Addition Public Transportation,
Final Edits

CPaaS.io

Contributors
Sylvain Prost (TTN)
Juan Antonio (OdinS)
Sylvain Prost (TTN)
Toshihiko Yamakami (ACC)
Martin Strohbach (AGT)
Katsunori Shindo (YRP)
Stephan Haller (BFH)
Sylvain Prost (TTN)
Stephan Haller (BFH)
Sylvain Prost (TTN)
Juan A. Martinez (OdinS)
Katsunori Shindo, Chiaki Ishikawa (YRP)
Sylvain Prost (TTN)
Katsunori Shindo, Chiaki Ishikawa (YRP),
Stephan Haller (BFH)

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Table of Contents
1

Introduction............................................................................................................................................................................... 6

2

Waterproof Amsterdam ........................................................................................................................................................ 6

3

4

5

6

7

8

9

2.1

Feedback ........................................................................................................................................................................... 6

2.2

CPaaS.io integration...................................................................................................................................................... 7

2.3

Performance and stability ........................................................................................................................................... 7

2.4

Validation report ............................................................................................................................................................ 8

Yokosuka Emergency Medical Care ................................................................................................................................. 8
3.1

Feedback ........................................................................................................................................................................... 8

3.2

CPaaS.io integration...................................................................................................................................................... 8

3.3

Performance and stability ........................................................................................................................................... 9

3.4

Validation report ............................................................................................................................................................ 9

Event Management – Enhanced User Experience..................................................................................................... 10
4.1

Feedback ......................................................................................................................................................................... 10

4.2

CPaaS.io Integration.................................................................................................................................................... 11

4.3

Performance and stability ......................................................................................................................................... 12

4.4

Validation Report ......................................................................................................................................................... 12

Event Management – Tokyo Management of Service Vehicle ............................................................................ 13
5.1

Feedback ......................................................................................................................................................................... 13

5.2

CPaaS.io integration.................................................................................................................................................... 14

5.3

Performance and stability ......................................................................................................................................... 14

5.4

Validation report .......................................................................................................................................................... 14

Tokyo Public Transportation ............................................................................................................................................. 15
6.1

Feedback ......................................................................................................................................................................... 15

6.2

CPaaS.io integration.................................................................................................................................................... 16

6.3

Performance and stability ......................................................................................................................................... 17

6.4

Validation chart............................................................................................................................................................. 17

Smart Parking – Murcia....................................................................................................................................................... 18
7.1

Feedback ......................................................................................................................................................................... 19

7.2

CPaaS.io integration.................................................................................................................................................... 19

7.3

Performance and stability ......................................................................................................................................... 21

7.4

Validation report .......................................................................................................................................................... 21

Sapporo Visitor Experience ............................................................................................................................................... 21
8.1

Feedback ......................................................................................................................................................................... 21

8.2

CPaaS.io integration.................................................................................................................................................... 22

8.3

Performance and stability ......................................................................................................................................... 22

8.4

Validation report .......................................................................................................................................................... 22

Outlook ..................................................................................................................................................................................... 23

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Appendix 1 - Enhanced User Experience, Color run feedback form ........................................................................... 24

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1 Introduction
Quite a few pilots and use cases accompany the CPaaS project. Even if they are part of the same project,their goals vary quite a lot, ranging from event to infrastructure protection. Nonetheless, they have same
objective, improve citizen life in cities as such the main source of feedback are the user (citizen). Use cases
on a city-wide scale are often backed or impact by governmental organisation, the owner of the use caseshould also then listen for feedback form this organisation. This document will resume the different
feedbacks received for each use cases which include user, stakeholder satisfaction reports but also because
the use cases work on large scale and/or large amount of data, stability, response time, scalability are
metrics that impact the user experience and the pertinence of a projects in the smart city context.
Each use cases will demonstrate how does it gather feedback, identify the main aspect and include it ineach of development cycle. They will also describe any tools and metrics that display the performance andstability of the application.

2 Waterproof Amsterdam
2.1 Feedback
In the current state the only shareholder of the use case is Waternet, the company in charge of the public
water system in Amsterdam. There haven't been any field tests conducted yet, so we don’t have any user
feedback for the application. Nonetheless, one important point of feedback is the success of the green
rooftop “De Dakdokters"1: Waternet has decided to finance them into producing and deploying new watertanks, and as they are our partners in the Waterproof application, this will also be beneficial for the workof the CPaaS.io project.
The scope defined by Waternet for the applications is to connect water tanks and through them receive
different kinds of information such as the water level, temperature, etc., but also to be able to command
the water tank to release or retain water. The application is successful in both tasks, although so far onlyin the lab.
The manufacturer of the tanks already has a working solution for their products (API, dashboard, etc…) but
is willing to make changes to its products to either forward the data or migrate the solution to CPaaS. We
have already identified what we need to work on with them, so we can connect them to a bigger network.

1 Cf. http://dakdokters.nl/en/green-roofs/

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2.2 CPaaS.io integration

Figure 1 - Waterproof CPaaS.io integration

The application makes use of three layers of the CPaaS.io service architecture. The IoT resource layer,
Integration layer, Virtual Entity layer and the SMART City services layer. Finally, Fog Flow is used toorchestrate the different services.
Main advantage to this disposition is the separation of the services and the handling of the services. All
the forwarding and mapping is handled inside CPaaS.io and put at disposition of the developer. It also
allows quick deployment and transferability.

2.3 Performance and stability
Our development cycle includes peer reviewing and continuous integration. It allows us to detect potential
bugs and deliver a reliable and performing product. We are also having manual test in our lab to ensure
end-to-end functionality.
On average The Things Network handle more than twenty-two thousand devices every day and is running
99% of the time. Furthermore, based on results of the first prototype of the version the CPaaS platform
can handle more than a hundred requests per second without increased latency in any service of theplatform. It is safe to say that there won’t be any issue in using both of these services.
A remark for the during the review meeting in Tokyo regarding the cross-region deployment and scalability
led us to rethink, the solution architecture. The current solution is fulfilling the main functional requirement.
However, the architecture is monolithic, as such it lacks flexibility, harder to maintain, scale and evolve. We
started to work toward a distributed architecture based with FogFlow, which is part of CPaaS. Using the
FogFlow service topology and automation we will have better reliability as well as transferability.

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2.4 Validation report
ID
WA-1

Description
Data collection

Fit Criterion
Existence of datacollection
infrastructure
Method for collecting
metadata andenriching sensor data

WA-2

Data
processing

WA-5

Automated
control overdevicePresence ofalgorithms

WA-11

Data
Harmonization

Ontologies for data
to be shared

ReportSensors data are collected via an IoT agent, that receive
messages from an HTTP bridge and a LoRaWAN bridge.
The data is then send to the IoT broker for storage.
Device are pushing data to either an HTTP bridge or thevia the LoRaWAN bridge. There the data is unpacked
and enriched following a configuration attached to thedevice. It is then formatted to ngsi10 and sent to the IoT
broker.
The analytic is on the last upper layer of the CPaaS
architecture. Now it used basic ruled based algorithm
but the objective is to make more complex calculation
by crossing several data source and device capabilities.
When formatting to ngsi10, the bridges are using thesame attribute fields and value type. Enabling sharing
but also the analytic service to work more efficiently.

3 Yokosuka Emergency Medical CareIn this section, we describe how we are validating the results of Yokosuka Emergency Medical Care
Application.

3.1 Feedback
In the current state, we are designing a use case system of Yokosuka Emergency Medical Care based on
the CPaaS.io architecture, and getting it ready for the first live experiment (planned for January 27, 2018)with the Yokosuka City Fire Bureau. Feedback from this experiment will be gathered and reported in thenext feedback and validation deliverable, D2.11.

3.2 CPaaS.io integration
Figure 2 shows how the Yokosuka Emergency Medical Care application has been integrated with the u2based CPaaS.io platform. Ambulances in this use case are identified by ucode.
Video transmission function in this use case enables doctors in hospital to see patients’ images or monitors
in real-time. Doctors who can see the images in ambulances are controlled by access control function in
IoT Aggregator.

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Figure 2: Integration of the Yokosuka Emergency Medical Care into the CPaaS.io platform. CPaaS.io components used arehighlighted in red.

3.3 Performance and stability
At the time of writing we are developing a system based on the CPaaS.io platform. We will have a video
transmission experiment, that is one function of this application, on January 27, 2018, connecting an
ambulance in Yokosuka City and a terminal for doctors at the Hongo campus of Tokyo University.

3.4 Validation report
ID
YEM-1

Description
Data collection

YEM-2

Consensus forusing medical andpersonal data bycity government
Privacy protectionof patients

YEM-3

YEM-4

H2020 EUJ-02-2016

Low-effort for
emergency
medical teamstaff members

Fit Criterion
Existence ofautomaticcollection
systemAgreementwith Yokosuka
city
government
Existence ofprivacy dataprotection
mechanism
Existence of
low-effort
mechanismand evaluation
score by theteam staff
members

ReportVideo transmission function and ambulance
location shared function is working well.

Yes, an agreement has been signed.

Yes, proper security protection mechanism basedon existing network security practices is
implemented.
Several training sessions and user feedback haveconfirmed that the system is easy to use. It is in
DAILY use in Yokosuka and Miura.

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ID
YEM-5
YEM-6

YEM-11

Feedback and validation report

Description
User feedback
collection
Metadata
management

YEM-14

Efficient query ofpatient, hospital
bed and doctor
data
Data sharing

YEM-17

Communication

Fit Criterion
Number ofuser feedbacks
Method forcollecting
metadata andenriching
sensor data
Query latency
in sec.

ReportYes, several user hearing sessions were held

Availability ofdata sharing
mechanisms
Number ofdata points
ingested per
second,processing
latency,storagefootprint

Yes, data collected in the ambulance is shared andhospital bed and doctor availability is shared now.

Vital data from the ambulance have beentransmitted to the hospitals. To this extent, this has
been archived.

Yes, it has been achieved and thus the propertransfer of patient to the right hospital is done now.

The application collects medical data of the patient
inside the ambulance and share this via
communication to doctors at various hospitals. The
current collection and transmission of data are
deemed sufficient by doctors of participating
hospitals.

4 Event Management – Enhanced User Experience
In this section we describe how we are validating the results of the Enhanced user Experience Application.

4.1 Feedback
Stakeholders of the Enhanced User Experience application are the end users, domain experts, and cities.
We have collected feedback via the following channels:••••

Feedback from event participantsCompany internal workshop with SVP Smart City and C level executives (see deliverable D7.4
Exploitation Report V1)
Feedback from city representatives at CeBIT 2017
Feedback from domain experts

For the feedback from the event participants we used feedback forms in which we asked general questions
about the participants. Those questions were related to general questions about the person (age, gender,
motivation for joining the event, fitness level, type of runner and color tastes). We also asked about how
participants experienced the run (emotions when reaching the start and finish, energy levels and emotions
over the run, activity rating of friends). We asked general questions about the whole event (memorable
event highlights, interest for joining next event). The form is attached in Appendix 1. And finally, we asked
miscellaneous questions, e.g. related to comfort of wearing the sensors, or any issues related to the device
setup and any other comments or suggestions. So far, this data has primarily use to validate the analytics,
e.g. for validating the emotions, detecting the running type and finding event highlights. For instance, one
participant stated that he had trouble keeping up with the group of ambitious runners and we could
validate that the detected walking anomaly happened near a drinking station when the group slowed
down.
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We also conducted AGT internal workshops in which we presented the capabilities of the application. In
particular at a workshop with the SVP Smart Cities we could validate that there is potential to use theanalytics on a more general Smart City level outside the pure event context. In another meeting with C
level executives, we could validate the overall business value based on the first analytics results we obtained
from the Color Run data.
As part of our participation at the CeBIT 2017 we engaged more closely with 22 participants that showedinterest in the Enhanced User Experience Application and validated the general value of the applicationand its use in a Smart City context.
As further feedback for the actual development of the application we engaged with domain experts at AGT
that are developing analytics and applications in similar domains related to sports and entertainment. In
regular intervals we used their feedback on guidance about visualizations and which analytics and metrics
to use in the application.
On the technical side, the CPaaS.io platform already proved to be a valuable tool for developing theapplication. The application is heavily relying on the Deployment Tool and deployment Knowledge base.
So far two major prototype versions have been built. While in the first prototype we have only used thedeployment tool and deployment knowledge base as platform components, for the second prototype we
integrated the application with the IoTBroker. Further integration with the security components is planned
for the 3rd prototype version.

4.2 CPaaS.io Integration
Figure 3 shows how the EUE application has been integrated with the CPaaS.io platform. In the resource
layer sensor data from Color Run Utrecht is collected via the AGT’s IoT analytics platform and inserted into
the IoTBroker via the IoTAgent. Via the NGSI to RDF mapper, data inserted in the IoTBroker is converted
to RDF data that is managed by the IoT Knowledge server. Examples include aggregated Twitter
sentiments. RDF data can be accessed in the application via SPARQL interface. To this end personal data isaccessed using the FIWARE components directly. This way we can benefit from the Security and and Privacy
components that provide access control and encrypted storage of the data in the CPaaS.io platform. In
addition, the application uses the deployment tool that provides information about how sensor devices
are deployed. For instance, it contains information about which wearables have been worn by which
individual. This information is also accessible via a SPARQL endpoint.

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EU FIWARE-based Platform Instance
SMART CITY Services Layer

SPARQL App
Developer

FIWARE App
Developer

Security &Privacy Pillar

IdM API

KeyRock

Knowledge Layer
SPARQLKAT Toolkit

Platform
Management ,Federation andOperation
Pillar

XACML
XACML

Semantic Data &Integration Layer

IoT Knowledge
Server
Deployment
Tool

NGSI to RDF
mapper

STH Comet
NGSI10KnowledgeServer API

Capability
API

NGSI10NGSI9NGSI10

PEP-Proxy

Virtual Entity Layer

FogFlowNGSI9

IoT Discovery

Capability
ManagerContext Broker

NGSI9

NGSI10

Encryption
API
NGSI10CP-ABE

IoT Data &Ingestion Layer
NGSI10IoT Broker
NGSI10SPARQL

Existing components
New/enhanced
components

IoT Resource Layer

SPARQL
Agent
Device API

IoT Agent

LoRaWANNGSIIoTA to to
NGSI
bridgeBridgeDevice

LoRa Device

Figure 3: Integration of the Enhanced User Experience Application into the CPaaS.io platform. CPaaS.io components used arehighlighted in red.

4.3 Performance and stability
At the time of writing we are developing performance tests to test the scalability and stability of the
CPaaS.io platform.

4.4 Validation Report
In we list the status of validated requirements as described in deliverable D2.5. Please note that we only
report on requirements that we marked for validation and that do not relate for federation. For instance,
we do not report on EUE-11 Semantic data integration and EUE-13 Access data from different platform
instances as the rational for this requirement is the sharing of data between different platform instances.

ID
EUE-1

Description
Data collection
infrastructure

Fit Criterion
Existence of a datacollection
infrastructure

EUE-2

Metadata
management

EUE-4

Data
processing

Method forcollecting metadata
and enriching
sensor data
Visual data accessfor end users

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Validation Report
As data collection infrastructure we use the available
components in the CPaaS.io resource layer. Data collected via
the IoTA platform is integrated via a dedicated IoTA Agent into
the IoTBroker.
The deployment knowledge base and user interface is used forcollecting the metadata. The application uses the SPARQL
endpoint for enriching the sensor data stored in the CPaas.io
platform.
A functional web application with a user interface has beendeveloped. The initial version has been described in D2.4 andan updated version will be described in D2.8
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EUE-5

Control overdata collection

UI for controllinguse of data

EUE-7

Efficient queryof user data

Query latency in ms

As described in D2.4 the web application provides functionality
for the user to manage data sets. In addition WP5 will provide
a dashboard for full control over the users data sets.
We experienced no perceivable delays in the application whenquerying the data from the SPARQL endpoint and the
IoTBroker respectively. Average query latencies are in the orderof a few hundred ms. In addition we benchmarked the averagetime to insert attribute values which are at 96ms.

5 Event Management – Tokyo Management of Service Vehicle
5.1 Feedback
Tokyo Management of Service Vehicle is mainly Y3 work. For exploration of management of service vehicle
field tests, we so far have contacted 23 wards of Tokyo Metropolitan government for possible TokyoManagement of Service Vehicle during March 2017 and May 2017. Results from these discussions are as
follows:•••••

The developed platform will enable route management of delivery, collecting, maintenance andmonitoring.
Use cases included garbage packers tracking and bird-hazard prevention.
Each ward office has different boundaries of office works and different IT policy.
Bird-hazard prevention is privacy-sensitive and inter-organization work for local government.
General feedback was insufficient to any ground-level deployment planning.

We are aiming to provide open-data-based general location-aware application platform for public
transportation for 2018, using the Tokyo Public Transportation Open Data Challenge.

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5.2 CPaaS.io integration

Figure u2-based architecture

TMS-3 Live Data collection and distribution: Service and ucode Manager manages data collection.
TMS-5 Secure and robust communication: IoT Aggregator secures IoT data secure communication.
TMS-11Visualization of data: Service and ucode Manager manages visualization.

TMS-12

Data processing: Service and ucode Manager manages analytic functions.

TMS-18

Data sharing: Service and ucode Manager manages analytic functions

5.3 Performance and stability
Tokyo Management of Service Vehicle is Y3 work. No performance and stability have been obtained sofar.

5.4 Validation reportID

TMS-11

Description
Live Data collection anddistribution
Secure and robust
communication
Visualization of data

Fit Criterion
Existence of automatic collection system anddistribution system of live operation data.
Existence of cryptographic communication
mechanism and fault tolerance mechanism.
Visual data access for users

TMS-12

Data processing

Availability of analytical services

TMS-18

Data sharing

Availability of data sharing mechanism

TMS-3
TMS-5

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ReportValidation
ongoing.
Validation
ongoing.
Validation
ongoing.
Validation
ongoing.
Validation
ongoing.
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6 Tokyo Public Transportation
6.1 Feedback
Tokyo Public Transportation application is part of a set of applications that use the CPaaS.io platform tomanage public transportation information. This sub-application focuses on integrating data from different
public transportation services. In order to promote the open data approach in the public transportation
sector in greater Tokyo metropolitan area, Association for Open Data of Public Transportation (AODPT)
had been formed with the participation of public transportation operators, ICT companies, government
offices as observers, etc. in 2015 before CPaaS.io started. The chair of CPaaS.io is Dr. Ken Sakamura(Yokosuka Telecom Research Park, Inc. (YRP), the CPaaS.io Japanese coordinator).
With the emerging CPaaS.io infrastructure based on u2 architecture in Japan, AODPT held a hackathon
called “Open Data Challenge for Public Transportation in Tokyo” with the support of CPaaS.io partners
including YRP, Microsoft Japan Co., Ltd. (MSJ) as the members of AODPT, and UoT as the co-host,. The
contest was held from December 7, 2017 to March 15, 2018.2 This open hackathon provided open datafrom 22 public transportation operators using the CPaaS.io-based application framework. During thecontest period, approximately 800 registrations including ones from outside Japan were recorded. By thedeadline, the hackathon received almost 100 submissions: in other words, 100 applications. The winningapps were announced and CPaaS.io logo was prominently shown in the result page.
With the successful operation and wide media coverage of the first hackathon, “The second Open Data
Challenge for Public Transportation in Tokyo” is already under way with the support of YRP and MSJ as themembers of AODPT and UoT as a co-host. The contest period is from July 17, 2018 to January 15, 2019.3

2 cf. also https://tokyochallenge.odpt.org/2017/en/index.html
3 cf. https://tokyochallenge.odpt.org/en/index.html

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6.2 CPaaS.io integration

Figure 4: u2-based Architecture for Tokyo Public Transportation: "Open Data Challenge for Public Transportation inTokyo"

Figure 5: High-level application architecture of Tokyo Public Transportation (from D2.1)

Based on the correspondence of the high-level application architecture of “Open Data Challenge of Public
Transportation in Tokyo,” we can explain the validation as follows. Data collection infrastructure is in thelower layer of the architecture (Open Data Distribution Platform). Meta-data is handled in Semantic Dataand Integration Layer. In this hackathon, visualization is done by the applications themselves. That is, theapplications by the contest participants add to the Smart City Services Layer.

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6.3 Performance and stability
Stability and performance are designed on u2-based CPaaS.io architecture. Access to the open data
sources via the open API, and Open API and documentation available on the developer site were available
over the course of contest period and could support approximately more than 800 registrants, i.e.,developers, and additionally the users of the released apps. The system was as robust as the underlying
cloud and proved very reliable.

6.4 Validation chart
In the following table, we list the status of validated requirements as described in deliverable D2.5. Please
note that we only report on the requirements that we marked for validation and that do not relate tofederation.

ID
TPT-2

Description
Multi-lingual data

Fit Criterion
Ratio of multilingual
information in all
information.
Existence of automatictranslation system.

TPT-3

Live data collection anddistribution

Existence of automaticcollection system anddistribution system of live
operation data.

TPT-4

Data format and API
standardization(Metadata + API)

Existence of standard data
format and standard API
specifications.

TPT-5

Secure and robust
communication

TPT-6

Developers' Site

Existence of cryptographic
communication mechanismand fault tolerance
mechanism.
Existence of developers' siteof Tokyo Public
Transportation Open Data.

TPT-7

Data License

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Existence of open data
licenseCPaaS.io

Validation Report
In the “Open Data Challenge for
Public Transportation in Tokyo,”some apps used the automatictranslation features. But wecould not force all the operators
to do so.
The 22 operators offered thelive, up-to-date data and suchdata was made available via thehigh-level applicationarchitecture to developers andapps.
Yes, we have the standard data
format and open API
documentation available at thedeveloper site, and the data
sources were preparedaccording to the specification.
The data, not compliant with thedata format, were converted into
the compliant form in u2-based
CPaaS.io instance to someextent.
Commonly available
cryptographic communication is
used.
Developer site was created forthe contest specifically aside
from the general developer site(https://developer.odpt.org/).
This was accessed by theapproximately 800 registrants toobtain the specification of data
format and API.
Data license was created for thecontest and the data providers
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TPT-8

Third party apps

TPT-15

Efficient query oftransportation open data

TPT-18

Data sharing

TPT-19Semantic data integration

Number of third party apps
or software.
Query latency in sec.

Availability of data sharing
mechanisms
Ontologies for data to besharedand contest participants abided
by it.
About 100 apps were submitted
during the 1st contest.
The data from the public
transportation operators were
provided in real-time including
the up-to-date operation status
and the location of trains andbuses, etc.
The data from 22 transportation
operators was shared.
The ontology for transportation
was published and used forformatting the data from 22transportation operators.

7 Smart Parking – Murcia
Besides the envisioned use cases, we have included a new one regarding a common problem that every
big city has to deal with day by day, the problem of traffic congestion. It is also means as an opportunity
to us by developing a new flexible framework able to handle complex processing tasks splitting them intoeasier to handle ones and distributing them into a shared cloud-edge environment to the different layers.
Murcia is a medium-size city in the south east of Spain with a population of 450.000 residents. This number
increases by about 5.000 residents per year, with a noteworthy distribution of people among the city
districts. Because of this and the fact that Murcia is the capital of the Murcia Region, a dramatic rise ofaccesses to the city centre has been detected in the last years. Day by day, commuters, tourists and families
traveling by car collapse the core of Murcia, wanting to park at commerce, financial and historical areas.
To improve the situation, the aim of this use case is that of taking advantage of the CPaaS.io platform toprovide a service that tracks the state of the free parking spots to provide the drivers with this information
beforehand, so they can better plan where they will park. This will also lead to a more fluid traffic in thecentre of the city, because there will be less drivers wandering, looking for a parking spot in areas that do
not have any available.
To do so, upon our CPaaS.io platform we have created a new service comprising the tasks presented in thefollowing picture. This service uses the FogFlow framework which provides a mechanism to support
dynamic processing flows over cloud and edges. It composites multiple NGSI-based processing tasks toform high level IoT services, orchestrating and optimizing their deployment within a shared cloud-edge
environment.

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Following a bottom-up approach, our Context Broker will provide the information about the registeredprivate parking sites, as well as the daily information about the tickets issued for the regulated parking
zones of the city. Such provision of information is reported with different frequency because of theavailability of the information.
Real-time prediction and estimation tasks will process their corresponding information and will provide
their results to the Parking Site Recommendation Task. This way, when user of this service represented as
the black vehicle, establish the destination, and request an arrival time estimation, the Parking Site
Recommendation Task will take these elements as inputs, as well as his location and will provide the routeto that specific destination.

7.1 Feedback
This use case will provide specific KPIs like the free parking slots in private parking sites, the estimation offree spots in the regulated parking zones of the city, the amount of time spent by the users of the service
to find a free parking spot and park his car, the most frequent destination points.

7.2 CPaaS.io integration
Regarding how Smart Parking is integrated inside the European architecture, Figure 6 shows theaforementioned architecture where the components involved in this use case are highlighted.

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Figure 6: Smart Parking components of EU architecture

According to this figure, devices provide information through their corresponding IoT Agent. This
component handles lightweight protocol messages and translates them to a NGSI representation. In casethe information coming from the parking sites and RPZs is integrated at platform layer, such integration
only involves the IoT Broker component.

Figure 7: Smart Parking flow of information

Following the diagram represented in Figure 7, once the information is distributed along IoTBroker
entities, each FogFlow edge node which have been deployed as virtualized services at edge layer (SP-5) isable to process its corresponding parking availability information to do the aggregating process. For acorrect operation, having the correct information is fundamental (SP-1, SP-2, SP-4).

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This information arrives to the Recommender which makes an analytical process (SP-3) when a request
for a destination is issued by a connected car. It will answer a recommendation of the best area and/or
private parking site according to the information provided in the request.

7.3 Performance and stability
Our development cycle includes peer reviewing and continuous integration. It allows us to detect potential
bugs and deliver a reliable and performing product. We are also having manual test in our lab to ensure
end-to-end functionality.
On average The Things Network handle more than twenty-two thousand devices every day and is running
99% of the time. Furthermore, based on results of the first prototype of the version the CPaaS platform
can handle more than a hundred requests per second without increased latency in any service of theplatform. It is safe to say that there won’t be any issue in using both of these services.
A remark for the during the review meeting in Tokyo regarding the cross-region deployment and scalability
led us to rethink, the solution architecture. The current solution is fulfilling the main functional requirement.
However, the architecture is monolithic, as such it lacks flexibility, harder to maintain, scale and evolve. We
started to work toward a distributed architecture based with FogFlow, which is part of CPaaS. Using the
FogFlow service topology and automation we will have better reliability as well as transferability.

7.4 Validation reportID
SP-1

Description
Data collection
infrastructure

SP-2

Edge processing

SP-3

Cloud processing

SP-4

Communication

SP-5

Virtualized
services

Fit Criterion
Existence of a datacollection
infrastructure
Existence ofdevices able tohandle processing
tasks in the edge.
Cloud services able
to processcomplex tasks
Online availabilityof services anddevices
Support for
virtualized services

ReportReal time information provided by parking sites
as well as daily logs of the expenders of RPZs
Edge nodes responsible for aggregating
information of parking availability and RPZ use.

The Recommender, located at cloud layer, is
responsible for the analytical part of the process
It is fundamental to have reliable and real-time
information regarding availability in order tomake an informed decision regarding the best
parking area
Edge nodes must support virtualized services
allowing them to dynamically run the specifictasks distributed by the FogFlow framework

8 Sapporo Visitor Experience
8.1 Feedback
Sapporo Open Data committee is now on service of data service for foreign tourist. Several open data
hackathon had been held in Sapporo. Some of attendee include citizen and private companies uses open
data and location data of Sapporo and build application. Almost of feedback from attendee were positive,
and many of developer tend to continue to develop application using the data. Many of participants want
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the data platform include open data and citizen service, so Sapporo Tourism Open Data will be a part ofwhole Sapporo City service.

8.2 CPaaS.io integration.

Figure 8 - The above is from WP3 PPT at the 2nd review meeting.

Data collection infrastructure is in the lower layer of the architecture (IoT Resource Layer), using various
sources. Efficient data query is handled by the databases in the Open Data Distribution Platform, while thelinking to virtual entities and metadata is handled in the Semantic Data & Integration Layer. Visualization
finally happens finally in the Service Layer, making also use of the Kokosil component.

8.3

Performance and stability

Stability and performance is designed on CPaaS.io architecture and running on Microsoft Azure as Public
Cloud platform, Basic services is guaranteed by cloud platform itself, and CPaaS architecture support
appropriate data providing that depends on beacon location data and device profile.

8.4 Validation report
In Table 4, we list the status of validated requirements as described in deliverable D2.5. Please note that
we only report on requirements that we marked for validation and that do not relate for federation.

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ID
SVE-1

Description
Data collection
infrastructure

Fit Criterion
Existence of a datacollection
infrastructure

SVE-2

Metadata
management

SVE-3

Visualizationof personal
data andopen data

Method forcollecting metadata
and enriching
sensor data
Visual data accessfor end users

SVE-7

Efficient queryof sightseeing
information

Query latency insec.

Validation Report

The data collection infrastructure is currently realized as
described in the high-level application architecture (seeD2.1) and marker information is also collected to learn
the whereabout of users in underground passages.
The metadata is managed and accessed by the high-levelapplication architecture (see D2.1).
Open data related to tourism and public transportation isincorporated and used for user interaction. In the current
high-level application architecture (See D2.1). Personal
data is not incorporated yet. (being planned for the
future).
In the current implementation, the query latency basedon the high-level application architecture fully satisfies
the visitor experience application requirements. Thisrequirement is extremely important for providing tourism
information as envisioned by the original application.

9 Outlook” For the next review, consider scaling use-cases up, highlight their strongest assets, and consider cross-borderdemonstrations” is the objective set by the committee during the year on review. In this regard, use cases
started to move they inner service to the CPaaS platform as much as possible.

In each use case, owner listen to the feedback of the stakeholders, being users or organisation and started
to design or redesign their solution to match expectation and policies. Currently the accent is put onenhancing the scalability of the solutions and strengthening the value brought to the stakeholder, see theSmart Parking use case.

With the current form of CPaaS, transferability and scalability is made easier by the addition of Fog Flowand the different mapping services. We have good indications that software transferability will bedemonstrate for next review meeting.

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Appendix 1 - Enhanced User Experience, Color run feedback form
Participant details:
Name:
Age:
Gender:
About yourself
1)
2)
3)
4)

What were your thoughts when you first heard about this event?
Why did you decide to join this event?
How would you rate yourself on a fitness scale from 1 to 10 ?
How often do you run (never, occasionally, regularly (once, twice, etc. per week/month …)? Whatdistances do you typically run?
5) What are your favorite colors? List 2 that you like the most and 2 that you don’t like.
The Run6) How did you feel when you reached the start of the color run?
7) How did you feel when you reached the finish of color run?
8) Please give us your energy levels (high , medium , exhausted) over the period of color run. Make
slots of 5 min from start to end and assign energy level and emotions (Happy, disgusted, afraid,
sad etc.) for the same.
9) Between your friends, who was the most active one in the run?
The whole event
10) What do you remember (interesting scene, own sentiment) about the color run? What are yourstrongest memories about it and why?
11) Will you discuss any of your experiences with friends after the event? If yes, what exactly did you
share with them (e.g., run details, color, and venue)? If not why.
12) Did you take any pictures or videos with your phone during event? If yes, did you take pictures of color
stations, other participants? Or did you take any selfies?
13) Would you like to join this event next time? Why? Why not
Miscellaneous14) Was it comfortable to wear all sensors during the run? What was not comfortable?
15) Did you encounter any other issues with the devices of the set up ?
16) Do you have any further comments, suggestions?

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