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

Deliverable D7.2

Data Management Plan (v1)
Version V1.0
December 28th, 2016

ABSTRACT:The CPaaS.io project is articulated around on the one-hand a comprehensive setof real-world scenario where large amount of raw data is produced, and on theother hand, on reasoning and analytics techniques that allows cities to exploitthat data – often referred to as the oil of the 21st Century”- through thegeneration of higher order meaningful knowledge.
This document gives details about all data either collected or generated by the
CPaaS.io project and the strategies put in place in order to manage, sustain andshare this large amount of information. In particular besides reminding thenature and purpose of the 5 CPaaS.io scenarios data-wise, each CPaaS.io partner
provides statement about roles undertaken as far as managing the collected
and generated data is concerned. This preliminary version of this living
document (updated and maintained continuously) will be complemented with
an official final version by the end of the project.

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 D7.2

Data Management Plan (v1)

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

François Carrez (UNIS)
EU consortium
Stephan Haller (BFH)

Delivery Type
Dissemination
Level
Contractual
Delivery Date
ActualDelivery Date
Keywords

R
Publicth

30 December 2016th

28 December 2016
Data management, Data openness, Privacy

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Revision History
Rev.
v0.1
v0.2
v0.3
v0.4
v0.5
v0.6
v0.7
v0.8
v0.9
v1.0

Date
23/11/2016
08/12/2016
08/12/2016
11/12/2016
13/12/2016
13/12/2016
14/12/2016
23/12/2016

H2020 EUJ-02-2016

Description
Extended ToC
Sections 2 and 3 update, AGT
Sections 2 and 3 update, UoS
Section 3 update, BFH
Section 2 and 3 update, TTN
Section 3 update, OdinS
Section 3 update, NEC
Final version for review
Review comments
Final version for delivery

CPaaS.io

Contributors
François Carrez
Martin Strohbach
François Carrez
Stephan Haller
Alexander Overtoom
Antonio Skarmeta
Gurkan Solmaz
François Carrez
Stephan Haller
François Carrez

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Table of Contents
1

Introduction ................................................................................................................................................................................ 5

2

CPaaS.io Research Data .......................................................................................................................................................... 6

3

2.1

Data from Enhanced User Experience application ............................................................................................... 6

2.2

Data from Waterproof Amsterdam ............................................................................................................................ 9

CPaaS.io Research Data management plan .................................................................................................................. 12
3.1

AGT International (AGT)................................................................................................................................................ 12

3.2

University of Surrey (UoS) ............................................................................................................................................ 12

3.3

Bern University of Applied Sciences (BFH) ............................................................................................................ 13

3.4

OdinS ................................................................................................................................................................................... 13

3.5

NEC ....................................................................................................................................................................................... 14

3.6

The Things Network ....................................................................................................................................................... 15

4

Conclusions & Next Steps ................................................................................................................................................... 16

5

References ................................................................................................................................................................................. 16

List of Tables
Table 1: Data collected for Managing Fun and Sport events scenario ........................................................................ 7
Table 2: Data generated for the Enhanced User Experience application .................................................................... 9
Table 3: Data collected for the Waterproof Amsterdam scenario ............................................................................... 10
Table 4: Data generated for the Waterproof Amsterdam application ....................................................................... 11

List of Acronyms
Acronym
DMP
HDF
XML

Definition
Data Management Plan
Hierarchical Data Format
eXtensible Markup Language

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1 Introduction
As described with the H2020 guidelines, Research funding organisations, as well as organisations
undertaking publicly funded research, have an obligation to optimize the use the funds they have been
granted. Part of this optimization is that data sets resulting from public funded research must be madeavailable to other researchers to either verify the original results (which is integral part of proper scientificapproach), or to build upon them.
In order to achieve this high-level objective, a data management policy has to be implemented andthoroughly followed by the CPaaS.io consortium as a whole, even if –per se- not all CPaaS.io partners
will be involved in all aspects of those policies/principles.
The Data Management Plan (DMP) is a living document (with two formal versions of the same deliverable
released in M6 and M30 respectively) that describes the data management policy (i.e. the management
principles) and collected and generated data sets. It covers all aspects introduced in the “Guidelines on
Data Management in Horizon 2020”, which are:
1. Precise description of the collected and generated data (nature of data, related domain
ontologies, standards and data formats used,…)2. Detail about various aspects of the data management (how it is stored, by whom, under which
responsibility, how it is secured, how it is sustained and backed up)
3. Sharing principles (licensing, access methods,...)
4. Detail about how the privacy is maintainedThis first version of the Data Management Plan gives a preliminary description of the data as collected
and generated by both the CPaaS.io platform and project partners through their legacy systems. At thetime of the document editing, some aspects of data management are still under discussion, mainly
because they are strongly depending on some technical decisions pertaining to the CPaaS.io system
platform design and how this architecture deals with partners’ legacy systems as far as storage, backup
and data flows are concerned.
Aspects such as data backup, sustainability, detail about data sharing and archiving will be thoroughly
developed through an intermediary and far more complete version of this deliverable.
In this current version we mainly provide detail (as known at M6) about the scenarios and collected data
(see Section 2) and roles of partners as far as Data Management in CPaaS.io is concerned (see Section 3).
Due to differences between EU and Japanese formal contracts and differences in data-related rules andconstraints, we have focussed in this initial version on scenarios and partners from EU only. However theliving document will aim at harmonizing the different views through a single and consultable internal
document.

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Data Management Plan (v1)2 CPaaS.io Research Data
This section introduces the different EU-side use-cases as described in the CPaaS.io Description of Work
document and the applications built upon them. It also describes the collected data (meaning thesemantically annotated raw-data with no extra added value) and the generated data (meaning thesemantic value-added information built from the annotated raw-data using various technics like analytics
or reasoning). Part of the information described in this section can be found in a more complete form in
CPaaS.io deliverable D2.1 [1].
The two scenarios considered in CPaaS.io for the EU-only side are:Managing Fun and Sport events
Waterproof Amsterdam

And the two derived applications are:

Enhanced User Experience
Waterproof Amsterdam

2.1 Data from Enhanced User Experience application
Short description
The core idea of this application is to use IoT sensors and analytics to enhance people’s experience while
visiting or participating at a fun or sports event. Wearables and mobile phones are used as sensors inorder to learn about the activities of event participants. Event participants may include members of theaudience, but also performing artists or athletes. For instance AGT has previously equipped referees andcheerleaders in basketball matches with wearable sensors and created content based on the analysed
data for consumption on site and for distribution via TV broadcasting, social media and other digital
distribution channels1. Furthermore the application uses sensor deployed at the venue to measure andanalyse fan behaviour and engagement.
Data collected for the Enhanced User Experience application (Color Run)
Table 1 summarizes the data from the Enhanced User Experience application as described in D2.1. Please
note that although the hosting field specifies that the most of the data is hosted external to the CPaaS.io
platform we are considering to use the storage capabilities in the next iterations. Further to the data sets
described in D2.1 we have added an additional mobile camera data set.

1

http://www.euroleague.net/final-four/berlin-2016/news/i/6vokoibj5fsgqg4q/heed-the-event-platform-based-joint-venturebetween-wme-img-and-agt-international-makes-first-official-foray-into-sports

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Table 1: Data collected for Managing Fun and Sport events scenario

Biometric data
Detailed Description

OGD or private data
Personal Data
Hosting
Data Provider
FormatUpdate Frequency
Update Size
Data Source
SensorNumber of Sensors perperson
GPS Traces
Detailed Description
OGD or private data
Personal Data
Hosting
Data Provider
FormatUpdate Frequency
Update Size
Data Source
SensorNumber of Sensors perpersonMotion Data
Detailed Description
OGD or private data
Personal Data
Hosting
Data Provider
FormatUpdate Frequency
Update Size
Data Source
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We will collect a range of biometric measurements fromwearables such as wristbands, chest straps and smart
sportswear that provides biometric measurements including
heart rate, breathing rate and galvanic skin response, burned
calories measurements and skin temperature.
PrivateYes
External
AGT
JSON
up to every 200ms~1 KB
SensorWristband, chest strap, smart shirts~6

GPS traces include positional data including altitude
information as delivered by GPS devices.
PrivateYes
External
AGT
common GPS formats (GPX, KML, CSV, NMEA)
Up to 1s
< 1KB
SensorGPS sensor in wristbands and mobile phones
1-2

Motion data that measures hand and body movements
based on accelerometer and gyroscope sensors
PrivateYes
External
AGT
JSON
Up to every 16 ms~ 200 byte per sensor reading
Sensors
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SensorNumber of Sensors perpersonStep Counts
Detailed Description
OGD or private data
Personal Data
Hosting
Data Provider
FormatUpdate Frequency
Update Size
Data Source
SensorNumber of Sensors perpersonEnvironmental Data
Detailed Description

OGD or private data
Personal Data
Hosting
Data Provider
FormatUpdate Frequency
Update Size
Data Source
SensorNumber of Sensors perpersonMobile Camera videos
Detailed Description
OGD or private data
Personal Data
Hosting
Data Provider
FormatUpdate Frequency
Update Size
Data Source
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Accelerometer and gyroscope sensors of mobile phones,
wristband and other wearables
2-3This data set contains step counts.
PrivateYes
External
AGT
JSON
Up to 1Hz
~ 200 byte per sensor reading
Sensors
Step count measurement of wristband
1-2This data set environmental data such light intensity andbarometric pressure. The data is primarily collected fromwearable sensors.
PrivateYes (tbc)
External
AGT
JSON
Up to 1Hz
~ 200 byte per sensor reading
Sensors
Sensors in wristband
1-2This data set contains videos recorded by mobile cameras
worn by Color Run participants.
PrivateYes
External
AGT
MP4
30fps
(~45kbps)
Mobile Camera
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SensorNumber of Sensors perpersonData Management Plan (v1)

GoPro Hero4 Camera
1

Data generated by the Enhanced User Experience application (Color Run)
The Enhanced User Experience application generates three types of data
1) User Activity2) Dominant Colors
3) Clothing Analysis
User activity is based mainly on motion data and therefore private information. A user activity is always
linked to a user and therefore personal information. The re-use of the data is possible within theboundaries defined in the consent forms used to collect the data.
Dominant Colour provides information about the prevailing colour in a video feed and is used for
detecting colour stations in the Color Run. The output is a colour value, duration and location. The
generated can be provided in anonymised form, but requires further examination to what degree it canbe opened.
Clothing Analysis uses deep learning techniques to determine metrics based on clothing styles derived
from images. By nature this metrics are linked to user and therefore reflect private data that can only bereused in the boundaries of the consent forms used to collect the data.
Table 2: Data generated for the Enhanced User Experience application

Types of generated data

Based on…

Anonymised
Y/N

User Activity

Motion DataN

Dominant Colour

Mobile Camera Videos

Y

Clothing Analysis

Mobile Camera Videos, Public Images

N

Open
Y/N
reusable,but not
open
Reusable,but not
fully open
Reusable,but notopen

2.2 Data from Waterproof Amsterdam
Short description
Extreme rainfall and periods of continued drought are occurring more and more often in urban areas.
Because of the rainfall, peak pressure on a municipality’s sewerage infrastructure needs to be loadbalanced to prevent flooding of streets and basements. With drought, smart water management isrequired to allow for optimal availability of water, both underground as well as above ground.
The Things Network develops the Amsterdam Waterproof application, which is a software tool creating a
network of smart, connected rain buffers, be it rain barrels, retention rooftops or buffer otherwise, thatcan be both monitored and controlled centrally by the water management authority. Third party
hardware providers will connect their buffers to this tool for uplink and downlink data transmission.
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Data Management Plan (v1)External data such as weather data and sewerage capacity are added, in order to calculate the optimal
filling degree of each buffer and so operate a pump or valve in the device. Waternet is the local water
management company who will be the main user of the application.
Data collected for the Waterproof Amsterdam application
In the section below are the data sets used for the Waternet application. It consists of device data (rainbuffer information), public weather data and government data about physical infrastructure. Device data
will be stored in the application and could be stored in CPaaS, especially as it contains private data likename and address of device owner. As this stage however we cannot determine whether this privacy data
will be shared by the vendors of the devices, who are also the ones maintaining them. They are the only
actor who has direct contact with the end user and/or owner of the device. (Historical) weather data ispublicly available on the web, so there is no need to store this data. It will be provided by a subscription
data feed from the web. The third data set is already owned and stored by Waternet, so there is also noneed for storage capabilities.
Table 3: Data collected for the Waterproof Amsterdam scenario

Weather data
Detailed Description
OGD or private data
Personal Data
Hosting
Data Provider
FormatUpdate Frequency
Update Size
Data Source
SensorNumber of Sensors
Rain buffer information
Detailed Description

OGD or private data
Personal Data
Hosting
Data Provider
FormatUpdate Frequency
Update Size
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Upcoming weather displaying periods of heavy rain or
drought
OGD
No
Platform
KNMI – Dutch weather forecast agency
HDF5/JSON
Hourly20kb
Sensors
Water sensor
unknown

Specific information about each rainbuffer (rooftop, barrel,
underground storage) Buffer size and type
 Filling degree
 Temperature Location
 Battery status
 Pump/valve capacity
 Active pump/valve hours
 Owner name, address, contact information
PrivateYes – anonymised and not open
PlatformRain buffer hardware provider
JSON
Hourly10b
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Data Source
SensorNumber of Sensors

Data Management Plan (v1)

Sensors
Water sensor or infrared sensor
1 per buffer

Sewerage processing capacity
Detailed Description
Geographical data on water infrastructure depicting
remaining capacity of sewerage
OGD or private data
PrivatePersonal Data
No
Hosting
External
Data Provider
Waternet
FormatXML
Update Frequency
HourlyUpdate Size
1kb
Data Source
Sensors, maps
Sensor
Water sensor
Number of Sensors
unknown
Data generated by the Waterproof Amsterdam application
The Waterproof Amsterdam generates different types of data.
1. Open/close command per buffer. This is the most important data generated, as it determines
when an actuator inside a buffer should be operated (valve open or pump on). Based on all data
sources available, an algorithm will determine which conditions are required to perform a certain
command. The commands can be open and close, or a value in between as different water
discharge mechanisms have different capacities (i.e. a percentage of full capacity)
2. Aggregated remaining buffer capacity per area. Waternet as the primary user of the applicationneeds to monitor the total remaining capacity to buffer rain water, to understand whether there
will be plenty capacity to catch up rain water in moments of heavy rainfall.
3. Aggregated litres of rain water processed per area. This is a metric to be used to show the impact
the micro buffer network has generated over time. These insights may be used for PR andmarketing purposes to stimulate individuals and companies to also buy and install such rain
buffers.
The open data in the table below can be reused to perform analytics on historical data, and could be
open data through a public (graphical or application) interface for third parties to interact with.
Table 4: Data generated for the Waterproof Amsterdam application

Types of generated data

Based on…

Open/close command per buffer

All data sets

Aggregated remaining buffer capacity(street, area, city level)
Aggregated litres processed by the
buffers

Individual rain buffers filling degreeand location, map
Individual rain buffer pump hours run
and pump capacity, map

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Anonymised
Y/N

Open
Y/N

Y

N

Y

Y

Y

Y

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3 CPaaS.io Research Data management plan
CPaaS.io project follows the principle that research data will be handled and managed by thoseorganisations/institutions that either collects or generates the research data. The CPaaS.io projectcomprise a number of partners that are involved directly in either:Producing the actual data during the trials, orDeveloping tools and enablers (e.g. analytics, reasoners, etc.) that are needed as core elements in
the CPaaS.io system architecture, orElaborating upon the produced data (using the aforementioned enablers) in order to produce
new value-added knowledge.

The individual roles and duties of such partners and the research data management plans that are inplace in the organisations taking part in CPaaS.io are described in the following sub-sections.

3.1 AGT International (AGT)
Data collection (from sensors)
The data collected by AGT has been described in Section 2.1 and is used for generated the data as
described in Table 2 and for developing the Enhanced User Experience application. As described in D2.2
the collected data is enriched with additional metadata.
Data generation
The data generated by AGT has been described in Table 2 and is used in the Enhanced User Experience
application.
Data Management
We have implemented appropriate technical and organizational measures to ensure generated data is
protected from unauthorized or unlawful processing, accidental loss, destruction or damage. We review
our information collection, storage and processing practices regularly, including physical security
measures, to guard against unauthorized access to our systems. We restrict access to generated data to
only those employees, contractors and agents who strictly need access to this information, and who aresubject to strict contractual confidentiality obligations.

3.2 University of Surrey (UoS)
ICS at the University of Surrey is not involved neither in the production of raw data nor in the
exploitation or generation of higher-level information out of it. However, UoS is focussing onarchitecture work where particular attention is paid to ensuring that 1/ all privacy-related requirements
are thoroughly taken into account 2/ important part of the data is publicly available following the projectOpen Data policy.
To this respect UoS is aiming at providing a bridge between CPaaS.io and another FIRE project called
FIESTA-IoT, two projects where UoS is actively involved. UoS will in particular aim at involving CPaaS.io ineither the 2nd Open Call of FIESTA-IoT or as a fellow contributor to that project via a cooperationagreement to be discussed between the two projects after both POs have been consulted on that matter.
In both cases, CPaaS.io could play two non-exclusive distinct roles:

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Data-provider: playing this role the CPaaS.io project would inject its data or part of its data (either
raw data or inferred data) to the FIESTA-IoT so that so-called experimenters can make use of it
using the FIESTA-IoT enablers; orExperimenter: playing this role, CPaaS.io could reuse additional data sets produced by the FIESTAIoT collaborators for testing our new own algorithms (e.g. Analytics) and techniques.

Data collection (from sensors)
UoS does not participate in any data collection
Data generation
UoS does not generate any new data from the project data sets
Data Management
UoS does not manage any gathered or generated data

3.3 Bern University of Applied Sciences (BFH)
The BFH is not directly involved in the implementation of the envisaged use cases. Its main research
focus is in the data management concepts – in particular the usage of Linked Data and Open
Government Data as well as data quality annotations, the application of MyData approaches, and in thevalidation of the use cases. Hence it is not collecting, generating or storing any data.
However, as part of its exploitation, validation and knowledge transfer activities, BFH is planning toconnect some sensors via the LoRa testbed network that another institute (Institute for Energy andMobility Research in Biel) is currently setting up. What data will be collected and for what purposes
exactly will be defined at a later stage; a related data management plan will be drawn up before any datacollection starts.
Data collection
BFH is not collecting any data for the main use cases of CPaaS.io. It may collect and make available some
sensor data through the LoRa network at BFH for testing and validation purposes; details will be
determined at a later stage.
Data generation
BFH is not generating any data for the main use cases of CPaaS.io. It may link public data sources (e.g.,
from the Swiss Open Government Data portal at www.opendata.swiss) with the sensor data collected
through the LoRa network at BFH for testing and validation purposes; potential use cases will be
determined at a later stage.
Data Management
BFH is not managing any data for the main use cases of CPaaS.io. Data collected and generated fortesting and validation purposes through the LoRa network at BFH will likely be made available publicly, inthe spirit of open data research, unless the data could allow to infer any information about individuals.
Details are to be determined at a later stage.

3.4 OdinS
OdinS as a partner involved on the security and privacy aspects, will check and support the project tocheck that data access and sharing activities will be implemented in compliance with the privacy anddata collection rules and regulations, as they are applied nationally and in the EU, as well as with
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Data Management Plan (v1)the H2020 rules. Concerning the results of the project, these will become publicly available based on the
IPRs as described in the Consortium Agreement.
Due to the nature of the data involved, some of the results that will be generated by each project phasewill be restricted to authorized users, while other results will be publicly available. Data access andsharing activities will be rigorously implemented in compliance with the privacy and data collection rules
and regulations, as they are applied nationally and in the EU, as well as with the H2020 rules.

Data collection (from sensors)
OdinS will not be involved in the data generation of data from sensors, working exclusively in thearchitecture aspects of the data collections and its consequence over the security and privacy
components.
Data generation
OdinS is not involved in the production of raw data, but as part of the Task 4.1 User Empowerment
Component Definition and the definition of access control policies and use consent solution, OdinS will
generate information associated to data for controlling access and sharing data between entities andcomponents that will use the platform.
Data Management
As the raw data included in the data sources, will be gathered from sensor nodes and information
management systems, those could be seen as highly sensitive. Therefore, access to raw data can only
take place between the specific end users based on the policies associated and the partners involved in
the analysis of the data. For the models to function correctly, the data will have to be included into the
CPaaS.io repository. The results of the data analytics are set to be anonymised and made available to thesubsequent layers of the framework, which will then allow the possibility for external industry
stakeholders to use the results of the project for their own purposes.

3.5 NEC
NEC is not directly involved in the production or raw data. NEC’s focuses are in the architecture (system
integration including transferability and semantic interoperability) area and cloud-edge processing of thedata. FIWARE’s resources such as the Generic Enablers and NEC’s IoT Platform can support storage andexploitation of data from use cases for generating higher-level analytical results. NEC pays particularattention to privacy related requirements as well as the Open Data policy of CPaaS.io.
Data collection
NEC is not planning to collect any raw data for the use cases of CPaaS.io.
Data generation
NEC is not generating data for the main use cases, NEC may exploit shared data from use cases andgenerate higher level data as a result. Potential use cases will be determined at a later stage.
Data management
While NEC is not directly involved with the use cases, it will take part in data transferability andmanagement via the provided IoT Platform. NEC has implemented necessary organizational andtechnical measures for the usage of the data and its protection from unauthorized persons.

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3.6 The Things Network
Data collection (from sensors)
The data collected by The Things Network has been described in Section 2.2 and is used for generated
the data as described in table 2 and for developing the Waterproof Amsterdam application. As describedin D2.4 the collected data is enriched with additional metadata.
Data generation
The data generated by The Things Network has been described in and is used in the Waterproof
Amsterdam application. Private data from owners of a rain buffer is anonymised. Based on an algorithm,
data from various sources is processed by the application to determine the optimal filling degree foreach individual rain buffer. The results may be used for automated control of buffers, or push
notifications to trigger manual control.
Data Management
Open data such as weather data will be streamed into the application and not stored locally.
Private data from external sources such as device location will be stored in the application and only
released in an anonymised and aggregated manner. Personal details about a device, such as name,address and contact details will also be stored in the application in a secure account server. These data
may be transferred to CPaaS.io at some time, easing security and privacy demands on the application
end and transferring those to CPaaS.io
Parts of the personal data, such as buffer location, size and processed litres, will be released in an
aggregated, anonymised manner (e.g. on a heat map) per area of a city or the city as a whole.
Readily available data from Waternet about sewerage capacity will abide by the policies of Waternet.
These policies are not yet clear at the moment.
We restrict access to generated data to only those employees, contractors and agents who strictly needaccess to this information, and who are subject to strict contractual confidentiality obligations.

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4 Conclusions & Next StepsIn this deliverable we presented the CPaaS.io approach towards data management as handled by the EU
CPaaS.io consortium. However at this early stage (M6), we do not have yet very precise information
about the data collected or generated by the project. Some functional aspects are also still underdiscussion which prevents giving much detail about type and location of data storage, backup
procedures, techniques used for generating data, and architecture-related detail in general.
However, being a living document, future iterations of this deliverable (even if not official deliverables)
will provide increasing level of detail about all data sets collected and generated by the project (including
the Japanese part, in order to provide a complete view). We will hopefully also be able to describe verysoon pre-requisite for reusing the public data sets and possibly concrete example of such reuse by thirdparties (some contacts have been already taken with the FIESTA-IoT FIRE H2020 project for instance).

5 References[1]

CPaaS.io Deliverable D2.1: “Requirements Specification”.

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