Deliverable D3.3

CPaaS.io System Architecture (v2)

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

Deliverable D3.3

CPaaS.io System Architecture(v2)
Version V2.0
September 30th, 2017

ABSTRACTThis third WP3 deliverable provides an update of the initial CPaaS.io architecture
D3.2. The architecture work follows the requirement collection phase (as alreadysummarised in Deliverable D3.1) and improves the previous version of thearchitecture, capturing the work achieved by other technical Work Packages
since D3.2 was released. This document provides some elements of the usedmethodology and then provides both the updated Functional and Information
Views, together with some Perspectives (including security and interoperability
in particular). The Functional View gives a second view about which functional
components need to be implemented in order to fulfil functional requirements.
Perspectives on the other hand focuses on how (through strategies and tactics)
the non-functional requirements can be fulfilled.
As explained in the DoW, we are using the IoT ARM methodology, which meansthat this document features a technology-agnostic logical architecture - which isagreed upon by both parties (Japan and EU) - in addition to two concrete
instantiations of this logical architecture (later referred as u2-based and
FIWARE-based concrete architectures) which are formally captured within two
so-called Instantiation Views.
Finally an accompanying technical annex in the form of a Volere template, keeps
track of the functional and non-functional requirement coverage, throughrespectively the architecture Views and Perspectives. This template is the resultof the requirement process, part of the IoT ARM methodology.
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/.

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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
EditorAuthors

François Carrez (UoS)
Editor +A. Skarmeta, J-A. Martinez, M-A. Zamora, A. Canovas (OdinS);
M. Strohbach (AGT),
S. Haller, M. Fraefel, A. Gschwend (BFH);
G. Solmaz, B. Cheng, M. Bauer (NEC);N. Koshizuka (UoT)
K. Shindo (YRP)

Reviewers

Stephan Haller (BFH);
Katsunori Shindo (YRP)

Delivery Type
Dissemination
Level
Contractual
Delivery Date
ActualDelivery Date
Keywords

R
Publicth

September 30 , 2017thSeptember 30 , 2017
Requirement analysis, functional decomposition, system use-cases,
system architecture, concrete platform instantiations, IoT ARM

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Revision History
Rev.
v0.1
v0.2
v0.3

Date
06/04/2017
12/06/2017
29/06/2017

Description
updated ToC
reshaping after Geneva F2F meeting
reshaping and updating

Contributors
F. Carrez (UoS)
F. Carrez (UoS)
M. Strohbach (AGT), F. Carrez
(UoS)

v0.4
v0.5

18/08/2017
21/09/2017

Updated inputs related to FIWARE
Updated inputs related to security components

v0.6
v0.7
v0.8

22/09/2017
24/09/2017
25/09/2017

Update of Edge Computing related material
Update Section 4 and alignment with Section 6
latest updates Sections 4 and 6 + fine tuning before
internal review

G. Solmaz (NEC)J.A. Martinez, A. Cánovas, M-A.
Zamora, A. Skarmeta (OdinS)
B. Cheng (NEC)F. Carrez (UoS)
A. Gschend (BFH). B. Cheng &G. Solmaz (NEC), F. Carrez
(UoS)

v0.9
v0.9b
v1.0

28/09/2017
29/09/2017
29/09/2017

Review comments
Update of the Japanese partner side (Section 6.1)
Final version for submission to EC

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K. Shindo (YRP)
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Table of Contents
1

Introduction ................................................................................................................................................................................ 8
1.1

2

3

4

5

6

Delta with previous version D3.2 ................................................................................................................................ 9

Methodology ............................................................................................................................................................................ 10
2.1

Introduction to the IoT ARM ...................................................................................................................................... 10

2.2

Using the IoT ARM in the context of CPaaS.io .................................................................................................... 20

CPaaS.io Requirements Analysis and Mapping ........................................................................................................... 20
3.1

Summary of requirement collection phase ........................................................................................................... 20

3.2

Requirement Analysis .................................................................................................................................................... 29

CPaaS.io Views ......................................................................................................................................................................... 30
4.1

Actors involved in data production.......................................................................................................................... 30

4.2

CPaaS.io Functional View ............................................................................................................................................. 31

4.3

CPaaS.io Information View .......................................................................................................................................... 40

Perspectives............................................................................................................................................................................... 50
5.1

Security ............................................................................................................................................................................... 50

5.2

Semantic Interoperability ............................................................................................................................................. 52

5.3

Performance ...................................................................................................................................................................... 52

5.4

Scalability ........................................................................................................................................................................... 53

Instantiation views .................................................................................................................................................................. 53
6.1

U2-based platform Instantiation View .................................................................................................................... 53

6.2

FIWARE-based platform Instantiation View .......................................................................................................... 62

7

Conclusions & Next Steps ................................................................................................................................................... 82

8

References .................................................................................................................................................................................83

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List of Figures
Figure 1: Simplified process leading to generating a concrete architecture ........................................................... 10
Figure 2: Simplified IoT Domain Model (feat. most important concepts) ................................................................ 11
Figure 3: IoT Domain Model (as in IoT-A D1.5 [1]) ............................................................................................................ 13
Figure 4: IoT Information Model (as in IoT-A D1.5 [1]) .................................................................................................... 14
Figure 5: IoT Functional Model (as in IoT-A D1.5 [1]) ....................................................................................................... 16
Figure 6: IoT Functional View (as in IoT-A D1.5 [1]) .......................................................................................................... 17
Figure 7: CPaaS.io logical functional architecture .............................................................................................................. 32
Figure 8: UML UC for Class-I RDP pre-configuration case ............................................................................................. 42
Figure 9: UML UC for Class-II RDP pre-configuration case ............................................................................................ 43
Figure 10: UML UC for Class-III RDP pre-configuration phase ..................................................................................... 44
Figure 11: Data production for Class-I RDPs........................................................................................................................ 46
Figure 12: Data production for Class-II RDP ........................................................................................................................ 47
Figure 13: u2 Architecture (ucR focus) ................................................................................................................................... 55
Figure 14: u2 architecture (IoT Aggregator focus)............................................................................................................. 56
Figure 15: u2 architecture ("Omotenashi" Cloud focus) .................................................................................................. 57
Figure 16: IoT Engine with IoT Engine Open Platform ..................................................................................................... 58
Figure 17: uID architecture ......................................................................................................................................................... 59
Figure 18: Matching of the u2 components to the CPaaS.io Functional View........................................................ 60
Figure 19: FogFlow Service Orchestrator............................................................................................................................... 66
Figure 20: Task Registration ....................................................................................................................................................... 67
Figure 21: Graphical editor of service topology ................................................................................................................. 68
Figure 22: Defining a requirement with a specified geo-scope.................................................................................... 68
Figure 23: FIWARE-based CPaaS.io architecture ................................................................................................................ 72
Figure 24: Matching of the FIWARE components to the CPaaS.io Functional View ............................................. 74
Figure 25: Interactions of the FIWARE FCs............................................................................................................................75
Figure 26. PAP Web configuration interface ........................................................................................................................ 79

List of Tables
Table 1: List of Non-Functional Requirements .................................................................................................................... 25
Table 2: List of Functional Requirements .............................................................................................................................. 27

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List of Acronyms
Acronym
ABE
ACP
AE
API
AuthN
AuthZ
BC
IoT ARM
CapBAC
CEP
CP-ABE
CPU
CRM
CSF
CSV
CT
DC
DoW
FC
FG
FREQ
FV
GDLI
GE
GUI
HFDS
HSM
IdM
IoT
IoT-A
JSON
KAT
IdM
IV
LDLI
LOD
M2M
MPU
NFREQ
NGO
NGSI
OMA
OMG
PAP
PDP
PDS
PEP
PE
PKC

Definition
Attribute Based Encryption
Access Control Policy
Augmented Entity
Application Programming Interface
AuthenticatioN
AuthoriZation
Business CaseIoT Architectural Reference Modelcapability-based access control model
Complex Event Processing
Ciphertext-Policy Attribute-Base Encryption
Central Process Unit
Customer Relationship Management
Common Service Function
Comma Separated Values
Capability Token
Design Constraints
Description of Work
Functional Component
Functional Group
Functional REQuirement
Functional View
Governement Data Listener & Ingestion
General Enabler
Graphical User Interface
Highly Functionally Distributed System
Hardware Security Module
Identity Management
Internet of Things
Internet of Things – Architecture
JavaScript Object Notation
Knowledge Acquisition Toolkit
Identity Management
Information View
Live Data Listener & Ingestion
Linked Open Data
Machine to Machine
Micro Processor Unit
Non Functional REQuirement
Non-Governmental Organisation
Next Generation Service Interface
Open Mobile AllianceObject Management Group
Policy Administration Point
Policy Decision Point
Personal Data Store
Policy Enforcement Point
Physical Entity
Public Key Cryptography

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Acronym
PKI
RDF
RDFS
RDP
RTOS
SCIM
SCP
SKC
SotA
SPARQL
SSN
UC
VE
VRM
XACML

CPaaS.io System Architecture (v2)

Definition
Public Key Infrastructure
Resource Description Framework
RDF Schema
Raw Data Producer (or Provider)
Real-Time Operating System
System for Cross-domain Identity Management(ETSI) Smart Card Platform
Symmetric Key Cryptography
State of the Art
SPARQL Protocol and RDF Query Language
Semantic Sensor Network
Use-Case
Virtual Entity
Vendor Relationship Management
eXtensible Access Control Markup Language

List of Editing Shortcuts
Shortcut
e.g.
etc
feat.
Gov
h/w
i.e.
incl.
p/f
resp.
s/w
tbd
vs.
w.r.t.

Definition
exempli gratia – for example
et ceterafeaturing
Government
hardwareid est – that isincludingplatform
respectively
softwareto be discussed/decided
versuswith respect to

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1 Introduction
This Deliverable D3.3 is the second version of the incremental architecture document that was initiatedwith D3.2. It will be completed and improved within two additional versions, D3.5 and D3.7 respectively
due M25 and M30.
Leveraging on D3.2, this version goes deeper in the CPaaS.io architecture, introducing more components
and more detail (see the following sub-section that goes in the detail of the delta between D3.3 andD3.5).
In the CPaaS.io project, we are reusing the architectural approach promoted by the IoT-A FP7 projectcalled the Internet of Things Architectural Reference Model (IoT ARM). As we explain in Section 2, the IoT
ARM is not an IoT architecture as such; instead it provides a whole methodology and framework forcreating such an IoT Architecture.
While an architecture team usually uses the IoT ARM for creating one single concrete architecture
considering one single use case (targeted product), CPaaS.io has to face an additional difficulty as the
project will produce two different concrete platform architectures: one for the Japanese side, based on
u2 technology and a second one for the European side based on FIWARE. The IoT ARM is therefore atool that helps the two involved communities for reaching common understanding and agreeing on themain architecture principles. The scope of this agreement includes:
•
•
•
•
•

•

A taxonomy of concepts (Domain Model);A logical structure for dealing with information (Information Model);A functional decomposition into logical components (Functional View);
System use-cases that illustrate interactions taking place between functional components forsome typical platform usage (Information View);
A strategy for ensuring interoperability, since the two concrete instantiations of a common“abstract & logical” platform architecture must eventually talk with each other and understandeach other in order to implement the interoperability and federation principles described as oneof the main technical objectives of the CPaaS.io project;
Strategies for ensuring additional system qualities (Perspectives).

The two later aspects – fully developed in the second iteration of the architecture- are usually dealt with,
with the adoption of common or compatible design and technology choices (e.g. adopting RDF as asemantic data internal format and JSON-LD for serialisation) and the definition of common interfaces(e.g. adopting REST, SPARQL etc.).
Finally this document is accompanied with a technical annex (Volere MicroSoft Excel document) thatsummarises the functional and non-functional requirements resulting from earlier project activities (seeD2.1 [2] and D3.1 [4] for more detail about Requirement collection and analysis).
This deliverable is then structured as follows.
Section 2 gives an introduction to the IoT ARM and in particular details the process part of the IoT ARM
methodology, focussing on requirement engineering, views and perspective definition.

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Section 3 focuses on requirements and provides some insights about the analysis of requirements
collected earlier form the scenario holders on the one hand and on the other hand on functional/nonfunctional requirements as seen from the platform perspective. It also shows the current coverage of theset of requirements by the functional decomposition discussed in Section 4.2.
Section 4 provides the revised views of the CPaaS.io architecture. The functional view depicts anddescribes a preliminary functional decomposition following the IoT ARM layered Functional Model, while
the Information View, at this stage, mainly consists of a collection of system use-cases that describeinter-FCs interactions for a selected set of basic activities at platform level, involving not only theplatform, but additional actors like platform clients and data producers as well.
Section 5 consists of the architecture Perspectives and gives insights about how the different nonfunctional properties of the system are dealt with.
Section 6 is an important section that bridges the common logical and abstract view upon the CPaaS.io
architecture to two distinct – still compatible- instantiations of it. It describes in particular the various
reused components (u2- and FIWARE- based technologies) and spots some new ones, which will be in
the centre of CPaaS.io specific software developments. It also shows how the concrete sets ofcomponents maps to the abstract and logical functional decomposition.
Finally Section 7 provides a conclusion and in particular gives some hints about the few limitations andgaps of this preliminary version. It also details next steps towards CPaaS.io architecture v3.

1.1 Delta with previous version D3.2
Deliverables describing the CPaaS.io architecture, namely D3.2, D3.3, D3.5 and D3.7, are incremental
documents. This section aims at describing updates brought since the previous version:
•
•

•
•
•
•
•

Section 4.1.1: Updated classification of RDPs;
Section 4.2: The list of functional components (and their descriptions) has been updated. The
Functional Architecture figure has been aligned to this updated list; Change in the classification of
Raw Data Producers;
Section 4.3.1: Revised and updated system Use Cases (aligned with their instantiated counterpartsin section 6);
Section 4.3.2: Short overview of foreseen usage for CPaaS.io ontologies;
Section 6.x.1, 6.x.2: Update to the two U2- & FIWARE- based platform instantiation views (moreFCs, adjustment, FC interfaces & functionalities);
Section 6.x.3: revised mapping tables;
Section 6.x.4: new sections with few useful and typical concrete system Use Cases (to be extended
in following versions).

The general idea of having System Use Cases described in this document is two-fold:••

Having a common understanding on how the platform behaves (meaning how FCs interacts witheach other) at both logical and concrete levels (that’s why system UCs appears in two different
sections, respectively section 4.2 and 6);
Proposing to CPaaS.io users, a catalogue of usage patterns (or reference implementations) thatcan be referred to when implementing new scenarios.

UCs consist of UML use cases and sequence diagrams.
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2 Methodology
2.1 Introduction to the IoT ARM
The IoT ARM methodology (see an overview of the whole process in Figure 1 below) covers the full
requirement process as introduced and described already in D3.1 [4], but also covers everything about
elaborating architectural Views and Perspectives. In the following sections we remind some aspects of
the requirement process (the one covered in D3.2) and introduce the Reference Model and Reference
Architecture (which ultimately makes the system architecture).

Figure 1: Simplified process leading to generating a concrete architecture

In this picture we have preliminary activities which start with the elaboration of architectural views(Physical Entity and Context Views – see Sections 2.1.4.3 & 2.1.4.4 for more details about those views)
that are depending on the scenarios and which basically describe in detail the aspects relating to IoT(what are the objects, what are the properties of interests, what is the hardware needed for capturing
those properties, how do we virtualise those objects, what are the actors outside the system and how dothey interact with the system etc.). Then based on those scenario specific information and businessconsiderations, we start a Requirement Process. This process is described in former deliverables D2.1 andD3.1 [2] [4] and consists of 3 steps: requirement collection (tackled in the two deliverables aforementioned), analysis and mapping (see sub-sections below). Finally starts the elaboration of remainingarchitectural Views (Functional, Information, Instantiation and Deployment & Operation Views 1).
2.1.1 Requirement Analysis
As soon as the raw material has been collected (see D2.1 and D3.1) there is a need for reviewing thoserequirements, removing duplicates, unifying/factorizing similar ones and translating/rewriting all
requirements using unified terms and concepts as introduced within the IoT ARM (Domain Model inparticular). The result of this phase is a collection of unified system requirements collected andsummarised within the Volere template (which is a technical annex to this document).

1

Deployment & Operation will be called Deployment View in the rest of this document

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2.1.2 Requirement Mapping
Functional unified requirements need to be mapped to the Functional View (FV) where one or more
Functional Groups (FG) and Functional Components (FC) can be identified. This will result into a firstfunctional decomposition of the targeted system. Some of the non-functional requirements can bemapped to the Information and Deployment Views. The CPaaS.io requirement analysis and mapping arecovered in Section 3.
2.1.3 IoT Reference ModelIn the IoT ARM methodology, the IoT Reference Model (RM) consists of a set of models which are used todescribe aspects of the IoT field which are meant to be agreed upon by architecture team members. In
the context of CPaaS.io we consider – for sake of simplicity – only three of them, namely the IoT Domain,
Information and Functional Models.
2.1.3.1 IoT Domain Model
The purpose of the IoT Domain Model (DM) as proposed by IoT-A [1] [10] is to introduce the concepts
pertaining to the IoT domain (see Figure 3 below) and the different relationships between thoseconcepts. Among the different concepts introduced by the DM, it is important to remind of the following
main ones (see a simplified view of the Domain Model in Figure 2 below):

Figure 2: Simplified IoT Domain Model (feat. most important concepts)

Physical Entities (PEs): Physical Entities are the objects from the real world that can be sensed and forwhich different aspects can be measured. They are virtualized in cyber-space using Virtual Entities.
Examples of PEs from the CPaaS.io project include people and geographical areas.
Virtual Entities (VEs): VEs are at the heart of an IoT system. They represent the PEs in the virtual world.
Aspects of the PE are captured by VE properties, and using sensors and actuators allows one to bridge
the physical and logical worlds and then to act on (or read about) properties. At the level of the VE, wewill consider a special kind of service, called a VE Service, which is used to manipulate or access those
properties. It is important to mention here that it is not compulsory that data providers provide
modelling of PEs into VEs and manage the associated VE Services. However it is highly important that
CPaaS.io provides the means for doing so. Actually the activity of defining VEs and their associatedproperties and then binding these properties to sensor readings for instance can be endorsed byadditional CPaaS.io actors bringing higher level of abstraction and added value services to the end-usersof the CPaaS.io platforms.
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IoT Devices: In CPaaS.io, IoT devices are the hardware supporting the sensing and actuation functions.
Micro-controllers, batteries, ROM memory etc. are also devices (but without the IoT prefix).
IoT Resources: IoT Resources are the software embedded in IoT Devices that provides the raw readings(for sensors) and actuations. The IoT Domain Model advises not accessing directly resources, but on thecontrary to access corresponding Resource-centric IoT Services (see below).
IoT Services: We can consider different kinds of IoT services depending on their level of abstraction:••

Resource-centric IoT services (r-IoT Service) are exposing the IoT Resources using
standardized interfaces and possibly adding metadata to the raw reading available at theresource level. They all connect to a sole resource (sensor or actuator). For instance getting
the reading of a temperature sensor (e.g. via a REST interface) is accomplished through an rIoT Service;
VE-centric IoT Services (ve-IoT Service) are associated to the VEs and are used for accessing
VEs attributes/status or to access VE-level services not directly connected to VEs attribute orsituation. In the Functional View the VE Service FC deals with such accesses. Getting the valueof the “hasTemperature” property of a room VE is an example of a ve-IoT Service.

Note: In the rest of this document IoT Services and VE Services are to be understood as respectively rIoT Services and ve-IoT Services.
Both kinds of IoT Services described above should be associated with service descriptions that can beused to discover particular sensing/actuation capabilities (as recommended by the Information Model).
Services: Services (without IoT prefix) are associated to VEs but do not relate to specific properties as
illustrated in the example above. Services are not part of the IoT Domain Model but could be added to
the global picture for the sake of clarity. For instance autonomous objects (with cognitive capabilities)
may expose services that do not relate de facto to any of their VE properties.
User: Different kinds of users are expected to interact with the CPaaS.io platform.

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User
0..*

0..*

invokes /subscribes
Human
User
DigitalArtefact

interacts
with
ActiveDigitalArtefact

contains

Passive
DigitalArtefact0..*Augmented
0..*
Entity1..*0..*Serviceisassociated
with

0..*

XOR

1

contains
contains
0..*0..1 Virtual

exposes1..*1

1
represents

Entity

0..1
0..*

1

1..*

0..*

0..*

is
attached
to

0..*

0..*
PhysicalEntity0..*0..*0..* 0..10..*
contains
Resource0..*is0..* associated
with

0..1
hosts

0..*

0..*
monitors

Device10..*
Network
Resource

On-Device
Resource

0..*
Actuator

Tag

reads
0..*

0..*
has Information
about / acts on

Sensor0..*
identifies

0..*
acts
on

Colour Scheme

Animate objects (humans, animals etc.)
Hardware
SoftwareNot clearly classifiable (e.g., combination)

Figure 3: IoT Domain Model (as in IoT-A D1.5 [1])2.1.3.2 IoT Information Model
The Information Model (IM) (see Figure 4 below) focuses on the description of the structure of Virtual
Entities as a representation in the cyber space of Physical Entities. The representation of the information(either it is encoded in eXtensible Markup Language - XML, RDF, binary or any other format) is kept
away from the Information Model and left to the architect’s choice, as part of the semanticinteroperability perspective.
The central part of the IM (referring to Figure 4 below) consists of the structure of the VirtualEntity
which is modelled using a set of Attributes and which are associated (via the Association
relationship) to the so-called Service Description. These associations are essential in the IoT IM as
they make the binding between a VE property – which as the name suggests is at the VE level – and a
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CPaaS.io System Architecture (v2)corresponding IoT Service (meaning a service exposing an IoT Resource), which is at the resource level.
This association must be managed in a dynamic way so that the binding between a VE property(attribute) and an IoT Resource (via an IoT Service) can vary in time.
The Attribute is the aggregation of one-to-many ValueContainers. Each of those containers
contains one single Value and one-to-many MetaData (e.g. time stamp, location, accuracy, etc.).
VEs are described using a ServiceDescriptions where each Service would be characterised (e.g. by
its interface) or any useful information that a look-up service can exploit.
As an IoT Service is exposing IoT Resources, which are themselves hosted by IoT Devices, the IM
authorises ServiceDescription to contain 0 to many ResourceDescription(s) and Resource
Description to contain 0-to-many DeviceDescription(s). The structure of descriptions is notconstrained by the IM and therefore left to the architect’s own choice.
Value

1

VirtualEntity
entityType
identifier

ValueContainer

Attribute
attributeName
0..* attributeType1..*

Association
serviceType0..*
Service
Description

MetaData
metadataName
metadataType
metadataValue

metadataMetadata

0..*

0..*
DeviceDescription

Resource
Description
0..1

Figure 4: IoT Information Model (as in IoT-A D1.5 [1])2.1.3.3 IoT Functional Model
The Functional Model (FM) (see Figure 5 below) proposed by IoT-A corresponds to a service-oriented
approach of IoT. It identifies 7 main Functional Groups (FGs) and 2 additional ones that are kept outside
the scope of the IoT ARM. The purpose of the section is to introduce the different layers (FG) of the FM
as they are central to the functional decomposition achieved later on in the Functional View (see Section
4.2).
The Functional Groups are defined as follows [1]:H2020 EUJ-02-2016

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•

•

•

•

•

•
•

CPaaS.io System Architecture (v2)

IoT Process Management FG: The purpose of this FG is to allow the integration of process
management systems with the IoT platform. For example, the formal definition of a task-based
application (supported by CPaaS.io platform) would fall into this category;
Service Organisation FG: This FG is responsible for composing and orchestrating services, acting
as a communication hub between other FGs. The execution of an application described within the
IoT Process Management FG would take place in this FG, like any other kind ofchoreography/orchestration engine;
Virtual Entity FG: This FG relates to VEs as defined in the IoT Domain model, and contains
functionalities such as discovering VEs and their associations with IoT Services. This FG also allows
access to the VE Service offered (formally “associated with”) by a Virtual Entity. In CPaaS.io those
VE Services can be accessed via a VE endpoint;
IoT Service FG: The IoT Service FG contains functions relating to Resource-centric IoT Services.
Those services expose the resources like sensors and actuators and provide the means for reading
sensor values or setting actuation. It also contains storage capability functionality. Morespecifically the IoT ARM states that: “A particular type of IoT Service can be the Resource history
storage that provides storage capabilities for the measurements generated by resources”;
Communication FG: The Communication FG is used to abstract the communication mechanisms
used by the IoT Devices. Communication technologies used between applications and other FGs
is out of scope for this FG as these are considered to be typical Internet technologies. A central
message bus offering publish/subscribe functionalities would also be part of this FG as we will see
when describing the CPaaS.io Functional View;
Security FG: The Security “transversal” FG is responsible for ensuring the security and privacy of
IoT- compliant systems. The management of security itself is also part of this FG;
Management FG: The Management “transversal” FG contains components dealing withconfiguration, faults, reporting, membership and state. It should be mentioned here that this FG
works in tight cooperation with the Security FG.

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Application

IoT
Process
Management

VirtualEntityIoTServiceSecurity

Management

ServiceOrganisation

Communication

DeviceFigure 5: IoT Functional Model (as in IoT-A D1.5 [1])2.1.4 IoT ARM Views
The IoT ARM comes with a comprehensive list of architectural Views (see Figure 1). However not all Views
will be touched in this document; the description of the CPaaS.io-related Physical Entity and Context
Views, which are scenario specific will fall in the scope of deliverables relating to scenario
implementation.
2.1.4.1 Functional View
In this section we provide a reminder (see Figure 6 below) of the “native” IoT-A Functional View as it
shows an illustrative example of functional decomposition resulting from a thoroughly conducted
requirement analysis (in that particular case it was the analysis of the IoT-A requirements coming fromthe different stakeholders resulting into the final list of UNIs [23]). We recommend referring to the IoT
ARM final architecture deliverable D1.5 [1] in order to get more detail about the purpose of each of the
Functional Components shown in that Functional View.

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Application

Management

ServiceOrganisation

IoT
Process Management

Virtual Entity

IoT Service

Security

Process
Modeling

Configuration

ServiceComposition

Fault

Service
Orchestration

Reporting

Process
Execution

Authorisation

Key Exchange &Management

VE & IoT
Service Monitoring

Service
Choreography

Trust & Reputation
VE Resolution

VE Service
Identity Management

MemberIoT Service
Resolution

State

IoT Service

Authentication

Communication
End To End
Communication

NetworkCommunication

Hop to HopCommunication

DeviceFigure 6: IoT Functional View (as in IoT-A D1.5 [1])Along with a description of the FCs within FGs it is equally important to get a concise – still precise –description of the different FCs implemented in the various data producers (which step outside the
CPaaS.io platform) and to understand also very clearly how they interact with and position w.r.t. othercomponents. At this point in time we concentrate on a clear textual description, but in the architecture
document System Use-Cases (UC) (see Section 4.3.1) we will formally elucidate those inter-component
interactions.
The CPaaS.io Functional View will be based on the native IoT (native) Functional View but will also
introduce those additional FCs which aim at capturing the CPaaS.io specifics.
2.1.4.2 Information View
We use the following various viewpoints for describing the information view:•

Information flow: shows how information flows between FCs;•

System use-cases: elucidate usage patterns and explicitly shows interactions between FCs;•

Structure of information: in the case of CPaaS.io we will describe the used ontologies andstrategies for storing of information;•

Sequence diagrams: temporal sequences of interactions between FCs using e.g. the UML
notation.

2.1.4.3 Physical Entity View
Considering a specific scenario, the PE View provides:

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The description of the Objects (hence so-called Physical Entities), their physical properties ofinterest and how those objects are virtualized into the cyber-spaces, explaining how the PEs are“translated” into VEs and how their physical properties can be modelled through VE properties;•

The physical association between objects and hardware devices, e.g. if a sensor is attached(touching, fixed) to the physical object or if the physical object is in the scope of a detachedsensor (e.g. a camera);•

A clear description of the information captured by devices, including meta-data.

There is no CPaaS.io Context View described in this document. Such views will be provided indeliverables relating to the scenario implementation.
2.1.4.4 Context View
According to the IoT ARM [1] and Rozanki & Woods [20] the Context View describes “the relationships,
dependencies and interactions between the system and its environment (the people, systems, external
entities, with which it interacts)” either using plain text or UML-like notations.
The concerns addressed by the Context View cover the following aspects:
•
•
•
•
•
•

System scope and responsibilities;
Identity of external entities and services and data used;
Nature and characteristics of external entities;
Nature and characteristics of external interfaces;
Other external dependencies;
Impact of the system upon its environment.

In the context of CPaaS.io, a dedicated Context View will be associated with each Scenario
implementation and therefore will be documented within deliverables relating to scenario
implementation (like for PE Views).
2.1.4.5 Deployment & Operation View
The Deployment & Operation View (or Deployment View) main purpose is to describe how the different
FCs and hardware (including gateways, sensors, actuators etc.) are deployed in the “real life” according to
specific scenarios. The Deployment View considers concrete FCs and individual scenarios and provides
the description of the concrete platform deployments (u2- and FIWARE- based) following:
1. The respective u2-based and FIWARE-based Instantiation Views (see Sections 6.1 and 0);
2. any additional deployment of components needed for implementing the scenarios (see the cases
relating to Class-I to Class-VI data providers, where some existing infrastructure do exists outside
the CPaaS.io platform).
Like for The PE and Context Views, the Deployment View is scenario specific. Each CPaaS.io scenario shalldescribe, using a Deployment View, how it is physically deployed in the field (with information about
infrastructure at the RDP side, network, storage and computing resources, including FCs that are part ofthat Scenario-specific infrastructure (e.g. Semantic Annotator, Resource Manager, Data and Service
endpoints…)).
2.1.5 IoT ARM Perspectives
According to Rozanski & Woods [20] an architectural Perspective “is a collection of activities, tactics andguidelines that are used to ensure that a system exhibits a particular set of related quality properties that
require consideration across a number of the system’s architectural views”.
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In this definition, a quality property is meant to be “an externally visible, non-functional property of asystem such as performance, security or scalability” [20].
As we can see architectural Perspectives are orthogonal to architectural Views; therefore any architecture
or design decision pertaining to non-functional or quality requirements often spans more than one
architectural View, if not all. Following the methodology from Rozanski & Woods [20], the IoT ARM
leverages this methodology and proposes to approach and structure those transversal aspects ofarchitecture using a comprehensive list of perspectives that focus on specific non-functional
requirements or desired quality properties of the architecture, with special focus on the IoT domain. The
IoT ARM proposes the following structure as far as “aspects” of an architecture are concerned:
•
•
•
•
•

•
•
•

Evolution (or Evolvability): is a quality of a system that has been designed in such a way it caneasily be adapted to new technologies;
Interoperability: ability of a system to easily interoperate with other systems at various levels like
technical, syntactical, semantic and organisational [12]Availability: ability of a system to be fully (or partly) available when required2
Resilience: ability of a system to effectively handle failure or attacks that could affect the system
availability2
Trust, Security and Privacy:o Security: ability of the system to reliably control, monitor and audit who can perform what
actions on what resources, to detect and recover from failures insecurity mechanisms andto resist to cyber attacks2o Trust: ability of a system to establish and enforce trusted relation between the different
parties involved in a system (end-users, component, data) in such a way system operation
and behaviours comply to expected oneso Privacy: ability of a system to deal with all kind of personal data and in particular to
implement reliably privacy policies about accessing, sharing that data or hiding people’s
identity
Performance: ability of a system to predictably perform its operations within its mandated
performance requirements and profile2
Scalability: ability of the system to cope with increasing demand in computing, networking,storage resulting from increasing volume of system usage2
Usability: quality that illustrate how easy a system can be used, how easy data can beapprehended by the end users, how easy the GUI is understandable and ergonomic while
maintaining efficient work

Of course the list of qualities can be updated or adapted according to architects’ needs.
Each desired quality will be then associated with a set of activities (for instance activities associated with
Trust, Security and Privacy are the collection of trust requirements, the conduction of risk and threat
analysis, the definition of a trust model, etc.
Then defining a certain number of tactics allows showing how the desired system quality can be
eventually reached. Because a tactic can span more than one view, the implementation of a tactic
through Design Choices (DC) can lead to more than one of those DCs (e.g. a tactic for realizing semanticinteroperability can lead to a collection of DCs relating to Data Structure for the Information View and
DCs relating to interfaces, storage and protocols for the Functional View).

2

adapted from [30]

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2.2 Using the IoT ARM in the context of CPaaS.io
In CPaaS.io we are using the IoT ARM in order to agree, between EU and Japanese colleagues, on the one
hand about the logical functionalities (i.e. FCs) that a CPaaS.io concrete platform must provide, and on
the other hand about the nature and sequences of interactions that must take place between thoselogical functional components in order to accomplish typical actions like producing data, consuming
data etc. (i.e. system UCs).
The following Section 4 is therefore about identifying and defining all needed FCs, based partly on
functional requirements (see Section 4.2), taking as a starting point the functional decomposition
provided by IoT-A [1] and describing such sequence of interactions (see Information View/system UCs in
Section 4.3.1).
2.2.1 Views
Since Japanese and European consortia are taking different technical approaches when it comes toimplementing a concrete platform (respectively u2- and FIWARE- based), we need to provide two
additional architectural views (called Instantiation Views), the role of which is:
1. to identify available and still-to-be-implemented u2-based (resp. FIWARE-based) concrete
components;
2. to show how that list of concrete components map to the list of logical FCs (so that we canidentifying coverage and gaps);
3. to provide an architectural view of the whole u2-based (resp. FIWARE-based) concretearchitecture, i.e. instantiation of the common ARM-driven logical system architecture;
4. to show concrete instantiations of the system UCs featured in Section 4.3.1, using the u2-based(resp. FIWARE-based) concrete components.
2.2.2 Perspectives
As far as CPaaS.io is concerned, we will need to synchronise and align, between Japanese and EU
partners, about 1/ the desired system properties or qualities and 2/ tactics to be followed, taking also
into account that two platforms based upon two different technologies (u2 and FIWARE), need to offer
the highest possible level of interoperability (interoperability being itself a perspective). The Volere
template shows an initial mapping of the non-functional requirements to the list of Perspectives listed in
Section 2.1.5.

3 CPaaS.io Requirements Analysis and Mapping
3.1 Summary of requirement collection phase
In the three following sub-sections we come back on the results of the two previous WP2 and WP3phases (achieved between M1 and M3 and concluded with respectively D2.1 and D3.1) and provide threelists of requirements:
1. Scenario requirements: requirements from the CPaaS.io scenario point of view (Section 3.1.1) as
introduced in D2.1
2. Platform requirements: functional requirements (Section 3.1.3) and non-functional requirements(Section 3.1.2) as introduced in D3.1
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The purpose of this section is then the analysis and unification of those requirements into a single list ofunified requirements (called UNIs in the IoT-A terminology). The following step will be the mapping toviews and perspectives, as explained in the previous introductory sections.
3.1.1 Scenario requirements
This section gives a summary of findings in term of scenario requirements, which still need translation
into platform functional and non-functional requirements (to be unified with the existing ones shown in
Section 3.1.2 and Section 3.1.3).
3.1.1.1 Event Management / Enhanced User ExperienceID

Description

Rationale

EUE-1

Data collection
infrastructure

All services require thecollection of real-time data

EUE-2

Metadata management

EUE-3

Visualization of personal
data

EUE-4

Data processing

EUE-5

Control over datacollection

EUE-6

Synchronized clocks
between IoT devices

EUE-7

Efficient query of user
data

EUE-10

Data sharing

EUE-11

Semantic data
integration

EUE-12

Scale to personal data

Sensor data can only beprocessed if it is known how
sensors were deployed ,operated, to which user, event
etc. the data relates etc.
The visualization component is
the front end towards end userthat provides him/her access toa record of his/her event
experience.
Data such as excitement levels
require processing of raw
sensor data
This allows users to be
empowered to make a decision
between what data is collected
and what services they can use
Time synchronization isrequired so that sensor datafrom different sensors can becorrelated by time
Personalized visualizations
require efficient retrieval of
media data and processed datafrom sensor streams
Users may want to share theirdata via various channels using
various platform instances
Data sharing between diverse
systems can only efficiently beachieved if the semantics of thedata are machine readable
The application will collectH2020 EUJ-02-2016

Refinement
of

CPaaS.io

Fit Criterion
Existence of adata collection
infrastructure
Method forcollecting
metadata andenriching
sensor data
Visual data
access for end
user

Availability ofanalyticalservices
UI forcontrolling useof data
Maximum clock
drift

Query latency in
msAvailability ofdata sharing
mechanisms
Ontologies fordata to besharedNumber of data
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sensor and media data formillions of people. The platformneeds to be able to efficiently
ingest, store, process user data.

EUE-13User Registration andManagement

This application offers
personalized services.

EUE-14

Semantic linking
between data anddeployment metadata

EUE-15

Semantic dataregistration

In order to display an
experience log the platformneeds to enrich data withmetadata
Data Registration: For legal
reasons some data may not be
stored by the platform directly.
Semantic Data Registration:
enables semanticinteroperability. Example 1:
User activity is available atstorage endpoint S

EUE-16Storage of semanticallyenriched personal data
streams

Semantically Enriched
Storage: data must be
discoverable and consumableby other application based on acommon ontology
Personal Data: the storage of
personal data is required forfurther processing and use byother applications

points ingested
per second,
processing
latency, storage
footprint
Existence ofuser
registration
management
component
EUE-11, EUE- Enriched data1
can besemanticallyqueried
EUE-11, EUE- Semantic
1
discovery
queries (e.g.
SPARQL) can beissued returninga semantic
description how
the data can beaccessed
EUE-11, EUE- Semantic
1, EUE-7
queries can beused to query
personal datafrom theplatform

3.1.1.2 Event Management / Tokyo Public Transportation
ID
TPT-1

Description
Automated data collection
mechanism

TPT -2Multi-lingual data

TPT -3

Live data collection anddistribution

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Rationale
Public transportation data tends to
update frequently. Efficient update ofthe data requires an automatic datacollection system (even for static datasuch as time tables and maps ofstations).
Foreign tourists may not haveJapanese language skills and needmultilingual information on public
transportation.

This use-case deals with real-time
operation information of public
CPaaS.io

Fit Criterion
Existence ofautomatic datacollection system for
publictransportation.
Ratio of multilingual
information in all
information.
Existence ofautomatictranslation system.
Existence ofautomatic collection
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TPT -4

Data format and API
standardization

TPT -5

Secure and robust
communication

TPT -6Developers' Site

TPT -7

Data License

TPT -8

Third party apps

TPT -9Services for the physicallychallenged

TPT -10User feedback collection

CPaaS.io System Architecture (v2)transportation such as trains, busses,
an air planes. It is necessary to provide
automatic data collection mechanism
for these data.
Since public transportation is operated
by many private and public sectors,
data distribution can only be achieved
efficiently based on standard data
formats and APIs.
Public transportation is a critical social
infrastructure. Communication amongplayers of the public transportation
open data system should be secureand robust.
For the open innovation with public
transportation open data, it isnecessary to provide an open
development environment on theplatform called "developers site".
Good data license is necessary for the
deployment of the data. For the data
license, the balance between dataholders’ merits and users' merits isessentially important.
Open data is a method for thecollaboration between data holders
and application developers.
Contribution to improve quality of lifeof the physically challenged and theelder in moving in Tokyo is one of themost important objectives.
User feedback is necessary for theevaluation of the use-case experiment.

system anddistribution systemof live operation
data.
Existence ofstandard data
format and standard
API specifications.
Existence of
cryptographic
communication
mechanism andfault tolerance
mechanism.
Existence ofdevelopers' site ofTokyo Public
Transportation
Open Data.
Existence of open
data license.

Number of third
party apps orsoftware.
Number of services
for the physicallychallenged.
Number of user
feedbacks.

3.1.1.3 Waterproof Amsterdam
ID
WA-1

Description
Data collection

WA-2

Data processing

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Rationale
The application collects and stores datafrom different sources, including
sensors and static cadastral andinfrastructural information
A rain buffer contains a set of standard
parameters, such as type, location,
capacity. These parameters should becomplemented with lifetime usage
data, such as times/open close, amountbuffered/released, active/inactive,
filling level, battery status, etc.
CPaaS.io

Fit Criterion
Existence of datacollection
infrastructure
Existence of datacollection
infrastructure

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WA-3

User hierarchies

WA-4

Manual control over device

WA-5

Automated control overdeviceWA-6

Adaptive control levels

WA-7

Visualisation of device status

WA-8

Operator control centre

WA-9

Push notifications

WA-10

User feedback collection

WA-11

Data harmonisation

WA-12

Physical network

WA-13

Multicast messaging

CPaaS.io System Architecture (v2)

Different user groups can exert
different levels of control over a device,
so a hierarchy between groups isestablished
Rules are set by individual users which
determine the level of control overtheir own device. Users may havedifferent preferences.
Circumstances need to be defined,
based on the various data sources, inwhich device control for user groups
are suspended and delegated to acentral user/authority
The rules and circumstances may
automatically trigger control levels as
they occur
Users can access information via agraphical front-end that displays arecord of the rain buffer status, such as
filling level
Data can be aggregated per
geographical area in order to monitor
the status of and trigger actions on anumber of devices altogether
Circumstances may trigger
communication of predefined
messages to users such as push
notifications.
Prompts in the application can be used
to manually collect user feedback.
Data collected from the various sources
needs to be useable and interpretableby the application
Data transmission is modulated with
LoRa, operated by The Things Network
network server. The application needs
to be integrated with TTN API
Bulk downlink messages may be sent
to control large quantities of actuators
at the same time.

Definition of usergroups, definitionof access rights
UI where users can
set their
preferences
Presence ofalgorithms

Control levels
definedUI that can beaccessed by user

UI for network
operator

Reception of
notifications

Collection of
feedback
Ontologies for data
to be shared
Data received inapplication server
via TTN API
Messages sent fromcontrol centre

3.1.1.4 Yokosuka Emergency Medical Care (YEM)
ID
YEM-1

Description
Data collection

H2020 EUJ-02-2016

Rationale
This use-case deals with real-time
operation information of such as
patients' conditions, and position of
ambulances. It is necessary to provide
automatic data collection mechanism
CPaaS.io

Fit Criterion
Existence ofautomaticcollection system

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YEM -2

YEM -3

YEM -4YEM-5CPaaS.io System Architecture (v2)

for these data.
The data may have direct or indirect
relations with personal information ofpatients. Before the trial, we need tohave formal consensus with city
government on the basis of official
guidelines for personal data.
Privacy protection of patients We must provide strong dataprotection mechanism such as securecommunication and secure storage.
Low-effort for emergency
Emergency medical team staffs are
medical team staffs
doing very hard work for taking care ofpatients. It is impossible for them tospend time and effort for the operationof this system. We must realize a goodwork-flow and user interface for theteam staff.
User feedback collection
User feedback is necessary for theevaluation of the use-case experiment.
Consensus for using medical
and personal data by city
government

Agreement with
Yokosuka city
government

Existence of privacy
data protection
mechanism
Existence of loweffort mechanismand evaluation
score by the teamstaffsNumber of user
feedbacks

3.1.2 Non-functional platform requirements
Those two following tables focus on Non-Functional REQuirement (NFREQ) and Functional REQuirements(FREQ) from the platform point of view (based on DoW ‘generic’ technical objectives and challenges
pertaining to Smart City domain).
Table 1: List of Non-Functional Requirements

ID #Description

Perspective

Priority

Category

NFREQ.1

Support different service level agreements(SLA)

Scalability

SHOULD

Platform,
Infrastructure

NFREQ.2

Process services and events on a set of
distributed nodes

Scalability

SHOULD

Platform,
Infrastructure

NFREQ.3

Continuously monitor quality of service atruntimeScalability

SHOULD

Platform,
Infrastructure

NFREQ.4

Balance its load at runtime

Scalability

SHOULD

Platform,
Infrastructure

NFREQ.5

Provide high availability

Availability

SHOULD

Platform,
Infrastructure

NFREQ.6

Guarantee infrastructure availability

Availability

SHOULD

Platform,
Infrastructure

NFREQ.7

Ensure network availability

Availability

SHOULD

Platform,
Infrastructure

NFREQ.8

Be able to perform self-healing

Availability

SHOULD

Platform,
Infrastructure

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City Data,

NFREQ.9

Expose data and services to authorized users

Security

MUST

NFREQ.10

Ensure services are always accessible toentitled users

Security

MUST

NFREQ.11

Ensure data freshness

Security,
Performance

MUST

Platform

NFREQ.12

Support access control mechanisms

Security

MUST

Platform

NFREQ.13

Have security mechanisms to protect datatransmission

Security

SHOULD

Platform,
Infrastructure

NFREQ.14

Make it difficult to spy on communicatedmessages

Security

SHOULD

Platform,
Infrastructure

NFREQ.15

Be able to perform to detect threats at runtime

Security

SHOULD

Platform,
Infrastructure

NFREQ.16

Provide trusted and secure communicationand information management

Trust

SHOULD

Platform,
Infrastructure

NFREQ.17

The platform infrastructure and services shallbe trustable

Trust

SHOULD

Infrastructure

NFREQ.18

Allow users to use free services anonymously

Privacy

SHOULD

Societal Needs,
Platform

NFREQ.19

Allow people to use free services anonymously

Privacy

SHOULD

Societal Needs,
Platform

NFREQ.20

Allow users to control which data they arewilling to provide and how their data should
be usedPrivacy

SHOULD

Societal Needs,
Platform

NFREQ.21

Keep users access-control rights/ policies
secured.

Privacy

SHOULD

Platform

NFREQ.22

Provide privacy protection for users interactingwith the platform

Privacy

SHOULD

Societal Needs,
Platform

NFREQ.23

Provide communication confidentiality

Privacy

SHOULD

Platform,
Infrastructure
Societal Needs,
City Data,
Platform,
Infrastructure

NFREQ.24

Be extensible for future technologies.

Evolvability

MUST

NFREQ.25

Provide standard interfaces for service
providers

Extensibility

SHOULDNFREQ.26

Be able to provide services in an interoperable

Extensibility

SHOULD

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Platform
City Data,
Platform

Platform,Business Needs
Platform
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CPaaS.io System Architecture (v2)mannerNFREQ.27

Data must be interoperable across thedifferent parties (data / service producers)

Interoperability

MUST

Platform

NFREQ.28

Provide data quality constraints for data sets

Trust

SHOULDBusiness Needs

NFREQ.29

Enable the linking of data sources acrossplatforms

Evolvability

MUST

Business Needs

Priority

Category

3.1.3 Functional platform requirements
Table 2: List of Functional Requirements

ID #Description

FREQ.1[REQ25]

Discovery and lookup service of IoT systems should allow
locating the physical entities based on geographicalparameters.

SHOULD

Discovery Look-up

FREQ.2[REQ31]The look-up service of CPaaS.io shall withhold or grantinformation depending on context such as application
involved, requesting entity, and security permissions.

MUST

Discovery Look-up

The IoT system shall enable the dynamic discovery of relevant
virtual entities and their related services based on respective
specifications.

MUST

Discovery Look-up

The IoT system shall enable the dynamic discovery of relevant
VEs and their related services based on a geographical location
scope.

MUST

Discovery Look-up

The IoT system shall enable the lookup of service descriptionsof specified services for Virtual Entities with the VE identifier as
key for the lookup.

MUST

Discovery Look-up

Object (Virtual Entities), services and resources shall besemantically described

MUSTInteroperability

FREQ.3[REQ36]
FREQ.4[REQ37]
FREQ.5[REQ38]
FREQ.09

FREQ.10

Any data at the platform level shall have data quality
annotations

MUST

FREQ.11

Data search and retrieval shall be achieved upon a collection of
criteria that includes location (of the Physical entity concerned),characteristics of the underlying IoT Resource reputation andthe quality of information.

MUST

Interoperability

FREQ.12The platform shall provide a SPARQL end-point for accessing
semantic information (either annotated data or semantic
descriptions)

MUSTInter-operability

FREQ.13

Any data at the platform level shall be following a accepted
set of standard schemas (ontologies)

MUST

Inter-operability

FREQ.14

The platform shall be able to aggregate data from various

MUSTinter-operability

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FREQ.20[REQ19]User anonymity shall be provided in order to enforce privacy(e.g. at communication level)

MUST

Security/Privacy &Trust

FREQ.21[REQ81]

Personal data in servers should be ciphered by a private key.

SHOULD

Security/Privacy &Trust

FREQ.22[REQ84]

Secure storage of data should be ensured.

SHOULD

Security/Privacy &Trust

FREQ.23[REQ34]

Data owners shall be able to set access-control rights/ policies(set up by data owners) to their data stored on resources.

MUST

Security/Privacy &Trust

FREQ.24

Access-control rights/ policies (set up by data owners) should
not be published publicly.

SHOULD

Security/Privacy &Trust

FREQ.25[REQ63]

Communicated data shall remain confidential.

MUST

Security/Privacy &Trust

FREQ.26

Service providers shall be able to set access-control rights/
policies (set up by service owners) to their services

MUST

Security/Privacy &Trust

FREQ.30[REQ116]

CPaaS.IO shall be able to evaluate access request dependingon access control policies.

MUST

Security/Privacy &Trust

FREQ.31[REQ117]

CPaaS.IO shall provide an authorization policy to data, object
and service for the different users or devices.

MUST

Security/Privacy &Trust

FREQ.32

User or device as part of the platform deployment shall be
registered before using any services provided by the platform.

MUST

Management
Security/Privacy &TrustUser or device shall be identified.

MUST

Management

User or device shall be authorized to use a service.

MUST

Security/Privacy &Trust

Objects should authenticate a person or object that try toaccess its data.

SHOULD

Security/Privacy &TrustAuthentication of user or service shall be carried out by devices
before delivering data.

MUST

Security/Privacy &TrustAuthentication of user or service shall be carried out to accessa service.

MUST

Security/Privacy &TrustUser's data shall be processed only with the user's consent.

MUST

Security/Privacy &Trust[REQ35][REQ 59]
FREQ.33[REQ60]
FREQ.34[REQ62]
FREQ.35[REQ69]
FREQ.36[REQ77]
FREQ.37[REQ 82]
FREQ.38[REQ123]

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FREQ.39[REQ133]
FREQ.50[REQ90]
FREQ.60[REQ20]

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CPaaS.io should support context-aware access policies.

MUST

Security/Privacy &TrustServices shall maintain a log of the operations done by users.
This log shall not be modified by attackers.

MUST

Security/Privacy &TrustTime stamps should be supported.

SHOULD

Platform

FREQ.70[REQ131]

The IoT system shall ensure that processing of information
takes place on trusted nodes.

MUST

Security/Privacy &Trust
Load Balancing &Bandwidth
Optimization

FREQ.71[REQ132]

The IoT system should optimize the processing of information
with respect to a cost function, e.g. communication,
computation, energy, etc.

SHOULD

Load Balancing

FREQ.72

The CPaaS.io platform shall be able to provide mechanisms for
migrating tasks to nodes participating the CPaaS.io systems
according to its characteristics

MUST

Load Balancing &Bandwidth
Optimization

FREQ.80

CPaaS.io platform shall be able to perform analytics, and event
detection/prediction using “live” and historical data

MUST

Platform

FREQ.90

The CPaaS.io platform shall provide publish / subscribe
functionality and criteria for filtering in/out data based (atleast) on geographical area, data type and data source

MUST

Platform

FREQ.91

The CPaaS.io platform shall provide Complex Event Processing
techniques that are able to infer high level events out of an
incoming flow of data, based on various criteria (condition forthat event to be triggered)

MUST

Platform

FREQ.92

The CPaaS.io platform provides data validation services

MAY

Platform

FREQ.93

The CPaaS.io platform functionality to map between common
RDF identifiers and vocabularies

MAY

Platform

3.2 Requirement Analysis
The starting point of the analysis is the set of functional and non-functional requirements introduced in
the earlier sections 3.1.2 & 3.1.3. Those requirements were consolidated, then duplicates were removed
and similar ones were merged in order to minimize their number. Some of scenario-originatedrequirements were also rewritten in order to remove ambiguities and/or to extend their scope.
Then all scenario-originated requirements were examined and compared to existing NFREQs and FREQs
and then were – depending on the result – linked to existing requirements or considered as new
requirements (classified then in FREQs, NFREQs or Design Choices). Existing requirements may also be
updated in order to cover the scope of the scenario-originated requirements.

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The result of this analysis consists therefore of a list of UNIs which are fully captured in the Volere
template (technical Annex to this document).
An initial requirement mapping as been also achieved, with mapping to Views, Perspectives, Functional
Groups and Functional Components.
Many comments are also associated to those requirements and will be used to feed forthcoming
technical discussions (towards the next architecture release e.g.).

4 CPaaS.io Views
4.1 Actors involved in data production
When defining the CPaaS.io logical architecture we needed to define various kinds of situations for data
provision, resulting in various classes of Raw Data Providers (RDP) numbered along decreasing
capabilities; Class-I being fully semantics-ready while Class-IV does not own/manage any local
infrastructure therefore relying fully on CPaaS.io platform for basically everything. The various RDP
classes are described below:
4.1.1 CLASSIFICATION OF RDPs
In CPaaS.io, RDPs are classified as passive RDPs or active RDPs. Active RDPs as feature in the CPaaS.io
architecture, correspond too data producers which act as a data sources and data providers, who
proactively push data to the CPaaS.io platform (see Class-I and Class-II in Figure 7) while passive RDPs
correspond to data sources that the CPaaS.io platform need to query or poll data from (see Class-III,Class–IV and Class-V RDPs in Figure 7).
••

Class-I RDP: This class of RDP produces data in semantic form and stores locally that data in its
own triple store (it may decide to push all or part of that data to the CPaaS.io platform as well). It
offers a SPARQL end-point so that semantic information can be queried. In addition a Class I-RDP
advertises its dataset to the CPaaS.io platform.
Optionally, a class-I RDP may offer an IoT Service layer that allows either to retrieve directly
observations from a sensor or to trigger actuation by an actuator. If so, a Class-I RDP mustdescribe semantically those IoT Services and store their descriptions either locally or at the
CPaaS.io side. In the former case, an IoT Service endpoint must be provided in order to allow on
the one hand discovery of those IoT Services at the CPaaS.io level as part of the overall federated
discovery feature and on the other hand, the handling of IoT Service invocation requests;
Class-II RDP: This class of RDP produces data in a non-semantic form (e.g. Json) and pushes it to
the CPaaS.io platform for storage. Data may be stored locally in a non-semantic format and suchdata sets may consequently have to be advertised to the platform, so that they can be queried by
CPaaS.io users.
Optionally, a class-II RDP may offer an IoT Service layer that allows either to retrieve directly
observation (in native non-semantic form) from a sensor or to trigger actuation from an actuator.
If so, Class-II RDP must describe semantically those IoT Services and store those descriptions at
the CPaaS.io side. An IoT Service endpoint must be provided in order to allow on the one hand
discovery of those IoT Services at the CPaaS.io level as part of the overall federated discovery
feature and on the other hand, the handling of IoT Service invocation requests;

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CPaaS.io System Architecture (v2)Class-III RDP: In the fourth case, the RDP only manages its IoT Resources locally and relies on
CPaaS.io for everything else. In an initial pre-configuration phase it needs to upload its
production policy as well as the IoT Services exposing its IoT Resources to the CPaaS.io platform(resp. to the Resource Manager FC and IoT Service FC). It does not provide locally any direct data
endpoint, however IoT Services can be of course invoked at the platform level;
Class-IV RDP: they refer in the CPaaS.io architecture to Web third parties, like social media
platforms producing live data e.g. twitter, news feeds, RSS …;Class-V RDP: they refer to open Government data, e.g., data stored in CKAN repositories.

NOTE: The various use-cases relating to Class-IV and –V will be dealt with in the third release of the
CPaaS.io architecture.

4.2 CPaaS.io Functional View
Each sub-section below provides a textual description for each FC and a list of functionalities provided by
the FC.
Below is a second version of the logical functional decomposition featuring the current list of FCs which
will be confirmed, updated and extended during the forthcoming architecture iterations. The legend is asfollows:•••

In Green: FCs which were already identified in D3.2.
In Red: the new FCS introduced in this iteration.
In Dashed lines: FCs which could be deployed at the edge, closer to the data.

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Figure 7: CPaaS.io logical functional architecture

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4.2.1 MANAGEMENT FG
The management FG groups a set of FCs offering management functionalities in order to manage
platforms and platform federation.
4.2.1.1 Web Front-End FC
The Web Front-End FC is a component that offers a web interface to many of the FCs implemented
across the various FGs. Those components include, but are not limited to:
•
•
•
•
•

All management functionalities;
Discovery and browsing of VE, VE properties, IoT service, IoT Resources, data sets etc.;
Look-up and resolution;
Definition of access and privacy policies;
Management of credentials.

4.2.1.2 Platform Management FC
Platform Management FC is responsible for managing configurations and platform-related aspects of
CPaaS.io IoT architecture.
This component deals with both intra- and inter- platform management. Different aspects are covered
like:
•

•

•
•

Platform federation: Registration of peers and inter-peer links, definition of nature of links
between the different platform instances and the functionalities they are supporting: federated
data query, discovery, task composing spanning multiple instances, etc.;
Task deployment: in order to enable smart and flexible deployment of tasks within the platform(see the Task Deployment FC) and in particular towards the platform edge, information about theplatform components like network capacity, CPU and memory capacities need to be known at theplatform level;
Management of security across the various platform instances and domains;
Publishing of new data/knowledge or replication of data from one platform instance to another(e.g. considering a hierarchical model from leaves towards the root) in order to allow multiple
input CEP or data processing.

Platform management is an essential requirement for realization of the CPaaS.io platform due to the
distributed nature of IoT. In the CPaaS.io platform, there will be vast number of IoT Devices (e.g. sensor
nodes) and various hardware and software components. Moreover, different deployments from Japanand Europe may be connected to each other and exchange information based on the federation concept.
4.2.1.3 Resource Deployment Tool FC
The Resource Deployment component is a web-based front end for managing the deployment of IoT
Devices. It covers an inventory for managing IoT Devices and sensors and allows IoT deployment
managers to plan a deployment of physical devices including a description (location, time, floor plan) of
deployments. The tool supports both the deployment of static and mobile devices. The management ofmobile devices is realized by associations with mobile entities such as people. The data generated by thistool is used for metadata enrichment of raw data and analytic results as described in deliverable D2.2
[3]".
Deployment metadata created by the tool instantiates a deployment ontology extending the SSN
ontology (see D6.1 for further details about the ontology) and is stored as RDF triples in a triple store(deployment knowledge base) within the platform. An initial version of the deployment tool is available
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to partners in the project. The deployment knowledge base currently contains inventory and deployment
metadata for the “Color Run” event.
4.2.2 IOT PROCESS MANAGEMENT FG
4.2.2.1 Task Composer FC
Task Composer FC is a component which can be configured by developers to create necessary tasks forcertain services. Each task is a data processing application that takes certain NGSI entity data in, performs
some internal data processing logic, and then produces some updates to existing or new entity data. A
service is a flow graph, which consists of multiple linked tasks to form some high-level data processing
logic. In more advanced settings, tasks can be composed automatically by the Task Composer FC itself.
To execute a service, there is need for defining the set of tasks. This FC decides how many tasks
necessary for the service as well as how the tasks are organized. Examples of task includes the FCs shown as dashed rectangles in Figure 7 - that could be deployed closer to the edge for execution
depending on the node capabilities. The concept of task (and related activities) will be developed further
in the next iterations of the CPaaS.io architecture.
4.2.2.2 Process Optimization FC
Process Optimization FC is a component that decides on how to execute the processes that are requiredfor providing services. This optimization includes or relies on aspects such as the order of execution of
the tasks, the necessary inputs and needed configurations for optimized performance, as well as the
optimization algorithms for the processes. Process Optimization FC makes decisions based on improving
the performance of the processes.
4.2.3 SERVICE ORGANISATION FG
4.2.3.1 IoT Resource Manager FC
The Resource Manager FC is an agent/component which can be configured and used by infrastructureless owners of IoT Resources (see the RDP Class-III case) in order to manage the reading of their IoT
Resources and its periodicity.
This FC allows RDPs for defining publishing policies and relies on CPaaS.io-hosted IoT Services which are
deployed by the RDPs Class-III and which are exposing the IoT Resources under the RDPs’ responsibility.
4.2.3.2 Service Orchestrator FC
Service orchestrator FC is the component which generates task instances and manages execution ofthese task instances based on the available resources. In order to perform this role, the Service
Orchestrator FC must know about the availability of the resources such as the cloud or edge resources(e.g., servers). It monitors the usage of resources and the status of all running tasks on the nodes.
This FC manages all the tasks and the execution of task instances over the edge nodes or cloud nodes.
Hence, the assignment of task instances to the available resources is handled by Service Orchestrator FC.
4.2.3.3 Task Deployment FC
Task Deployment FC takes the assignment from Service Orchestrator FC as an input and performs thenecessary deployment of task instances. It aims to minimize the cross-edge node and cross-site network
traffic while doing these deployments by generating a deployment plan and performs the task
deployment on the available cloud or edge resources based on a deployment plan.

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4.2.3.4 Task Execution Engine FC
Task Execution Engine FC is the component that executes the tasks that are defined by the Task
Composer FC and deployed by the Task Deployment FC based on the available resources. The
instantiation of this FC is a worker that executes the necessary tasks.
4.2.4 VIRTUAL ENTITY FG
As explained earlier in Section 2.1.3.3 this FG deals with all aspects pertaining to Virtual Entities likestorage of VE-related observations and VE/VE Service Descriptions, discovery, look-up and resolution of
VEs and VE Services, discovery, look-up and resolution of Associations and VE Service endpoint.
Functionalities shared with the IoT Service FG are handled in Section 4.2.6.
4.2.4.1 Semantic VE Data repository FC
This FC provides a database for storing VE-related data (i.e. historical data) as well as a SPARQL endpoint
for accessing that VE-related data.
4.2.4.2 Non-Semantic VE Data repository FC
This FC provides a database for storing VE data in a non-semantic form. The semantic Integration layer
can later on ingest it before storing it in a semantic data store. Typical example would be –for the
FIWARE based instantiation of it- VE context data expressed in the FIWARE internal NGSI format.
4.2.4.3 VE Registry FC
VE Registry FC is the component that stores, queries and retrieves VE semantic descriptions. This FC
could be merged with the VE Resolution FC or can be implemented by a stand-alone component that is
integrated with the VE Resolution component.
4.2.4.4 VE Resolution FC
This FC is an extension of the IoT-A native component that can be used to discover and retrieve
associations existing between VEs and IoT Services. Using Associations (see Figure 4) the entity managing
VE Services can know about which IoT Services should be used in order to return the value of a VE
Property (as associations between VEs and IoT Services are dynamic in order to deal with mobility). In
addition to providing means for retrieving those associations, this component can be used to by VEs toregister their availabilities. The existing VEs that are available and registered can be discovered based on
fitting the searching criteria. This search can typically be done by the VE Service endpoint FC for
identifying the IoT Service to be triggered for answering a READ/UPDATE request upon a specific VE
property.
4.2.4.5 VE & IoT Service Monitoring FC
This component is an IoT-A native FC responsible for automatically finding new associations, which are inturn stored by the VE Resolution FC.
4.2.4.6 VE Service End-Point FC
This FC is an extension of the IoT-A native FC that provides a unique endpoint to accessing (Read/Set)
Virtual Entities and their properties. This FC is responsible for:•

Maintaining and publishing a snapshot of VE state at regular interval (sampling rate to be
configured) as VE observations which are therefore also stored within the Semantic VE Data
Repository FC;

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Handling and answering requests for accessing VE properties. As explained in case 2/ in Section
4.3.1.4, answering such requests requires identifying the appropriate corresponding IoT Service
via existing associations between device-level and thing-level information.

4.2.5 IOT SERVICE FG
This IoT Service FG deals with all aspects pertaining to IoT data like storage of IoT Data, discovery, lookup and Resolution of IoT Services, invocation of IoT Services, storage, discovery, look-up and resolutionof IoT Service descriptions and IoT Resource descriptions.
Functionalities shared with the Virtual Entity FG are handled in Section 4.2.6.
4.2.5.1 Semantic Data Repository FC
Semantic Data Repository FC is used for storing semantic data managed at CPaaS.io level. Semantic
information can be annotated sensor readings (annotated raw data coming from RDP) in the form ofobservation or any kind of semantic data resulting from applying analytics or from processing within the
Semantic Integration FC. This FC provides a SPARQL endpoint that can be used for searching andretrieving the data from the database (either it is a full-fledge triple store or relational database withSPARQL endpoint).
4.2.5.2 Non-Semantic Data Repository FC
The Non-Semantic Data Repository is used to store non-semantic data coming e.g. from Raw Data
Producers. It can be then used by other components for various tasks like CEP, analytics etc… This data
can also be queried by the Semantic Integration FC in order to produce and store semantically annotateddata.
4.2.5.3 IoT Service & Resource Registry FC
The IoT Service/Resource Registry 3 FC provides an API for registering a Resource and the associated IoT
Services within a registry with associated metadata. This particular API can be used either by CPaaS.io
users for registering virtual resources and associated IoT services or by RDPs which do not handle locallythe definition of the IoT Services that expose their resources (see definition of Class-II and -III).
This registry allows also to look-up IoT Services exposing IoT Resources based on various criteria(metadata).
4.2.5.4 Live Data Listener & Ingestion FC
This component is responsible for pulling data from external data sources (like e.g. Twitter RSS or news
feed-which correspond to RDPs Class-IV) and putting it in an appropriate form with needed meta-data
before pushing it to the appropriate repository. This FC therefore may use as well the Semantic
Integration FC and may publish related knowledge to the communication FG for other components/users
to use. Finally this component can also push the data to the Analytics FC or/and Machine Learning FC for
further analysis (e.g. classification) before 1/ some higher-level data is published or 2/ the incoming datais enriched its meta-data before being published.
4.2.5.5 Government (Gov) Data Listener & Ingestion FC
This component behaves in a similar way to the Live Data one, but applies to open-data data sources likegovernment, institutions or NGOs for instance (RDPs Class-V). Such data is usually accessible via CKAN
3This FC correspond to the IoT Service Resolution FC in Figure 6

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CPaaS.io System Architecture (v2)repositories. This FC will be further developed in a next iteration of the architecture in accordance withproject scenario needs.
4.2.5.6 IoT Service FC
This FC provides a REST endpoint for accessing resource data (resources are exposed to CPaas.io users
by IoT Services).
4.2.6 COMMON TO VE AND IOT SERVICE FGs
4.2.6.1 Semantic Integration FC
As described in D6.1, CPaaS.io is using Linked Data and RDF to facilitate integration of data fromplatforms like FIWARE and u2. Neither of the two base platforms is currently supporting RDF and Linked
Data out of the box. To address this, CPaaS.io is providing and integrating a semantic layer that enables
mapping existing data to RDF.
This semantic integration is implemented in different phases and levels. In its first form, the semantic
layer is simply exposing metadata as Linked Data, using common vocabularies and best practices as
described in D6.1 [7]. This enables users to query information about available data within the FIWARE
and u2 platform as Linked Data. To access this metadata layer the current implementation provides a
SPARQL endpoint that can be queried.
In a second step, data residing in FIWARE or u2 can be mapped to RDF. For that, one needs to extend theappropriate data model used and introduced in D6.1, Section 3.2 [7]. This is done by a semantic
middleware that can be configured accordingly. By providing appropriate tools and user interfaces,
FIWARE users can thus map existing data to RDF representations. The u-code data model of u2 is close
to a RDF as it stores information in a triple-like data model. The semantic integration layer needs to map
internal u-code IDs to publicly used and dereferencable URIs, preferably as HTTP URIs to allow Linked
Data usage. In some use-cases like the Tokyo Metro real-time data system, this is already being done
and can be used as an example implementation.
To be able to query this kind of data, a SPARQL endpoint will proxy requests to the platform 4. Users
should be able to run SPARQL queries on data residing in FIWARE or u2.
4.2.6.2 Pre-processing FC
Pre-processing groups all kinds of algorithms that could apply straight away to the raw data before it isannotated and published. More precise description about needed algorithms will be provided in the next
iteration of the architecture as soon as we get more detail about the CPaaS.io scenario developments.
4.2.6.3 Analytics FC
The Analytics FC provides a large variety of algorithms that can take as input a broad range of structured
data (e.g. video streaming, time series, t-uple series) in order to perform for instance statistical analysis,
machine learning, prediction, anomaly/event detection before publishing their result through thecommunication channel. Example of analytics (input formats, algorithms, output format) can be found in
the two Instantiation Views.

4

See for example R2RML implementation in Stardog: http://docs.stardog.com/#Virtual%20Graphs

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4.2.6.4 Machine Learning FC
This component is still under discussion, but it will at least provide support for supervised classification
tasks, like for instance determining if social data stemming from social media platform – e.g. tweetsshould be considered as Like/Don’t Like or Neutral. See Section 6.2.1 for more detailed description of its
current version.
4.2.7 COMMMUNICATION FG
4.2.7.1 Communication Channel FC
The communication channel is a FC that provides a message bus with support for publish/subscribe andpublishing/consuming along topics. In the architecture it is used as a way 1/ to provide subscribers with“live data” 2/ to store data using a pre-determined topic (see UCs in Section 4.3.1)
4.2.8 SECURITY FG
The Security FG follows closely the recommendation for IoT-A native FCs as shown in Figure 6. This FG is
made of several components already identified in the IoT-A Native Functional View:•

AuthN: Authentication of CPaaS.io users;•

AuthZ: Access-Control policies, decision and enforcement;•

KEM: Key Exchange and management (including as well Group Management);•

Identity Management (including also The key/X509 certificate generation).

Those different FCs are described here after in more detail.
4.2.8.1 Authentication (AuthN) FC
This AuthN 5 component is responsible for enforcing the authentication of registered CPaaS.io users.
Based upon a user credential passed to the AuthN FC we can make an assertion about the identity of theuser i.e. that they are a known CPaaS.io users.
The AuthN component interacts with the User Management FC when the user registers in order toproduce the user credentials (such credentials can take multiple forms e.g. both username/password andan X509 certificate).
When access to a resource or service is requested by a user, the request is captured by a decision point
as whether to grant or deny the request based upon whether the requester is authorized to do so. At thispoint, the AuthN FC can be contacted by the decision point to assert the authenticity of the requester.
4.2.8.2 Authorization (AuthZ) FC
The AuthZ 6 component makes decisions about access control requests (intercepted at access decision
points) based upon Access Control Policies (ACPs) which are based on two different technologies: XACML[38] and CapBAC [39] which are based on the concept of a Capability Token (CT) which has been
explained in D5.1 and D5.2. The first technology is used for validating the privileges of a resource accessrequest issued by a service or application. The second technology is applied in order to provide the CT

5
6

AuthN correspond to the one proposed in Figure 6 as part of the Security FG
AuthZ correspond to the one proposed in Figure 6 as part of the Security FG

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CPaaS.io System Architecture (v2)which is presented as a proof of possession to be validated by the service which contains the requested
resource.
Access control can be applied at the level of look up, e.g.:•

for a request to search for a list of VEs/resources in a specific domain or part of a domain
meeting some criteria; or•for direct access to resources via their interfaces.

Access is denied if the assertions made about the request and requester do not comply with the AccessControl Policy. Usually the three parameters of an access request include an assertion (data guaranteeing
for the occurrence of an authentication of a user client at a particular time using a particular method ofauthentication), the targeted resource and requested operation (read or write for instance).
In the same sense, ACPs can also be applied with a different approach. Specifically, the AuthZ component
can also make decision about the access to certain information which is being broadcasted. That is, it can
implement ACPs based on the attributes of the users’ profiles to the information itself, allowing only the
consumers with the right permissions to access to it.
4.2.8.3 Access Policy Administration FC
This sub-component of AuthZ FC provides a Policy Administration Point to the AuthZ FC where accesspolicies are defined and managed. A GUI is provided to the owner of resources who wish to protect
access; using the GUI the owner can create new access policies, attach them to resources, update accesspolicies and delete access policies.
The Access Policy Administration FC will typically be used by Raw-data producers signing up to CPaaS.io
and registering their resources. When they register a resource, they can assign an access policy (applying
a default rule e.g. all CPaaS.io users can perform a read operation on the API, or define their own policy
using the GUI).
Access Policies defined in this FC will be followed by the AuthZ component when making access control
decisions.
4.2.8.4 Key Exchange and Management FC
The Key Exchange and Management (KEM) component manages the exchange of security information
between two parties. In particular, it ensures that the keys required to construct a secure and trusted
communication channel is carried out in a secure manner.
4.2.8.5 Identity Management
The Identity Management (IdM) component takes into account the management of the identity lifecycle
and also the interoperability issues with components like Authentication, Authorization and KEM. While
the Authentication component is responsible for asserting the identity of subjects using different IoTframeworks (different identifiers, cryptographic algorithms, key strength), the IdM component must
support pseudonym creation across different identity frameworks.
The IdM component needs to interact with the Authentication, Authorization and IoT Service Resolution
component in order to provide its functionalities.
•

Relationship with the Authentication component;

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CPaaS.io System Architecture (v2)Authenticate the requesting subject;
Authenticate the subject for which a pseudonym is requested.

Relationship with the Authorization component:••

Get the access rights of the requesting subject and of the subject for which a pseudonym isrequested;
Create a new access policy associated to the pseudonym.

One of the tasks of the IdM FC is to enable security features between different IoT frameworks bycreating pseudonyms for identities pertaining to other IoT frameworks. The IdM component addresses
this kind of problems by issuing pseudonyms and accessory information to trusted subjects so that they
can operate (use or provide Services) anonymously. Pseudonyms are temporary identities of fictional
subjects (or groups of subjects) whose credentials can be used by a subject or a set of subjects wheninteracting with other subjects instead of using its own credentials. Access rights associated topseudonyms depend on those of the identities of the request issuing subject(s). As pseudonyms are
identities, they can be used in turn to request other pseudonyms.

4.3 CPaaS.io Information View
The purpose of the Information View is to provide system UCs and information about the information
structure. This second aspect of the IV will be dealt with in the next iteration of the architecture, in theform of 1/ CPaaS.io core ontology for data & VE observation, VE/VE Service/IoT Resource/IoT Service
Descriptions and 2/ scenario related domain ontologies.
4.3.1 System use-cases
This section provides a non-exhaustive list of system use-cases which are used to illustrate the accepted
platform behaviours and to reach common understanding between the various partners:••••

Configuration phase (pre-operational phase);
Production of data (depending of various cases) and sub-sequent data flows between
components;
Consumption of data (ranging to simple consumption to usage of reasoning and analytics);
Any other typical usage.

When a common understanding is reached each single use-case needs to be “instantiated” within the
Instantiation View using concrete components (for both u2- and FIWARE-based platforms).
4.3.1.1 Management phases
Note: The scope of management FC is still under discussion, the following list gives a non-exhaustive
overview of the topics to be considered:
•
•
•

•
•
•

To assign the platform with a unique identifier;
To define CPaaS.io clients (and actors in general);To create and configure a set of roles that the platform users can endorse i.e. actors producing
data, accessing data to/from the platform or creating/running application using CPaaS.io
capabilities;
To set-up access rights for roles and actors;
To register platform components capabilities (as an enabler to Edge Computing);
To link platform instances with each other (for federation purpose).

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4.3.1.1.1 Platform manager creates a new platform user
Creating a new user in the platform involves different activities like:•••

Gathering and storing “administrative” data like name, organisation, department, …
Creating / Allocating and storing security credentials;
Setting access policy up.

Some of those actions involves the IdM Functional Components. Please refer to D5.2 [6] for detailed
system Use Cases.
4.3.1.2 RDP pre-configuration phases
We describe in this section UCs (in the form of UML Use-Cases) that should occur at the RDP level before
operation phase. They include actions such as:•••

Declaring IoT services, VE services and Data endpoints to the platform;
Describing and registering IoT Services, IoT Resources, VE services semantic descriptions;
Advertising data sets, …

4.3.1.2.1 Data producer Class-I pre-configuration
In order to be either forwarded SPARQL data requests by CPaaS.io platform instances or be invoked an
IoT Service, the different endpoints (Semantic data endpoints and IoT Service endpoints) must be
declared to the platform instance, the RDP is linked to (See Figure 8 below). This also allows discovery
requests to reach Class-I RDP as they locally manage IoT Service and IoT Resource semantic descriptions.
Optionally, data sets can also be described and advertised so that data can be accessed directly at the
RDP level by the CPaaS.io client without requesting that data at the platform level and relying onfederation. Finally access-rights must be defined so that discovery, look-up and access can be eventually
granted (or denied) to the pre-registered CPaaS.io clients.

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Figure 8: UML UC for Class-I RDP pre-configuration case

4.3.1.2.2 Data producer Class-II pre-configuration
The main difference with the Class-I case is that a Class-II RDP needs to provision a Semantic Annotatorthat will be used to annotate raw data so that it complies with the CPaaS.io ontology (see Figure 9
below). As a Class-II RDP is not semantic-ready, only a non-semantic data endpoint is provided in orderto accommodate direct access by a CPaaS.io client to the locally stored raw data (following e.g. prioradvertisement of non-semantic data sets as illustrated in Section 4.3.1.4.2).

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Figure 9: UML UC for Class-II RDP pre-configuration case

4.3.1.2.3 Data producer Class-III pre-configuration
As explained in Section 4.1 RDPs Class-III are infrastructure-less parties that only own IoT Resources. They
rely on the CPaaS.io platform for:
•
•
•
•
•

Registering their IoT Resources so that they can be discovered or looked-up;
Serving, registering and managing IoT Services that expose their IoT Service so that they can be
discovered, looked up and invoked (for synchronous live data consumption);
Pulling data from the IoT Resources;
Annotating semantically the data;
Producing the data towards the Semantic Data Repository.

The two first items of this list pertain to a configuration phase that occurs prior to any data production(which is elucidated in Section 4.3.1.4). The UML use-case below (Figure 12) explains in details thoseactions.

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Figure 10: UML UC for Class-III RDP pre-configuration phase

RDP Class–III have the responsibility of providing IoT Services exposing their IoT Resources, they also
provide IoT Service and IoT Resource semantic descriptions. The sequence of actions is therefore to 1/ todeploy IoT Service to the CPaaS.io (the needed IoT Service server is not shown in the architecture but
will be part of the Deployment and Instantiation Views) 2/ to register the descriptions to the IoT Service
and Resource registry FC 3/ to provide a polling policy to the Resource Manager FC.
4.3.1.3 Declaring a new platform to the CPaaS.io eco-system
For the sake of platform federation, a new platform instance needs to be known from existing CPaaS.io
platform instances. This UML use-case will be fully described as soon as aspects pertaining to federation
have been fully dealt with (architecture next release) from both EU and Japanese sides.
4.3.1.4 Production of data
There are basically two main ways to producing data:
1. Asynchronously: it is meant here that the data is produced on its own and then fed into theplatform (published, stored) at the initiative of the data producers, following its own production
policy (including sampling rate). In this scenario producers can either fully rely on platform
capabilities (we will see later on what this exactly means) or rely on their own infrastructure. In
this later case they will decide which part of the data needs to be published to the CPaaS.io
platform;
2. Synchronously (or on-demand): it is meant in this second mode, that the data will be pulled fromthe IoT Resource (i.e. sensor) only when there is a request from “the top” to receive or consume
such data, and this is done basically by triggering an IoT Service exposing that IoT Resource.
Again this case refines into two sub-cases:
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The IoT Service is triggered explicitly, i.e. through a direct call like for instance a REST GET;The IoT Service is triggered implicitly whenever a platform user wants to access a VE
property (as VE properties are bound to IoT Service through the use of Associations). Thissecond case makes the assumption that the VE endpoint implements this mechanism.

If the asynchronous mode is used, accessing a VE property results in the VE endpoint retrieving the latest
stored value for the IoT Resource bound to the property via the Association. Alternatively it is possiblethat the direct invocation to the IoT Service is made in the case the RDP provides an IoT Service endpoint
as well (even if working mostly in the asynchronous mode).
Publishing of data asynchronously is made through the Communication Channel which offers
publish/subscribe functionalities. Implicit storage can be achieved when publishing along specific topic(see Section 4.3.1.6).
This section gives illustrations to those different possibilities, while section 4.3.1.4.3 will focus on data
consumption.
4.3.1.4.1 Data Producer Class-I produces data to the platform
We develop here three different cases:•••

Case I.a/: corresponds to classical publishing of semantic data (without federation capability);
Case I.b/: corresponds to classical publishing of semantic data (with federation capability);
Case I.c/: corresponds to the publishing of data/data set descriptions.

The detail of those 3 cases is discussed hereafter:•••

Case I.a/: The general idea (Case I.a/) explained in Figure 11 (shown with the plain red arrows) isthat a Class-I RDP follows its own publishing policy and therefore pulls and stores data at a predetermined rate and then publishes its semantic data (or part of it) to the CPaaS.io semantic
repository using the Communication Channel FC (which can be seen as a message bus withpublish/subscribe capability (see the Asynchronous case in 4.3.1.4 1/)). Relevant interactions inFigure 11 are 1/, 2/, 3/, 4/ & 5/. Corresponding data consumption is illustrated in section 4.3.1.5.1
Case II.a/.
Case I.b/: This case is very close to Case I.a/. Main difference is that, since the semantic data isalready stored locally there is no additional publishing towards CPaaS.io platform. Data stays local
and any attempt to consume data using a SPARQL request at the CPaaS.io level is forwarded tothe data endpoint provided by the CPaaS.io-registered Data Providers. Relevant interactions inFigure 11 thus are only 1/, 2/ and 3/.
Case I.c/: This last case illustrates a quite different scenario, where the RDP stores the semantic
data within his own repository (therefore locally outside the CPaaS.io) and only publishes to
CPaaS.io semantic repository a semantic description of the data sets that can be therefore
accessed only locally. This scenario is quite different from 1.a/ but is still very relevant in practice.
Following this strategy, a data consumer will not consume the semantic data from the CPaaS.io
repository like in case II.a/ (see Section 4.3.1.4.1) but indeed will discover which data is available
and how to access it. The final part of the consumption process involves a classical SPARQL
request sent out to the RDP data endpoint in order to retrieve the semantic data from the local
semantic data store. Relevant interactions are 1/, 2/, 3/, 4bis/ and 5bis/.

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Figure 11: Data production for Class-I RDPs

4.3.1.4.2 Data Producer Class-II produces data towards the platform
In the Class-II case there is no local support to semantic so only non-semantic data is stored locally. The
RDP then needs to annotate the raw data (aligning it with the CPaaS.io ontology) before publishing to
the platform. Only a non-semantic data endpoint is provided by such RDPs.
••

Case II.a/: This case follows the general idea already presented in section 4.3.1.4.1 Case 1/. Data isretrieved by the Resource Manager FC and stored locally, then annotated in order to comply with
CPaaS.io ontology and published towards CPaaS.io Semantic repository via the Communication
Channel FC. Relevant interactions in Figure 12 are 1/, 2/, 3/, 4/, 5/ & 6/;
Case II.b/: This second case is similar to Case I.b/ described in section 4.3.1.4.1. Instead ofsystematically annotating and publishing data towards CPaaS.io, the RDP publishes only
descriptions of available data or data set (incl. information about how to access that data).
Relevant interactions in Figure 12 are 1/, 2/, 3/, 4bis/ and 5bis/.

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Figure 12: Data production for Class-II RDP

4.3.1.4.3 Data Producer Class-III produces data towards the platform
Since a RDP Class-III is passive, it does not per se push data towards the platform. On the contrary anagent (namely IoT Resource Manager FC) located at the platform side is responsible for polling data fromthe resources owned but the RDP and pushing it to the platform.
A WEB front-end allows the owner of the IoT Resources to specify a polling policy like for instance a timeschedule, sampling rate and topic to be used, would the data be forwarded to the Communication
Channel for real-time consumption. Instead of using this channel, the RDP may decide to store it directly
within the non-semantic data repository.
4.3.1.4.4 Data Producer Class-IV & V produce data towards the platform
Class-IV and –V RDP are yet another two similar classes of passive RDP which are relying on platform
agents for polling data and passing it on towards the platform. Like for Class-III RDPs, they rely
respectively on the two Live Data Listener & Ingestion (LDLI) and Government Data Listener & Ingestion(GDLI) FCs part of the IoT Service FG. Both FCs are instrumented via either a web front-end page where
the nature of data to be retrieved / subscribe to is specified or directly via the FC API (this is the mode
used by the ML component).
The LDLI is focussed only on Twitter in its preliminary version. It can retrieve data in two different modes:••

Streaming data: it then subscribes to a tweet feed according to a given list of hash tags andstrings and sends to the Communication Channel;
Historical data: it retrieves in one-go a bunch of social media data and sends it to theCommunication Channel or back to the invocating party (e.g. the ML component).

4.3.1.5 Consumption of data
Consumption of data can be done as follows:•

Live datao Invoking an IoT Service, in order to either retrieve annotated current readings from a sensor
resource, or setting a new actuator value;

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CPaaS.io System Architecture (v2)o Invoking a VE-service in order to get directly current value of VE properties;
Historical data (stored data)o Retrieving VE-level observations or annotated sensor historical data using SPARQL queries.

4.3.1.5.1 CPaaS.io client consumes data originating from RDP Class-I
Data consumption from Class-I RDP offers 5 alternative methods:
•

•

•

•

•

Case I.a/: Data previously published by a Class-I RDP can be consumed from the CPaaS.io
semantic repository via a SPARQL request (this corresponds to the case where the RDP either
duplicates all its semantic data or publishes surveys or part of its semantic data within CPaaS.io
repository) to the CPaaS.io semantic data repository;
Case I.b/: Alternatively if the RDP local platform has registered its data endpoint to the CPaaS.io,
the federation capabilities of the platform, makes sure that any incoming SPARQL request is
forwarded to the RDP. The request is therefore answered by the RDP itself;
Case I.c/: In this last case, the data endpoint of the RDP is accessed directly by the CPaaS.io client
as means to access the data/data set and data/data set descriptions were previously advertised tothe platform and therefore could be discovered by the client prior to data consumption (See Case
I.c/ in Section 4.3.1.4.1);
Case I.d/: As far as live data is concerned, the CPaaS.io client can invoke directly an IoT Service(following discovery and resolution) via the IoT Service endpoint and therefore being returned thecurrent annotated value;
Case I.e/: Subscribing to the Communication Channel in order to receive automatically the flow of
produced data.

4.3.1.5.2 CPaaS.io client consumes data originating from RDP Class-II
Data consumption from Class-I RDP offers 4 alternative methods:
•

•

•
•

Case II.a/: like in Case I.a/ above the historical data can be accessed from the CPaaS.io Semantic
data Repository FC via a SPARQL request sent to the Semantic Data Repository SPARQL data
endpoint;
Case II.b/: Corresponding to Case I.b/ above, {historical data/data set} has been previously
advertised and therefore can be accessed directly by the CPaaS.io client from the RDP SPARQL
data endpoint, without involving the CPaaS.io platform (apart for the discovery phase);
Case II.c/: Subscribing to the Communication Channel in order to receive automatically the flowof produced data;
Case ii.d/: As far as live data is concerned, if the CPaaS.io client is not satisfied with the sampling
rate implemented by the RDP, it can also invoke directly an IoT Service (following discovery andresolution) via the IoT Service endpoint and therefore being returned the current annotated value
in near real time.

4.3.1.5.3 CPassS.io client consumes data originating from RDP Class-III
Data consumption from Class-III RDP is achieved in 3 ways:•••

Using SPARQL request at the CPaaS.io platform level in order to query historical data from the
Semantic Data Repository;
Invoking IoT Services exposing the RDP resources via the IoT Service server at the platform level;
Subscribing to the Communication Channel for live data consumption.

4.3.1.5.4 CPassS.io client consumes data originating from RDP Class-IV or Class-V
Data consumption from Class-IV orClass-V RDPs is achieved in 3 ways:
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Using SPARQL request at the CPaaS.io platform level in order to query historical data from the
Semantic Data Repository;
Invoking the API from (respectively) the LDLI and GDLI FCs;
Subscribing to the Communication Channel for live data consumption.

4.3.1.6 Storage of Data
Storage of IoT data or VE data can be achieved explicitly using the two Semantic/Non-semantic Data
Repository FC and Semantic/Non-semantic VE Data Repository FC APIs or implicitly when publishing thedata to the Communication Channel along a pre-determined topic.
By default the four data repositories (VE-level and IoT-level) are listening the Communication Channeland storing everything coming along pre-determined topics they are assigned to.
4.3.2 CPaaS.io ontologies and internal formats
Ontologies will be used for describing PEs (via the modelling of VE properties), for metadata like quality
information, and for linking of data sets from different providers. In particular for the federation of
implementation platforms between Europe and Japan, as the implementation platforms in the tworegions provide the data in different forms (NGSI in Europe, ucR in Japan).
As mentioned in Deliverable D6.1 [7], the project will be implementing the W3C proposed
recommendation on “Data on the Web Best Practices” 7 and make use of existing ontologies and evolving
standards. At the time of writing it has however not been decided yet which concrete ontologies andvocabularies will be used.
A second round of discussions discarded the FIESTA-IoT ontology due to some doubt about its
sustainability. In the meanwhile a second version of SSN including actuation has been released by theW3C consortium (Spring 2017). It is still lacking some concepts and relations but we are confident it
could be extended in order to cover those missing ones, using for instance fragments of OWL-S or IoT-A
ontologies (for covering the descriptions of IoT-services and others like e.g. analytics-as-a-service).
We envision that the main usages for the CPaaS.io ontology will be:
•
•
•
•
•
•
•

To model/describe entities of interest (PEs) and their virtual counterparts, the VEs (classes andinstances);
To model/describe IoT Resources (i.e. Sensors and Actuators);
To model/describe Sensor and VE observations (only sensor observations are covered by SSN2);
To model/describe Actuation actions and VE actions;
To model/describe Services profiles (IoT Services and general purpose services e.g. analytics);
To describe data-sets (either stored locally or within the platform);
To model Domain-related concepts and relations.

The full specification of the CPaaS.io ontology will be provided during Year 2 of the project, anddocumented partly in D3.5 and fully in WP6 deliverables.
For more details, please refer to Deliverable D6.1 [7] and forthcoming D6.3 [8].

7https://www.w3.org/TR/dwbp/

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5 Perspectives
This section provides a preliminary description of the CPaaS.io perspectives and starts sketchingstrategies for reaching the identified targeted properties/qualities of the CPaaS.io platform. The objective
here is to refine strategies and find synchronisation points between the Japanese and European
approaches so that interoperability can be eventually ensured. See Section 2.1.5 for an introduction to
Perspectives.

5.1 Security
5.1.1 Policy Based Authorization for Access Control
Description: The Access Control module within CPaaS.io is responsible for making authorization
decisions based on access control policies. These polices define the permissions that a subject (smart
object or user) has over certain target resources (e.g. IoT service). Thus, the policies specify which
particular actions that subjects or groups are allowed to perform over a target resource under certain
conditions. These conditions usually refer to the context information provided by the context manager
module. In this regard, the eXtensible Access Control Markup Language (XACML) [25] policy language will
be adopted in CPaaS.io to deal with authorization policies.
Additionally, it is desirable to employ access control solutions specially designed for IoT scenarios.
Recently, the capability-based access control model (CapBAC) has been postulated as a realistic andpromising approach to be deployed on IoT scenarios. In this sense, the authorization component is able
to perform capability-based access control based on the mechanism presented in [26].
In a typical CapBAC scenario, an entity (subject) tries to access a resource of another entity (target).
Usually, a third party (issuer) generates a token for the subject specifying which privileges it has. Thus,
when the subject attempts to access a resource hosted in the target, it attaches the token which was
generated by the issuer. Then, the target evaluates the token granting or denying access to the resource.
Therefore, a subject which wishes to get access to certain information from a target, requires sending thetoken together with the request. Thus, the target that receives such token already knows the privilegesthat the subject has and a local Policy Decision Point (PDP) only needs to verify the capability token isvalid. This simplifies the access control mechanism, and it is a relevant feature in scenarios with resourceconstrained devices since complex access control policies are not required to be deployed on
constrained end-devices.
Additionally, the token must be tamper-proof and unequivocally identified in order to be considered in areal environment. Therefore, it is necessary to consider suitable cryptographic mechanisms to be usedeven on resource-constrained devices which enable an end-to-end secure access control mechanism.
Moreover, given its high level of flexibility, CapBAC could be also extended to consider contextual
conditions related to parameters which are locally sensed by end-devices. Also, this model could becomplemented by other access control models by providing automated tools to infer the privileges to beembedded into the token.
On the other hand, this component is also responsible for providing access control to the information
broadcast in a shared media allowing only the consumers with the right credentials to gain access tosuch information.
To do so, this component uses CP-ABE [35], an encryption schema that ciphers information under acertain policy of attributes, and the keys of the users are associated with sets of attributes. This enables
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dissemination of information using different information sharing models where the producer of theinformation is always on control of how the information is accessed.
CP-ABE is considered, because IoT Systems need not only effective ways of coping with security issues
but also efficient ones. In this sense, although the classic security schemes such as Secret Key
Cryptography (SKC) and Private Key Cryptography (PKC) provide proven security primitives, they may not
be suitable for distributed systems as IoT. The use of CP-ABE is an interesting improvement to the otherciphering schemes. With CP-ABE, the information can only be ciphered with the policies previously
defined, and all users whose keys satisfy the policy are able to decipher the information. This not only
saves resources, but might also be well related with the attribute-based nature of XACML, giving
consistency to the security functionality.
5.1.2 Authentication
Description: Authentication ensures that an identity of a subject (user or smart object) is valid, i.e., thatthe subject is indeed who it or what it claims to be. The authentication module enables authenticating
users and smart objects based on the provided credentials. The credential can be in form oflogin/password, shared key, digital certificate.
Traditional authentication mechanisms based on for instance login-password or electronic IDs have been
already solved in by other projects and solutions. CPaaS.io focuses on alternative and more sophisticatedways of performing authentication ensuring, at the same time, privacy and minimal disclosure of
attributes. Thus, this kind of alternative privacy-preserving way of authentication using anonymous
credential system is handled in CPaaS.io framework by the Identity Management Component.
The IdM is able to verify anonymous credential and then, in case the identity is proved, the IdM interacts
with the authentication module which is the one that actually delivers authentication assertion to be usedduring a transaction.
5.1.3 Privacy Preserving Identity Management
Description: The Identity Management (IdM) module is responsible for managing the identities of smart
objects and users. The module is able to take into account privacy concerns to manage subjects’credentials (from users or smart objects), in a privacy-preserving way relying on anonymous credential
systems. It is able to endow subjects with mechanisms to ensure anonymity, which is done mainly issuing
pseudonyms and proof of credentials to minimal personal data disclosure.
The IdM system is responsible for managing and storing the credentials, which are used by subjects torequest information from an IoT services. The IdM allows users to obtain the proper credentials that canbe use afterwards to derive proofs of identity to be presented to a Verifier (e.g., IoT Service provider) inorder to prove the identity condition while disclosing the minimum amount of private information.
The component is envisioned as endowed with a cryptographic engine like Idemix [27] responsible forlow level cryptographic operations required by the anonymous credential system.
5.1.4 Extension to Key Exchange and Management
Description: The Key Exchange and Management (KEM) component assists peers involved in acommunication in the process of establishing a security context, like for instance, setting up tunnels for asecurity communication. It involves cryptographic key exchange and provides interoperability between
the peers in order to reach an agreement regarding the security functions to use for the communication.
This component is also in charge of storing and generating the cryptographic keys.
The Group Manager module as an extension of the KEM, enables sharing information in a secure andH2020 EUJ-02-2016

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private way, with those groups of entities that satisfy certain particular sets of identity attributes values.
These particular sets of attributes are represented by attribute sharing policies, which are influenced bycontext information where the data sharing is being performed.
This module is envisioned to manage dynamic sharing groups. To this aim, the group manager makes
use of attribute based encryption mechanism, namely the Ciphertext-Policy Attribute-Base Encryption(CP-ABE) scheme [28]. With this scheme, data to be shared within a group is encrypted under a policy of
attributes, while keys of participants are associated with sets of attributes. Only those target users thatsatisfy particular identity attributes (those given in the policy) possess the cipher keys to decrypt thedata. In this way, a data producer can exert full control over how the information is disseminated to otherentities, while a consumer’s identity can be intuitively reflected by a certain private key.

5.2 Semantic Interoperability
Information Model
Description: The FIWARE focuses on a common data model and powerful interfaces for searching andfinding information. FIWARE is using the OMA Next Generation Service Interface (NGSI) data model as thecommon information model of IoT-based systems and the protocol for communication. We describe the
NGSI9 and NGIS10 below.
NGSI9 is used to manage the availability of context entity. A system component can register theavailability of context information, and later on the other system component can issue either discover orsubscribe messages to find out the registered new context information.
NGSI10 is used to enable the context data transfer between data producers and data consumers. NGSI10
has query, update, subscribe and notify context operations for providing context values. A context broker
is necessary for establishing data flow between different resources as well as consumers or providers.

5.3 Performance
Service Orchestrator
Description: The Service Orchestrator module is responsible for generating processing task instances
and assigning them to proper resources (e.g., edge or cloud nodes) for task deployment, based on the
topology defined by developers, the current system status, and also some parameters (e.g., the scope ofdata sources) given by the operator on the fly. Meanwhile, the Service Orchestrator is managing all task
instances over the resources at the global level.
Service orchestrator allows leveraging edge for more efficiency and higher performance in computing
and communication. Leveraging edge computing with Service Orchestrator allows significant bandwidth
reductions, especially for scenarios such as input streams (e.g., video streams captured by cameras) arefiltered or even analysed on the edge and only the filtered values or analytical results are sent to cloud as
opposed to sending the raw data as it is.

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5.4 Scalability
Federation Agent
Description: Federation agent of the IoT Broker provides capability of forwarding data from one IoT
Broker to another with hierarchical or mash-up topologies. For instance, one deployment in a certain
area using an IoT Broker can be accessed by another deployment with another IoT Broker. Hence,Federation Agent separates IoT data in different domains and enhances the privacy by giving control ofthe data access to the IoT domain administrators.
IoT Broker federation allows local deployments to be handled by a local IoT Broker and multiple IoT
Brokers can access data from each other when necessary. Moreover, it may also allow deployments fromdifferent domains in the same region (e.g., a smart city with multiple business domains) having different
IoT Brokers. Compared to the centralized context brokering approach where there is a context broker inthe cloud which collects and manages all information, using Federation Agent is more scalable anddistributed approach which is suitable for IoT deployments.

6 Instantiation views
6.1 U2-based platform Instantiation View
The TRON Project, since its inception in 1984, has been aiming at the realization of Highly FunctionallyDistributed System (HFDS) where any objects worth connected in our surroundings will be embeddedwith computers and work in a collaborative manner to offer better service to human users. The conceptof HFDS has been realized partially by the concept of the IoT and/or ubiquitous computing. In a sense,
the world has finally caught up with the project. So far the project has attempted to realize HFDS bydeveloping technological components that would be necessary such as node devices, identification
infrastructure, information distribution infrastructure, etc. We are now at the stage when we can combine
the components to build a system as a whole and fill in the details since the component technologies
have matured.
6.1.1 Description of u2 Functional Components
6.1.1.1 Pre-requisites
In this short section we introduce some concepts which are at the centre of the u2 technology.
6.1.1.1.1 ucode
To identify individual objects, spaces, and concepts in the real world, unique identifiers are assigned torespective objects, spaces and concepts that we wish to identify. This model is called a ucode model. The
target in the real world to be identified in the ucode model is called an "entity,” and the unique identifierassigned for identifying the entity is called a ucode.
The entity consists of objects, places, and concepts. "Objects" in the ucode model include tangible
objects such as industrial products and agricultural crops, and intangible objects such as content andprograms. "Places" include features in the real world such as roads and buildings and more detailed
smaller components in the real world such as rooms and corridors. "Concepts" include the relationships
between "objects" and "places" and the information which can be the real-world context. In a nutshell,
the entity is a target to be identified among objects, places, or concepts in the real world, and theidentifier to identify the entity is a ucode.
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6.1.1.1.2 ucR: ucode Relation
ucR: u2 architecture represents the real-world context by modeling the relation on a real-world entity asa relational representation among ucodes allocated to entities or between the ucode and atom , which isa value such as a string, a number or a date. This representation model is called a ucode Relation model(ucR model). When a relationship is represented by ucR model, it is called a Relation.
ucR Unit: The basic unit of the ucR model consists of two ucodes or a ucode and atom, and theirrelationships. Moreover, the respective relationships between the two are provided with a logical ucodecalled a relation ucode. This basic unit consisting of the above triplet is called a ucode relation unit (ucRunit). When this triplet is set in a sentence where the relationships are represented as the predicate, theucode as the subject is called a subject ucode, and the ucode as the predicate which is a relation part iscalled a relation ucode, and the ucode as the object or complement is called an object ucode. The atom
can be substituted for the object ucode. For example, in a description, "R of S is O" or "there is a relation
R between S and O," S refers to the subject ucode, R to the relation ucode, and O to the object ucode or
atom. When it is not necessary to uniquely identify any of the three elements comprising a ucR unit, theirrelevant parts can be left blank.
ucR Graph: The information and context about an entity are represented by combining relations among
multiple entities. For example, for information on an entity indicating a “stuffed cabbage,” pieces of
information such as its recipe, a cabbage as ingredients, and a production area of the cabbage are
combined. To represent such information, ucR units are combined, and a digraph where multiple ucodes
and atoms are connecting by relation ucodes is built. The digraph generated based on the above
method is called a ucode Relation graph (ucR graph). More specifically, the ucR graph is a massive graphstructure, in which ucodes are connected by relations and pieces of information described as atoms areassociated with each ucode.
6.1.1.2 ucode Resolution Component
The wide-area distributed database which manages ucR graphs is called a ucode Relation 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 database is basically an open database which anyone can use to reference or register
information, but it can also implement an access control.
u2 architecture does not specify the internal configuration of the ucR database. However, the ucR
database must provide an interface for ucode resolution explained in the following section.
When a user physically accesses an entity in the real world, u2 architecture identifies the information
appropriate to the situation from the ucR database, based on the ucode assigned to the entity. This
process is called ucode resolution. Moreover, the information associated with ucodes, namely, the ucRgraph is registered in the ucR database. The protocol for accessing the ucR database in this manner iscalled ucode Resolution Protocol (ucodeRP).

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Figure 13: u2 Architecture (ucR focus)

6.1.1.2.1 Functionalities
The main functionalities of the ucode Resolution Component are listed below:
•
•
•
•
•

Query ucode: Searching ucode information;
Issue ucode: Generating a new ucode;
Get ucode: Acquiring information about a certain ucode;
Update ucode: Updating information about a certain ucode;
Delete ucode: Deleting information about a certain ucode;

6.1.1.2.2 Interfaces
The interfaces of the ucode Resolution Component are based on HTTP/1.1.
The ucode Resolution Component provides the Query ucode interface based on the SPARQL 1.1 andreturns JSON-formatted response. The component provides the RESTful otherwise interface and returns
JSON-formatted response.
6.1.1.3 IoT Aggregator Component
Aggregate computing uses all devices, services and systems that are connected to the network toachieve desired services. Let us take an example of the optimum control of air conditioning in our living
environment. Aggregate computing uses air conditioners, of course, but it also employs other objects
such as windows, air vents, gas stoves, temperature sensors, humidity sensors, and any other devices thatare related to the control of the air in our living space to achieve the desired optimum control of the
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CPaaS.io System Architecture (v2)environment beyond the confine of the original manufacturers. While we aim to achieve such computing
environment, we also have to pay due attention to the governance of the usage of functions. Users
should have the final say about who can control what and when instead of the vendors of the devices orservice providers. Thus the users should guide the governance, and we need to provide flexible access
control that can be dynamically changed according to the current context. However, note that theembedding of complex and comprehensive access control in each node will negate the general efforts toachieve the miniaturization and energy-efficiency of such devices. Yet, we can still achieve theminiaturization, low-cost, energy-saving by moving the complex advanced functions such as access
control, which is auxiliary to the original inherent function of the device, to the cloud. Thus "Aggregate
Computing" tries to offer a holistically optimized IoT service by mixing various devices and services as awhole.
The reference implementation for it is "IoT-Aggregator". It has not been easy to connect devices fromdifferent manufacturers and make them collaborate with each other. IoT-Aggregator will act as lubricant
and mediator among the devices, systems and services that would not have been capable ofcollaborating with each other easily.

Figure 14: u2 architecture (IoT Aggregator focus)

6.1.1.4 "Omotenashi" Cloud Component
The TRON Project has been aiming to realize the vision of so-called HDFS environment in which many
computers in our surroundings cooperate together to offer better services to improve the quality of
everyday living. To realize such an environment, we need the mechanism to let objects to communicate
with each other to collaborate. But we also need a mechanism to learn and collect the attributes of eachindividual user such as the language spoken, age, many physical characteristics, preferences, etc. Suchknowledge must be reflected to create an optimized environment. For this purpose, TRON Project is now
building a general-purpose Personal Data Store (PDS) that uses the IoT technology. It is called"Omotenashi (hospitality) cloud" under its nickname. "Omotenashi" cloud is a project supported byindustry-government-academia and now has officially started with the funding from the Ministry ofInternal Affairs and Communications.

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In the short time span, this "Omotenashi cloud" can be used to advance the hospitality service to theincreasing number of overseas tourists who are expected to peak in 2020. Customers (consumers) store
their individual characteristics such as preferred spoken language, food taboos, passport information forsales tax-exemption in the cloud. Once the data is stored, such information is easily retrieved and passedon the spot to service providers such as stores and public services by using the smartphone apps ortransportation IC cards as retrieval key. OPaaS.io has been designed to collaborate with IoT-Aggregator.
The collaboration will implement an HFDS environment.
The party who receives a service escrows the personal data in the PDS cloud, and lets it pass the data to
the service vendors as the need arises. So it is the customer (consumer) that controls the vendors. This
concept, Vendor Relationship Management (VRM) is the reverse of the conventional idea of Customer
Relationship Management (CRM). OPaaS.io plays the important role of PDS in the scheme. This
framework has been designed to be useful as legacy service platform in Japan after the year 2020 when it
will be utilized very much for hospitality service to foreign tourists. Small regional service vendors who
did not have the resources to build conventional CRM can join OPaaS.io service framework and can now
provide better services suited for the new IoT age. Omotenashi Cloud will be useful for invigorating
regional economy and also is expected to add to e-inclusion by accelerating the realization of
Enableware.

Figure 15: u2 architecture ("Omotenashi" Cloud focus)

6.1.1.4.1 Functionalities
The main functionalities of "Omotenashi" Cloud Component are listed below:•••

Authorization: Starting authentication, logging in by IDm, and acquiring an access token;
User Management: Searching users, creating users, and updating users’ information;
Access Authority Management: Searching authorized services, searching authorized user
attributes, and updating authority;

6.1.1.4.2 Interfaces
The interfaces of the "Omotenashi" Cloud Component are based on HTTP/1.1.
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CPaaS.io System Architecture (v2)The "Omotenashi" Cloud Component provides RESTful interface and returns JSON-formatted response.
6.1.1.5 IoT-Engine Component
IoT-Engine is a standard development platform, standardized by TRON Forum, for Open IoT to realize"Aggregate Computing". IoT-Engine standard specifies the connector on the Micro Processor Unit (MPU)
board, and the RTOS used by the MPU, and requires the function to connect to the cloud services on theInternet. The RTOS used on the IoT-Engine is µT-Kernel 2.0 which TRON Forum releases as open source.
IoT-Engines collaborate with the cloud platform called IoT-Aggregator. By doing this, edge nodes can beconnected via the cloud and they act as the components of the "Aggregate Computing", a vision of thecomputing architecture framework in next generation. IoT-Engine project has the participation of sevensemiconductor companies from six countries and regions of the world (as of December 2016): Toshiba
Microelectronics, Renesas Electronics, Cypress, Imagination Technologies, Nuvoton Technology, NXP
Semiconductors, and STMicroelectronics.

Figure 16: IoT Engine with IoT Engine Open Platform

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6.1.2 Description of CPaaS.io u2-based platform architecture

Figure 17: uID architecture

For historical reasons, u2 architecture was designed by a top-down manner driven by the needs ofapplications. Applications themselves have held many functions, which correspond to FGs withinthemselves. This is in contrast to today’s CPaaS.io architecture overview, so it is not easy to create
concrete mapping since some functional modules in u2 architecture is still offered as part of applications.
In Figure 18 below, yellow round corner rectangles corresponds to the base u2 architecture functions(ucR DB, resolution, etc.) Blue and violet round corner rectangles correspond to functions offered in
Omotenashi (Hospitality) Cloud. Red rounded corner rectangles correspond to modules from IoTAggregator. But do note that the u2 architecture itself is in flux, and many applications will offer
additional modules in the future, the mapping today may be slightly changed in 6-12 months time.

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Figure 18: Matching of the u2 components to the CPaaS.io Functional View

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u2 Components

Ucode resolution
ucR DBIoT Aggregator
Device Manager
modulesIoT Aggregator p2p
securecommunication
Omotenashi
ID/Authentication
Omotenashi VRM
Apps

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x
x
x
x
x
x
x
x
x

Web Front-end

Platform Management

Resource Deployement Tool

Pre-Processing (VE/IoT)

Post-Processing (VE/IoT)

Rule-based Reasoner

Analytics (VE/IoT)

Resource Manager

Task Execution Engine

Processing Optomizatiom

task Deployment

Task Composer

Kem

AuthZ

AuthN

VE Service Endpoint

VE Semantic Integration

VE & IoT Service Monitoring

VE Resolution

VE Registry

Semantic VE Data Repository

IoT Service

IoT Semantic Integration

Live Data Manager

IoT Service & Resource Registry

Semantic Data Repository

Communication Channel

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6.2 FIWARE-based platform Instantiation View
6.2.1 Description of FCs used in the FIWARE-based platform
In this short section let us introduce components which are at the centre of the FIWARE IoT Chapter aswell as some components from the European side coming from previous or ongoing EU projects (e.g.
KAT Toolkit or FIESTA-IOT IoT Registry).
6.2.1.1 IoT Broker
The IoT Broker is specified as a lightweight and scalable middleware component that separates IoT
applications from the underlying device installations. The IoT Broker implementation available throughthe FIWARE Catalogue is the reference implementation of this Generic Enabler. This implementation
satisfies all properties described in the specification of the Generic Enabler. The main features of the IoT
Broker are:•••Offering a single point of contact to the user, hiding the complexity of the multi-provider natureof the Internet of Things.
Collecting and aggregating information about thousands of real-world objects on behalf of theuser.
Provide means to assemble lower-level device information (device-centric access) into higherlevel Thing information (information-centric access).

IoT Broker is integrated with three other components: IoT Discovery (ConfMan), IoT Knowledge Server,and FIWARE (Orion) Context Broker.
6.2.1.1.1 Functionalities
The main functionalities of the IoT Broker are listed below. Detailed information regarding some of thestandard functionalities can be accessed in the specification document of IoT Broker [30].
•
•
•
•
•

Context query: Accessing context information (for data consumers);
Context subscription: Subscribing for a change or update for a context;
Context subscription update: Modifying an existing subscription;
Context notification: IoT Broker sending notifications to the subscribers;Context data push: Context update from data providers.

Other than these basic context management functionalities, IoT Broker provides access to historical data
(via its historical agent functionality), allows geo-scoped queries, federation (via federation agent), andscalability (having multiple server instances).
6.2.1.1.2 Interfaces
The main aspects of the IoT Broker are listed below. Detailed information regarding the interface can befound in the interface specification for NGSI-10 [32].
The IoT Broker is implemented based on the FIWARE OMA NGSI 10 interface and it provides a RESTful
API via HTTP. The details about the NGSI-10 interface can be found in [33]. The purpose of the NGSI-10
interface is to exchange context information. There are three main interactions types:
1. One-time queries for context information (Context query);
2. Subscriptions for context information updates (Context subscription) / Notifications base on thesubscriptions;
3. Unsolicited updates (invoked by data providers).
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The IoT Broker is a RESTful web service supporting HTTP/1.1 with JSON or XML data serialization formats.
Some of the operations of NGSI 10 for exchanging context information are listed as follows:
queryContext,
contextEntities,
contextEntityTypes,
subscribeContext,
unsubscribeContext,
contextSubscriptions, updateContext, updateContextSubscription.

6.2.1.2 ConfMan
The NEC Configuration Management or NEC ConfMan is an implementation of the FIWARE IoT Discovery
Generic Enabler. This implementation is specifically designed to interwork with the IoT Broker GE FIWARE
reference implementation, serving as the registry of FIWARE NGSI context providers. The detailed
information about the IoT Discovery GE can be found in [31].
ConfMan is responsible for discovering the availability of context. The availability of context must be
through the registrations made by IoT Agents (i.e., data providers). By registering, data providers maketheir access endpoints available for the data consumers. Data providers offer information about context
entities (i.e., virtual entities) and their attributes (e.g., measurement values, metadata). The role of IoTAgents can be taken by either the Data Handling GE in IoT Gateways or the Back-end Device
Management GE (see Figure 23). Other IoT Backend systems may also provide context information.
Typically, context source availability information is forwarded to the FIWARE Context Broker GE (Figure23). This allows context information (i.e., information generated by or coming from the “Things” i.e. thePhysical Entities) to be available and accessible by the applications. ConfMan is integrated with two othercomponents: IoT Broker and IoT Knowledge Server.
6.2.1.2.1 Functionalities
The main functionalities of the ConfMan (IoT Discovery) are listed below. Detailed information regardingsome of the standard functionalities can be accessed in the specification document of ConfMan [31].
ConfMan allows applications and services to register, discover, and subscribe to the context availability
information. Context availability information can be one of the following.
•••

Information about IoT resources such as data sources or IoT devices and the variables theymeasure.
Information about “Things” (i.e. PEs) and their attributes (i.e associated VEs attributes).
Information about associations between entities. For instance, the attributes of a thing can bederived from attributes of other things.

ConMan allows geographic discoveries of context availabilities based on the defined geo-scopes such as
a location (point), circle or a polygon.
6.2.1.2.2 Interfaces
The main aspects of ConfMan are listed below. Detailed information regarding the interface can befound in the interface specification [32].
The NEC ConfMan API is fully based on FIWARE NGSI-9 which is a RESTful API via HTTP. ConfMan
supports the following operations for exchanging context availability information:•

The registerContext operation is available to register information about data sources in the
ConfMan's database;•The discoverContextAvailability operation can be used to query ConfMan for data sources.

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Users can subscribe for data sources using the subscribeContextAvailability,
updateContextAvailabilitySubscription and unsubscribeContextAvailability operations. Subscribed users
need to expose a resource for receiving notifyContextAvailability messages from ConfMan.
6.2.1.3 IoT Knowledge Server
IoT Knowledge Server adds semantic information into NGSI messages and enhance these NGSI messages
with semantic reasoning. IoT Knowledge Server component has an internal triple-store where NGSI (orother) ontologies are kept. It serves semantic knowledge such as entity subtypes or supertypes andprovides high-level access to the semantic ontologies via query/subscription functionalities.
IoT Knowledge Server has the ability to serve high level queries (e.g., get subtypes of a sensor).
Moreover, it is a flexible component which can be extended with new features on the fly. IoT Knowledge
Server is composed of two HTTP web servers and two databases along with their HTTP servers. One ofthe database is HSQLDB (a SQL database) and the other is given by Apache Jena Fuseki server (a RDF 3store).
IoT Knowledge Server is currently integrated with the ConfMan component.
6.2.1.3.1 Functionalities
The current functionalities of IoT Knowledge Server are listed as follows.
•
•
•
•
•
•

Query functionalities : getSubTypes, getSuperTypes, getAttributes, getAllSubTypes,
getAllSuperTypes (current list based on NGSI ontology, new features could be easily added)
Add new queries: New queries with one or zero variables (e.g. Entity Type) can be added to a fileand we can start using as a new functionality (other than the 5 above)
Register new queries: Adding new queries by HTTP request on the fly (without restarting theserver)Forward SPARQL queries : To provide single point of contact even for direct SPARQL queries
along with the high level ones (getSubTypes)
Subscribe functionality : Subscribing to queries and regular (fixed time) updates on change to thesubscribers by the IoTKnowledgeServer.
Caching mechanism : Caching mechanisms for fast respond (without asking SPARQL server) toqueries (both for Queries and for Subscriptions)6.2.1.3.2 Interfaces
IoT Knowledge Server offers service via its own REST API and sends response/updates with JSON format.
Some example requests for IoT Knowledge Server are listed below. Other than those, IoT Knowledge
Server also supports REST-based SPARQL interface via HTTP requests (similar to the Apache Jena Fusekiinterface).
GET SUBTYPES: (HTTP.GET)
http://<ip_address+port>/query?request=getSubTypes&entityType=<entity_type>
Example Use: http://localhost:8080/query?request=getSubTypes&entityType=Node
GET SUPERTYPES: (HTTP.GET)
http://<ip_address+port>/query?request=getSuperTypes&entityType=<entity_type>
Example Use: http://localhost:8080/query?request=getSuperTypes&entityType=Sensor
GET ATTRIBUTES: (HTTP.GET)
http://<ip_address+port>/query?request=getAttributes&entityType=<entity_type>
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Example Use: http://localhost:8080/query?request=getAttributes&entityType=Sensor
REGISTER NEW QUERY:(HTTP.POST)

http://<ip_address+port>/registerquery
Example Use: http://localhost:8080/registerquery

6.2.1.4 FIWARE Data Context Broker
FIWARE’s Data Context Broker (or simply Context Broker) is a specific middleware for brokering thecommunication between IoT data providers and IoT applications based on NGSI-standard as an opensource generic enabler and it can provide functionalities such as queries pertaining to historicalinformation, federation, entity compositions (aggregating information), and so on. FIWARE Context
Broker has functionality for discovery of information sources (for things/ entities) and the information
sources and what information they can provide are registered to this Context Broker. FIWARE Context
Broker is used for discovering information sources and then accesses/forwards the information.
6.2.1.4.1 Functionalities
The functionalities of FIWARE Context Broker include standard context management operations such as:
•
•
•
•
•

Update context;
Query context;
Subscribe context;
Update context subscription;
Unsubscribe context.

Similar to the IoT Broker, FIWARE Context Broker gives access to the information based on virtualentities, meaning that it provides an abstraction to the context, which makes easier application
development and data analytics.
As opposed to having two stand-alone components such as IoT Broker and IoT Discovery, FIWARE
Context Broker is a single component which also has context discovery functionalities (standard
operations [34]) such as:
•
•
•
•
•

Register context;
Discover context availability;
Subscribe context availability;
Update context availability subscription;
Unsubscribe context availability.

6.2.1.4.2 Interfaces
The communication between the Context Broker for context value data forwarding as well as registrationof entity registrations are based on the NGSI protocol (NGSI-9 and NGSI-10). The API of the Context
Broker is very similar to the APIs of the IoT Broker and ConfMan components. For detailed information
and APIs, please refer to [34]. This document contains HTTP requests with JSON messages for both NGSI9 and NGSI-10 interfaces.
While the FIWARE Context Broker (Orion) supports two NGSI versions (NGSI v.1 and v.2), the first version
is considered as the standard to follow for both Orion and IoT Broker/ConfMan. The major difference of
the FIWARE Context Broker for NGSI v.1 is the lack of “domainMetadata” in the JSON structure. Fullcompatibility between the two brokers can be satisfied with minor modifications.

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6.2.1.5 FogFlow Service Orchestrator
Nowadays more and more stream data are constantly generated by a huge number of IoT devices thathave been widely deployed at different locations in our cities. By outsourcing more and more dataprocessing logics from the central cloud down to the network edges, such as Base stations, IoT gateways,
even some endpoint devices, we are able to largely reduce the data transmission cost and also enablereal-time data analytics service with fast response time and better privacy-preserving capability. In such a
cloud-edge environment, we need to decompose our IoT services into smaller tasks so that the small
tasks could be dynamically migrated between cloud and edges. However, it is a big challenge todynamically orchestrate a decomposable service and manage its tasks over cloud and edges, due to thehigh heterogeneity, openness, and dynamics of such system environments. The FogFlow service
Orchestrator is designed and implemented to solve this problem. It provides efficient programming
model and tools for service providers to easily program decomposable IoT services and help them
manage the service orchestration during the runtime.
The FogFlow Service Orchestrator is implemented within FogFlow, which is going to be an open source
FIWARE GE soon. The FogFlow Service Orchestrator is mainly to dynamically generate, configure, anddeploy data processing flows over cloud and edges 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 type of output data is expected, and which type of schedulingalgorithm is preferred.
The FogFlow Service Orchestrator consists of two major parts: a Global Orchestrator in the Cloud and a
Local Orchestrator at edges. As illustrated in Figure 19, according to a service topology defined byservice developers, FogFlow dynamically launches necessary data processing tasks from the provided
service topology and the launched data processing tasks can process the raw NGSI context informationfrom context producers like sensors and then generate real-time results required by context consumers
like actuators. The distributed context management can be provided by other components in the virtualentity layer.

Figure 19: FogFlow Service Orchestrator

6.2.1.5.1 Functionalities
FogFlow Service Orchestration provides the following functionalities to service developers and service
operators via its UI based task designer.
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For service developers:•••

Registering an implemented docker image as a data processing task;
Editing and defining a service topology;
Managing all registered service topologies;

For service operators:•••

Triggering and generating the data processing logic from a specific service topology based on arequirement;
Deploying the generated data processing tasks over cloud and edges;
Optimizing the deployment plan based on the locality of producers and consumers and also
device mobility;

6.2.1.5.2 Interfaces
Currently, the following based interfaces are provided via a UI task designer:•

Register a task with a docker image;

Figure 20: Task Registration•

Editing and submitting a service topology;

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Figure 21: Graphical editor of service topology•

Trigger a service topology with a geo-scope based requirement;

Figure 22: Defining a requirement with a specified geo-scope

6.2.1.6 KAT Toolkit
The Knowledge Acquisition Toolkit (KAT) provides a set of methods for labelling data sets after initial preprocessing and data abstraction. In particular the tool provides an algorithm workflow that is
implemented sequentially: pre-processing method, along with dimensionality reduction e.g. principle
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CPaaS.io System Architecture (v2)component analysis), data abstraction (e.g. clustering) and finally labelling/annotation of the resulting
abstracted data is carried out. Input data format is time series in a tuple format or Comma Separated
Values (CSV). Some newly developed machine learning techniques for data prediction will be integrated
to the existing toolkit in the course of the project.
6.2.1.7 Analytics FC
The Analytics FC is provided as AGT background to CPaaS.io as an external service that enables access toanalytics results in semantically rich abstraction level that is meaningful for applications. The service canbe accessed via a GraphQL 8 interface that provides access to a data represented in an underlying
semantic data model. In CPaaS.io the service can be pre-configured to execute the full data ingestion
and analytics pipeline as required for the My Experience application. For instance the service can deliveruser activity and emotions of people as well as provide contextual information, e.g. location of colour
stations in a Colour Run event as derived from video analytics pipeline.
6.2.1.8 Machine Learning
The Machine Learning (ML) component implements different algorithms fitting different purposes andfocuses. In a first step, it is used to extract information from available Social Media (we chose Twitter for
the first prototype), before publishing it to the CPaaS.io platform (this case corresponds to a RDP ClassIV case). The first version of the ML module is based on Naïve Bayes and provides a classification of twits
according to the mood/sentiment they convey i.e. Positive, Negative or Neutral. This classification of
event-related social media data can for instance be exploited by the Colour Run event as an additionalsource of event-related information.
6.2.1.9 Authorization Component
The authorization component is able to perform capability-based access control based on themechanism presented in [26]. It describes authorization tokens specified in JavaScript Object Notation(JSON) and optimizations to manage access control on constrained devices. The access control
component it is controlled by XACML policies
The CPaaS.io Authorization scenario will consists of the following main entities:
•
•

•
•

•

8

Capability Client: performs requests to the Capability Manager to obtain capability tokens, which
are used to perform actions over entities that are registered with the Context Manager;
Capability Verifier: a server receiving access requests from Capability Clients. Such access requests
contain a capability token, which is evaluated by the Capability Verifier in order to deny or grant
the requesting action. This entity makes use of the Capability Evaluator functionality, which isintended to validate capability tokens;
Capability Manager: a server accepting requests for capability tokens generation. Additionally,
this entity acts as a client requesting authorization decisions to the Policy Decision Point;
Policy Decision Point (PDP): It is a server that accepts XACML requests to make authorization
decisions. The PDP is contacted by the Capability Manager before generating a capability token
for the Capability Client;
Policy Administration Point (PAP): It is a web application responsible for defining and managing
the access control policies. It provides the functionality so users can define XACML policies in auser-friendly way. The PAP has a GUI to facilitate the generation of policies.

http://graphql.org

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6.2.1.9.1 Functionalities
This component, as described will be responsible for making authorization decisions. In this sense, thiscomponent will have the following functionalities:••••

Policy generation: In order to provide new access control rules, thanks to the PAP, administrators
will be able to add new access control policies;
Policy validation: Every receiving access request must be validated by the PDP;
CT generation: If the access to a specific resource is granted, the authorization component
provides a Capability Token in which such authorization is granted;
Secure group communications: In this sense, the authorization component can encrypt the
information to be provided to a group of consumers allowing only the ones with the rightcredentials to decrypt that information.

6.2.1.9.2 Interfaces• According to the specific functionalities defined in the last section, each of this functionality will
have the corresponding interface in order to grant authorization access to the resources to theappropriate consumers. In this sense, the following interfaces or end points should be provided
by our authorization component:Policy generation: A certificate must be provided in order tovalidate the profile and permissions of the user requesting such generation. In addition, the triplet(resource/s, subject/s, action/s) must be provided too;
• Policy validation: This interface should receive the request in a JSON format, the identity of theuser and an authentication token which make possible to generate an access control decision. If
the access is granted, a CT will be provided to the requester;• Secure group communications: The authorization component must have an interface which allow
requesters to provide the kind of policy (attribute-based encryption) must be applied.
6.2.1.10 Identity Management
The IdM module is responsible for managing the identities of the smart objects and users. The module isable to take into account privacy concerns to manage subjects’ credentials (from users or smart objects),
in a privacy-preserving way relying on anonymous credential systems. It is able to endow subjects withmechanisms to ensure anonymity, which is done mainly issuing pseudonyms and proof of credentials tominimal personal data disclosure.
The Identity Management system is responsible for managing and storing the credentials, which are usedby subjects to request information from an IoT services. The IdM allows users to obtain the proper
credentials that can be use afterwards to derive proofs of identity to be presented to a Verifier (e.g., IoT
Service provider) in order to prove the identity condition while disclosing the minimum amount ofprivate information.
The component is endowed with the Idemix cryptographic engine that is responsible for low level
cryptographic operations required by the anonymous credential system. The information provided by theContext Manager module is used by the IdM to handle the usage of partial identities under a specificcontext.
The CPaaS.io IdM has been integrated within the FIWARE IdM, i.e., the Keyrock IdM. The CPaaS.io IdMIssuer, which is in charge of generating the Idemix credentials, is able to communicate with the Keyrock
to generate the credentials based on the attributes stored in the Keyrock for the user requesting thecredential. To this end, the CPaaS.io IdM provides a Java library to interact with Keyrock by means of the
SCIM standard [29]. This library provides methods such as add, remove or update users.
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Because CPaaS.io IdM relies on Keyrock, users profile management is delegated to Keyrock, the rest of
the components of the CPaaS.io framework interacts with Keyrock through the CPaaS.io IdM.
The IdM components that have been designed and implemented in the scope of CPaaS.io are
enumerated below:
•

•

•
•

•

CPaaS.io IdM Client: This is a client application that allows obtaining Idemix credentials from theIssuer Server. It also allows interacting with the Verifier server which can validate the partial
identity derived from the credential;
CPaaS.io-Issuer-Server: This is a web application which allows generating Idemix credentials forclients. The client must be authenticated against the Issuer using a valid certificate. The Issuer also
supports the verification functionality;
CPaaS.io-Verifier-Server: This is a web application, which is able to validate partial identities
presented by the client application;
CPaaS.io-IdM-Enabled-Capability Manager: This is a web application that allows users to obtaincapability tokens using their partial identities. In other words, it allows authenticating anddemonstrating their attributes by means of Idemix proofs of having a valid credential issued by
the Issuer;
CPaaS.io IdM KeyRock Client: This is a library that provides a basic API for identity management
by implementing a client to interact with the FI-WARE KeyRock server.

6.2.1.10.1 Functionalities
The main functionalities provided by this component are related to registering and authentication
operations. The first one, which should be done by the administrator providing for this purpose his
credentials, and the second one which must be opened to all the users to allow them to log into theplatforms.
6.2.1.10.2 Interfaces
In light to the previous functionalities two interfaces will be provided:• Registering new users: The interface provided by the IdM will need the credentials of theadministrator, his username and password, and a parameter indicating the new user and theircorresponding attributes;• Authentication: This interface will receive the username and password of the users in order to bevalidated.
6.2.1.11 Authentication
The authentication module verifies if a subject is truly who or what it claims to be. Authentication services
are provided by the CPaaS.io IdM system. Besides the conventional password-based and key-based
mechanisms, the CPaaS.io IdM also supports authentication based on anonymous credentials.
•

Login password-based authentication: The CPaaS.io IdM relies on the Keyrock IdM to perform
this kind of authentication. To this end, the CPaaS.io IdM library provides a method that given theusername and password authenticates the user against the keyrock and generates anauthentication token that can be used afterwards to perform other actions against the Keyrock
IdM. The CPaaS.io Keyrock IdM client library provides two main methods for dealing with thiskind of authentication. The first method is for authenticating a user registered in the Keyrock
given the password and username and its domain. The second method, provided by the Keyrock
IdM client, is for validating a given token ID;

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PKI-based authentication: CPaaS.io framework allows users to authenticate using their digital
certificates. It requires a Public Key Infrastructure (PKI) where the user’s certificates are signed by
the Certificate Authority (CA) and validated on the server side. The IdM server performs client
authentication based on asymmetric-key cryptographic. This is done actually by the IdM web
server that authenticates the client that must possess the private key associated with its public
X509 certificate (the server trusts the CA);
Anonymous credentials based authentication: This kind of authentication is done by the
CPaaS.io privacy-preserving IdM, which offers a claim-based solution for authentication. The
CPaaS.io IdM relies on an anonymous credential system, namely Idemix from IBM [27], to ensurea privacy and minimal disclosure of personal information.

6.2.1.11.1 Functionalities
Since the authentication operations are based on the identity attributes defined by the IdM all thefunctionalities have been defined in the previous section.
6.2.1.11.2 Interfaces
Again, the interfaces provided by IdM have been provided in the previous section.
6.2.2 Description of CPaaS.io FIWARE-based platform architecture

Figure 23: FIWARE-based CPaaS.io architecture

The Figure 23 above shows the FIWARE IoT architecture which is based on the FIWARE IoT Services
Enablement Architecture [31]. This figure is a modified version which includes the IoT Knowledge Server,
SPARQL agent, and FogFlow Service Orchestration components in addition to the existing components in

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CPaaS.io System Architecture (v2)[31]. The architecture mainly consists of three layers: IoT Devices, Context & Process Management, andSecurity Layer. These layers are described as follows:••

IoT Devices: It is made of all on-field IoT infrastructure elements needed to connect physicaldevices to FIWARE Apps or SPARQL Apps. Typically, it comprises: IoT end-nodes, IoT gateways
and IoT networks (connectivity);
Context and Process Management: Context Management comprises the set of functions, logicalresources and services hosted in a Cloud data centre as well as on the Edge. Up north, it isconnected to the data chapter ContextBroker, so IoT resources are translated into NGSI Context
Entities. South-wise the Context Management is connected to the IoT devices, that are all thephysical IoT infrastructure. Process management has the FogFlow Service Orchestrator, whichenables processing optimization between the Cloud and Edge resources.

FIWARE’s IoT architecture follows the FIWARE OMA NGSI context management information model, whichallows performing context operations such as query, update, subscribe, and notify. The NGSI9 API ismainly used for discovering VEs while NGSI10 is mainly for accessing context information.
Considering the architecture from a bottom-up (south to north) perspective, in the south IoT devices areconnected to the IoT Broker and IoT Discovery components via the IoT NGSI gateway. The IoT NGSI
Gateway can be implemented with an adapter so that the devices with various APIs (shown as Device
API) can be registered via NGSI9. Moreover, the context information can be sent to the IoT Broker via
NGSI10. The IoT Broker then forwards the context to the data context broker and to the application
developers. In FIWARE, each FC in this architecture is realized by Generic Enablers (GEs). Detailed
information about specific GEs can be found in the FIWARE catalogue [32].
Currently, the FIWARE-based CPaaS.io platform provides the possibility for devices to push SPARQL orNGSI data through IoT or SPARQL Agents. The agents are responsible for converting device data (device
API) to NGSI or SPARQL data. Moreover, the CPaaS.io platform provides app developers the flexibility touse either the NGSI or the SPARQL interface to access data. The SPARQL interface is provided by the IoT
Knowledge Server, whereas the NGSI interface is provided by the IoT Broker (AeronBroker) and the Data
Context Broker (Orion Context Broker). In the next phase of the project, the conversion from NGSI to
SPARQL may allow pushing NGSI data through IoT Agents and accessing this data by SPARQL app
Developers.
We listed the components in Section 6.2.1 and showed them in the above figure in the Context andProcess Management Layer. These components, namely AeronBroker, ConfMan, IoT Knowledge Server,Orion Context Broker, and FogFlow Service Orchestrator are supported by FIWARE. Now, in the below
figure we match these components to the CPaaS.io FCs based on where the already implemented
FIWARE components possibly fit in this Functional View.
The Security Layer provides the necessary authentication and authorization mechanisms to the CPaaS.io
platform. The listed components in the Security Layer (Fig. 23) are KeyRock, XACML, Cap Manager, PEP
Proxy, and CP-ABE. The layer is responsible for providing secure access control to the data providers andconsumers. Currently, these components are already integrated with the Orion Context Broker and they
are being integrated with IoT Broker. FIWARE app developers (data consumer) as well as the providers(IoT Agents, IoT Broker) can access content through the PEP Proxy component.

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Figure 24: Matching of the FIWARE components to the CPaaS.io Functional View

In the Figure 24 above, the FIWARE components are matched with the FCs of CPaaS.io. Note that the
FIWARE components are placed in arbitrary places for visual purpose, while their places actuallycorrespond to the places of the marked FCs in CPaaS.io functional view.
While CPaaS.io architecture is clearly more advanced, having each FC has various requirements, somerequirements of the FCs are not completely met by the FIWARE components. For instance, considering
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CPaaS.io System Architecture (v2)the requirements that are listed for use-cases, Semantic VE Data Repository may require more advanced
and novel features compared to the existing functionalities of the IoT Knowledge Server component. For
this reason, some FIWARE components may be either extended or improved or new components should
be developed during the CPaaS.io project when necessary. At this stage, the decisions have not been
finalized for the implementation necessities of the proposed CPaaS.io architecture. On the other hand,some components can already meet the listed requirements of some of the FCs without further
implementation effort. For instance, VE Resolution is provided by IoT Discovery and therefore IoT
discovery component could be integrated without much effort.
We provide an instantiation view for the FIWARE components below. The Figure 25 below includes IoT
Broker instances, IoT Knowledge Server, Context Broker, as well as IoT Discovery GEs including also theirinteractions (APIs).

Figure 25: Interactions of the FIWARE FCs

In the above Figure 25, the historical data is stored in CouchDB by IoT Broker. IoT Broker also forwards
the context to the context broker or applications. Moreover, subscriptions are stored in and accessedfrom a shared SQL database by the distributed IoT Broker instances. Registrations are saved in theRegistration Respository (ConfMan) via NGSI9. Lastly, IoT Agent is responsible for providing context of
VEs in NGSI9/10 information model and therefore it can be considered as an NGSI device.

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Concrete
FIWARE
Componentsrdand 3 party
components

KAT Toolkitrd(3 party)
Context
Broker(FIWARE)IoT Broker(FIWARE)IoT Discovery(FIWARE)
IoT
Knowledge
Server(FIWARE)
FogFlow
Service
Orchestrator
SocIoTal
FIWARE
based
Authentication
SocIoTal
FIWAREX

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X
X
X

PA

CPaaS.io
X
X
X
X

X

X
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X
PA

X
X
X

X
X
PA

X
X
PA
PA

Web Front-end

Platform Management

Resource Deployement Tool

Pre-Processing (VE/IoT)

Post-Processing (VE/IoT)

Rule-based Reasoner

Analytics (VE/IoT)

Machine Learning

Resource Manager

Task Execution Engine

Processing Optomizatiom

task Deployment

Task Composer

Kem

Identity Management

AuthZ

AuthN

VE Service Endpoint

VE Semantic Integration

VE & IoT Service Monitoring

VE Resolution

VE Registry

Semantic VE Data Repository

IoT Service

IoT Semantic Integration

Live Data Manager

IoT Service & Resource
Registry

Semantic Data Repository

Communication Channel

Deliverable D3.3
CPaaS.io System Architecture (v2)

6.2.3 Mapping FIWARE / System ArchitectureDeliverable D3.3

CPaaS.io System Architecture (v2)basedAuthorization
SocIoTal
FIWARE
based
Identity
Management
ML
component

X

X

X: covers the FC currently
NR: covers the FC in Next Release of the concrete FC
PA: covers PArtly the FC

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6.2.4 Concrete System Use-cases
In this section we revisit some of the System Use-cases that have been already described in Section 4.3.1
in order to illustrate typical operation usage patterns in the form of interactions taking place between“logical” function components and actors that stand outside the CPaaS.io platform. We focus here on
describing how such interactions between logical FCs and external actors derives into interactions
between those actors and the concrete FCs as introduced in this chapter. While section 4.3.1 refers todifferent classes of Raw Data Producers we will in this section refer more explicitly to the project
scenarios (e.g. Waterproof Amsterdam or ColourRun) in order to 1/ give a concrete grounding of thearchitecture work within the project scenarios and actual platform development and 2/ give more
support to the implementation and integration phase running in parallel to this architecture paperwork
activity.

6.2.4.1

Configuration phases

This section revisits Section 4.3.1.2 and describes few concrete system UCs dealing with system
configuration where the system consists of the CPaaS.io platform and platform clients (actors defined in
the scenarios). Most of those UCs are relevant to all scenarios. This ‘catalogue’ of UCs will be extended
within the next iterations of the CPaaS.io architecture document.
6.2.4.1.1 Register a user/actor to the CPaaS.io platform
As already expressed before in this document the architecture of our platform contains a specificelement for managing the properties and attributes of its users. This component receives the name of
Identity Management.
So, in order to register a new user/actor into the CpaaS.io platform, first we need to interact with thiscomponent in order to define the different parameters of the user’s profile. Specifically, we have defined
the following attributes for it:•

NickName nickName: the casual way to address an entity user in real life•

Name name: the components of the user’s name•ArrayList<Address> addresses: the physical mailing addresses of an entity user•

ArrayList<Email> emails: the e-mail addresses of an entity user•

Organization organization: it identifies the name of an organization•

Department department: it identifies the name of a department•

ArrayList< X509Certificate> x509Certificates: the X.509 certificates associated with the entity•

String password: the entity's clear text password•

boolean active: a value indicating the entity's administrative status

To do so, the Identity Management provides and endpoint to receive such registration operations using
a RESTful API and the POST method. The URL that will handle such registrations is similar to the followingone:
https://URLIdentityManagement:443/v3/OS-SCIM/v2/Users/id_user
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The body of this request should contain a JSON text that must meet the specification of SCIM core (RFC7643 SCIM Core Schema). Since this operation must be done by the administrators of the platform,
such operation requires also the addition of a certificate in order to securely validates the origin of such
request.

6.2.4.1.2 Create and assign security credential
Although after applying the previous operation the user has been registered in the CPaaS.IO platform, hedoes not have any access rights over the information stored in it. So, we need to establish the security
policies for such information. This is done thanks to the XACML component which comprises thefollowing entities: Policy Administration Point (PAP), Policy Decision Point (PDP) and Policy Enforcement
Point (PEP).
Thanks to an administration web page, the administrator can establish the different security policies thatwill be applied to the user and the resources stored in the platform.

Figure 26. PAP Web configuration interface

After having logged in, the administrator will be able to generate new XACML policies. These are
identified by a triplet (resource, subject, action) which means that for a specific resource or resources, theselected subjects or users will be able to perform the specified actions over them. Applied to ourplatform, the resources will be the different stored entities with their corresponding attributes, thesubjects are the different users of the platform, and finally, the actions could be register, updating orrequesting the information.
Thanks to the definition of these policies, the Capability Manager, after receiving an authorization
request will forward it to the PDP which will carry out the validation operation over it. If the validation
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process is successful, it will answer the Capability Manager that will generate the Capability Token thatwill allow the requester of such information to access to it.
6.2.4.1.3 A CPaaS.io user / RDP creates a privacy/security policy
Another kind of authorization that the platform can perform is the authorization over the broadcast
information. In this sense, a specific authorization policy can be applied over the transmitted informationin such a way that only the receivers with the appropriate credentials will be able to reveal it.
Such authorization or privacy policy is based on Attribute Based Encryption (ABE) so, the users and RDPs
which are intended to apply this authorization policy must specify the combination of attributes that will
be able to decrypt such information, i.e. organization = OdinS and department = R&D. Such combination
implies that only the users which are employed by OdinS and are working on R&D will be able to decrypt
the information. If there are any users from OdinS which working in other areas, they will not be able todecrypt it.
The following example describes how the information must be registered in our CPaaS.IO platform.
"contextElements": [{"type": "Test","isPattern": "false","id": "Test:1","attributes": [{"name": "cipheredAttribute","type": "ciphertext","value": "hello","metadatas": [{"name": "cpabe-policy","type": "string","value": "organization = OdinS and department = R&D"
}]
}]
}
]

According to it, the information to be stored in our platform is an entity whose id is Test:1 of the type
Test. It only contains one attribute whose name is cipheredAttribute of the type ciphertext. The security
information is included in the metadata field where we specify that this attribute should be encryptedusing a CP-ABE policy using the combination of aforementioned attributes.
This information is included in the NGSI request to register information into our platform. It will bereceived by our PEP_Proxy which will make the validation tests first, and afterwards it will proceed toencrypt the information using the appropriate CP-ABE key.
On the other hand, the registered users will have received a specific CP-ABE key which is based on theattributes of their identities stored in the Identity Management component. This way, after receiving thisinformation, only the one with the appropriate attributes will be able to decrypt it.

6.2.4.2 Data production
6.2.4.2.1 An RDP Class-I pushes semantic data to the CPaaS.io platform
Semantic data can be pushed to CPaaS.io in two ways:
1. Via NGSI-10 to the Context or IoT Broker
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2. Via SPARQL to the Semantic Data Repository
When using the NGSI-10 interface the RDP must follow certain conventions in order to enable semanticinteroperability. For instance entity types must link to RDFS classes (rdfs:class or owl:class) and entity id
must use the same identifiers as RDF instances. Similar conventions apply for attribute types and names.
Details will be described in the upcoming deliverable D6.3 [8].
Alternatively the data can directly pushed to the Semantic Data repository using SPARQL INSERT or
UPDATE requests.

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7 Conclusions & Next Steps
We have provided in this second version, a first consolidation of the CPaaS.io logical architecture(architectural Views and Perspectives with requirement tracking) and an update of the two instantiation
views that shows how this logical architecture is being instantiated using two different technologies,
namely u2 for the Japanese side and FIWARE for the European counterpart.
This second version of the CPaaS.io architecture focuses on the main aspects of the CPaaS.io architectureand does not fully cover yet some aspects like task distribution, federation and FIWARE-based / u2based interoperability, which are topics still under discussion.
The next version of the CPaaS.io architecture (D3.5) will be updated in M24 and will provide the following
additions and improvements:
•
•

•
•
•
•
•

Updated Functional View: refinement of the logical architecture (updated list of FCs) with special
focus on Edge Computing and Semantic Integration;
Updated Information View:
o Updated list of system UCs covering more scenarios and more FCs;o Overall description of the CPaaS.io ontology
Updated Instantiation views (u2- and FIWARE- based concrete architectures) with more detailed
information about the interfaces and concrete system UCs;
Solutions for inter-platform interoperability;
Solution for platform federation;
Updated Volere template (update of requirement mapping);
Updated system Perspectives.

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8 References[1]

F. Carrez et al., “IoT-A Deliverable D1.5 – Final Architectural Reference Model for the IoT v3.0”,
www.iot-a.eu/public/public-documents/[2]

D2.1: CPaaS.io deliverable “Requirement Specification (V1)”, Martin Strohbach ed.

[3]

D2.2: CPaaS.io deliverable: “Prototype, 1st release”, Martin Strohbach ed.

[4]

D3.1: CPaaS.io deliverable “Requirement Collection and Analysis”, François Carrez ed.

[5]D5.1: CPaaS.io deliverable “ Empowerment Components Definition”, Antonio Skármeta, Juan A.
Martínez ed.

[6]

D5.2: CPaaS.io deliverable “User Empowerment Architectural”, Antonio Skármeta, Juan A. Martínez
ed.

[7]

D6.1: CPaaS.io deliverable “Holistic Data Management State of the Art”, Mariane Fraefel ed.

[8]

D6.3: CPaaS.io deliverable “Holistic Data Management: Initial Concepts and Models”, Adrian
Gschwend ed.

[9]

ETSI TR 103 167Machine-to-Machine Communications (M2M); Threat analysis and countermeasures to M2M service layer[10]

Haller, S et al: “A Domain Model for the Internet of Things”, in iTHINGS’2013 proceedings, Beijing,
China (see also IEEE eXplore)[11]

Ho, E. & al., “IoT-A Deliverable D6.1 – Requirements list”, www.iot-a.eu/public/public-documents/[12]

IoT Semantic Interoperability: Research Challenges, Best Practices, Recommendations and Next
Steps [IERC – European Research Cluster on the IoT], Position Paper available at www.internet-ofthings-research.eu/pdf/IERC_Position_Paper_IoT_Semantic_Interoperability_Final.pdf[13]

IoT-A web site: http://www.iot-a.eu[14]

oneM2M white paper, January 2015, http://www.onem2m.org/images/files/oneM2M-whitepaperJanuary-2015.pdf[15]

oneM2M Functional Architecture, http://www.onem2m.org/images/files/deliverables/TS-0001Functional_Architecture-V1_6_1.pdf[16]

OneM2M security solutions”, oneM2M-TR-0008-Security-V1.0.0, 2014[17]

Pastor, A. “Updated Requirements List”, www.iot-a.eu/public/public-documents/[18]

OneM2M security solutions”, oneM2M-TR-0008-Security-V1.0.0, 2014[19]

“Requirements Specification for Urban Plarforms v2.2” prepared by Demand-Side Engagement
Stream I the context of the European Innovation Partnership for Smart Cities & Communities(EIP_SCC) Integrated Infrasturcture Action Cluster – Urban Platform[20]

N. Rozanski and E. Woods, “Applying Viewpoints and Views to Software Architecture” , Addison
Wesley, 2011[21]

SMARTIE Project Deliverable D2.2 “Requirements”[22]

SPARQL Protocol And RDF Query Language http://www.w3.org/TR/rdf-sparql-query/[23]

UNIs: List of IoT-A UNIfied requierementS. Available at http://www.iot-a.eu/public/requirements[24]

Volere Requirements Resources, http://www.volere.co.uk/

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E. Rissanen, extensible access control markup language (xacml) version 3.0 oasis standard (2012).

[26]

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logic into smart things through ecc optimizations,” International Journal of Computer
Mathematics, pp. 1–22, 2014.

[27]

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[29]

System for Cross-domain Identity Management. (SCIM). Internet Engineering Task Force (IETF),
RFC 7643. ISSN: 2070-1721.

[30]

www.viewpoints-and-perspectives.info/home/perspectives/ Last accessed 26/01/2017[31]

FIWARE Internet of Things (IoT) services enablement architecture. Available
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oT.Backend.IoTBroker[34]

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