D7.1 Portfolio of Use-Case Concepts · 2020. 4. 24. · 2 Title: Document Version: D7.1 Portfolio of Use-Case Concepts 1.0 Project Number: Project Acronym: Project Title: 779852 IoTCrawler

D7.1 Portfolio of Use-Case Concepts

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 779852

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Title: Document Version:

D7.1 Portfolio of Use-Case Concepts 1.0

Project Number:

Project

Acronym: Project Title:

779852 IoTCrawler IoTCrawler

Contractual Delivery Date: Actual Delivery Date: Deliverable Type* - Security**:

31/07/2019 31/07/2019 R - PU * Type: P - Prototype, R - Report, D - Demonstrator, O - Other

** Security Class: PU- Public, PP - Restricted to other programme participants (including the

Commission), RE - Restricted to a group defined by the consortium (including the Commission),

CO - Confidential, only for members of the consortium (including the Commission)

Responsible and Editor/Author: Organization: Contributing WP:

Sebastian Christophersen AAR (City of Aarhus) WP7

Authors (organizations):

Sebastian Christophersen (AAR), Marianne Krogbæk (AAR), Michail Beliatis (AU), Mirko Presser (AU)

Juan A. Martinez (OdinS), Pedro González Gil (UMU), Antonio Skarmeta (UMU), Mirko Ross (DW),

Narges Pourshahrokhi (UoS), Roonak Rezvani (UoS), Payam Barnaghi (UoS) Tom Collins (AU),

Andreas Fernbach (SIE), Martin Strohbach (AGT), Stefaniia Legostaieva (AGT)

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Abstract:

In workpackage 7 we move beyond technical demonstration and into creating prototypes that

can also showcase how an IoT search engine can create value in the domains we are addressing

in the IoTCrawler project. Seven testbeds have been identifying challenges and opportunities in

their respective domains through a user-centric process, where external stakeholders and end-

users have been engaged to create meaningful concepts. A baseline of design methods was

created and adapted to the individual testbeds. This deliverable is a portfolio of the concepts that

have come out of this process, which is presented through visual scenarios and mock-ups with

details about the challenges and opportunities being addressed. At least 5 of the presented

concepts will be developed into real hands-on prototypes in task 7.2 in workpackage 7.

Keywords:

Co-creation, prototypes, mock-ups, design process, story boards, scenarios, concepts, smart city,

smart home, smart healthcare, industry 4.0, smart campus, smart grids, mobility, experimentation,

Disclaimer:

The present report reflects only the authors’ view. The European Commission is not responsible for

any use that may be made of the information it contains.

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Abbreviations

AAR City of Aarhus (IoTCrawler partner)

AU Aarhus University (IoTCrawler partner)

IoT Internet of Things

UMU University of Murcia (IoTCrawler partner)

UoS University of Surrey (IoTCrawler partner)

DW Digital Worxx (IoTCrawler partner)

SIE Siemens Österreich (IoTCrawler partner)

MVP Minimum Viable Product

RPZ Regulated Parking Zone information

TTN The Things Network (LoRaWAN community)

GSM Global System for Mobile Communications

NBIoT Narrow-band Internet of Things

LoRaWAN Low Power Wide Area Network

NHS National Health Service (UK)

OCPP Open Charge Point Protocol

VPP Virtual Power Plant

OSCP Open Smart Charging Protocol

EV Electric Vehicle

CSP Charge Service Provider

DSO Distribution Service Provider

CEM Customer Energy Manager

EVSE Electrical Vehicle Supply Equipment

PV Photovoltaic

TGO Transmission Grid Operators

BAS Building Automation Systems

GUI Graphical User Interface

MQTT Message Queuing Telemetry Transport

UTI Urinary Tract Infection (UTI)

AIA Detection of Agitation, Irritation, Aggression

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Executive Summary

In workpackage 7 we move beyond technical demonstration and into creating prototypes

that can also showcase how an IoT search engine can create value in the domains we are

addressing in the IoTCrawler project. The IoTCrawler partners from seven testbeds have

been identifying challenges and opportunities in their respective domains through a user-

centric process, where external stakeholders and end-users have been engaged to create

meaningful concepts. A baseline of design methods was created and adapted to the

individual testbeds. This deliverable is a portfolio of the concepts that came out of this

process, which is presented through visual scenarios and mock-ups with details about the

challenges and opportunities being addressed. At least 5 of the concepts presented below

will be developed in real hands-on prototypes in task 7.2 in workpackage 7:

CompariSense (AAR): This concept is an IoT Product Validation platform that provides

reference data sets (real and virtual) by crawling all available IoT devices across a city. Using

the data from these IoT devices allows IoT startups and their customers to validate the data

being generated from their own solutions.

Pop-up Experimentation Space (AAR): This concept is an IoT experimentation platform

where flexible urban experimentation spaces in a city can be created by geo-fencing an area.

The experimentation space then automatically integrates any IoT devices being

implemented in the space from approved citizens or companies.

Smart Parking (UMU/OdinS/UoS): This concept is a parking app that crawls and searches

for Regulated Parking Zone information across the city of Murcia to find the best parking spot

for the citizens based on different parameters. This scenario will also develop a web portal

and associated web services to analyses the traffic flow in cities and gives high-level

information about traffic patterns (low, medium high), which can also be used to support a

smart parking scenario. The data from the City of Aarhus will be used for the latter.

SmartConnect (AGT): This concept is a solution that provides automatic integration of Smart

Home products across different vendors into Smart Home platforms. This provides value to

the end-user in the Smart Home and to the Smart Home platform developers who can easier

create platforms that handles different types of devices.

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Room Booking (AU): This concept is a room booking monitoring system for students at a

campus that gives analytical insights about room and equipment availability by using

automatic sensor discovery.

Elderly Care (UoS): This concept is a healthcare platform that discovers and integrates health

related sensors at a home in order to save integration costs and to provide data that can be

analyzed to give a status of the wellbeing of an elderly person with dementia.

Machine Monitoring (DW): This concept will use IoTCrawler-enabled data search to add a

digital data layer on approved KAIZEN processes on the industrial shop floor to identify

anomalies.

Flexibility trading for small assets (SIE): In this concept, IoTCrawler scans IoT networks for

assets (EV, buildings, homes) that can be used for flexible energy trading of control power,

which will enable small energy prosumers to join the trading market via an aggregator.

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Disclaimer

This project has received funding from the European Union’s Horizon 2020 research and

innovation programme under grant agreement No 779852, but this document only reflects

the consortium’s view. The European Commission is not responsible for any use that may be

made of the information it contains.

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Table of Contents

1 Introduction .............................................................................................................................................................. 10

1.1 Moving beyond technical demonstrators in workpackage 7 .................................... 10

2 User-Centric Approach ..................................................................................................................................... 11

3 Testbed overview ................................................................................................................................................ 15

4 Smart City Concepts: CompariSense & Pop-up experimentation space by Aarhus

Municipality ......................................................................................................................................................................... 19

4.1 Co-creation Process description ..................................................................................................... 20

4.2 Description of available technologies and data sources in the domain ........... 22

4.3 Description of stakeholders and users and current practices .................................. 22

4.4 Identified challenges and opportunities .................................................................................... 23

4.5 The Concept: CompariSense .............................................................................................................. 25

4.6 The Concept: Pop-up Experiment Space ................................................................................. 31

5 Smart City Concepts: Smart Parking by University of Murcia, Odins and University

of Surrey ................................................................................................................................................................................36





5.5 The Concept .................................................................................................................................................... 41

6 Smart Home Concept: Smart Home Data Integration by AGT .......................................... 53



6.3 Description of stakeholders and users and current practices ................................. 56

6.4 Identified challenges and opportunities ................................................................................... 56

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6.5 The Concept .................................................................................................................................................... 57

7 Smart Campus Concept: Room Booking by Aarhus University ....................................... 64

7.1 Co-creation Process description .................................................................................................... 64

7.2 Description of available technologies and data sources in the domain .......... 66


7.4 Identified challenges and opportunities ................................................................................... 69

7.5 The Concept .................................................................................................................................................... 71

8 Smart Home Concept: Elderly Care by University of Surrey .............................................. 78

8.1 Co-Creation Process description .................................................................................................... 78

8.2 Description of available technologies and data sources in the domain .......... 80



8.5 The Concept ....................................................................................................................................................84

9 Industry 4.0 Concept: Machine monitoring by Digital Worx ................................................ 93

9.1 Co-Creation Process description .................................................................................................... 93



9.4 The Concept ................................................................................................................................................... 96

10 Smart Energy Concept: Flexibility trading for small assets by Siemens .................. 101

10.1 Co-Creation Process description ................................................................................................. 102

10.2 Description of available technologies and data sources in the domain .... 102

10.3 Description of stakeholders and users and current practices .......................... 104

10.4 Identified challenges and opportunities ............................................................................ 106

10.5 The Concept ............................................................................................................................................ 107

11 Next steps ................................................................................................................................................................ 119

12 Conclusion .............................................................................................................................................................. 120

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1 Introduction

In the IoTCrawler project the goal is not solely to develop the IoT search engine

technology and demonstrate it, but the ambition is also to show how an IoT search

engine can deliver value within different domains of our society. This is the main

objective of workpackage 7, where we move beyond technical demonstrators and

into meaningful prototypes for real people and businesses. This objective is split up

into four individual objectives:

• Objective 7.1: Co-creation of use-cases with stakeholders from the industry and

cities to identify business opportunities based on the IoTCrawler technologies

in terms of new uses and adoption.

• Objective 7.2: Develop and deploy hands-on prototypes that can be used by

users in controlled environments such as a small-scale pilot, lab, exhibition or

other controlled location.

• Objective 7.3: Evaluation of user experiences and business opportunities to

identify the most promising prototypes for scaling within the project frame.

• Objective 7.4: Wider deployment of show cases and business development to

create a first case installation for the hosting organizations to demonstrate the

value proposition.

The first objective is central to this deliverable, which is a portfolio of concepts that

have been created and validated in a user-centric design process. The portfolio will

have a focus on real-world needs and opportunities identified in the domains and

contains visual representations of the concepts. Each concept will also include an

assessment of the expected impact, the development feasibility, and demonstration

value.

1.1 Moving beyond technical demonstrators in

workpackage 7

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The scenarios created in workpackage 2 and described in the deliverable “D2.1

Requirements and Design Templates For IoT Crawling” explore possible applications of

an IoT search engine to support the development of the core components and

enablers. These scenarios were therefore mostly based on internal assumptions

about the potential applications of IoTCrawler and had limited stakeholder

involvement. From a technical perspective these scenarios served as relevant

starting point, however the ambitions of the IoTCrawler project is also to demonstrate

how the search engine can provide value to real people and make an impact in

selected domains. This requires a deeper understanding of the challenges and

opportunities seen from the perspective of the stakeholders within these domains

and that they are engaged in the development of the concepts. This was the purpose

of the task 7.1 “Co-creation of Use-case Scenarios” in workpackage 7. The concepts

that has been generated in this user-centric design process will continue into task 7.2

“Development of prototypes - MVP”, where they will be developed into Minimum

Viable Products (MVP’s). In task 7.3 “Evaluation of User Experience and Business Model

Testing” the user experience of the MVP’s will be assessed, and the business

opportunities further explored, after which the 2-3 most promising MVP’s will be

scaled up for wider uptake in task 7.4 “Wider Deployment and Uptake of most

Promising Solutions”.

The user-centric approach in task 7.1, which was used to develop the concepts in this

deliverable will be described in the following chapter.

2 User-Centric Approach

The various domains the partners work with in the IoTCrawler project all represent

testbeds with very different contexts and stakeholders. Therefore, an adaptable set

of user-centric methods and formats for task 7.1 was selected and each testbed was

assigned to an experienced process designer to assist with adapting the methods

and guide the process.

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Why?

The IoTCrawler project is to a large degree a technical research and innovation-

oriented initiative, where the main focus is on the technology that is being

developed. Nevertheless, the user focus is relevant for all cases, in terms of creating

a holistic approach for creating the best overall results and to generate an impact.

The goal of the co-creation process was to identify real needs and challenges in the

domains that the IoTCrawler technology can be applied to. Visualising how the end

user will benefit from what it does, is an important step towards prioritizing and

understanding the functionalities of the applications and opens up the conversation

about what the IoTCrawler components will enable and possibly what components

might be missing for developing a specific application. The ambition was to land the

concepts for the MVP’s within the “sweet spot” of user needs, the exploitation

potential of the testbed organization, and the capabilities of the IoTCrawler

technology (see the figure above).

How?

Aarhus Municipality (Task 7.1 leader) and Aarhus University (Workpackage 7 leader)

have worked together on gathering suggestions for the methods used and to guide

the IoTCrawler partners from the testbeds through the steps presented in the

timeline presented:

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Aarhus Municipality, Aarhus University, AGT, Digital Worxx, Siemens, University of

Surrey and University of Murcia together with OdinS was all presented to this user-

centric approach. Aarhus University and Aarhus Municipality offered guidance to the

other partners in deciding a course of action; which methods to use to gather user

insights to obtain empathy with the end users, and finally helping the partners

define the format and scope of the workshops.

What?

The methods proposed to the partners were:

• Cultural (Digital) probes: This is a method where users are prompted to share

data about their lives, values and thoughts within a specific domain through

digital or analogue tools.

• People Shadowing: Observing the behavior of people in a given

domain/context.

• Semi-structured interviews: Interviews that leaves room for the interviewee to

divert into meaningful paths of conversation.

• A workshop format: The format is centered around a mapping of assets,

actors, environments, moods, actions for a thorough analysis and

understanding of the context, challenges, and opportunities, which is then

used to create ideas for the concepts together with the participant in the

workshop.

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• SAP scenes: A set of ready-made visual material to be used to create

storyboards that can communicate concepts, value propositions and user

insights to external stakeholders.

The set of methods is based on an IoT Methodology Kit developed by C4IoT1,

Design Thinking2, Google Ventures Design Sprint3, U4IoT’s Co-creation kit4 and

supporting methods and tools to generate empathy. The workshop format was

subjected to some alterations, but with the mapping as a central element.

1 http://www.iotmethodology.com/ 2 http://designthinkingforlibraries.com/ (this version of Design Thinking the City of Aarhus has helped

made together with IDEO) 3 https://www.gv.com/sprint/ 4 https://u4iot.eu/

https://experience.sap.com/designservices/approach/scenes

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Each individual case process was structured using a selection of the methods: e.g.

University of Murcia and OdinS decided with their Smart Parking case to learn about

their user needs through Cultural Probes, a finalizing 1,5 hour workshop and

describing the most important learnings in SAP scenes. SAP scenes were used by

all partners to present their cases. Storyboards provide an effective way to show the

value of ideas and product visions in their context of use. They make messages

more memorable and understandable and provided the project with a valuable

inside understanding of each other’s cases. The SAP Scenes are also used to

present the concepts in this deliverable.

Task 7.1 was finalized with a workshop with an invitation to all partners and

participants of IoT Week 2019, with the purpose of having each concept validated

by partners and external experts within the IoT community. Following this the

concepts were assessed during a partner meeting for the development activities in

task 7.2.

3 Testbed overview

In IoTCrawler we are working with seven testbeds operating within different domains,

which are presented below:

Aarhus Smart City Testbed by Aarhus Municipality

In Aarhus Municipality we have created Aarhus City Lab, which is an outdoor smart

city experimentation space located by the harbor in the city, but it is also a

recreational area. Citizens use the space for activities such as playing sports on the

basketball and football courts, fishing, cultural events or meeting with friends. Smart

City businesses are invited to demonstrate and test their technologies in this real

urban setting. There is a set of basic environmental sensors put up in this space that

connect to the municipality’s city-wide LoRaWAN, which transmits the data to the

Open Data DK portal, which also collects 155 other open data sets from the city. This

way citizens, businesses, knowledge institutions, and the city can use the data to gain

insights about the city and develop solutions together.

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Murcia Smart City Testbed by University of Murcia and Odins

Thanks to the collaboration with the City Council of Murcia, and the work carried out

during the Smart City project for the city, a FIWARE-based platform pilot has been

deployed. This platform contains heterogeneous information coming from different

sources such as public transport (bus, tramp, bicycle), solar panel information, traffic

information, private parking availability or Regulated Parking Zone (RPZ) information.

The platform exposes NGSIv2 API thanks to an instance of Orion Context Broker which

provides also a publish/subscribe pattern. In the scope of this project private parking

and RPZ is processed.

Smart Home Testbed by AGT

AGT is one of the partners in the GrowSmarter project (www.grow-smarter.eu) where

multiple smart solutions were implemented to make Europe smart and sustainable.

As part of the GrowSmarter project, AGT provided a web-based application for

device-level energy awareness in Smart Homes. The energy application was

deployed and tested with tenants from the city of Cologne. It supports the integration

of sensors via homee Smart Home gateway and targets in particular Smart Home

sensors that can measure energy consumption such as smart plugs. The energy

awareness dashboard provides statistics about energy consumption of individual

devices as well as historical overview.

We will use the GrowSmarter installation as a testbed to collect sensor metadata and

data beyond energy usage which will enable us to better validate the SmartConnect

MVP. For this we are currently investigating possibilities to scale up the testbed.

Smart Campus Testbed by Aarhus University

Aarhus university has developed a testbed including mixed sensors deployed inside

university and in industrial partners for didactic purpose and research. This testbed

1st is a room booking monitoring system that gives analytical insights about room and

equipment availability by using automatic sensor discovery and 2nd includes sensors

deployed in various industrial partners for monitoring the health conditioning of

manufacturing machineries.

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Dementia Testbed by University of Surrey

TIHM (Technology Integrated Health Management) for dementia is a major new

research study funded and monitored by NHS England. It will test how cutting-edge

technology placed in people’s homes could be used to improve the lives of people

with dementia and their carers.

The three year ‘Internet of Things’ Test Bed is being led by Surrey and Borders

Partnership NHS Foundation Trust and has involved over 150 people with dementia

and their carers living in Surrey and North East Hampshire. Partners involved in the

project include: Alzheimer’s Society, University of Surrey, six local clinical

commissioning groups and a technology company. This testbed provides living lab

and adaptable environments for connected devices to develop scenarios for elderly

care and dementia care. The testbed currently offers an integrated system from living

lab environment (2 living labs) for monitoring physiological and environmental data in

home environment. The back-end system for the data collection and integration and

related information governance and security mechanisms are also developed in the

testbed. This flexible testbed environment allows test and development of new

scenario and data analysis methods for designing new care pathways and solutions

for various healthcare challenges. In IoTCrawler the focus is on developing secure

and privacy-aware data stream crawling solutions that can analyse patterns and

events in the home environment and designing machine learning methods that use

the results of the crawled patterns/event to design higher-level health and care

analysis solutions.

Industry 4.0 Testbed by Digital Worxx

Digital Transformation on the industrial shop floor (industry 4.0) is a key challenge to

straighten the European manufacturing industries. The industrial testbed is provided

together with WAFIOS AG machine building company. The testbed allows access to

process data pools connected to wire and tube bender machines. This allows to

search and analyze data on the manufacturing process of single machines and pools

of machines. The data is provided by MQTT standard and the testbed includes access

on reference data by virtual machines (digital twins) and live processing data of

machines in the WAFIOS technology center.

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Smart Energy Testbed by Siemens

Siemens is very active in the field of energy management systems. In the recent past,

Siemens was involved as a system integrator in a project of smart buildings equipped

with state-of-the-art building automation systems, photovoltaics, battery systems,

heat pumps and electric vehicle charging stations. These IoT-enabled services

provide the ideal conditions to act as a testbed for the IoTCrawler Smart Energy use

case. Since the dimension of this testbed is still too limited to show the scalability (one

major requirement to the IoTCrawler discovery mechanisms) in a proper way, there

will be an additional testbed of virtual assets acting as a complement to the physical

one. This will be realized by means of simulating the behavior of the aforementioned

prosumers deployed in today’s smart buildings.

In the next chapters the concepts that have been developed in the testbeds will be

presented.

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4 Smart City Concepts: CompariSense & Pop-up

experimentation space by Aarhus Municipality

Aarhus is a city with 345.000 citizens and has a strong profile as an innovative smart

city. One of the more recent initiatives to support the smart city-related activities is

the establishment of Aarhus City Lab. The City Lab is an outdoor area by the harbor

planned to be an open experimentation space, where smart city solutions can be

demonstrated and tested in a real urban setting together with citizens, start-ups,

academia and public institutions. Aarhus City Lab is also a recreational space, where

citizens can sit and enjoy the view over the harbor, play sports, fish, and take part in

cultural events. Aarhus has a focus on multiple helix collaborations across the city

and acknowledges that the societal problems that cities are facing today cannot be

solved by the municipality alone but requires experimentation and collaboration with

many different stakeholders. When it comes to IoT and publishing data, Aarhus was

the first city in Denmark to have an open data portal, however this open data

represents just a small portion of the IoT data that exists in the city: Some citizens

have open weather stations, makers and students create small-scale experiments,

and start-ups create new IoT devices all which generate data that could be used to

create better services and solutions in the city if their data was made accessible. The

City of Aarhus can see a great potential in using IoTCrawler to leverage all the open

data that is available across the multiple helix ecosystem in the city. The mission we

therefore set out in the Aarhus testbed was to find out how IoTCrawler could help

support experimentation and co-creation of new IoT solutions and services.

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4.1 Co-creation Process description

To get a basic understanding of the current practice of Aarhus City Lab and its

opportunities we used observation as a method and did short on-the-spot interviews

with people using the space. After that we carried out semi-structured interviews with

IoT startups to further explore the challenges and opportunities, that presents

themselves when collaborating with the municipality to develop IoT solutions. We

also had internal sessions in the municipality with smart city staff to understand some

of these aspects seen from their perspective. Based on the insights gathered we

invited the smart city stakeholders and IoT startups to a co-creation workshop to

develop solutions to support the experimentation and use of the City Lab. The insights

are also informed by the experiences and insights generated in the EU funded project

Organicity (Grant Agreement No. 645198). Below is picture of the co-creation

workshop with IoT startups and Smart City stakeholders:

To understand the opportunities of experimenting with IoT with the people already

using City Lab, we carried out a workshop with a high school that uses City Lab for

gym classes.

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The concepts that came out of the co-creation workshops were validated with

relevant stakeholders and discussed during IoT Week 2019 in Aarhus.

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4.2 Description of available technologies and data sources in

the domain

In Aarhus City Lab there is a basic set of environmental sensors installed, where the

data generated from these are presented on a large public screen at Aarhus City Lab.

Around the city there exists a number of implemented IoT devices that measure

everything from real time traffic on the roads, books checked in and out of the library,

to solar panel energy on schools, etc. These IoT data sources are available on the

public open data portal; www.opendata.dk. There is also a city-wide LoRaWAN that

Aarhus Municipality has set up to increase to uptake of IoT solutions into the city.

Alongside of this there is also an Aarhus chapter of The Things Network, the citizen-

driven IoT Infrastructure based on LoRaWAN. This infrastructure is especially

interesting when it comes to IoT experiments and new sources of data and we know

that some of startups in the city is using this to develop their solutions on a small

scale. OS2 IoT is another software solution under development in the national

municipality-driven open source community OS2, which will collect data from various

types of transmission and integrate it into the open data platform.

4.3 Description of stakeholders and users and current

practices

After the initial observations of Aarhus City Lab and validation of insights by smart city

staff, we identified two main target groups that connects to this space: Producers and

consumers. “Producers” we define as the startups, students, makers or similar who

are able to develop and test smart city solutions together with the City of Aarhus and

to use Aarhus City Lab as a test facility in this process. The producers that we have

approached in this process has been IoT Startups.

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The “consumers” in Aarhus City Lab we define as everyday citizens, who might gain

value from taking part in testing the solutions that the City or producers provide. The

consumers are also the ones who use Aarhus City Lab already e.g. they use the

football and basketball court for sports, use the path through Aarhus City Lab to run,

bike or walk, they use the harbor side for breaks and fishing. There is also a local high

school that uses this space for gym classes.

4.4 Identified challenges and opportunities

Not a clear entry point for IoT experimentation

The way an IoT startup ends up collaborating with the municipality does not happen

in a very systematic way ‑ often it is more luck and chance encounters with relevant

stakeholders that makes things happen. Finding the right person to talk to in the

municipality can be difficult as the municipality is a large organization with multiple

departments and magistrates ‑ this becomes increasingly difficult if a suggested

experiment or IoT solution goes across these.

Not being aware that Aarhus City Lab is an option for doing early proof of concept

testing

“I just put up one of my early prototypes on a lamppost outside my office to test it,

however since it was not approved by the municipality it was removed shortly after”

That was one of the stories that we learned from a startup that was involved in our

co-creation activities. By creating more awareness of Aarhus City Lab and the

opportunity to use it as a testbed maybe this situation could have been avoided.

There is a need to be close to the municipality to understand the needs

Because IoT solutions are still not widely used in cities, then it seems that the IoT

producers does not always have a clear understanding about what societal

challenges their technology can help solve. And from the perspective of the

employees at the municipality the opportunities of applying IoT is not widely known

or is on the top of their minds. Therefore, they are not able to connect it with the

challenges and opportunities they see in city. This supports that spaces for IoT

experimentation and dialogue about IoT is needed.

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What data generates value for a city is based on assumptions

When translating raw data into meaningful data sources that the city can use it seems

that the producers, do not think that e.g. anomalies in the data is interesting to the

city, but instead the companies tries to correct it and align with expected data

readings. From a city perspective it could very well make sense to see all unexpected

data readings. So therefore, there should be transparency in the way that the data

providers model their data and the municipality should whenever possible have

access to the raw data.

The producers of Aarhus City Lab are interested in following other experiments

The producers that we have talked to thinks that it would be of great value if they

could see data from other experiments and they would also share their own. The

argumentation for this was that they could use the data to validate the readings of

their own devices, and there is also a possibility to correlate different data sources to

understand new aspects of their own readings and use them for new services. This

was also were the startups could see the potential of an IoT search engine.

Students are interested in spaces for experimentation

The consumers, which in this is specific case was the high school students that use

the City Lab for classes and breaks, expressed an interest in having dedicated areas

of Aarhus City Lab, where they could carry out experiments with IoT. Many of the

concepts that they developed during our workshop had a focus on sustainability and

sought to find a right solution and adapt the space to the people using the City Lab.

Based on this insight and the interest from the innovation class teacher we identified

a need to have spaces, where we could allow this type of experimentation to happen

and given the current restrictions of using city these spaces would need to be

temporary and flexible.

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Sports equipment in the City Lab sports courts is lost

During our observations and interviews at Aarhus City Lab to understand the current

practice we learned that the wind can sometimes pick up the balls and lead them

astray, and possible they fall into the harbor. During our workshop with the high

school we also learned from one of the high school gym teachers, that they keep

their sports equipment for gym class in the benches in the City Lab sports courts.

They do not have any sports courts or gyms that they can use at the high school for

these classes, so they rely on the ones at City Lab. However, when a big cultural event

takes place at City Lab, these sport courts and benches are moved away, which has

caused problems since more institutions also keep their equipment in other benches.

This has caused that the high school has lost a great deal of their equipment. So

tracking of assets at City Lab could be an area to explore with the help of IoTCrawler.

In the next chapters we will present two concepts that builds on the insights above;

we have called them “CompariSense” and “PoP-up experimentation space”.

4.5 The Concept: CompariSense

In the early stages of an IoT startup’s product development there is a need to find out

if the product generates valid data by comparing it to other data sources.

E.g. IoT startups that develops parking sensors, can use webcams as an additional

data source to validate if their sensors are giving a correct reading about the

availability of a parking spot. Or a startup the creates products for environmental

monitoring in a city might be asked to benchmark their product against other

measurement units already in place in the city to prove their business case.

To support this practice the concept “CompariSense” that is presented below makes

use of IoTCrawler to give IoT startups and customers of IoT solutions an easier way

to test and benchmark IoT products, by comparing it to others that are already

installed in the environment. If a data source is missing, then there is an option to

create a new virtual sensor by fusioning existing sensors.

4.5.1 A visual scenario of the concept

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4.5.2 Wireframes

4.5.3 User value

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During our interview sessions we identified that there was a potential for IoTCrawler

to support the development of IoT solutions by giving the companies and their

customers a way to validate and benchmark their products up against existing ones.

Having a large number of IoT sensors available could also be used to create virtual

sensors that again could assist in this process. Another way that this concept delivers

value is that it could be used to explore patterns in a city by observing the correlation

between different data sources, which could present itself as a new business

opportunity. On a larger scale, then this can also be used the by IoT industry to explore

different cities to see what types of data a city has available, and if a city does not

have e.g. parking data, then a startup that creates parking sensors can use this to

generate new business leads. Or Smart Cities can identify what cities have similar

solutions available in their cities, so they can establish meaningful collaborations.

4.5.4 Integrated IoT platforms and data from the testbed

CompaniSense is planned to be connected to data devices integrated on TTN, the

municipality’s IoT infrastructure, SmartCitizen.me, and OpenWeatherMaps, but will be

open for other data sources once they are identified.

4.5.5 Impact, Feasibility, and Demonstration Value

Impact:

CompariSense is expected to have a positive impact on the experimentation capacity

of the IoT startups in Aarhus. This can create an open ecosystem of IoT experiments,

where the IoT startups can learn from each other’s experiments and make better

solutions and identify new business opportunities.

The municipality will have an easier way to explore if a new IoT solution that is being

tested delivers the promised effect.

Feasibility:

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From the City of Aarhus, we are relying on a well-documented framework for

IoTCrawler, that is accessible to partners who have not developed the core

components themselves. Guidance will be needed but is easy to find within the

consortium of the more technical partners and therefore we deem the concept as

feasible to be developed into an MVP. On the scale of the MVP, then we will have a

relevant set of data sources to integrate, however to fully realize the vision of the

CompariSense concept, then all available and open data in a city is integrated into

the platform. Therefore we rely also on the general uptake of IoT.

Demonstration Value:

CompariSense will be a good concept to demonstrate the crawling capabilities of

IoTCrawler. The privacy aspect is also built into the concept, since the users of the

platform should be able to keep the data from their own IoT device private, while still

comparing it to other public data sources. Indexing, ranking, data quality and semantic

annotation are also key components of this concept. The virtual sensor functionality

might not be ready for the MVP level of the prototype, but will be a relevant

component to showcase for IoTCrawler.

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4.6 The Concept: Pop-up Experiment Space

Another idea we wish to use as a basis for our continued work is aimed at the non-

tech users we hope to engage in Aarhus City Lab. It overlaps to a certain extent with

learnings from IoT start-ups and Smart City stakeholders, since both has a need for

improved experimentation spaces and a better overview of the possibilities

involved. The concept is based on insights from our workshop with Aarhus

Katedralskole – a local high school, where we introduced IoT and Aarhus City Lab.

We explored the students’ and teachers’ needs to access outdoor areas for learning

opportunities for e.g. Innovation class. The students are very interested in working

with IoT focused on sustainability, real-life solutions and the city showing a similar

engagement in providing a greener city scape. Their ideas for monitoring and

managing the plants in this public space, could also be used to support biology

class. This ended up being the concept “Pop up experimentation space”, which is a

platform that creates geo-fenced flexible urban experimentation spaces with

automatic integration of IoT devices that invites citizens to join together to create

meaningful IoT solutions for the city.

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4.6.1 5.6.1 A visual scenario of the concept

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4.6.2 Wireframes

IoTCrawler crawls the geofenced area that defines the experimentation space and

automatically integrates all IoT sensors and devices from users, who have been

allowed to be part of this particular experimentation space.

A user can subscribe to the data generated in the experimentation space, which

IoTCrawler monitor the value of and its quality.

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4.6.3 User value

The main user value is that it allows citizens, such as the high school students or even

IoT startup to take part in IoT experiments together in an easy way without having to

worry about integrating their devices to a specific platform. The citizens will learn

about the opportunities and barriers of using IoT and will also potentially come up

with new meaningful solutions for the city. If we could further integrate easy tools for

creating triggers connected to the data, then we could optimize the user value even

further, so the experimenters are not just monitoring the data.


The concept makes use of data sources from IoT experiments on TTN’s LoRaWAN

gatewats, but also data from other transmission types such as GSM, Wifi, Sigfox,

NBIoT will be collected. Besides using data from approved experiments that is

targeting a specific challenge, then data sources from citizens sharing data from e.g.

open weather stations or the city’s open data is also accessible from the platform to

enable validation of the data from the experiments.


Impact:

From Aarhus Municipality’s perspective the impact potential is high. Providing better

access to the City Lab and between the devices installed, would make it possible for

us to interact with students and start-ups in creating new services. We plan to take

the concepts back to the students in August, when the new school year starts.

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Feasibility:

As mentioned in the CompariSense concept, then we rely on user friendly

documentation of the IoTCrawler framework. In this specific concept we also rely on

getting the first flexible experimentation spaces approved at Aarhus City Lab to test

the MVP, which should be feasible. The concept also integrates this aspect in a

flexible way, by introducing “Experimentation Spaces rules”, where the users are

giving guidelines and limitations about experimenting in the space. E.g., maybe they

can only measure data with their device while they are there with it and should bring

it back with them after the readings have been carried out.


The demonstration value of Pop-up experimentation space is also deemed high as it

also showcases IoTCrawles dynamic crawling capabilities within the geofenced

experimentation areas. The experimentation spaces that are created on the portal

should also have the option to keep their data private and only approved

experimenters will have their devices integrated into the space and platform.

Indexing, ranking, data quality and semantic annotation are also key components of

this concept.

5 Smart City Concepts: Smart Parking by University of

Murcia, Odins and University of Surrey

Traffic congestion is one of the significant problems associated with large cities. One

of the leading causes for this is the time dedicated to finding a free parking spot in

the desired destination area. This is the problem addressed in our use case. The goal

of this use case is to take advantage of IoTCrawler to develop a solution that allows

users to specify a destination, time of arrival and other preferences and to use this

information to provide the user with a recommendation for the specific area where it

is more likely to find a free parking spot. The co-creation process for the concept was

carried out in Murcia by University of Murcia and Odins, but the concept can be

applicable for other cities, so University of Surrey will also explore how it can be

scaled to Aarhus with additional functionalities.

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The co-creation process took part in two sessions of roughly one hour each, in which

16 participants (plus 2 organizers) participated in a number of activities, designed with

the purpose of introducing users to the problem at hand, and later helping or assisting

them in the process of exploring different solutions and devising a first prototype of

a technology to solve it.

The problem addressed, in this case, was to improve the experience of commuters

trying to find parking in the city center of Murcia, although this case could easily be

applied to any other city with minimum effort. Users were introduced to the problem

with the help of some graphic material (context maps), and descriptions obtained

from the probing phase.

After this initial introduction, users were engaged in the process of creating POVs and

HMWs (Points Of View and How Might We), capturing user needs and insights from

the problem space, which were later shared and discussed in common.

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The following session started by revisiting the POVs and HMWs of the previous one,

to follow shortly afterwards with a quick ideation phase, in which users had little time

to capture any ideas to solve the problem, that would cross their mind. Following the

rapid ideation, ideas where presented in common, clustered into bigger/more

general groups and voted by them, producing a set of candidates for the next phase.

The final phase of this workshop, was to create prototypes (or wireframes) for the

selected solutions, using graphic templates and material provided by the organizers.

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After the workshop, all the ideas and material produced was gathered by the

organizers, sorted and further processed to extract valuable concepts that where the

again validated by users and relevant stakeholders, locally and during IoT Week.


the domain

Additionally, to the identifier and type associated to every entity stored in our Orion

Context Broker, the following information is also associated to the Smart Parking use

case:

Private parking site information in Murcia

Each private parking site registered in the platform provides the following

information:

• Location: GPS coordinates of the parking site's location

• Name: Name of the parking site

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• Total Spot Number: The total number of spots offered by this parking site

• Free Spot Number: The number of free spots provided by the parking site

Regulated Parking Zone information in Murcia

For this aspect we can count with information regarding parking meters and

information provided by the tickets issued by them.

Parking meters

• name associated to the parking meter

• location: GPS coordinates of the parking site's location

• available Spot Number: The number of available spots associated to the

parking meter.

• sector: The parking meter is associated to an area or sector of the RPZ.

Tickets

• issued time: the timestamp when the ticket was issued

• from: this is the first end of the time interval reserved for the parking activity,

i.e. the timestamp associated to the beginning of the parking task

• to: this is the other end of the time interval reserved for the parking activity, i.e.

the timestamp associated to the end of the parking task

• parking meter: parking meter which issued the ticket

• amount: the cost associated to the reservation of the parking spot

• rate: the rate associated to the ticket

• sector: although this information is also provided in the parking meter

information, it can also be available in the tickets information too.

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practices

The activity of reducing traffic congestion involves different stakeholders. Our smart

parking solution aims at citizens that day-by-day commute to go to work to the city

center, as well as others which go to the city center for making administrative

processes with the city council or any other regional or state agency.

For this reason, we consider the City Council of the city, in addition to end-users, one

of the most significant entities to collaborate within the scope of this use case.


As a result of the interaction with users and stakeholders during the co-creation

sessions and the validation activities, the following challenges were put forward:

• Lack of quality public transport hinders possible inter-modal solutions.

• Interacting with a mobile app should be avoided during driving.

• In general, the parking congestion problem stems from the fact that there is

more demand of parking spots than there is offer of effective available parking

places. This trend keeps increasing relentlessly.

At the same time, the main opportunity that was extracted from these sessions was

that there is a real existing need for parking solutions, that only grows with more and

more people living and working in the cities and IoTCrawler provides a valuable tool

to address it, by applying it to very different solutions.

5.5 The Concept

We have addressed one of the most common problems that large cities, which is

traffic congestion. Mainly we have dealt with one of its causes, which is the number

of vehicles wandering around the city finding a free parking spot. Our solution is

presented as an App since smartphones are usually nearby users and can even be

fully integrated with the most modern vehicles.

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This app allows the user to select his preferences and establish a destination area

and time of arrival. With this parameter, our solution generates a recommendation

specifying the most appropriate destination to find a free parking spot with a high

probability.

5.5.1 A visual scenario of the concept in Murcia

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5.5.2 Wireframes for the Murcia concept

5.5.3 Visual scenario for the extended concept in Aarhus

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5.5.4 Wireframes for the Aarhus concept

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5.5.5 User value

As previously commented, this use case addresses the challenge of finding a free

parking spot in a destination area. To do so, our solution takes two sorts of information:

the availability of the private parking sites, and the information coming from the

tickets issued by the parking meters the day before.

These two sorts of information are processed differently. On the one hand, private

parking site availability is considered by our solution only when a certain number of

free parking spots are available, reducing this way the possibility to recommend a

free parking spot that is no longer available at the time of arrival. On the other hand,

RPZ information is used to train a model which provides parking meters sector

availability at a specific time of arrival.

All this information, together with rate policies and user preferences, is processed to

issue the most appropriate recommendation for parking in the desired destination

location.

The extension of the Murcia scenario to the City of Aarhus’ open data search

addresses the needs for a more environmentally friendly traffic management by

integrating the information about different sort of parking areas (private parking and

regulated parking zones) into a service that will allow the users to reduce the time

spent for a free parking spot in a specific destination. The real-time search and pattern

query interface that will be created for this concept will also allow more enhanced

access to the data and will support citizens and city planners and managers to have

an overall view of the data and access to the detailed patterns and changes in the

continuous data.


The information obtained for the Murcia use case is available in the MiMurcia Smart

City platform, thanks to the collaboration with the city of Murcia. This use case

processes both private parking site availability and RPZ information.

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For the extended use case in Aarhus the Open Data DK portal will provide relevant

data such as parking information, traffic count, and pollution.


Impact:

With our solution we expect to reduce the time dedicated by citizens and commuters

to look for a free parking spot. Reducing this time, a significant reduction in the traffic

congestion is expected. Additionally, this situation will contribute also to improve the

quality of living of the citizens in two ways: reduction of the noise associated with

working engines of the vehicles, and a reduction in the pollution in the air.

The impact can be also extended to reduction in the CO2 emissions associated to the

vehicles; improving the traffic management because of the reduction of the time in

pursuing a free parking spot and enhanced planning and management of the city

services and mobility around the city of citizens by having access to the patterns and

changes in the data.

Feasibility:

Since we already count with the information required to develop this use case, we

expect to obtain an MVP within the project lifetime.


Thanks to the use of IoTCrawler, our use case accesses the IoT information using a

standard and homogeneous representation of information. Additionally, IoT providers

can define security restrictions in the access of the information integrated into the

IoTCrawler framework.

Since also policy rates are considered in this use case, semantic search allows

introducing more specific queries from the user’s point of view, which gives more

accurate and useful information. For instance, application and services will be able to

search for specific parking sites whose rate is less than an amount of money.

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6 Smart Home Concept: Smart Home Data Integration by

AGT

Sensors deployed in smart home environments are used in various applications such

as home automation, home safety and energy management. Where the individual

smart home devices can be accessed with the help of smart home gateway that acts

as a central control unit. Those are usually developed either by the vendor of the

sensor or the third-party company. The later usually provides the capabilities to

integrate multiple smart home systems. An enormous number of sensors has

emerged on the market over the last years from the range of vendors that use

different communication protocols such as ZigBee, Z-Wave, WiFi, EnOcean,

Bluetooth, etc. However, smart home environments are extremely heterogeneous.

Each vendor uses its own communication channel and data format for exchanging

messages. As a result, smart home application developers are forced to integrate with

vendor specific APIs that increase the development time and efforts. It is important to

address the challenge of accelerating the integration of smart home devices to

provide a common semantic abstraction layer for integration of existing smart home

systems in a common infrastructure such as the one provided by IoTCrawler and to

address the problem of interoperability of smart home systems and sensors.


First insights were captured from the experience that AGT gained during the EU

funded project GrowSmarter, where the energy awareness application for the smart

home domain was developed. The application provides energy consumption

statistics, costs calculation and fine-grained historical data of connected appliances

to the smart plugs with measuring capabilities. The developed system supports

collection of energy measurements from Pikkerton smart plugs that are accessed via

our own gateway and Fibaro smart plugs via a third-party smart home gateway.

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For the co-creation activities, we brought together people interested in smart home

technologies at different experience levels. This included Smart Home expert users

over less experienced users to people that already bought Smart Home equipment

but have not really used it yet. People that participated at the workshop were partly

consisting of AGT colleagues with technical background that were not involved in the

project as well as one external person with no technical background. First, the

individual interviews were conducted with our participants to identify challenges and

extract insights about the experience of using smart home systems as well as

integrating smart home sensors using existing open source home automation

platforms. After that, the first version of use cases was developed based on the

insights gathered from the individual discussions. Finally, the co-creation workshop

was organized to present and validate developed use-cases with our participants.

After that, a two-hour session with participants was conducted to brainstorm ideas

that would extend and enhance presented use cases. A major outcome of this

discussion was that the participants were struggling a lot with the heterogeneity of

technologies in the smart home domain and that even simple home automation

scenarios required huge effort. The concepts that were developed during the co-

creation workshop and learnings from the previous project were used to develop the

first version of MVP that was presented during IoTWeek.

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the domain

The enabling technology for the smart home data integration is from one hand

integration of the existing smart home devices into the IoTCrawler platform and from

another hand is the semantic understanding of the discovered sensors. Shared

understanding provides the interoperability of sensors from different vendors and

abstract access layer across gateways. Addressing the first challenge, there are many

smart home platforms such as OpenHAB, Vera, Home Assistant, Homee, etc, that

integrate various IoT sensors and actuator. The main distinction between those

platforms is whether it is open source and was developed by the community and

requires more technical background in order to use it, or it is a commercial product

that can be purchased off the shelf. To tackle the second aspect of semantic

understanding of the connected sensors, the number of ontologies for the smart

home domain is available. However, all existing ontologies are tailored to the specific

use case and do not provide a holistic semantic description of the smart home

environment. Extensive work has been done to provide the comprehensive overview

of the existing ontologies in the IoT domain, for example Lov4IoT5.

5 https://lov4iot.appspot.com/

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practices

We have identified two main group of users that are involved in the process: tech-

savvy smart home users and smart home application developers. Tech-savvy users

are users that have a basic understanding of technologies and a limited level of

experience of basic software development. A smart home user can describe the

concrete sensor device that was discovered in his home to integrate it into the

IoTCrawler platform. An application developer is acting as a consumer of the added

data sources that are secure, privacy preserved and semantically enriched, to build a

third-party smart home application.


The most common challenges that were identified from the co-creation workshop

with tech-savvy smart home users can be clustered in the following groups.

Interoperability between sensors and systems

One of the tech-savvy users pointed out that it is difficult to find one smart home

system that would solve the task that he has. As a result, he had to combine multiple

solutions to have the capabilities that he needs. However, it is very time consuming

and expensive to build your own application that consists of multiple smart home

systems.

“I had to buy the second system in order to have needed functionality for my garden

sensors that was not provided by the vendor application”.

“There are various smart home systems and smart devices on the market, but none

provides comprehensive solution. As a result, I had to integrate several smart home

systems to obtain the expected functionality”.

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Semantic sensors understanding

Whereas one of the end users of a smart home system mentioned that their smart

home system lacks the semantic understanding of the sensors connected. It is usually

fixed assignment of the sensors to the specific functionality and it is difficult to

remember sensors’ names when the number of connected sensors grow, as a result

you cannot remember the specific command to activate the sensor and the smart

home system becomes non-functional.

“Smart home systems annoys me when the home automation rules don’t work, for

example to turn on and off lights.”.

6.5 The Concept

The smart home data integration scenario addresses the problem of integration of

IoT data sources. It provides the capabilities to automatically crawl IoT environments

such as smart home. With the ability to discover new sensors that are available in the

certain environment and understand the type of sensor that has been discovered. For

example, it can identify that the discovered sensor is of type "smart plug" and has the

energy measuring capabilities. And how to integrate these sensors in a common

infrastructure such as IoTCrawler, providing semantic understanding of the

discovered sensors. Those sensors will be available in the IoTCrawler platform for

subscription and use on the abstraction layer by other applications.

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6.5.2 Wireframes

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6.5.3 User value

By using the IoTCrawler platform with data integration capabilities, the Energy

Awareness application could have been easily integrated with smart plugs from other

vendors and with sensors that provide energy measurements. Without the need to

modify the application during the integration process.


As a testbed for the prototype development and validation, we have integrated the

following smart home platforms: Home, OpenHAB and Domoticz. To each of the

selected gateways the various smart home sensors were connected such as smart

plugs, motion sensors, door and window sensors, multisensors.


Impact:

The smart home data integration scenario helps to accelerate the process of

integrating new IoT data sources into the common infrastructure such as IoTCrawler.

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Feasibility:

The smart home data integration tool will be developed as an MVP. The prototype of

an application will be developed to show the applicability of the developed

technologies only.


The IoTCrawler provides a common infrastructure to integrate and access data from

various smart home sensors in the secure and privacy aware mode that were

integrated with the help of presented concept.

7 Smart Campus Concept: Room Booking by Aarhus

University

Smart Campus Herning is an initiative at Aarhus University in Herning campus to

provide an open Living Lab providing better services, data insights and a

technological infrastructure that students can access for their own experiments in a

working environment via internet. Various investments in connectivity and sensing

platforms have laid the foundation to connect different IoT enabled resources, and a

new data analysis layer is required to expose the value to students, staff and

researchers in a less technical way. Under this umbrella platform several concept test

beds/pilots are under development, where IoTcrawler components will be used.


Co-creation has been the key method for user engagement and use case

development activities. Students, support staff and researchers have all taken part in

an extensive series of IoT co-creation workshops exploring IoT and IoTCrawler at the

campus.

A complete co-creation kit has been devised for IoTCrawler integrating IoT

technologies and local data sources to help to explain the components in a more

intuitive way. This includes exploration flashcards, inspiration cards, situation

definition templates and a range of UI, Data and Device mockup templates.

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The workshops have contextualized IoTCrawler as a new way to solve challenges

within the campus and local areas. Participants are challenged in a two-hour session

to explore their environment, define opportunities, ideate, paper prototype solutions

and present their new concepts.

Over 15 concepts have further been devised by the end-user groups. Several of which

are being considered for implementation, with one project already starting with an

intelligent Room Booking system.

Partner Co-creation Workshop training

The design methods and Co-creation tools were further demonstrated in a training

and validation session with IoTCrawler partners, with the goal to help them carry out

design tasks and user engagement in their own testbeds.

The exploration activities of the local testbed included a ‘walkaround’ tour and expert

interviews to get to know the domain, users and key challenges. The partners goal

where to relate their own experiences to the domain and consider the empathy

exercises that we previously carried out.

Preview of the IoTCrawler Co-creation method used in the testbed

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An example report has been created to show partners the overall process, and how

their co-creation and design activities can be an effective communication tool for

stakeholders. The report explains the overall process, the tools used and the

individual problems and concepts that were co-created.


the domain

To support application development at Smart Campus Herning, a modular IoT

Gateway has been developed and is being deployed across campus. The solution

extends IoT connectivity via existing LAN opening up mesh and BLE interfaces for

low-power autonomous devices and a LoRaWan network.

The gateway is being deployed with eight different environmental sensors, collecting

base data to compare in dedicated experiments. This includes CO2, sound level,

temperature, humidity, light level and a range of gas sensors. The gateway can also

‘sniff’ bluetooth UUID’s indicating presence in certain locations. Furthermore we have

deployed LoRa PIR and IR senors in 10 study rooms which they can monitor in

presence in the rooms allowing for the students to check which rooms are available

and book then in advance to carry on their group assessments and individual studies.

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This base data helps to understand popular location, usage times, underused

resources and helps keep the work environment accountable to key health factors.

Graphical representation of network infrastructure and heatmap data on activity

With a data set for correlation, active experiments can understand how their trials

influence the campus.

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To create valuable change, a process to identify trends and activity hot spots is being

developed, using open-source web apps to support time-series and heatmap data

analysis of the campus. This will work with IoTCrawler components to expose data in

a highly searchable way allowing different experiment trials to share insights and

build upon each other's work.


practices

To fully include the users in the environment, each “IoT Point” is presented with a

dedicated and unique poster explaining the purpose, technology, benefits and

privacy details. All code and data on the device are publicly available to anyone on

the campus to remix the code or explore data for their own unique projects.

‘IoT Point’ Awareness poster

More specific for the student room availability pilot three stakeholder groups are

identified:

• Group 1: students for day to day use,

• Group 2: research academic staff for research and

• Group 3: facility maintenance staff for monitoring the environment conditions

in the rooms (heat, clean air, etc.)

Similar for the Aarhus University industry 4.0 pilot three stakeholder groups are

identified:

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• Group 1: students for day to day use,

• Group 2: research academic staff for research and

• Group 3: the company owners and employes.


Room booking challenges

For the students of Herning Campus it can often be frustrating and difficult to find an

available room for study activities, without having to check all of campus. This

happens because AU Herning suffers from capacity problems with regards to student

work facilities, which is negatively impacting the students and their ability to be

productive. This will eventually have to lead to the need for expanding the facilities,

by constructing new workspaces for the students, since the University wants to

supply the students with enough rooms for working in groups. This construction will

be quite costly, estimated to 1.000.000 - 2.000.000 DKK, based on information from

our research and Jan Møller Nielsen. For many years, the University has tried to

improve the Resource Booking System, to lower the downtime of facilities and avoid

physical expansions but have failed to find a proper solution. The problem can be

solved by deploying an IoT scalable solution which can be implemented at a low cost.

The implementation of Internet of Things based presence sensors, as part of the

Resource Booking system, will diminish the issue significantly, by providing the

students with sufficient information about the availability of AU Herning’s work

facilities. More specifically, it does allow the students to check which rooms are

actually available, instead of having to interrupt the work of other groups, by

physically entering the rooms. It will remove the stress that comes from having to

book facilities one week in advance, and more spontaneous meeting will be possible

without the inconvenience of having to check all the rooms.

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Community development

The IoTCrawler project has helped to bring students and researcher staff interested

in IoT around a real initiative to solve and implement real solutions. The ‘IoTClub’ is a

student led initiative looking at how Internet of Things can improve the campus and

the local area. Coding workshops, co-creation exercises, exploration exercises and

general meetups have connected students and help to get projects started. Such as

the intelligent Room Booking system which IoTCrawler will support.

Several other students are also supporting prototype development upon the heat

map and times series analytics visualisation systems.

Technology transfer - Industry 4.0 application of uptime and heatmap solutions

From the experience and development of the SCH-gateway solutions, a range of co-

created cases and potential verticals have been identified. The Smart Campus

Herning project is being mapped and implemented with a local textile factory to

create an IoT ‘Ninja Shadow Infrastructure’ next to their existing production line, and

legacy monitoring line to support rapid deployment of new IoT sensors and new

analytic services.

Utilising a different hardware platform, the same data collection and analysis

methods will be employed to collect data and more efficiently analyze time series

and heatmap data. Similarly, we expect the IoTCrawler components to further add

value in this testbed as it is providing in the Smart Campus Room Booking project.

The textile factory case will specifically identify machinery that is either unproductive

(available and not being used), in an unplanned downtime state, and to tracking

general uptime for accountability.

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7.5 The Concept

The Room boking concept is to discover and integrate different sensors at smart

campus and industry in order to save integration costs and offer more flexibility to

use a wide range of sensor vendors for offering monitoring services to stakeholders.

In our case automatic discovery of presence, air quality and vibration sensor to get

instant analytical insights for rooms and equipment availability.

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7.5.2 Wireframes

7.5.3 User value

This pilot makes use of the discovery of IoT capabilities, as well as the secure access

to the contextualized information. The usage of IoT Crawler features will make

possible to discover available space in university campus and to easily deploy new

discoverable sensors over different facilities thanks to the searching tools of the

IoTCrawler project.


The pilot testbed utilize Microsoft Azure server with Ubuntu 18.04 Linux where on top

of that a docker daemon is running hosting containers of NodeRed for data filtering,

InfluxDB for data storage, and Graphana for data visualization and user interface,

furthermore a custom made dashboard is made on HTML, CSS and JS to display the

data from gateways and Industry 4.0 setup.

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Impact:

This solution has a primarily impact on the students of Campus Herning and resource

optimization. Most students have experienced the situation where a room was

needed to work in, and it can often be difficult to get an overview of where to find

one, because the bookings people make are often misguiding, as they do not show

up or leave earlier than anticipated. The societal impact of the solution involves

reducing frustrations, giving an overview of the availability of rooms and provide

study-facilities for as many groups/people as possible. Furthermore, one of the

important factors of this is reducing the risk of disturbing students while working or

having a meeting in the study-rooms, which will result in a better work environment

for the users of Herning Campus and cost reductions facilities

operation/maintenance. Finally, the testbed it can be used for student projects on IoT

and software development areas.

Feasibility:

It is implemented and being further iterated. Integration and correlation with the

actual booking system will be the next steps, the aim is to do this.



various smart campus sensors in a secure and privacy aware mode that were

integrated with the help of the presented concept allowing to better utilization of

facilities, space and equipment.

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8 Smart Home Concept: Elderly Care by University of Surrey

The technology of Smart Homes, as an instance of ambient assisted living

technologies, is designed to assist the homes’ residents accomplishing their daily-

living activities and thus having a better quality of life while preserving their privacy.

A Smart Home system is usually equipped with a collection of inter-related software

and hardware components to monitor the living space by capturing the behavior of

the resident and understanding his activities. By doing so the system can inform about

risky situations and take actions on behalf of the resident to his or her satisfaction. It

can also be used in the healthcare domain for monitoring patients and assisting

especially elderly people who suffer from disease such as dementia. The goal of this

use case is to take advantage of IoTCrawler to develop a solution that allows users

who suffer from dementia to have better quality of life and also by monitoring patients

it can help clinical team in decision making.

8.1 Co-Creation Process description

This process was informed by the user research from an existing 3-year research

project, which aim is to assist people who suffer from dementia.

The testbed provides an IoT environment for healthcare and elderly care. The testbed

is currently linked to a national project funded by the NHS, called TIHM for Dementia.

Surrey offers 2 living labs; one for engineering design and one for clinical and trial

tests. The main testbed for the TIHM project is a part of a trial with over 200 patients

and care givers and the technologies (in collaboration with 7 SMEs) are deployed in

patient homes. For the purpose of the IoTCrawler we will provide test and trial

facilities and also test data from our living labs. The crawling and search methods

then work on the results of the machine learning algorithms to find and extract

specific types of patterns and events that happen in single or multiple sensory data

analysis scenarios. The following picture presents the living lab that is used in the

testbed.

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a picture of our test bed for smart home environment for people with dementia

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the domain

Smart Homes and Smart Healthcare rely on the ability to gather data from a variety

of sources including the environment, the home and the patient themselves. The

TIHM Medical Device is a Software Device which comprises of an alert and learning

module, a graphical user interface (GUI) and biomarkers and sensors. Health data for

dementia patients are analyzed both online and offline by a machine learning (ML)

and analytics module generating insights. The observations and measurements of

raw data and data trends are presented through the Integrated View (iView) graphical

user interface, which provides information to the Monitoring Team about the

wellbeing and change of health of the patients. The device adheres to standard NHS

IG/ IT data security protocols in regards the use and management of personal data.

Following epicures presents these devices and their usability.

Door sensor to detect the status of the door

if it is closed or open

This sensor is used on the fridge door to

check if person used the fridge or not

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PIR sensor is used to measure the activity of

person in house

physiological measurement such as heart-

rate, blood pressure and temperature.

weight scale to measure the weight of

patient

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practices

Interfaces of a Smart home must be designed in a way that empowers the users

(stakeholders) to interact effectively and comfortably with the Smart Home system.

In the case of Smart Homes for healthcare, we can distinguish four groups of users:

- Residents (e.g., dementia patients, disabled people, elderly people, etc.)

- Informal caregivers (e.g., family members of older adults)

- Social caregivers (e.g., care homes, professional caregivers)

- Formal caregivers (e.g., doctors, nurses)

Therefore, the design requirements of the interface must be specific for these user

groups. For instance, a formal caregiver is interested in receiving updates about the

progress of the resident’s disorder by capturing physiological signs such as blood

pressure, blood sugar and body temperature. However, such information is not

necessarily relevant to the informal caregivers. Moreover, choosing an adequate

interaction medium for a stakeholder needs particular considerations. As an instance,

people with dementia might not be able to learn how to operate a new equipment;

thus, Smart Homes for people with dementia should be able to operate regardless of

the residents’ capacity.

The three year ‘Internet of Things’ Test Bed is being led by Surrey and Borders

Partnership NHS Foundation Trust and will involve over 100 people with dementia

and their carers living in Surrey and North East Hampshire.

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First, one of the most pressing concerns for the smart home technologies is

associated with the privacy and security of the transmitted data. The data may contain

sensitive, protected or confidential information that can endanger residents’ privacy

and safety, if breached. Therefore, ensuring strong data encryption, database security

as well as secured communication channels is critical for smart homes.

Second, smart homes use a wide range of sensors, actuators and other wireless

devices, thus generating a large volume of data. Therefore, the communication

protocols, hardware and computation resources impose bottlenecks for the

connectivity.

Third, a smart home is a complex system with many discrete devices and systems

connected in a common platform. However, the system needs to be carefully

designed to deal with integration issues among different devices and also to have

optimum number of sensors in order to avoid redundant data, minimize infrastructure

and maintenance cost as well as energy consumption without losing key information.

Fourth, the sensing systems of the smart homes, are aimed for long-term monitoring

purposes. Therefore, these systems need to be energy efficient, which can be

achieved by using low-power components and efficient batteries.

Fifth, modularity, expansion capability of the system and interoperability among

different smart home platforms are vital for achieving flexibility and widespread

acceptance among the users.

Sixth, ensuring a highly reliable, accurate and robust implementation of AI

technologies particularly for decision making and execution purposes is critical for a

trustworthy and safe operation of the smart homes. In addition, in order to make the

best use of AI driven features such as machine learning, robotics and big-data

computing in the smart home, standardized protocols need to be developed and

implemented.

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8.5 The Concept

The concept of “Elderly Care” is to discover and integrate health related sensors at a

home in order to save integration costs and offer more flexibility to use a wide range

of vendors for offering health related services to elderly people. In this concept this

functionality is shown through automatic discovery of a blood pressure sensor to get

an instant analytical insight for an elderly person.


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8.5.2 Wireframes

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8.5.3 User value

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This study will potentially deliver fundamental change to future dementia care. This

is vital because there are an estimated 850,000 people living with a confirmed

diagnosis of dementia in the UK. It is predicted that this figure will be over a million by

2025.


IoT Crawler has a role of a mediating agent that connects unknown devices to the AI

bot with minimal a priori knowledge about the devices. It facilitates device integration.

This scenario focuses on integrating IoTCrawler into an IoT environment for

healthcare and elderly care in order to detect unusual patterns and events (e.g. social

isolation and changes in daily activities). In this case, IoTCrawler will enable search

and discovery of changes in activity data streams, especially changes in sensory

observation and measurement and environmental monitoring data.

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Impact:

Active aging enables elderly people to live at their own homes and promotes

preventing hospitalization.

Feasibility:

We have already developed machine learning algorithms for analyzing daily patterns,

the risk of Urinary Tract Infection (UTI), Detection of Agitation, Irritation, Aggression

(AIA), daily risk awareness scores and body vital signal processing for detecting

conditions such as Hypertension. IoTCrawler will provide generic change and event

search and discovery for the data streams that will allow creating more advance

algorithms using the information.



various smart home sensors in a secure and privacy aware mode that were integrated

with the help of presented concept allowing to better utilization of time, life and

equipment to improve quality of life of people for instance people who suffer from

dementia to have more independent and healthy life with respect to their privacy.

Further, we will demonstrate that by using connected sensors and algorithms that

was developed in the TIHM project and IoTCrawler project we can create patterns

from continuous and dynamic sensory observation and measurement data. We will

then use these patterns with machine learning algorithms for clustering and

classification. Following this the crawling and search methods will work on the results

of the machine learning algorithms to find and extract specific types of patterns and

events that happen in single or multiple sensory data analysis scenarios.

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9 Industry 4.0 Concept: Machine monitoring by Digital Worx

In general, the testbed aims to optimize industrial processes in the shop-floor by

supporting worker and shop-floor managers with digital views on process data and

integrating it as digital layers to existing optimization methods of KAIZEN. This

method of lean production is providing principles and physical tools to identify, track

and solve problems in industrial environments. Usually many KAIZEN visualization

tools are "non digital" e.g. Shift-boards for continuous improvements, where workers

are noting their recommendations and remarks during a shift. The WAFIOS testbed

enables a digital enrichment of lean production optimization by linking data layers to

remarkable incidents during a production shift. IoTCrawler will allow a better

identification of problems in the data sets of production processes. Linking this digital

information to lean production principles will increase the quality and efficiency of the

production.


The pilot use case is embedded into the general industry 4.0 strategy and activities

of the company. For the co-creation activities a joint project team of WAFIOS

engineering and Digital Worx was working together.

The design principle had to be lean and agile and was a process that covered Ideation

in jour-fixe meetings of all team members, visualizations of MVP’s, and Concept

Presentations for Clearance at the Management board. The development and

progress have been organized in the SCRUM Methodology. Concepts have been

validated to customer interactions in the Technology Center and on Fairs.

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Concept of mobile machining data search, which was presented for the Managing

Board.

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Validation Concept studies with customer interaction on fairs and expositions

9.2 Description of available technologies and data sources

in the domain

The data is provided by Gateway APIs on machining centers by MQTT protocol and is stored

on MongoDB databases. Data can be accessed for search analytics directly from machining

processes via Gateway or for retrospective analytics via REST calls on MongoDB.

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practices

Working on industrial environments at the shop floor requires to fulfill the needs of

workers and shop floor management. Both groups of stakeholders have different

skills and needs. Workers are mainly operating in harness environments, the tools

they are using are often "non digital" as e.g. whiteboards to note remarks on a

production shift. When they are using digital tools, the devices need to be robust

and the user-interfaces have to be simple: less information and less functionality.

While shop floor managers are requiring digital mobile access on the whole data

universe to get an overall view on processes and to deep dive into particular

production steps. In practice these two different requirements are leading into a

divided situation, where workers are mainly using "non digital" tools and shop floor

managers can access a wide variety of digital assets.

9.4 The Concept

Industrial machining on the shop floor is creating a mass of data from sensors during

the production process. It is a challenge for humans to identify critical processes or

anomalies with the amount of process data on manufacturing sites.

Powerful searching is needed to create added value from industrial process data

e.g. to detect conditions for predictive maintenance or optimize the production

process. With support of the IoTCrawler data search, we can add digital data layers

on approved KAIZEN processes on the industrial shop floor. This will support the

shop floor managers to dive into process data and find links between incidents on

the shop-floor and data points of the production process. By that, troubleshooting

and continuous improvement will be speed up.

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9.4.2 Wireframes

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9.4.3 User value

The concept presented here will enable the shop floor managers to optimize the

shop floor processes much faster through a mobile user interface, when an incident

happens on the shop floor. Examples of incidents could for example be a single

machining production step, where the tool loader is unprecise, which will causes

problems later on the in the production process and workpiece quality. Failures like

these are often linked to complex production chains and it is hard to identify the

cause of trouble in the chain. Identifying and solving the trouble at the source of

problem is much easier on the shop floor, when data can be searched on the

involved processing components. This will lead into better production quality,

reducing downtimes and increasing the productivity of production lines.


This concept integrates machining process data based on MQTT that is sourced

from the machine gateways.


Impact:

Data is nothing without knowledge of the industrial processes. The concept support

to merge both worlds: the massive amount of data from machining processes at the

shop-floor and the need to search and extract data points and values to tie them to

the human knowledge of workers and shop-floor managers. Increasing the ability to

optimize shop floor processes by merging both worlds has a high impact on

increasing the industrial productivity.

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Feasibility:

MVP of the concept is under construction and the realization is depending on the

progress of overall digitization efforts at WAFIOS AG. Currently we have access on a

data pool by virtual machining (digital twins) and live data of machining test cycles. It

is feasible to finish the MVP during the project by the current progress of

development.


Searching on process data is a huge challenge due to the amount of data and the

dependencies of data from process knowledge. The demonstrator will show how

valuable data points can be searched and used to optimize shop-floor processes.

10 Smart Energy Concept: Flexibility trading for small assets

by Siemens

Deviations between injection to and withdrawal from electricity networks must

constantly be balanced out by increases or reductions in the output of control energy

suppliers’ power stations. Distinctions are drawn between primary, secondary and

tertiary control energy. Usually this control energy is provided by controllable assets

that change their energy consumption/production on request with respect to a given

operating point. Depending on the power of the assets, they need to be aggregated

by a so-called Virtual Power Plant (VPP) in order to offer a relevant amount of control

energy to the market. In order to guarantee availability, assets need to fulfill several

preconditions (communication, accountability, etc.) for participating in the balancing

market. This process of approving these capabilities by the transmission grid operator

is called prequalification.

Prequalification of assets is a complex process that is not feasible (from both

economic and technical perspectives) for small assets in terms of their available

control energy (e.g. domestic homes equipped with photovoltaic and batteries) which

in turn restricts accessibility of energy markets for these small assets.

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For organizational reasons, the conduction of a co-creation workshop as originally

planned in this working package was not feasible at Siemens. As a substitute, an

interview with colleagues from the Smart Infrastructure business unit was carried out.

The use case concept was presented during a virtual meeting and feedback was

collected from the representatives. The gained suggestions were regarded, and

adaptions were made to the original intent accordingly. A follow-up feedback

discussion with representatives will take place soon.

10.2 Description of available technologies and data sources

in the domain

For operation of the IoTCrawler as an enabler of a VPP for small assets, the following

domains and technologies come into question:

Electric vehicle charging:

The Open Charge Point Protocol (OCPP) and the Open Smart Charging Protocol (OSCP)

published by the Open Charge Alliance are a designed to integrate EV charging

stations into a smart grid. It provides not only means for gaining information about

charging processes but also to perform demand response actions, i.e. to adapt

charging power of a station remotely. A charging station might have spare charging

power such that less charging power is actually consumed than the current forecast

allows. This way, flexibility can be provided by the central controller of a Charge

Service Provider (CSP). To achieve this, e.g. the UpdateCableCapacityForecast

message of the OSCP can be utilized which is originally intended to be used by the

Distribution Service Provider (DSO) to reduce charging power.

Smart Home Energy Management:

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EEBus is an open communication and application standard for connecting energy

management-enabled appliances in homes, industry, e-mobility and power grids. It

therefore defines a number of device types and functional profiles (Entity Types) with

according interfaces. Examples are listed in the following:

• Device Types: EnergyManagementSystem

• Entities Types: Battery, BatterySystem, CEM (Customer Energy

Manager, ElectricityGenerationSystem, ElectricityStorageSystem, EV, EVSE

(Electrical Vehicle Supply Equipment), PVSystem, SmartEnergyAppliance,

• Feature Types: SmartEnergyManagementPs

Communication in EEBus takes place via messages relying on the WebSockets

protocol.

MODBUS

Modbus is used by numerous vendors of photovoltaics (PV) inverters and battery

controllers to access and control their equipment. Integration of MODBUS-talking

devices requires individual configuration since there is no application-specific

information model or standardized schema for MODBUS register addresses. PV and

buffer equipment can this way be interfaced by IoT gateways or edge computing

platforms making their datapoints accessible for IoTCrawler.

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Desigo CC SODIAP

The Desigo CC SODIAP allows third party applications to connect to the Siemens

building management system Desigo Insight and hereby enables access to BACnet

building automation systems and other subsystems. This way, the current state of

building services can be read out or setpoints can be modified. The Rest API provides

a browse service to traverse through different views of the designation hierarchy

exposed by the plant. Full access to BACnet datapoints is given by the value service.

This interface is relevant for the IoT crawler to access building energy management

systems (BEMS) allowing to get information about current and historical energy flows.

Additionally, energy-related actions can be performed by modifying power setpoints

or switching loads by activation or deactivating components of the building

automation systems.

PV Inverter APIs:

SMA is providing an open source communication library called YASDI to interface

SMA devices like inverters. A similar API is provided by Fronius. It allows to read out

measurement values and to set device parameters (only SMA). These interfaces can

be used by the IoTCrawler to get information about the current state of inverters in

photovoltaic plants like current feed in power.


practices

• Transmission Grid Operators (TGOs) are responsible for prequalification, i.e. a

procedure to approve the ability of a plant operator to participate in the control

energy (balancing) market. They are also in charge of assuring grid stability.

Therefor required control energy is publicly advertised where prequalified

operators can bid.

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• Distribution System Operators (DSOs) are responsible for the low voltage grids.

These grid segments directly connect small end-consumers and prosumers of

electric energy like single households. DSOs must be able to handle

bottleneck situations like caused by EV-charging stations or PV plants. These

bottleneck situations can be countered by local flexibilities like load shifts

discovered and made accessible by the IoTCrawler.

• Virtual power plant operators (VPPs or aggregators) participate in the control

energy market and trade their aggregated flexibility with TGOs. The IoTCrawler

performs its discovery mechanisms on small flexible assets like proposed as a

key application for the IoTCrawler.

• Small flexible asset owners act as providers of micro flexibilities. They are

currently not enabled to directly participate in the control energy market but

may use an aggregator to get indirect access. The discovery of micro

flexibilities and provision to aggregators is achieved by the IoTCrawler.

• Potentially in the future, Siemens might act as an IoTCrawler system provider.

The goal is to support the Siemens-internal stakeholders, i.e., the Smart

Infrastructure business unit (BU), in the development of smart energy

applications. To this aim, an interview has been carried out with colleagues

from this BU to define the requirements in this domain. The result of this work

was presented at the IoTCrawler booth during IoT Week 2019 in Aarhus which

is shown in the following picture.

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One of the most relevant statements from the stakeholder interview regarding the

further proceeding of the project was the following:

“The major problem faced in building automation engineering is still 3rd party technology

integration. Tasks like M-Bus, heat pump and PV inverter integration consume a lot of

resources.”

Due to this fact, the focus of the Siemens demo case is temporary shifted from the

originally intended smart energy field to service discovery in building automation

systems (BAS). A prototype showing the feasibility of service discovery in BAS using

the IoTCrawler methodology shall this way act as a starting point for future Smart

Energy applications. However, the underlying problem of making a heterogeneous

device and service landscape accessible by applications in a unified way is inherent

to both areas.

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Another suggestion by the stakeholders was to not only use service discovery for

creating a context image describing building services but also to regard configuration

information from BAS engineering tools. This way, insights into plant topology and

functional relationships between devices can be gained.

10.5 The Concept

The already mentioned increasing number of small assets capable of providing

control energy is currently excluded from participating in the control energy market.

This can be circumvented using an aggregator collecting flexibility from these small

assets and acting as a market player on behalf of them. A yet unsolved issue is the

discovery and rating of available flexibilities by the aggregator provided from

inherently unreliable sources. For this purpose, the IoTCrawler might bring the

desired solution. Its continuous discovery and rating mechanisms create a context

image representing the availability of micro-flexibilities. The aggregator can use the

semantic search interface to receive the endpoints of flexible assets depending on

current market needs. Using these endpoints, the aggregator can collect the required

flexibilities and put an offer to the energy market. After the transactions has taken

place, the aggregator returns feedback on the reliability of each asset which

influences the IoTCrawler rating systems and this way improves future search results.

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10.5.2 Wireframes

Smart Energy Web frontend showing a map of the selected location and a form for

specifying the search criteria. It provides a view for building-related queries and one

for energy-related queries.

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The map now shows the results when querying for temperature sensors related to

heating modules.

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If one of the returned temperature sensors is selected, detailed information about the

sensor including current value, quality of information and nearby temperature

sensors.

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Here, the Energy Flexibility view of the Web frontend shows the query results for solar

modules output in form of a heatmap.

10.5.3User value

The IoTCrawler enables owners of small assets to participate in flexibility trading. This

results on the one hand in financial benefits to this stakeholder group but also creates

consciousness in the relevance of renewable energy sources and smart grid rollout.

These environmental and financial incentives might result in acceleration of PV and

battery systems rollout in the domestic area.

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As a testbed for the building automation discovery use case, equipment of a real

building which already acts as a prototype for another research project will be

interfaced. This includes a BACnet network of devices delivering live sensor values

and device data. This way, live measurement values of the inside as well as the

outside conditions like temperature, humidity, brightness values, datapoints from a

heatpump, battery and PV inverters are available.

For the smart energy use case, which is an extension the former setup, the live

building automation process image is complemented with values from a scalable

simulation setup reflecting a multitude of energy-related assets. For this purpose, the

FIWARE Device Simulator will be used.


Impact:

The smart energy scenario allows a broad and even growing number of small energy

assets also partly acting as prosumers to access the control energy market. This

means also a strong gain in grid stability which is necessary due to continuous

deployment of renewable sources.

Feasibility:

The Siemens research groups involved in the IoTCrawler project do not develop

products but only prototypes. Product development takes place in this case in e.g.,

the Smart Infrastructure business unit which might eventually take up and continue

the development of the IoT Crawler prototype developed during this project.


The Siemens smart energy demonstrator covers following features of the IoTCrawler

concept:

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• Semantic enrichment of IoT devices and services (meta data)

• Rating and ranking by a monitoring component/application feedback

• Semantic search engine

• Integration of heterogeneous (also legacy) resources from various domains

Apart from the intended new application scenario of enabling small-scale prosumers

to flexibility trading, the IoT Crawler concept can also be used to facilitate

engineering/reconfiguration/adaptation of Building Energy Management System

(BEMS) applications.

11 Next steps

The next steps in workpackage 7 is that the concepts presented above will be

grouped into clusters where there are synergies and similar functionalities between

the concepts and at least 5 of them will be developed into MVP’s. This will happen in

task 7.2, where the development of the prototypes will take place. The first selection

of concepts that will be developed into the MVP’s are the ones listed below.

• Smart Parking

• SmartConnect

• Room Booking

These concepts have a clear relation to the technology demonstrators created during

the project to demonstrate some of the IoTCrawler components and enabler and are

therefore further along in their development. These first MVP’s will be used to test

the integration of the components in IoTCrawler, where after the other concepts will

be developed.

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12 Conclusion

The system of technologies we refer to as IoT can be difficult to grasp to the end-

users and is further complicated by introducing the concept of an IoT search engine.

Therefore, a great deal of effort will be used for translating the opportunities of the

technology and extracting needs from the relevant stakeholders in order to create

meaningful and valuable concepts and prototypes. The user aspect is not always a

priority in technology-centric development processes, as the processes can be

demanding and resource intensive, but it will pay off in the end. The documentation

of the process makes it possible to validate concepts and prototypes by people who

has expressed a need for the solution can meet, which makes a long term impact far

more likely. The user-centric approach in this project has sought to enable this

translation between different disciplines and domains to create the concepts that

lands in the “sweet spot” of user needs, technology demonstration, and are relevant

to the organisations involved in the development of the solutions. In the next stages

of the IoTCrawler project we will continue this balancing act of demonstrating the

technology, while also trying to solve challenges in real-life situations. The goal of the

concepts presented in deliverable 7.1 is that they will act as a sounding board for the

development of the MVP’s in task 7.2 to bring in the user perspective. The work has

already started and the most mature concepts in terms of feasibility and technology

readiness are chosen to be developed into the first MVP’s.

The visual scenarios in this deliverable was created with Scenes™ by SAP AppHaus

(https://experience.sap.com/designservices/scenes)

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 779852

@IoTCrawler IoTCrawler EUproject /IoTCrawler www.IoTCrawler.eu [email protected]

Documents

D7.1 Portfolio of Use-Case Concepts · 2020. 4. 24. · 2 Title: Document Version: D7.1 Portfolio of Use-Case Concepts 1.0 Project Number: Project Acronym: Project Title: 779852 IoTCrawler