These slides were used at the first Aarhus Follower Group meet-up for the EU-funded project IoTCrawler. They entail an introduction to the project aswell as a more in depth presentation of the difference between web search and Internet of Things (IoT) search an the development of Internet of Things. Furthermore some of the scenarios from the project are presented.

1. Meet-up
Godsbanen 18.06.2018
www.iotcrawler.eu

2. Agenda
• Welcome by Michelle Bach
• Introduction to IotCrawler by Antonio
Skarmeta, Ralf Toejnes, Payam Banarghi,
• Use cases in IoTCrawler – concrete Smart
City scenarios by Martin Strobach,
Sebastian Holmgaard,
• Questions and debate
• Free networking

3. General Presentation
University of Murcia
skarmeta@um.es
www.iotcrawler.eu

4. Partners
Participant No Participant organisation name Short name Country
1 Universidad de Murcia UMU Spain
2 University of Surrey UniS United Kingdom
3 University of Applied Sciences
Osnabrück
UASO Germany
4 Aarhus University AU Denmark
5 Siemens AG Österreich SIEMENS Austria
6 NEC Corporation NEC Germany
7 AGT Group (R&D) GmbH AGT Germany
8 digital worx DW Germany
9 Odin Solutions S.L. OdinS Spain
10 City of Aarhus AAR Denmark

5. Vision
• To develop the next generation of Internet search engines that
support crawling, discovery, search and integration of IoT data.
• To provide tools and mechanisms to respond to machine initiated
search, offer adaptive and dynamic solutions for resource ranking and
selection,
• To develop distributed crawling and indexing mechanisms to enable
real-time (or near real-time) discovery and search of massive real
world (IoT) data streams in a secure and privacy- and trust-aware
framework.
• To integrate the security and privacy properties of smart object within
the registry and lookup procedure
• To change the way that the data (especially new forms such as IoT
data) can be published, discovered and accessed in large-scale
distributed networks.
• Providing enablers for security-, privacy and trust-aware discovery
and access to IoT resources in constrained IoT environments
• To pave the way for creating new applications and services that rely
on ad-hoc and dynamic data/service query and access.

6. Main areas
• Integration
• Interoperability
• Discovery
• Knowledge-based search
• Dynamic services
• Security and Privacy
• Pattern creation and
abstraction
Security,Privacy&Trust
IoT Resources: sensors and actuators
Use cases
Machine initiated semantic sear ch
IoT discovery
Context management
Monitoring & fault r ecovery
Multi-criteria ranking
Adaptive indexing
Edge
broker
Edge
broker
Edge
broker
Cloud
broker
Distributed
IoT framework
Dynamic
crawling
Search
Dataanalysis
API
Smart city Social IoT
Smart
energy
Industry
4.0

7. Key Expected results
• Search and Discovery
• Suitable schema evolution to the information/content discovery, description
mechanisms such as the Resource Description Framework (RDF) and JSON-LD
integrated on overlay networks based on DHT
• Designing adaptive intelligent methods that can process quality of multivariate
and multi-modal IoT data and provide knowledge-based and context-aware query
and search and mechanisms for IoT data/services
• Common data property modelling maybe aligned with ETS-ISG-CIM
• Security and Privacy
• Advanced cryptographic techniques based on Attribute-Based Encryption (ABE).
Specifically, it analyses the application and extension of the Ciphertext-Policy ABE
(CP-ABE) for privacy aware communications
• Blockchain alternatives to simply encrypting transaction to allow a certain subset
of nodes to exchange sensitive transactions without involving other nodes
• Lightweight access control scheme for IoT integrated on a discovery and registry
mechanism

8. Pilots
Prototype Organisation Description
Smart Cities:
Sharing Economy in
Smart Cities
AAR, OdinS, AU,
UniS, UMU, UASO
A web-based interface showing shared assets (e.g. bikes, tools,
toys) including a simple booking system to reserve assets.
Social IoT AGT, DW, AU A web-based interface for discovering, searching and sharing data
sets that quantify people’s experience and emotions when
participating at sports and entertainment events such as the
Colour Run or Basket Final Four. Identify context patterns to
improve mental health state of aging people.
Smart Energy:
Predictive
Maintenance and
Energy Prediction
SIEMENS, AGT,
OdinS, UASO
A web-based interface for semi-automatic integration and vetting
of new data sources in industrial predictive maintenance and
energy management scenarios.
Industry 4.0:
Lean Shopfloor
DW, SIEMENS,
UniS, AU
Web and mobile App based interfaces for industrial
manufacturing processes data to increase quality and optimise
services of a stakeholder in production sites.
Dynamic Crawler
and IoT Search
Portal
UniS, SIEMENS,
OdinS, UMU,
A web-based search engine for IoT resource crawling, discovery
and search with RESTful APIs to support machine-to-machine
interactions.

9. Organization
WP1-ProjectManagement
WP8-DisseminationandExploitation
WP2 - Scenarios and IoTCrawler Framework
WP3 - IoT Security,
Privacy and Trust
WP6 - Integration, Benchmarking and Testing
WP4 - Crawling and
Indexing of Large-
Scale Dynamic IoT
Resources
WP7 - Deployment: Use-case Scenarios and
Business Solutions
WP5 - Machine
Initiated Semantic
Search

10. Outcomes
• Scalability issues of the Internet of Things with regards to data
integrity and authentication, common KPI and approaches
• Identity and Identification approach for IoT
• Efficient Crypto solution for authorization and testing of
interoperability aspects
• IoTCrawler will define open-source and common toolkits and
enablers to encourage rapid application and service development
based on the IoTCrawler innovations and solutions
• Definition of common methodology for GDPR impact and PIA
assessment

11. THANK YOU!
mail@iotcrawler.eu
/iotcrawler.eu
/iotcrawler.eu
www.iotcrawler.eu
mail@iotcrawler.eu

12. A Search Engine for the Internet of Things
- Technical Approach -
Ralf Tönjes
University of Applied Science Osnabrück
www.iotcrawler.eu

13. How to discover IoT Ressources?
The search for IoT Ressourcen is still in its infancy
• Internet search engines work with text data.
However, IoT data has different formats
and is often streaming data
• Internet search is initiated by humans
IoT search is automatically/machine initiated
and is context dependent
• IoT Ressources are often
mobile and transient
=>Need for distributed indexing
and discovery methods in
rough dynamic
environments

14. IoT-Crawler
Problems of current
IoT search engines:
(Shodan, Thingful)
• Only central indexing
or manual input
of meta data necessary
• Too static or outdated
• Security and privacy
neglected
=>Scalable methods for
• Crawling, discovery,
indexing and ranking
of IoT ressources
• Machine initiated
semantic search
Security,Privacy&Trust
IoT Resources: sensors and actuators
Machine initiated semantic sear ch
IoT discovery
Context management
Monitoring & fault recovery
Multi-criteria ranking
Adaptive indexing
Edge
broker
Edge
broker
Edge
broker
Cloud
broker
Distributed
IoT framework
Dynamic
crawling
Search
Dataanalysis

15. Information Federation with Layered
Architecture

16. Internet Scan of IoT devices
Ten thousands of industrial devices (Scada, Modbus, etc.) discoverable!
=> security gap!
Weltweite Verteilung
Quelle: Li et al.: Understanding the Usage of Industrial Control System Devices on the Internet;
IEEE IoT Journal 2018.
=> IoTCrawler: Security/Privacy by Design incl. role models (CP-ABE, Blockcains)

17. Distance Sight Way Track/Vehicle
Propagation
Radial Radial with blocking Distinct Grid
Restricted Layer on
base Grid
Example Pollution Light Street System Subway Ride
Feasibility Simple Complex Medium Medium
Problem: unreliable sensors/data
 Semantically annotated measures for quality (accuracy, age, …)
 Modell based correlation of data

18. Sensor net in Aarhus: quality monitoring

19. Internet of Things - The story so far
Payam Banarghi
University of Applied Science Osnabrück
www.iotcrawler.eu

20. Internet of Things: The story so far
RFID based
solutions
Wireless Sensor and
Actuator networks
, solutions for
communication
technologies, energy
efficiency, routing, …
Smart Devices/
Web-enabled Apps/Services,
initial products,
vertical applications, early
concepts and demos, …
Motion sensor
Motion sensor
ECG sensor
Physical-Cyber-Social
Systems, Linked-data,
semantics,
More products, more
heterogeneity,
solutions for control and
monitoring, …
Future: Cloud, Big (IoT) Data
Analytics, Interoperability, Enhanced
Cellular/Wireless Com. for IoT,
Real-world operational use-cases
and Industry and B2B
services/applications,
more Standards…
P. Barnaghi, A. Sheth, "Internet of Things: the story so far", IEEE IoT Newsletter, September 2014.
20

21. Search on the Internet/Web in the early days
2121

22. Dynamic and (near-) real-time
22
Off-line Data analytics
Data analytics in dynamic environments
Image sources: ABC Australia and 2dolphins.com

23. Web search is already adapting this model
23
Image credits: the Economist

24. Data Collection and Processing
−Smart Data Collection
−Intelligent Data
Processing (selective
attention and
information-
extraction)
−Region Beta Paradox
24
(image source: KRISTEN NICOLE, siliconangle.com)

25. 25
Event Visualisation

26. 26
Image courtesy: IEEE Spectrum

27. Scenarios Overview
AGT International
mstrohbach@agtinternational.com
www.iotcrawler.eu

28. Relationship between scenarios
07/10/2018 28
22+ Scenarios
8 Domains
3 Cross Domains

29. WP2 Scenario: Homemade Digital
Citizen Services & Experiments (IFTTT)
City of Aarhus
Sebastian Christophersen – sech@aarhus.dk
ww.iotcrawler.eu

30. General description
• Problem:
• Open Data Sources are not widely used by citizens
• Citizen Services are often created for citizens and not by citizens
• Purpose:
• Engage citizens in Smart City experiments of their own
• Allow citizens to create digital micro-citizen services for themselves
• Make Open Data more useful to ”normal” citizens
• Approach:
• Use all available open data sources in the city to act as triggers to create
an action in other services to create tailored digital micro-citizen services.

31. Solution and Scenario
• Susanne is a 26 years old student at Aarhus University.
She is fairly tech-savvy but does not know how to
code.
• Susanne has previously heard about Aarhus being a
Smart City and knows that there is something called
open data, but it has never been something that
caught her interest.
• But now a friend of hers mentioned she could use
open data sources in Aarhus to trigger an action in
some of her favorite platforms like Facebook, Twitter,
and Spotify. Her friend mentioned a few examples of
how she used it e.g. to change the color of her Phillips
Hue lights to red if her morning-bus was delayed, so
she would know that she had to take the bike instead.
• Susanne searches for CO2 data in Aarhus to act as the
trigger in the morning and then she connects it to
Facebook Messenger to send her a message with a
happy smiley if the CO2 levels are low and a sick
smiley if the CO2 levels are high.

32. IoTCrawler support
• Discovery of IoT resources (Open
Data DK, The Things Network,
OpenWeatherMap, Open IoT
Devices from citizens…)
• Secure crawling
• Secure access to IoT resources
• Context-aware searching (user-
centric search)
• Linked with IFTTT.com or
Zapier.com

33. Smart Paking Use Case

35. THANK YOU!
@iotcrawler.eu
/iotcrawler.eu
/iotcrawler.eu
www.iotcrawler.eu
mail@iotcrawler.eu