Environmental awareness and knowledge may help people to take more informed decisions in their everyday lives, ensuring their health and safety. The Web of Things enables embedded sensors to become easily deployed in urban areas for environmental monitoring such as air quality, electromagnetism, radiation, etc. In this presentation, we propose an eco-system for urban computing which combines the concept of the Web of Things, together with big data analysis and event processing, towards the vision of smarter cities that offer real-time information to their habitants about the urban environment. We touch upon near real-time web-based discovery of sensory services, citizen participation, semantic technologies and mobile computing, helping people to take more informed everyday decisions when interacting with their urban landscape. We then present a case study where we demonstrate the feasibility and usefulness of this eco-system to the everyday lives of citizens. This research has been supported by the P-SPHERE project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No 665919.

1. A Web of Things Based Eco-System for
Urban Computing - Towards Smarter Cities
Andreas Kamilaris
Marie Curie Postdoc Fellow
IRTA-UAB Barcelona, Spain
ICT2017
Limassol, Cyprus
May 4, 2017

2. • Real-time discovery of data streams and sensing of the
environment
• Understanding of data discovered
• Fast data processing
• Efficient processing of complicated event logics
• Filtering of relevant data
• Useful information to the user in different urban scenarios.
Requirements for Smart City Frameworks

3. Web of Things
• Designed to connect
“things” to the Web
• A combination of
• Approaches
• Software Architectures
• Interfaces

4. • Increase Interoperability among IoT platforms
• Mitigate Silo Architecture
• Avoid Multiple and Conflicting Standards
• Global and Easy Discovery of Devices
• Datasets (produced by WoT devices) available
as Open Data on the Web
Why we need Web of Things?

5. A WoT-Based Eco-System for Smart Cities

6. Eco-System Components
1. WoT-based sensor data streams
2. Discovery of WoT devices, services and sensor data streams
3. Middleware performing big data analysis and CEP
4. Publish/subscribe messaging queues
5. ICT technologies such as mobile applications
6. Service composition, i.e. urban mashups
7. Semantic web technologies

7. Component #1: WoT-based sensor data streams
• Devices fully integrated to
the Web.
– Directly by embedding
Web servers on them.
– Indirectly by means of
gateways.
• Expose their sensing
services as RESTful web
services.

8. Component #2: Discovery of WoT devices & services
• Machines needs to automatically discover
devices/things and their description
• Search Space is the whole Web
• Geo-Spatial Mapping
• Movable Objects/Things
• Require Frequent Updates in Indexes
• Semantic Annotation to describe things

9. Component #2: WOTS2E
• A Search engine to discover
semantic meta-description of
things
• Crawls the Web to discover
Linked Data Sources
• Analyzes Linked Data sources
to identify relevant WoT
devices
• SPARQL queries and data
endpoints
Andreas Kamilaris, Semih Yumusak and Muhammad Intizar Ali. WOTS2E: A Search Engine for a Semantic Web of
Things. In Proc. of the IEEE World Forum on Internet of Things (WF-IoT), Reston, VA, USA, December 2016.

10. Component #3: Middleware for big data analysis
• Middleware between smart city applications and sensor data streams
are needed for processing complex events and for analyzing big data.
• Real-world applications in the WoT space require reasoning
capabilities that can handle incomplete, diverse and unreliable input.
• The Automated Complex Event Implementation System (ACEIS) is a
quality-aware adaptive CEP platform for urban data streams.
1. Each sensor data stream is annotated with QoS and QoI metrics.
2. ACEIS receives an event service request and composes the most
suitable data streams.
3. It then transforms the event service composition into a stream
query to be deployed and executed on a stream engine (i.e.
CQELS, C-SPARQL) to evaluate the complex event pattern
specified in the event service request.

11. Component #3: ACEIS
Semantic Annotation
ACEIS Core
Resource
Management
Application
Interface
Knowledge Base
QoI/QoS
Stream
Description
Data Mgmt,
Indexing,
Caching
User Input
Event Request
Data
Federation
Resource Discovery
Event Service Composer
Composition Plan
Subscription Manager
Query Transformer
Query Engine
Query
Results
Constraint
Validation
Constraint
Violation
Adaptation
Manager
Data Store
IoT Data
Stream
Social Data
Stream
F. Gao, M. I. Ali, and A. Mileo. Semantic Discovery and Integration of Urban Data Streams. In Proc. of the Fifth
International Conference on Semantics for Smarter Cities, 2014.

12. Component #4: Publish/subscribe messaging queues
• Offload total network traffic.
• Decouple producers from consumers.
• RabbitMQ
RabbitMQ - Messaging that just works: https://www.rabbitmq.com/

13. Component #5: ICT technologies
• Mobile apps
• Web apps
• Big data analysis on the cloud
or regional (fog)
• Pervasive apps – augmented
reality

14. Component #6: Service composition
• Mobile apps targeting smart cities locate and interact with
environmental services, provided by sensors installed at various
urban locations.
• Informing the user about existing environmental conditions:
– A local view of the urban environment, and are able to take only
local decisions,
– Communicate with smart city middleware (such as ACEIS),
which would assist them in taking more informed, broader
decisions, taking into account the whole city infrastructure

15. Component #6: UrbanRadar
A. Kamilaris and A. Pitsillides. The Impact of Remote Sensing on the Everyday Lives of Mobile Users in Urban Areas. In Proc.
of the International Conference on Mobile Computing and Ubiquitous Networking (ICMU), Singapore, January 2014.
• Urban Mashups: Web mashups involving real entities
• Opportunistic physical mashups, validated only when the local
environmental conditions support the sensor-based services defined by
the mashups.

16. Component #7: Semantic web technologies
• Semantics provide seamless data integration, combination and reuse
with minimal effort.
• Use of lightweight information models that are developed on top of
well-known ontologies, such as SSN and OWL-S.
– Streams coming from urban sensors using the Stream Annotation
Ontology (SAO)
– Events detected relating to smart cities using the Complex Event
Ontology (CEO)

17. Component #7: Semantic web technologies

18. Component #7: Semantics in ACEIS
• A sensor service description is annotated as:
sdesc = (td, g, qd, Pd, FoId, fd)
type grounding QoS
Observed
Properties
Feature Of
Iterest
Pd → FoId
• Similarly, a sensor service request is annotated:
sr = (tr, Pr, FoIr, fr, pref, C)
type Requested
Properties
Feature of
Interest
Pd → FoId
no
grounding
NFP Constraint and
Preferences

19. All Components of the WoT-Based Eco-System
1 2
3
45
6
7

20. Case Study: Traffic Monitoring and Journey Planner
• 449 pairs of traffic sensors were deployed at the city of
Aarhus, Denmark
• Travel Planner mobile app
• ACEIS calculated the ideal route for its users while
commuting, taking into account their current context
• User preferences: weather conditions, traffic and people
intensity, traffic schedules, QoS and QoI.
• Real-time data analytics, continuously monitoring user
context and relevant events (e.g. traffic accidents) on the
planned route.

21. Case Study: Traffic Monitoring and Journey Planner
1
3
CityPulse-Journey-Planner: https://github.com/CityPulse/CityPulse-Journey-Planner
2
4

22. Conclusion
• Semantic search and real-time discovery are essential for Web
of Things, especially in smart city scenarios.
• Mobile location-based services and real-time big data
analytics will facilitate the filtering of vast amount of sensory
data into relevant information that would enhance the quality of
life of citizens, while moving within their cities.
• Semantic interoperability is key for future intelligence in urban
eco-systems.
• Technology is already here!

23. Future Work
• Improve the search mechanism of WOTS2E.
• A user-friendly website, to incrementally let users to access the
discovered lists of services in a well-organized way.
• Larger-scale case studies/deployments in various cities, involving
thousands of sensor devices/services such as dust, water pollution,
radiation, dangerous chemicals and heavy metals in foods.
• Assess the eco-system’s acceptance to citizens involved, their potential
engagement and behavioral change in a participatory-based model.
• Privacy and security.

24. Thank you!
Andreas Kamilaris
(andreas.kamilaris@irta.cat)
ICT2017
Limassol, Cyprus
May 4, 2017

25. WOTS2E: ArchitectureWOTS2E: Implementation/Analysis
• Discovered patterns are used
as an input to our web
crawlers, in order to search the
web for available SPARQL
endpoints.
• For web crawling, we used a
meta-crawling service called
SpEnD.
• SpEnD exploits the search
functionality available over
popular search engines to
accelerate the performance of
web crawling.

26. WOTS2E: ArchitectureWOTS2E: Evaluation
• From the discovered 638 active SPARQL endpoints, we
examined them one by one for relevance to IoT/WoT
Ontology Number of Endpoints
SSN 13
DBPedia 13
SmartBuilding 3
DogOnt 2
DUL 2
km4city 2
OpenEI 2
RDFS, SKOS 4
Fan Fpai, Fiemser, IoT,
PROV, SAREF
5 (once each ontology)

27. WOTS2E: ArchitectureWOTS2E: Evaluation
• IoT/WoT-specific triples from the endpoints
Ontology Number of Triples
SSN 1.433,248
DUL 182
km4city 56
Fiemser 50
OpenIoT 44
SmartBuilding 36
DogOnt 24
SAREF 4
Fan Fpai 2