CityPulse is a project that aims to develop a distributed framework for processing large-scale real-time IoT and social data streams to extract knowledge for smart city applications. The framework will semantically integrate heterogeneous data sources, perform large-scale analytics, and adapt processing in real-time based on context. It will be tested through prototype smart city applications to evaluate its ability to support future smart city platforms and products. The key issues addressed are virtualization of data, on-demand federation of sources, large-scale aggregation and analytics, real-time interpretation and processing control, and reliable information extraction.

1. 1
CityPulse: Real-Time IoT Stream
Processing and Large-scale Data
Analytics for Smart City Applications

2. 2
CityPulse– a quick snapshot
− CityPulse aims to support the integration of dynamic data
sources and context-dependent on-demand adaptations of
processing chains during run-time.
− CityPulse aims to bridge the gap between the application
technologies on the IoT and real world data streams.
− It will use Cyber-Physical and Social data and will employ
big data analytics and intelligent methods to aggregate,
interpret and extract meaningful knowledge and
perceptions from large sets of heterogeneous data streams.

3. 3
An Integrated Approach

4. 4
CityPulse Consortium
Industrial
SIE (Austria,
Romania),
ERIC
SME AI,
Higher
Education
UNIS, NUIG,
UASO, WSU
City BR, AA
Partners:
Duration: 36 months

5. 5
The main objective in CityPulse is:
− to develop, build and test a distributed framework for the
semantic discovery and processing of large-scale real-time
IoT and relevant social data streams for knowledge
extraction in a city environment.
− It will prototype and demonstrate its major concepts in a
city environment and evaluate the results for exploitation
towards future smart city delivery and development
platform and testing products.

6. 6
Processing steps and Life-cycle stages
Analytics
Toolbox
Context-aware
Decision
Support,
Visualisation
Knowledge-
based
Stream
Processing
Real-Time
Monitoring&
Testing
Accuracy&
Trust
Modelling
Semantic
Integration
On Demand
Data
Federation
Open
Reference
Data Sets
Real-Time
IoT Information
Extraction
IoT Stream
Processing
Federation of
Heterogenous
Data Streams
Design-Time Run-Time Testing
Exposure APIs

7. 7
The Key issues addressed
− Virtualisation: Semantic annotation of heterogeneous data
for automated discovery and knowledge-based processing
− Federation: On demand integration of heterogeneous
Cyber-Physical-Social sources
− Aggregation: Large-scale data analytics
− Smart Adaptation: Real-Time interpretation and data
analytics control
− User centric decision support: Context aware customized
IoT information extraction
− Reliable Information Processing: Testing and monitoring
accuracy and trust
− Smart City Applications: Application programming interface
for rapid prototyping

8. 8
Overall Structure
WP1ProjectManagement
CityPulse Framework
WP6 Integration & Evaluation:Smart City Applications
WP5 Real-Time IoT Intelligence
WP4
Reliable
Information
Processing
WP3 Large-Scale IoT Stream Processing
WP2 Requirements and Smart City Framework
Configuration
Semantic
IOT Stream
Monitoring
& Testing
WP7DisseminationandExploitation

9. Contact
Payam Barnaghi, University of Surrey, UK
Email: p.barnaghi@surrey.ac.uk
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