At The Digital Catapult Centre Brighton event, Tech Beyond The Screen: Connectivity & Infrastructure on Wednesday 2nd March, Dr Nour Ali from The University of Brighton spoke about mobile and self adaptive ambients in service oriented architecture.

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Mobile and Self-Adaptive Ambients in Service
Oriented Architecture
Dr Nour Ali
School of Computing, Engineering and Mathematics
2nd of March, 2016
N.ALI2@BRIGHTON.AC.UK

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COLLABORATORS

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INTERNET OF THINGS ARCHITECTURE
Smart Objects
(Things)
End UserNetwork
Wireless
Internet
Cloud
Services

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CONTENTS
 Modelling Internet of Things Applications
 Automatic Code Generation and Deployment
 Self-Adaptation to Services and Resources
 Conclusions and Further Work

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 Solves Interoperability problem.
 What are the generic building blocks for IoT devices and
services?
Use models and then generate/configure devices and code
to specific platforms.
Model independently of the kind of device.
MODEL DRIVEN DEVELOPMENT

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 An ambient is a place, delimited by a boundary,
where computation happens.
 Examples of ambients are:
Devices
Car
Data packets
Firewalls
Networks
A Building or an airplane
AMBIENT CALCULUS
Cardelli and Gordon, 1998
m
n
in m P
Q
R

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AMBIENT-SERVICE ORIENTED RUNTIME META-
MODEL

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AMBIENT RUNTIME MODEL

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SMART HOME

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 Device Ambients:
 Smart TV
 Sensors
 Alarm
 Mobile ambients:
 Car
 Mobile Device that represents the human.
 Location Ambients
 House
 Rooms
AMBIENTS

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MODELLING MOBILE AMBIENTS IN SMART
HOMES
House
Garage
Reception Room
Living Room
Mobile
Device
enter

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MODELLING MOBILE AMBIENTS IN SMART
HOMES
House
Garage
Reception Room
Living Room
Mobile
Device
enter

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MODELLING MOBILE AMBIENTS IN SMART
HOMES
House
Garage
Reception Room
Living Room
Mobile
Device
enter

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AMBIENTS IN SERVICE ORIENTED
ARCHITECTURE

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ENTERING MOBILITY SERVICE CONTRACT
15

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CONTENTS
 Modelling Internet of Things Applications
 Automatic Code Generation and Deployment
 Self-Adaptation to Services and Resources
 Conclusions and Further Work

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 To model service oriented architecture of distributed
and mobile systems.
 automatically generate and execute them at runtime.
AUTOMATIC CODE GENERATION AND
DEPLOYMENT
Transformations
ATL
Declarative Languages for OSGI
Modelling Tool
EMF/GMF

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CONTENTS
 Modelling Internet of Things Applications
 Automatic Code Generation and Deployment
 Self-Adaptation to Services and Resources
 Conclusions and Further Work

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MOTIVATION: ADAPTATION TO RESOURCES
How can we self-adapt at runtime to resources?
Internet
Or Cloud
services
Home Cinema
CPU,
battery,
etc
Internet
Or Cloud
services
CPU,
battery,
etc
NOT ALL APPLICATIONS AND SERVICES HAVE THE SAME PRIORITY

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OUR APPROACH
Internet
Or Cloud
ARCHITECTURAL MODEL @ RUNTIME
SYSTEM @ RUNTIME
Planning Mobile
Adaptation
Architectural
Metamodel
Discrete Swarm Optimization Algorithm

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AUTONOMIC AMBIENTS

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AMBIENT-SERVICE ORIENTED RUNTIME META-
MODEL

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 Mobile DEVICE BEFORE ENTERING CINEMA
SCENARIO
enter

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 Create Possible Solutions
 Calculate Utility Functions resource costs, utility
and current value of resource
POSSIBLE CANDIDATE SOLUTIONS AND
UTILITY FUNCTION
} Uf()=0
Battery COST with
DATA (mA)
BatterY COST
WITH WLAN (mA)
Utility
Health App 70 50 100
VideoStreaming Service 60 60 50
Friends Service 70 50 10
Restaurant Service 50 30 10

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MOBILE USER INTERFACE FOR ALGORITHM
Total No of Resources, Services and Apps
No of Iterations
Name of Service and the Utility of Service

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MOBILE USER INTERFACE FOR ALGORITHM
Services and their Dependencies

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OUTPUT
4G[0]WLAN[1]Health App [1]VideoService [1]Friends Service[1]

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MOBILE DEVICE IN CINEMA

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IMPLEMENTATION AND EVALUATION
-The maximum number of iterations to perform is 1000.
- We executed the algorithm 10000
When the number of particles increases,
the percentage of success increases.
The best execution time was 0.99 ms when
25 particles were used, with an average of
46.4 iterations and 96.4% success.

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 25% of the battery
EVALUATION ON MOBILE DEVICE

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CONTENTS
 Modelling Internet of Things Applications
 Automatic Code Generation and Deployment
 Self-Adaptation to Services and Resources
 Conclusions and Further Work

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 We use Model Driven Engineering to develop and
manage applications in a technology independent
way.
 We use autonomic computing to allow applications to
self-manage.
 Further Work:
 Developing a tool that includes:
architectural modelling visualizations, monitoring, etc
 Allow users to change the utility of the resources provided at
runtime.
 New case studies to apply our work.
CONCLUSION AND FURTHER WORK

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QUESTIONS?
Dr. Nour Ali
Principal Lecturer in Software Engineering
University of Brighton
Home page: www.cem.brighton.ac.uk/staff/na179/
Email: n.ali2@brighton.ac.uk

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 Ali, Nour and Solis, Carlos (2015) Self-Adaptation to Mobile
Resources in Service Oriented Architecture In: 2015 IEEE
International Conference on Mobile Services (MS), New York
City, NY, USA, 27 June - 2 July 2015.
 Ali, Nour and Solis, Carlos (2014) Mobile architectures at
runtime: research challenges In: 1st ACM international
conference on mobile software and engineering systems
(Mobilesoft), Hyderabad, India, 2-3 June 2014.
 Ali, Nour, Solis, Carlos and Chen, Fei (2012) Modeling support
for Mobile Ambients in Service Oriented Architecture In: 1st
international conference on Mobile Services (MS), Honolulu,
Hawaii, 24-29 June, 2012.
 Ali, Nour, Ramos, I. and Solis, Carlos (2010) Ambient-PRISMA:
ambients in mobile aspect-oriented software
architecture Journal of Systems and Software, 83 (6). ISSN
0164-1212
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