Presentation I gave to the University of Ulster Faculty of Computing and Engineering Research Graduate School Conference on the 15/01/2013. This presentation gives a very high-level overview of my PhD project designed for a general audience.

1. Magee Campus
GQP2PS: A context-aware framework for
facilitating high-quality multimedia streaming
in disaster recovery scenarios
Fraser Cadger
Supervisors:
Dr. Kevin Curran
Dr. Jose Santos
Dr. Sandra Moffett
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http://isrc.ulster.ac.uk

2. Magee Campus
Introduction
 Disaster recovery includes natural and man-made
disasters
 First responders are usually emergency services,
military, special civilian agencies, or volunteers
 Dedicated medical personnel such as doctors are limited
 Multimedia technology could be used to communicate
with remotely located doctors who can observe and
diagnose an injured person
• Emergency telemedicine
 Lack of telecommunications infrastructure makes this
difficult
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http://isrc.ulster.ac.uk

3. Magee Campus
GQP2PS Overview
 Geographic QoS P2P Streaming Framework will:
• Provide streaming multimedia content in disaster recovery
scenarios with limited or no infrastructure
• Requires no infrastructure to operate
• Operate on WiFi wireless mesh networks (WMN) formed by
devices such as smartphones and tablets
• In WMNs the devices are the network
• Use context information (with a focus on location and mobility) to
make routing and streaming decisions that optimise Quality of
Service (QoS)
 Overall aim is to provide users with high-quality
multimedia when infrastructure is limited or unavailable
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http://isrc.ulster.ac.uk

4. Magee Campus
GQP2PS Design
 GQP2PS consists of two components:
• Location-Aware P2P Streaming Environment (LAPSE)
• Geographic QoS Predictive Routing (GQPR)
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http://isrc.ulster.ac.uk

5. Magee Campus
GQP2PS - Implementation
 GQP2PS will be implemented as an Android application
 Current testbed of six Android smartphones
• Five Samsung Galaxy Minis and one HTC Nexus One
 GQP2PS app will be written in a mixture of Java (GUI) and
C (networking/p2p) using Android NDK
 GQP2PS will make use of code developed by the Serval
Project for their Serval Mesh app
• Serval Mesh code is open source and licensed under the GPL
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http://isrc.ulster.ac.uk

6. Magee Campus
LAPSE - Design
 Uses p2p streaming to distribute multimedia content
• Similar to p2p filesharing
• Content is split and distributed amongst peers
• Peers get pieces of a file from each other and not a server
 P2P streaming is distributed, decentralised, and fault-
tolerant
 LAPSE will form and maintain a p2p overlay network and
facilitate multimedia streaming
 LAPSE will use QoS predictions provided by GQPR for
overlay building and peer selection
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http://isrc.ulster.ac.uk

7. Magee Campus
LAPSE - Implementation
 Development is scheduled to begin around April near
completion of GQPR
 End-user application will be written in Java
 Streaming code will be written in C using existing code
from the Serval Project
 Serval Project already contains code for VoIP over mesh
calls
• Modify this to support video
 On-demand streaming will either be written from scratch
or use a modified version of Serval’s Rhizome filesharing
system
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http://isrc.ulster.ac.uk

8. Magee Campus
GQPR – Design I
 Operates on top of a hybrid wireless mesh network; a
form of ad-hoc network
• In ad-hoc networks all connected devices are end-users and can act as
intermediates for relaying messages
• Hybrid WMNs allow for the incorporation of infrastructure where
available
• Without infrastructure, they function like a typical ad-hoc network
 Uses context information to predict QoS available from
neighbouring nodes
 Emphasis on location/mobility predictions provided by an
Artificial Neural Network
 These predictions will be used for routing and by LAPSE
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http://isrc.ulster.ac.uk

9. Magee Campus
GQPR – Design II
 Based on geographic routing
• Requires only limited network knowledge
 Location/mobility prediction allows devices to anticipate
neighbour mobility
• Previously geographic routing protocols only used basic location
prediction algorithms
• Infrastructure networks used more advanced methods based on machine
learning algorithms – unsuitable for ad-hoc networks
 We developed a neural network location prediction
algorithm for use in ad-hoc networks
 GQPR will use this in combination with other context
information to make QoS predictions
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http://isrc.ulster.ac.uk

10. Magee Campus
GQPR - Operation
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http://isrc.ulster.ac.uk

11. Magee Campus
GQPR - Implementation
 Currently under development:
• Phase One: Geographic Predictive Routing (GPR): complete
• Phase Two: Geographic QoS Predictive Routing: in progress
 GPR uses neural network-powered location predictions
and other context information to make routing decisions
 GPR performs well compared to established ad-hoc
routing protocols such as AODV, DSR, and DSDV in
multimedia simulations
 GPR is the foundation for GQPR
 Development of GQPR will take place in ns-2 simulator
and Android testbed using GPR and Serval code
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http://isrc.ulster.ac.uk

12. Magee Campus
Conclusion
 GQP2PS will facilitate high-quality multimedia streaming
in WiFi mesh networks without the need for infrastructure
 The intended application of GQP2PS is for emergency
telemedicine in disaster recovery scenarios
 GQP2PS will use context information such as location
predictions to predict QoS
 These QoS predictions will then be used for routing and
p2p streaming purposes
 A high-level of network QoS guarantees a high-level of
multimedia for the user
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Thank you for your time and patience
Questions?
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