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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.
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http://isrc.ulster.ac.uk
Magee Campus
GQP2PS: A context-aware framework for facilitating high-quality multimedia streaming
in disaster recovery scenarios
Fraser Cadger
Supervisors:
Dr. Kevin CurranDr. Jose Santos
Dr. Sandra Moffett
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http://isrc.ulster.ac.uk
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
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
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
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
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
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
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
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
Magee Campus
GQPR - Operation
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http://isrc.ulster.ac.uk
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
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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http://isrc.ulster.ac.uk
Magee Campus
Thank you for your time and patience
Questions?
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