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.
Automating Google Workspace (GWS) & more with Apps Script
GQP2PS: A context-aware framework for facilitating high-quality multimedia streaming in disaster recovery scenarios
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
1
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
2
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
3
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
8
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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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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
11
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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13. Magee Campus
Thank you for your time and patience
Questions?
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