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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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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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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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GQP2PS Design GQP2PS consists of two components:

• Location-Aware P2P Streaming Environment (LAPSE)• Geographic QoS Predictive Routing (GQPR)

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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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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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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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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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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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GQPR - Operation

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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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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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