Robust Wireless Multicast using Network Coding

Dawn Project Review, UCSC Sept 12, 06

Mario GerlaComputer Science Dept, UCLA

gerla@cs.ucla.edu; www.cs.ucla.edu/NRL

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Background – Network Coding

Traditional multicast: store and forward

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Background – Network Coding Network Coding:store-mix-forward

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a+b a+bba

ba

a

a

b

b

a

aa

a

a a

Network Coding : wireless net

Wu et al. (2003); Wu, Chou, Kung (2004) Lun, Médard, Ho, Koetter (2004)

optimal routingenergy per bit = 5

network codingenergy per bit

= 4.5

a

a a,b

a a ba,b b,a

Store-mix-forward

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Random Network Coding

x y z

Random combinati

on

buffer

Sender

Destination

Aαx + βy + γz

Every packet p carries e = [e1 e2 e3] encoding vector prefix indicating how it is constructed(e.g., coded packet p = ∑eixi where xi is original packet)

Intermediate nodes randomly mix incoming packets to generate outgoing packets

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Robust NC Multicast Most studies have evaluated NC M-

cast in static networks; no errors In tactical nets one must consider:

Random errors; External interference/jamming

Motion; path breakage Target application:

Multicast (buffered) streaming Some loss tolerance Some delay tolerance (store & playback

at destination) - non interactive

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Network Coding in static wireless nets For cost efficiency

Médard et al. “Min-cost operation over coded Networks.” IEEE T-IT

Fragouli et al. “A network coding approach to energy efficient broadcasting…”, INFOCOM ’06

Wu et al. “Minimum-energy multicast in mobile ad hoc networks using network coding.” IEEE TComm.

For reliability Médard et al. “On coding for reliable

communication over packet networks.”

Others… Ephremides et al. “Joint scheduling and wireless

network coding.” In Proc. NETCOD 2005.

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NC vs Conventional M-cast comparison

Conventional Multicast: ODMRP Mesh “fabric”; Redundant paths Robust to motion and to errors

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NC-Multicast evaluation Simulation study

Scenarios with errors and motion Reported in IEEE Wireless Communication

Magazine Oct. 2006 issue Performance bounds

Static grid - “corridor” model Uniform, random errors Idealized MAC protocol (time slotting;

non interfering sets of hyperarcs) Linear programming optimal solutions Manually computed optimal solutions Reported in MILCOM 2006

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Simulation experiments Settings

QualNet 100 nodes on 1500 x 1500 m2

5 Kbytes/sec traffic (512B packet) - light load

Single source; multiple destinations Random Waypoint Mobility 20 receivers

Metrics Good packet ratios: num. of data packets

received within deadline (1sec) vs. total num. of data packets generated

Normalized packet O/H: total no. of packets generated vs no. of data packet received

Delay: packet delivery time

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ODMRP vs NC: Reliability

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

1.01

0 10 20 30 40

Max Node Speed (m/sec)

Delivery Ratio CodeCast-8-dp0CodeCast-8-dp10CodeCast-4-dp0UDP-dp0UDP-dp10

Goo

d P

acke

t Rat

io

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ODMRP vs NC: Efficiency

0

0.5

1

1.5

2

2.5

3

3.5

0 10 20 30 40

Max Node Speed (m/sec)

Normalized Packet OH |

CodeCast-8-dp0CodeCast-8-dp10CodeCast-4-dp0UDP-dp0UDP-dp10

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ODMRP vs NC: Delay

0

0.1

0.2

0.3

0.4

0.5

0.6

0 10 20 30 40

Max Node Speed (m/sec)

Average End-to-End Delay (sec)

CodeCast-8-dp0CodeCast-8-dp10CodeCast-4-dp0UDP-dp0UDP-dp10

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ODMRP vs. NC: Highway scenario

Randomly moving 200 nodes on 10kmx50m field. All nodes are receivers.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 10 20 30 40Node Speed (m/sec)

Normalized Packet OH

NC-dp0

NC-dp10

ODMRP-dp0

ODMRP-dp10

0.91

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

1.01

0 10 20 30 40

Node Speed (m/sec)

Delivery Ratio

NC-dp0

NC-dp10

ODMRP-dp0

ODMRP-dp10

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Robustness of NC approach

Robust to random errors Robust to mobility

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Throughput Bounds

Max NC-MCAST throughput in wireless networks? Previous simulation results based on light

load. As load is increased, congestion leads to performance collapse

Our approach: evaluate max throughput analytically for a simple grid structure, the “corridor”:

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Linear Programming approach To calculate and compare maximum throughputs

with and without NC, we use LP formulation

Maximum multicast throughput LP models exist for wired networks

We developed LP models for maximum throughput in unreliable wireless networks based on: LP model developed for min-cost problems in

unreliable wired network by Muriel et al. wireless medium contention constraints

Also, we solve with LP for max throughput of conventional multicast (single tree and tree packing)

LP solutions matched with “manual” solutions

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Related Work – Throughput Bound Previous works show the gap between NC

and S/F for wired networks with no loss (e.g. log(n))

For wireless networks Ephremides et al. “Joint scheduling and

wireless network coding.” In Proc. NETCOD 2005.

Wu et al. “Network planning in wireless ad hoc networks: a cross-layer.” IEEE JSAC 2005.

=> Both show throughput gain of NC calculated using link scheduling heuristics

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maximize f

Wireless medium contention constraints

Wireless flow conservation constraints

Linear Programming Formulation

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Maximum Multicast Throughput Comparison: NC vs Conventional

Receivers

Sender

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.1 0.2

Link Error Probability

End-to-end Throughput (Link Capacity=1)Network Coding

Multicast with Tree PackingMulticast with Single Tree

CORRIDOR MODEL

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F

A+B

E

E F

D C

A B

G H

F E

C D

H

C+D

G

A B

A B

B

C D

A

C D

(1) (2) (3) (4) (5) (6)

(7) (8) (9) (10) (11) (12)

Network Coding: Link schedule achieving throughput of 2/3

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A

A

A

B

BA

B

B

C

(1) (2) (3) (4) (5)

(6)

Multicast with multiple embedded trees (no NC): Link schedule achieves 2/5 throughput

C

C

D

DC

D

(7) (8) (9) (10)

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(1) (2) (3) (4) (5) (6)

An “optimal” Single Tree multicast schedule that achieves 1/3

A

A

A

B

B

B

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Future Work in Network Coding Implement NC - Mcast congestion

control and ETE recovery above UDP If loss used as feedback, key

problem is discrimination between random error and congestion

TCP over Network Coded unicast Network Coding solutions for

intermittent connectivity Models that include mobility

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Vehicular Sensor Networks - Epidemic Dissemination

Models Car-Car or Car-Infostation communications using

DSRC DSRC: Dedicated Short Range Communication 802.11p

IEEE Task group and derived from 802.11a

VSN-enabled vehicle

Inter -vehiclecommunications

Vehicle -to-roadsidecommunications

Roadside base station

Vide o Ch e m.

Sensors

S to ra g e

Systems

P ro c.

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Vehicular Sensor Applications Environment

Traffic congestion monitoring Urban pollution monitoring

Civic and Homeland security Forensic accident or crime site

investigations Terrorist tracking

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Accident Scenario: storage & retrieval

Private Cars: Periodically collect images on the street (store data

locally) Process the data and classify the event Create Meta-Data for event -- Summary (Type, Option,

Location, Vehicle ID, …) Post it on a “distributed index”

The police access data from distributed storage

CRASH

- Sensing - P rocessing

Crash Summary Reporting

Summary Harvest ing

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Epidemic Posting & Harvesting

Exploit “mobility” to create index and disseminate summaries

Vehicles periodically broadcast summary of sensed data to their neighbors Data “owner” advertises only “his” own

summaries to his neighbors Neighbors listen to advertisements and store

them into their local storage A mobile agent (the police) harvests

summaries from mobile nodes by actively querying mobile nodes Vehicles return all “summaries” collected so

far

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Epidemic Diffusion - Idea: Mobility-Assist Summary Diffusion

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Epidemic Diffusion - Idea: Mobility-Assist Summary Diffusion

1) “Periodically” Relay (Broadcast) its summary to Neighbors 2) Listen and store other’s relayed summaries into one’s storage

Keep “relaying” its summary to its neighbors

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Epidemic Diffusion - Idea: Mobility-Assist Summary Harvesting

Sum. Req

1. Agent (Police) harvestssummaries from its neighbors

2. Nodes return all the summariesthey have collected so far

Sum. Rep

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Harvesting AnalysisMetrics

Fraction of harvested summaries F(t)Analysis assumption

Discrete time analysis (time step Δt)N disseminating nodesEach node ni advertises a single summary si

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Harvesting Analysis-Regular Nodes

Expected number (α) of contacts in ∆t: ρ : density of disseminating nodes v : average speed R: communication range

Incremental number of summaries harvested by a regular node ∆Et = Et - Et-1: Prob. of meeting a not yet infected node is 1-Et-

1/N

2Rs=vΔt

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Harvesting Analysis- Agent Node Agent harvesting summaries from its

neighbors (total α nodes) A regular node has “passively” collected so

far Et summaries Probability that agent can collect a specific

summary=Et/N Specific summary collected from α neighbors

with probability 1-(1-Et/N)

Let E*t = Expected number of summaries harvested by the agent

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Harvesting Analysis - Harvesting Fraction

Numerical analysis

Area: 2400x2400m2

Radio range: 250m # nodes: 200Speed: 10m/sk=1 (one hop relaying)k=2 (two hop relaying)

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Simulation Simulation Setup

Implemented using NS-2 802.11a: 11Mbps, 250m

transmission range Network: 2400m*2400m Mobility Models

Random waypoint (RWP)

Urban map model: Group mobility model Random Merge and split

at intersections Westwood map

Westwood Area

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Simulation Summary harvesting results with

random waypoint mobility

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Simulation Summary harvesting results with

urban map mobility

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Future Work Further investigate dependence of

dissemination/harvesting from motion

Enhance track models to reflect realistic (urban, open) scenarios

Motion pattern characterization NCR (Neighborhood Change Rate) Fraction of “traveling buddies”, etc

Data mining in large spatial-temporal databases on mobile platforms