robust wireless multicast using network coding dawn project review, ucsc sept 12, 06 mario gerla...
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Robust Wireless Multicast using Network Coding
Dawn Project Review, UCSC Sept 12, 06
Mario GerlaComputer Science Dept, UCLA
[email protected]; 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
A
αx + 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-dp0
CodeCast-8-dp10
CodeCast-4-dp0UDP-dp0
UDP-dp10
Goo
d P
acke
t R
atio
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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-dp0
CodeCast-8-dp10
CodeCast-4-dp0
UDP-dp0
UDP-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-dp0
CodeCast-8-dp10
CodeCast-4-dp0
UDP-dp0
UDP-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 40
Node 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 CodingMulticast 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
Vid e 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 Harvesting
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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 Analysis
MetricsFraction of harvested summaries F(t)
Analysis assumptionDiscrete 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