First item planned. Add more text as necessary. Second item planned. Add more text as necessary. Add other points as necessary Topic/project/effort description

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Topic/project/effort description

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MAIN ACHIEVEMENT:

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HOW IT WORKS:

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ASSUMPTIONS AND LIMITATIONS:

• Limitation or assumption

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•First key point

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What is the state of the art and what are its limitations? (DELETE THIS BOX OF TEXT AND INSERT DIAGRAM(S)

What are the key new insights? (REPLACETHIS BOX AND INSERT DIAGRAM(S))

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Analytical model of NC static multicast scenario shows superior goodput and graceful degradation with packet loss

Network coding (NC) for efficiency & robustness

Network coding moves information rather than packets. It exploits computing (λ) and storage ( ) to provide robust performance in degraded and congested settings.

Analysis indicates potential to meet Phase 1 metrics. Partial network stack demonstrated.

MAIN RESULT:

Analyzed and implemented network coding algorithms for dynamic wireless networks.

HOW IT WORKS:

Topology information is collected to compute subgraphs. Source nodes mix packets which forwarded by subgraph nodes to unicast or multicast destinations.

ASSUMPTIONS AND LIMITATIONS:• Needs further integration with reliable hyperlink

protocol.

• Needs further integration with channel access protocol.

• Control overhead for baseline and CONCERTO protocols not included in analysis.

• Potential additional gains from inter-session coding not included in analysis.

Traditional packet copying (C) and forwarding (F) is inefficient and fails to exploit the availability of inexpensive memory and CPU resources.

Demonstrate 10x bandwidth reduction compared to baseline MANET implementation using realistic scenario and traffic load

C F

C F

C F

C F

C F

C FC FC F

C FC FC F

C FC FC F

C FC FC F

C FC FC F

S

D1

D2 random combination

buffer

hyperarc

random combination

buffer

hyperarc 0 20% 40% 60% 80% 100%

5

20

Probability of Loss

Network CodingNORM

Multicast ARQUnicast ARQ

Goodput

BandwidthSavingsRatio

5

10

15

Network CodingNORMMulticast ARQUnicast ARQ

GOODPUT

Bandwidth savings ratio (BSR)

Phase 2

BSR Target

Phase 1 BSR Target

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Philosophy of “network coding as infrastructure” reduces number of protocols dramatically, simplifying configuration and algorithm development.

Network coding as a unifying architectural principle

Network coding subsumes unicast, multicast, multiple path routing, opportunistic routing, packet level FEC, ARQ and rateless coding.

CONCERTO’s network coding approach simplifies MANET architecture

MAIN RESULT:

Simplified network stack architecture based on coding

HOW IT WORKS:

Unicast, broadcast and multiple-path routing are special cases of multicast subgraphs. Rateless coding integrates packet level FEC and ARQ.

ASSUMPTIONS AND LIMITATIONS:• Analyzed, but have not implemented, network-

coding compatible backpressure, admissions control and rate control algorithms.

Existing protocols were developed to solve specific problems (unicast, multicast, link level reliability, end-to-end reliability) and do not form a cohesive whole.

Incorporate intra-session coding. Demonstrate that multiple protocols can be replaced with network coding.

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Header Gen / Information Extraction

SubgraphConstruction

DataProcessorDataProcessor

ConcertoSystem

ApplicationSockethandle publish

NetworkCoding

Queuing &Scheduling

Local & E2EQoS

Admission &Rate Control

Sending Rate

Topology

NS Info Ctrl

ExtractedNS Info Ctrl

Cngst Price (remote)

Cngst Price(remote)

SG Ctrl

Piggyback CtrlFields

SendingRate

Subgraph

QoSSpec TopologyDiscovery

Topology& remoteupdates

GroupsTopology

Appl.Adapt Info

Topology

Topo Ctrl

SubgraphHyperarc

HyperArcFlow Request

ChannelAccess

HyperArcDiscovery

ModularXmt

HyperArc(local: real & used))

Neighbors (1-hop)

HyperArcFlow

Request

Neighbors (2-hop)


QoSSpec

Subgraph

Cngst Price (local)

Cngst Price (outbound remote)

COPE




ConcertoSystem


NetworkCoding

Queuing &Scheduling

Local & E2EQoS


Sending Rate

Topology

NS Info Ctrl



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SG Ctrl


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Subgraph



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SubgraphHyperarc


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ModularXmt


Neighbors (1-hop)

HyperArcFlow

Request

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QoSSpec

Subgraph

Cngst Price (local)


COPE




ConcertoSystem


NetworkCoding

Queuing &Scheduling

Local & E2EQoS


Sending Rate

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NS Info Ctrl



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Subgraph



GroupsTopology

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SubgraphHyperarc


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HyperArcFlow

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Subgraph

Cngst Price (local)


COPE




ConcertoSystem


NetworkCoding

Queuing &Scheduling

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Sending Rate

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D1

Unicast Multiple Path Multicast

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Unicast Multiple Path Multicast

Protocol Example Unicast Routing

OLSR

Multicast Routing

SMF

Multiple Path Routing

Mid Hop Route

Opportunistic Routing

ExOR

Unicast End-to-end Reliability

TCP

Multicast End-to-end Reliability

NORM

Hop-by-hop Reliability

Hybrid FEC/ARQ

Protocol Example Unicast Routing

OLSR

Multicast Routing

SMF

Multiple Path Routing

Mid Hop Route

Opportunistic Routing

ExOR

Unicast End-to-end Reliability

TCP

Multicast End-to-end Reliability

NORM

Hop-by-hop Reliability

Hybrid FEC/ARQ

NetCoding

Subgraph Construction

Rateless Coding and Per-Generation ARQ

RC-MAC in MARCONI achieves near-optimal channel access without TDMA overhead.

Progress on a backpressure-informed media access control

Backpressure congestion signal specifies urgency of channel access across nodes, not just within a node

Our “regulated contention” MAC approaches optimal channel utilization without the overhead of TDMA

MAIN RESULT:Implemented and demonstrated differentiated random access with backpressure signaling

HOW IT WORKS: • Basic RC-MAC: Nodes with highest “urgency” have

highest channel access probability

• MARCONI RC-MAC: Normalized backpressure signals specify max urgency wi of each node

• P(access) 1 for most urgent nodesP(access) 0 for least urgent nodes

ASSUMPTIONS AND LIMITATIONS:• Assumes queue length metric includes all criteria

that determine message “urgency”

TDMA-based protocols require close coordination and tight time sync to achieve optimal channel utilization

Random access approaches are simple, but have poor utilization

• Refine urgency weighting function for delay-sensitive traffic

• Refine urgency vs. fairness tradeoff

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+ t0+Δtt0+2Δt

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t0

t0

t

State of the art (802.11) on small packets (e.g. VoIP

Current RC-MAC

Ideal TDMA

2-user shared medium

% user 1 accesstime

% u

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cess

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Most “urgent”: P(access) ≈ 1

tLeast “urgent”: P(access) ≈ 0

: wfwf

wfp

j j

ii

Urgency = wi =

-backpressureMost urgent flow: +2Least urgent flow: -2

0 0.2 0.4 0.6 0.8 10

0.2

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1

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)/exp

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In severely challenged networks, admission control rejects some flows to guarantee QoS of others, improving overall delivery of bulk files (green vs. yellow) and streaming video (blue vs. red)

Progress on Joint Routing and Admission Control

Backpressure complements channel utilization and link capacity in determining the feasibility and admissibility of a new route

Our JRAP protocol aligns routing and admission goals with optimal control objectives

MAIN RESULT:

Implemented route discovery and admission control protocol that tests for flow feasibility and decides feasibility using backpressure signal

HOW IT WORKS: Forward sweep (“join query”) identifies possible paths to destination

Return sweep (“join reply”) rejects infeasible paths, choosing one with greatest surplus capacity

Flows admitted only after route discovery identifies a path with sufficient resources

ASSUMPTIONS AND LIMITATIONS:• Problem formulation collapses all capacity and QoS

into a scalar routing metric

• Current design & implementation unicast only

• Ad hoc routing metrics may not match network goals

• Resource reservation infeasible for MANETs

• Route discovery does not check that network can support new traffic

• Implement multicast routing

• Improve route adaptation to manage changes in MANET dynamics and account for network-wide impact of flows

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Benefits of admission control

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0 10 20 30 40 50 60 70 80

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inelastic_WITH

inelastic_WITHOUT

elastic_WITH

elastic_WITHOUT

Min latency: What about other flows?

Max throughput: What about reliability?

Min hop count: What about throughput?

Flow rejected unless capacity exists and congestion is feasible

Priority Route RateTUS

PreemptibleTraffic

EnoughCapacity?

CUS

ContentionCount

RequiredCapacity

AvailableCapacity

NNS

Flow Information

Sensor Data

Decision Procedure

Forwarding node selects less-congested path to destination

For inelastic flows, forwarding node checks for delay & rate feasibility

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First item planned. Add more text as necessary. Second item planned. Add more text as necessary. Add other points as necessary Topic/project/effort description