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First item planned. Add more text as necessary.
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Topic/project/effort description
First key insight. Add more text as necessary.
Second key insight. Add more text as necessary.
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A sentence why it is important/useful
MAIN ACHIEVEMENT:
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HOW IT WORKS:
Placeholder explanatory text paragraph. Replace with text and diagrams as necessary.
ASSUMPTIONS AND LIMITATIONS:
• Limitation or assumption
• Another limitation or assumption
Primary answer here. Add more text as necessary.
•First bullet point
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•First key point
•Additional as necessary
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))
CHARACTERIZE THE QUANTITATIVE IMPACT (DELETE THIS BOX OF TEXT AND INSERT TABLE, GRAPH, OR OTHER SUITABLE VISUALIZATION)
What are the end-of-phase goals?(REPLACE WITH DIAGRAM/TEXT/THRESHOLD CRITERIA)
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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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CONCERTO ACHIEVEMENT
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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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CONCERTO ACHIEVEMENT
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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)
HyperArcFlow Request
QoSSpec
Subgraph
Cngst Price (local)
Cngst Price (outbound remote)
COPE
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)
HyperArcFlow Request
QoSSpec
Subgraph
Cngst Price (local)
Cngst Price (outbound remote)
COPE
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)
HyperArcFlow Request
QoSSpec
Subgraph
Cngst Price (local)
Cngst Price (outbound remote)
COPE
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)
HyperArcFlow Request
QoSSpec
Subgraph
Cngst Price (local)
Cngst Price (outbound remote)
COPE
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Unicast Multiple Path Multicast
S
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D1
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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MARCONI ACHIEVEMENT
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+ t0+Δtt0+2Δt
…
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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2 ac
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ti
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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
0.4
0.6
0.8
1
x
y=ex
p(kx
)/exp
(k)
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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MARCONI ACHIEVEMENT
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Benefits of admission control
0
20
40
60
80
100
120
0 10 20 30 40 50 60 70 80
time (s)
Pac
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of
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traf
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ts *
10
0
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