1. 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 First key insight. Add more text as necessary. Second key insight. Add more text as necessary. Add other points as necessary A sentence why it is important/useful MAIN ACHIEVEMENT: Placeholder explanatory text. Replace with text and diagrams as necessary. 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 Additional as necessary Primary answer here. Add more text as necessary. 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) QUANTITATIVE IMPACT END-OF-PHASE GOAL [ PROJECT-NAME ] ACHIEVEMENT STATUS QUO NEW INSIGHTS

2. 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 0 20% 40% 60% 80% 100% 5 20 Probability of Loss Network Coding NORM Multicast ARQ Unicast ARQ Goodput Bandwidth Savings Ratio 5 10 15 Network Coding NORM Multicast ARQ Unicast ARQ GOODPUT Bandwidth savings ratio (BSR) Phase 2 BSR Target Phase 1 BSR Target QUANTITATIVE IMPACT END-OF-PHASE GOAL CONCERTO ACHIEVEMENT STATUS QUO NEW INSIGHTS

3. 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. QUANTITATIVE IMPACT END-OF-PHASE GOAL CONCERTO ACHIEVEMENT STATUS QUO NEW INSIGHTS

4. 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 w i of each node P(access)  1 for most urgent nodes P(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 QUANTITATIVE IMPACT END-OF-PHASE GOAL MARCONI ACHIEVEMENT STATUS QUO NEW INSIGHTS +  t0+Δtt0+Δtt 0 +2Δt … t0t0   t0t0 t State of the art (802.11) on small packets (e.g. VoIP Current RC-MAC Ideal TDMA 2-user shared medium % user 1 access time % user 2 access time Most “urgent”: P(access) ≈ 1 t Least “urgent”: P(access) ≈ 0 Urgency = w i = -backpressure Most urgent flow: +2 Least urgent flow: -2

5. 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 QUANTITATIVE IMPACT END-OF-PHASE GOAL MARCONI ACHIEVEMENT STATUS QUO NEW INSIGHTS  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 Forwarding node selects less-congested path to destination For inelastic flows, forwarding node checks for delay & rate feasibility