Robust Wireless Multicast using Network Coding

1. Robust Wireless Multicast using Network Coding Dawn Project Review, UCSC Sept 12, 06 Mario Gerla Computer Science Dept, UCLA gerla@cs.ucla.edu; www.cs.ucla.edu/NRL

2. Background – Network Coding Traditional multicast: store and forward

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

4. a+b a+b b a a a a a a b a a b a b a Network Coding : wireless net • Wu et al. (2003); Wu, Chou, Kung (2004) • Lun, Médard, Ho, Koetter (2004) Store-mix-forward a a,b a a a a,b b,a b optimal routingenergy per bit = 5 network codingenergy per bit = 4.5

5. Random Network Coding Sender Every packet p carries e = [e1e2e3] encoding vector prefix indicating how it is constructed (e.g., coded packet p = ∑eixiwhere xiis original packet) x y z A αx + βy + γz buffer Random combination Intermediate nodes randomly mix incoming packets to generate outgoing packets Destination

6. 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

7. Network Coding in static wireless nets • For costefficiency • 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.

8. NC vs Conventional M-cast comparison • Conventional Multicast: ODMRP • Mesh “fabric”; Redundant paths • Robust to motion and to errors

9. 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

10. 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

11. ODMRP vs NC: Reliability Good Packet Ratio

12. ODMRP vs NC: Efficiency

13. ODMRP vs NC: Delay

14. ODMRP vs. NC: Highway scenario Randomly moving 200 nodes on 10kmx50m field. All nodes are receivers.

15. Robustness of NC approach Robust to mobility Robust to random errors

16. 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”:

17. 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

18. 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

19. Linear Programming Formulation maximize f Wireless medium contention constraints Wireless flow conservation constraints

20. Maximum Multicast Throughput Comparison: NC vs Conventional CORRIDOR MODEL Sender Receivers

21. Network Coding: Link schedule achieving throughput of 2/3 A B C D C B D A B A (3) (1) (2) (4) (5) (6) E F G H G H E F F E D C A B C D A+B C+D (9) (7) (8) (10) (11) (12)

22. Multicast with multiple embedded trees (no NC): Link schedule achieves 2/5 throughput B A A B A B A (3) (1) (2) (4) (5) D C C D C D C B (9) (7) (8) (10) (6)

23. An “optimal” Single Tree multicast schedule that achieves 1/3 A B A B A B (3) (1) (2) (4) (5) (6)

24. 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

25. Car-Car or Car-Infostation communications using DSRC DSRC: Dedicated Short Range Communication 802.11p IEEE Task group and derived from 802.11a Vehicular Sensor Networks - Epidemic Dissemination Models

26. Vehicular Sensor Applications • Environment • Traffic congestion monitoring • Urban pollution monitoring • Civic and Homeland security • Forensic accident or crime site investigations • Terrorist tracking

27. 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

28. 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

29. Epidemic Diffusion - Idea: Mobility-Assist Summary Diffusion

30. Keep “relaying” its summary to its neighbors 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

31. Sum. Rep Sum. Req Epidemic Diffusion - Idea: Mobility-Assist Summary Harvesting • Agent (Police) harvestssummaries from its neighbors • Nodes return all the summariesthey have collected so far

32. Harvesting Analysis • Metrics • Fraction of harvested summaries F(t) • Analysis assumption • Discrete time analysis (time step Δt) • N disseminating nodes • Each node ni advertises a single summary si

33. s=vΔt 2R 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

34. 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

35. Harvesting Analysis - Harvesting Fraction • Numerical analysis Area: 2400x2400m2Radio range: 250m # nodes: 200Speed: 10m/sk=1 (one hop relaying)k=2 (two hop relaying)

36. 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

37. Summary harvesting results with random waypoint mobility Simulation

38. Summary harvesting results with urban map mobility Simulation

39. 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