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

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

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

4. 4 a+b ba ba aabbaaaa aa Network Coding : wireless net  Wu et al. (2003); Wu, Chou, Kung (2004)  Lun, Médard, Ho, Koetter (2004) optimal routing energy per bit = 5 network coding energy per bit = 4.5 a aa,b aab b,a Store-mix-forward

5. 5 Random Network Coding xyz Random combination buffer Sender Destination A α x + β y + γ z Every packet p carries e = [e 1 e 2 e 3 ] encoding vector prefix indicating how it is constructed (e.g., coded packet p = ∑e i x i where x i is original packet) Intermediate nodes randomly mix incoming packets to generate outgoing packets

6. 6 Problem Statement  Multicast streaming in mobile wireless networks is non-trivial Streaming requires: high reliability (but not 100%), low delay (but not 0) But network is: unreliable, bandwidth- limited  Major concern: packet drops Lossy wireless channel (uncorrelated, random like errors) Route breakage due to mobility, congestion, etc (correlated errors)

7. 7 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

8. 8 Conventional vs NC Multicast  Conventional Approaches Time diversity => O/H, delay?  Recovery scheme a la ARQ (Reliable Multicast)  (End-to-end) Coding (FEC, MDC, …) Multipath diversity (ODMRP, …) => O/H?  NC Approach Main ingredient: Random network coding (by M édard et al., Chou et al.) Exploit every(?) diversity available Controlled-loss (near 100%), bounded-delay (hundreds of ms) Suitable for buffered streaming Real time version (tens of ms delay bound) possible using progressive decoding

9. 9 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.

10. 10 NC vs Conventional M-cast comparison  Conventional Multicast: ODMRP Mesh “fabric”; Redundant paths Robust to motion and to errors

11. 11 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

12. 12 Simulation experiments  Settings QualNet 100 nodes on 1500 x 1500 m 2 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

13. 13 ODMRP vs NC: Reliability Good Packet Ratio

14. 14 ODMRP vs NC: Efficiency

15. 15 ODMRP vs NC: Delay

16. 16 ODMRP vs. NC: Highway scenario Randomly moving 200 nodes on 10kmx50m field. All nodes are receivers.

17. 17 Conventional Forwarding/Routing SourceReceiver forwarders Select least number of nodes as forwarders to form a path b/w a S-R pair and each forwarder transmits each packet once

18. 18 Problems with Conventional Routing Receiver forwarders What if random error occurs? forwarders What if route breaks?

19. 19 Network Code Forwarding SourceReceiver forwarders Select most nodes in between a S-R pair as forwarders and each forwarder transmits one packet per generation once; each node asks its neighbors for more packets if it fails to get a whole generation A node becomes a forwarder if (hop count to Source + hop count to Receiver) is less than {hop distance of S-R pair + ∆}

20. 20 Robustness of NC approach Robust to random errors Robust to mobility

21. 21 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”:

22. 22 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

23. 23 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

24. 24 maximize f Wireless medium contention constraints Wireless flow conservation constraints Linear Programming Formulation

25. 25 Maximum Multicast Throughput Comparison: NC vs Conventional Receivers Sender CORRIDOR MODEL

26. 26 F A+BA+B E EF DC AB GH FE CD H C+DC+D G AB A B B CD A C D (1)(2) (3) (4)(5)(6) (7)(8) (9) (10)(11)(12) Network Coding: Link schedule achieving throughput of 2/3

27. 27 A A A B B A 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 D C D (7)(7)(8)(8) (9)(9) (10)

28. 28 (1)(2) (3) (4)(5)(6) An “optimal” Single Tree multicast schedule that achieves 1/3 A A A B B B

29. 29 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

30. 30 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

31. 31 Vehicular Sensor Applications  Environment Traffic congestion monitoring Urban pollution monitoring  Civic and Homeland security Forensic accident or crime site investigations Terrorist tracking

32. 32 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

33. 33 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

34. 34 Epidemic Diffusion - Idea: Mobility-Assist Summary Diffusion

35. 35 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

36. 36 Epidemic Diffusion - Idea: Mobility-Assist Summary Harvesting Sum. Req 1.Agent (Police) harvests summaries from its neighbors 2.Nodes return all the summaries they have collected so far Sum. Rep

37. 37 Harvesting Analysis  Metrics Fraction of harvested summaries F(t)  Analysis assumption Discrete time analysis (time step Δ t) N disseminating nodes Each node n i advertises a single summary s i

38. 38 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 ∆ E t = E t - E t-1 : Prob. of meeting a not yet infected node is 1-E t-1 /N 2R s=v Δ t

39. 39 Harvesting Analysis- Agent Node  Agent harvesting summaries from its neighbors (total α nodes)  A regular node has “passively” collected so far E t summaries Probability that agent can collect a specific summary=E t /N  Specific summary collected from α neighbors with probability 1-(1-E t /N)   Let E* t = Expected number of summaries harvested by the agent

40. 40 Harvesting Analysis - Harvesting Fraction  Numerical analysis Area: 2400x2400m 2 Radio range: 250m # nodes: 200 Speed: 10m/s k=1 (one hop relaying) k=2 (two hop relaying)

41. 41 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

42. 42 Simulation  Summary harvesting results with random waypoint mobility

43. 43 Simulation  Summary harvesting results with urban map mobility

44. 44 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