Content sharing in the Vehicle Grid Comp Science Dept Retreat Oct 20, 2006

1. Content sharing in the Vehicle GridComp Science Dept RetreatOct 20, 2006 Mario Gerla www.cs.ucla.edu/NRL In collaboration with Uichin Lee and Dr. JS Park

2. Outline • Opportunistic “Ad Hoc” Wireless Networks • The emerging Vehicular Grid • V2V applications • Car Torrent • MobEyes • Autonomous evacuation • Network layer optimization • Network Coding • Conclusions

3. New Roles for Vehicles on the road • Vehicle as a producer of geo-referenced data about its environment • Pavement condition • Probe data for traffic management • Weather data • Physiological condition of passengers, …. • Vehicle as Information Gateway • Internet access, infotainment, dynamic route guidance, ……

4. Vehicle Roles (cont) • Vehicle & Vehicle, Vehicle & Roadway as collaborators • Cooperative Active Safety • Forward Collision Warning, Blind Spot Warning, Intersection Collision Warning……. • In-Vehicle Advisories • “Ice on bridge”, “Congestion ahead”,…. • All of these roles demand efficient communications

5. The urban wireless options • Cellular telephony • 2G (GSM, CDMA), 2.5G, 3G • Wireless LAN (IEEE 802.11) access • WiFI, Mesh Nets, WIMAX • Ad hoc wireless nets (manly based on 802.11) • Set up in an area with no infrastructure; to respond to a specific, time limited need

6. Wireless Infrastructure vs Ad Hoc Infrastructure Network (WiFI or 3G) Ad Hoc, Multihop wireless Network

7. Ad Hoc Network Characteristics • Instantly deployable, re-configurable (No fixed infrastructure) • Created to satisfy a “temporary” need • Portable (eg sensors), mobile (eg, cars) • Multi-hopping ( to save power, overcome obstacles, etc.)

8. Typical Ad Hoc Network Applications Military • Automated battlefield Civilian • Disaster Recovery (flood, fire, earthquakes etc) • Law enforcement (crowd control) • Homeland defense • Search and rescue in remote areas • Environment monitoring (sensors) • Space/planet exploration

9. Ad hoc nets in battle • In 1971 (two years after ARPANET), DARPA starts the Packet Radio Program • DARPA, Army and Navy support ad hoc net research • Over the years, ad hoc net technology has climbed to high sophistication and to “large scale” • Virtually all funding comes from Defense

10. Portable Radio circa 1973

11. SURVEILLANCE MISSION AIR-TO-AIR MISSION STRIKE MISSION RESUPPLY MISSION FRIENDLY GROUND CONTROL (MOBILE) SATELLITE COMMS SURVEILLANCE MISSION UAV-UAV NETWORK COMM/TASKING COMM/TASKING Unmanned UAV-UGV NETWORK Control Platform COMM/TASKING Manned Control Platform Typical Ad Hoc Network

12. Traditional ad hoc net characteristics • Tactical battlefield: • No infrastructure • Instant deployment • Specialized missions (eg, UAV scouting) • Civilian emergency: • infrastructure, if present, was destroyed • Critical: scalability, survivability, QoS, jam protection • Non critical: Cost, Standards, Privacy • These architectures are not suitable for “every day” urban vehicular communications • Enter: “Opportunistic” Ad Hoc Networks

13. New Trend: “Opportunistic” ad hoc nets • Driven by “commercial” application needs • Indoor W-LAN extended coverage • Group of friends sharing 3G via Bluetooth • Peer 2 peer networking in the vehicle grid • Cost is a major issue • Access to Internet: • available, but; • “bypass it” with “ad hoc” if too costly or inadequate • Critical: Standards -> cost reduction and interoperability • Critical: Privacy, security

14. Car to Car communications for Safe Driving Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 65 mphAcceleration: - 5m/sec^2Coefficient of friction: .65Driver Attention: YesEtc. Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 75 mphAcceleration: + 20m/sec^2Coefficient of friction: .65Driver Attention: YesEtc. Alert Status: None Alert Status: None Alert Status: Inattentive Driver on Right Alert Status: Slowing vehicle ahead Alert Status: Passing vehicle on left Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 45 mphAcceleration: - 20m/sec^2Coefficient of friction: .65Driver Attention: NoEtc. Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 75 mphAcceleration: + 10m/sec^2Coefficient of friction: .65Driver Attention: YesEtc. Alert Status: Passing Vehicle on left

15. The emerging vehicle communicationsstandards

16. Convergence to a Standard: • Fed Communications Commission creates DSRC • The record in this proceeding overwhelmingly supports the allocation of spectrum for DSRC based ITS applications to increase traveler safety, reduce fuel consumption and pollution, and continue to advance the nations economy. • FCC Report and Order, October 22, 1999, FCC 99-305 • Amendment with licensing rules in December 2003 • IEEE creates IEEE 802.11p • http://grouper.ieee.org/groups/scc32/dsrc/ • Automotive companies create Vehicle Safety Communications Consortium (VSCC) • Final Report Submitted January 2005 • USDOT/CAMP creates Cooperative Intersection Collision Avoidance (CICAS) Consortium • http://www.its.dot.gov/cicas/cicas_workshop.htm

17. The Standard: DSRC / IEEE 802.11p • Car-Car communications at 5.9Ghz • Derived from 802.11a • three types of channels: Vehicle-Vehicle service, a Vehicle-Gateway service and a control broadcast channel . • Ad hoc mode; and infrastructure mode • 802.11p: IEEE Task Group for Car-Car communications

18. DSRC Channel Characteristics

19. CarTorrent : Opportunistic Ad Hoc networking to download large multimedia files Alok Nandan, Shirshanka Das Giovanni Pau, Mario Gerla WONS 2005

20. You are driving to VegasYou hear of this new show on the radioVideo preview on the web (10MB)

21. One option: Highway Infostation download Internet file

22. Incentive for opportunistic “ad hoc networking” Problems: Stopping at gas station for full download is a nuisance Downloading from GPRS/3G too slow and quite expensive Observation: many other drivers are interested in download sharing (like in the Internet) Solution: Co-operative P2P Downloading via Car-Torrent

23. CarTorrent: Basic Idea Internet Download a piece Outside Range of Gateway Transferring Piece of File from Gateway

24. Co-operative Download: Car Torrent Internet Vehicle-Vehicle Communication Exchanging Pieces of File Later

25. BitTorrent: Internet P2P file downloading Uploader/downloader Uploader/downloader Uploader/downloader Tracker Uploader/downloader Uploader/downloader

26. CarTorrent: Gossip protocol A Gossip message containing Torrent ID, Chunk list and Timestamp is “propagated” by each peer Problem: how to select the peer for downloading

27. Selection Strategy Critical

28. CarTorrent with Network Coding • Limitations of Car Torrent • Piece selection critical • Frequent failures due to loss, path breaks • New Approach –network coding • “Mix and encode” the packet contents at intermediate nodes • Random mixing (with arbitrary weights) will do the job!

29. Network Coding - Background Traditional multicast: store and forward Destination Source Destination

30. Network Coding - Background Network Coding:store-”encode”-forward

31. “Random Linear” Network Coding e = [e1e2e3 e4] encoding vector tells how packet was mixed (e.g. coded packet p = ∑eixiwhere xiis original packet) buffer Receiver recovers original by matrix inversion random mixing Intermediate nodes

32. Single-hop pulling (instead of CarTorrent multihop) Buffer Buffer Buffer B1 *a1 B2 *a2 *a3 File: k blocks B3 + “coded” block *ak Bk Random Linear Combination CodeTorrent: Basic Idea Internet Re-Encoding: Random Linear Comb.of Encoded Blocks in the Buffer Outside Range of AP Exchange Re-Encoded Blocks Downloading Coded Blocks from AP Meeting Other Vehicles with Coded Blocks

33. Simulation Results • Avg. number of completion distribution 200 nodes40% popularity Time (seconds)

34. Simulation Results • Impact of mobility • Speed helps disseminate from AP’s and C2C • Speed hurts multihop routing (CarT) • Car density+multihop promotes congestion (CarT) Avg. Download Time (s) 40% popularity

35. Vehicular Sensor Network (VSN)IEEE Wireless Communications 2006Uichin Lee, Eugenio Magistretti (UCLA)

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

37. Accident Scenario: storage and retrieval • Designated Cars: • Continuously collect images on the street (store data locally) • Process the data and detectan event • Classify the event asMeta-data (Type, Option, Location, Vehicle ID) • Post it on distributed index • Police retrieve data from designated cars Meta-data : Img, -. (10,10), V10

38. How to retrieve the data? • “Epidemic diffusion” : • Mobile nodes periodically broadcast meta-data of events to their neighbors • A mobileagent(the police) queries nodes and harvests events • Data dropped when stale and/or geographically irrelevant

39. Epidemic Diffusion- Idea: Mobility-Assist Meta-Data Diffusion

40. Keep “relaying” its meta-data to neighbors Epidemic Diffusion- Idea: Mobility-Assist Meta-Data Diffusion 1) “periodically” Relay (Broadcast) its Event to Neighbors 2) Listen and store other’s relayed events into one’s storage

41. Meta-Data Rep Meta-Data Req Epidemic Diffusion- Idea: Mobility-Assist Meta-Data Harvesting • Agent (Police) harvestsMeta-Data from its neighbors • Nodes return all the meta-datathey have collected so far

42. Simulation Experiment • Simulation Setup • NS-2 simulator • 802.11: 11Mbps, 250m tx range • Average speed: 10 m/s • Mobility Models • Random waypoint (RWP) • Real-track model (RT) : • Group mobility model • merge and split at intersections • Westwoodmap

43. Higher mobility decreases harvesting delay Number of Harvested Summaries Time (seconds) Meta-data harvesting delay with RWP

44. Number of Harvested Summaries Time (seconds) Harvesting Results with “Real Track” • Restricted mobility results in larger delay

45. Protecting vehicles against road perils

46. Evacuation from a Tunnel after a Fire: Emergency Video Streaming • Multimedia type message propagation helps road safety • Precise situation awareness via video • Drivers can make better informed decisions Real-time Video Streaming Fire inside the Tunnel Source: http://www.landroverclub.net/Club/HTML/MontBlanc.htm

47. Emergency Video Streaming • Problems • Potential volume of multimedia traffic • Unreliable wireless channel • Multimedia data delivery service must be reliable and efficient at the same time • Our Approach: Random network coding

48. Emergency Video Streaming • Highway Data Mule: Data is store-carry-and-forwarded via platoons in opposite direction • Random network coding for delayed data delivery

49. Simulation Results (Delivery Ratio)

50. U-VeTUcla - Vehicular Testbed E. Giordano, A. Ghosh, G. Marfia, S. Ho, J.S. Park, PhD System Design: Giovanni Pau, PhD Advisor: Mario Gerla, PhD