Communication and Content sharing in the Urban Vehicle Grid Qualnet World Oct 27, 2006 Washington, DC

1. Communication and Content sharing in the Urban Vehicle GridQualnet WorldOct 27, 2006Washington, DC Mario Gerla www.cs.ucla.edu/NRL

2. Outline • New vehicle roles in urban environments • Opportunistic “Ad Hoc” Wireless Networks • V2V applications • Car Torrent • MobEyes • 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 • Weather data • Physiological condition of passengers, …. • Vehicle as Information Gateway • Internet access, infotainment, P2P content sharing, …… • Vehicle collaborates with other Vehicles and with Roadway • Forward Collision Warning, Intersection Collision Warning……. • Ice on bridge,… These roles demand efficient communications

4. 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

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

6. Ad Hoc Network Characteristics • Instantly deployable, re-configurable (No fixed infrastructure) • Created to satisfy a “temporary” need • Portable (eg sensors), mobile (eg, cars)

7. Traditional 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

8. 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 • Access to Internet: • available, but; it can be “opportunistically” replaced by the “ad hoc” network (if too costly or inadequate)

9. Urban “opportunistic” ad hoc networking From Wireless to Wired network Via Multihop

10. 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

11. 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

12. DSRC Channel Characteristics

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

14. You are driving to VegasYou hear of this new show on the radio:Video preview on the web (10MB)

15. One option: Highway Infostation download Internet file

16. 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

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

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

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

20. 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

21. Selection Strategy Critical

22. 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!

23. “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

24. 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

25. Simulation Results • Histogram of Number of completions per slot (Slot = 20sec) 200 nodes40% popularity Time (seconds)

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

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

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

29. 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

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

31. 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

32. 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

33. 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

34. Higher mobility decreases harvesting delay Number of Harvested Summaries Time (seconds) Meta-data harvesting delay with RWP Police Private cars

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

36. 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

37. Project Goals • Provide: • A platform to support car-to-car experiments in various traffic conditions and mobility patterns • Remote access to U-VeT through web interface • Extendible to 1000’s of vehicles through WHYNET emulator • potential integration in the GENI infrastructure • Allow: • Collection of mobility traces and network statistics • Experiments on a real vehicular network

38. Big Picture • We plan to install our node equipment in: • 50 Campus operated vehicles (including shuttles and facility management trucks). • Exploit “on a schedule” and “random” campus fleet mobility patterns • 50 Communing Vans • Measure freeway motion patterns (only tracking equipment installed in this fleet). • Hybrid cross campus connectivity using 10 WLAN Access Points .

39. Conclusions • Vehicular Communications offer opportunities beyond safe navigation: • Dynamic content sharing/delivery: Car Torrent • Pervasive, mobile sensing: MobEyes • Massive Network games • Research Challenges: • New routing/transport models: epidemic dissemination, P2P, Congestion Control, Network Coding • Searching massive mobile storage • Security, privacy, incentives

40. Publications Uichin Lee, Eugenio Magistretti, Biao Zhou, Mario Gerla, Paolo Bellavista, Antonio Corradi. MobEyes: Smart Mobs for Urban Monitoring with a Vehicular Sensor Network. IEEE Wireless Communications, Sept 2006. J.-S. Park, D. Lun, Y. Yi, M. Gerla, M. Medard. CodeCast: A Network Coding based Ad hoc Multicast Protocol. IEEE Wireless Communications, Oct 2006. J.-S. Park, D. Lun, M. Gerla, M. Medard. Performance Evaluation of Network Coding in multicast MANET. Proc. IEEE MILCOM 2006. U. Lee, J.-S. Park, J. Yeh, G. Pau, M. Gerla. CodeTorrent: Content Distribution using Network Coding in VANET. Proc. of MobiShare, Los Angeles, Sept 2006.

41. Support This work was supported by: ARMY MURI Project “DAWN” (PI JJ Garcia) 2005-2008; UCLA CoPI: Rajive Bagrodia ARMY Grant under the IBM - TITAN Project (PI, Dinesh Verma, IBM) 2006-2011; UCLA CoPIs: Deborah Estrin, Mani Srivastava NSF NeTS Grant - Emergency Ad Hoc Networking Using Programmable Radios and Intelligent Swarms; 2005-2009; PI: Gerla, UCLA CoPIs - Soatto, Fitz, Pau

42. The End Thank You