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

Outline New vehicle roles in urban environments Opportunistic “Ad Hoc” Wireless Networks V2V applications –Car Torrent –MobEyes Network layer optimization –Network Coding Conclusions

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

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

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

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

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

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)

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

Car to Car communications for Safe Driving Vehicle type: Cadillac XLR Curb weight: 3,547 lbs Speed: 65 mph Acceleration: - 5m/sec^2 Coefficient of friction:.65 Driver Attention: Yes Etc. Vehicle type: Cadillac XLR Curb weight: 3,547 lbs Speed: 45 mph Acceleration: - 20m/sec^2 Coefficient of friction:.65 Driver Attention: No Etc. Vehicle type: Cadillac XLR Curb weight: 3,547 lbs Speed: 75 mph Acceleration: + 20m/sec^2 Coefficient of friction:.65 Driver Attention: Yes Etc. Vehicle type: Cadillac XLR Curb weight: 3,547 lbs Speed: 75 mph Acceleration: + 10m/sec^2 Coefficient of friction:.65 Driver Attention: Yes Etc. Alert Status: None Alert Status: Passing Vehicle on left Alert Status: Inattentive Driver on Right Alert Status: None Alert Status: Slowing vehicle ahead Alert Status: Passing vehicle on left

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

DSRC Channel Characteristics

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

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

One option: Highway Infostation download Internet file

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

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

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

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

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

Selection Strategy Critical

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!

“Random Linear” Network Coding Receiver recovers original by matrix inversion random mixing buffer Intermediate nodes e = [e 1 e 2 e 3 e 4 ] encoding vector tells how packet was mixed (e.g. coded packet p = ∑e i x i where x i is original packet)

CodeTorrent: Basic Idea Internet Downloading Coded Blocks from AP Outside Range of AP Buffer Re-Encoding: Random Linear Comb. of Encoded Blocks in the Buffer Exchange Re-Encoded Blocks Meeting Other Vehicles with Coded Blocks Single-hop pulling (instead of CarTorrent multihop) “coded” block B1 File: k blocks B2 B3 BkBk + *a 1 *a 2 *a 3 *a k Random Linear Combination

Simulation Results Histogram of Number of completions per slot (Slot = 20sec) 200 nodes 40% popularity Time (seconds)

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

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

Accident Scenario: storage and retrieval Designated Cars: –Continuously collect images on the street (store data locally) –Process the data and detect an event –Classify event as Meta-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

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

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 Keep “relaying” its meta-data to neighbors

Epidemic Diffusion - Idea: Mobility-Assist Meta-Data Harvesting Meta-Data Req 1.Agent (Police) harvests Meta-Data from its neighbors 2.Nodes return all the meta-data they have collected so far Meta-Data Rep

VSN: Mobility-Assist Meta-Data Harvesting (cont) Model Assumption –N disseminating nodes; each node n i advertises event e i “k”-hop relaying (relay an event to “k”-hop neighbors) –v: average speed, R: communication range –ρ : network density of disseminating nodes –Discrete time analysis (time step Δ t) Metrics –Average event “percolation” delay –Average delay until all relevant data is harvested

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 Westwood map

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

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

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

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

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.

Related Car to Car Projects UMassDiesel (UMass) –A Bus-based Disruption Tolerant Network (DTN) – VEDAS (UMBC) –A Mobile and Distributed Data Stream Mining System for Real-Time Vehicle Monitoring and diagnostics – CarTel (MIT) –Vehicular Sensor Network for traffic conditions and car performance – RecognizingCars (UCSD) –License Plate, Make, and Model Recognition –Video based car surveillance –

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

Future Work Extending the P2P sharing concepts to pedestrians Health Networking Security, privacy in vehicular content sharing Network Coding –Implement CodeCast congestion control and ETE recovery above UDP If loss used as feedback, key problem is discrimination between random error and congestion –Network Coding solutions for intermittent connectivity –Models that include mobility Vehicular tesbed experiments

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 J.-S. Park, D. Lun, Y. Yi, M. Gerla, M. Medard. CodeCast: A Network Coding based Ad hoc Multicast Protocol. IEEE Wireless Communications, Oct J.-S. Park, D. Lun, M. Gerla, M. Medard. Performance Evaluation of Network Coding in multicast MANET. Proc. IEEE MILCOM 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.

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

The End Thank You