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Communication and Content sharing in the Urban Vehicle Grid Qualnet World Oct 27, 2006 Communication and Content sharing in the Urban Vehicle Grid Qualnet World Oct 27, 2006 Washington, DC Mario Gerla www. cs. ucla. edu/NRL

Outline • New vehicle roles in urban environments • Opportunistic “Ad Hoc” Wireless Networks Outline • New vehicle roles in urban environments • Opportunistic “Ad Hoc” Wireless Networks • V 2 V applications – Car Torrent – Mob. Eyes • Network layer optimization – Network Coding • Conclusions

New Roles for Vehicles on the road • Vehicle as a producer of geo-referenced 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, P 2 P 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 – 2 G (GSM, CDMA), 2. 5 The urban wireless options • Cellular telephony – 2 G (GSM, CDMA), 2. 5 G, 3 G • Wireless LAN (IEEE 802. 11) access – Wi. FI, 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

Wireless Infrastructure vs Ad Hoc Infrastructure Network (Wi. FI or 3 G) Ad Hoc, Wireless Infrastructure vs Ad Hoc Infrastructure Network (Wi. FI or 3 G) Ad Hoc, Multihop wireless Network

Ad Hoc Network Characteristics • Instantly deployable, re-configurable (No fixed infrastructure) • Created to 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 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 New Trend: “Opportunistic” ad hoc nets • Driven by “commercial” application needs – Indoor W-LAN extended coverage – Group of friends sharing 3 G 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 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, Car to Car communications for Safe Driving Vehicle type: Cadillac XLR Curb weight: 3, 547 lbs Speed: 75 mph Acceleration: + 20 m/sec^2 Coefficient of friction: . 65 Driver Attention: Yes Etc. Vehicle type: Cadillac XLR Curb weight: 3, 547 lbs Speed: 65 mph Acceleration: - 5 m/sec^2 Coefficient of friction: . 65 Driver Attention: Yes Etc. Alert Status: None Alert Status: Inattentive Driver on Right Alert Status: Slowing vehicle ahead Alert Status: Passing vehicle on left Vehicle type: Cadillac XLR Curb weight: 3, 547 lbs Speed: 75 mph Acceleration: + 10 m/sec^2 Coefficient of friction: . 65 Driver Attention: Yes Etc. Alert Status: Passing Vehicle on left Vehicle type: Cadillac XLR Curb weight: 3, 547 lbs Speed: 45 mph Acceleration: - 20 m/sec^2 Coefficient of friction: . 65 Driver Attention: No Etc.

The Standard: DSRC / IEEE 802. 11 p • Car-Car communications at 5. 9 The Standard: DSRC / IEEE 802. 11 p • Car-Car communications at 5. 9 Ghz • Derived from 802. 11 a • three types of channels: Vehicle-Vehicle service , a Vehicle-Gateway service and a control broadcast channel. • Ad hoc mode; and infrastructure mode • 802. 11 p: IEEE Task Group for Car-Car communications

DSRC Channel Characteristics DSRC Channel Characteristics

Car. Torrent : Opportunistic Ad Hoc networking to download large multimedia files Alok Nandan, Car. Torrent : 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: You are driving to Vegas You hear of this new show on the radio: Video preview on the web (10 MB)

One option: Highway Infostation download Internet file One option: Highway Infostation download Internet file

Incentive for opportunistic “ad hoc networking” Problems: Stopping at gas station for full download Incentive for opportunistic “ad hoc networking” Problems: Stopping at gas station for full download is a nuisance Downloading from GPRS/3 G too slow and quite expensive Observation: many other drivers are interested in download sharing (like in the Internet) Solution: Co-operative P 2 P Downloading via Car-Torrent

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

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

Bit. Torrent: Internet P 2 P file downloading Uploader/downloader Tracker Uploader/downloader Bit. Torrent: Internet P 2 P file downloading Uploader/downloader Tracker Uploader/downloader

Car. Torrent: Gossip protocol A Gossip message containing Torrent ID, Chunk list and Timestamp Car. Torrent: 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 Selection Strategy Critical

Car. Torrent with Network Coding • Limitations of Car Torrent – Piece selection critical Car. Torrent 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 e = [e 1 e 2 e 3 e 4] “Random Linear” Network Coding e = [e 1 e 2 e 3 e 4] encoding vector tells how packet was mixed (e. g. coded packet p = ∑eixi where xi is original packet) buffer Receiver recovers original by matrix inversion random mixing Intermediate nodes

Code. Torrent: Basic Idea • Single-hop pulling (instead of Car. Torrent multihop) Buffer Internet Code. Torrent: Basic Idea • Single-hop pulling (instead of Car. Torrent multihop) Buffer Internet File: k blocks Buffer B 1 B 2 B 3 *a 1 *a 2 *a 3 *ak + “coded” block Bk Random Linear Combination Buffer Re-Encoding: Random Linear Comb. Outside Range the Buffer of Encoded Blocks inof AP Exchange Re-Encoded Blocks Downloading Coded Blocks from AP Meeting Other Vehicles with Coded Blocks

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

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

Vehicular Sensor Applications • Environment – Traffic congestion monitoring – Urban pollution monitoring • 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 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), V 10

How to retrieve the data? • “Epidemic diffusion” : – Mobile nodes periodically broadcast 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 Epidemic Diffusion - Idea: Mobility-Assist Meta-Data Diffusion

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

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

Simulation Experiment • Simulation Setup – – NS-2 simulator 802. 11: 11 Mbps, 250 Simulation Experiment • Simulation Setup – – NS-2 simulator 802. 11: 11 Mbps, 250 m 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 Number of Harvested Summaries • Higher mobility decreases harvesting Meta-data harvesting delay with RWP Number of Harvested Summaries • Higher mobility decreases harvesting delay Police Private cars Time (seconds)

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

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

Project Goals • Provide: – A platform to support car-to-car experiments in various traffic Project Goals • Provide: – A platform to support car-to-car experiments in various traffic conditions and mobility patterns – Remote access to U-Ve. T 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 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.

Conclusions • Vehicular Communications offer opportunities beyond safe navigation: – Dynamic content sharing/delivery: Car Conclusions • Vehicular Communications offer opportunities beyond safe navigation: – Dynamic content sharing/delivery: Car Torrent – Pervasive, mobile sensing: Mob. Eyes – Massive Network games • Research Challenges: – New routing/transport models: epidemic dissemination, P 2 P, Congestion Control, Network Coding – Searching massive mobile storage – Security, privacy, incentives

Publications Uichin Lee, Eugenio Magistretti, Biao Zhou, Mario Gerla, Paolo Bellavista, Antonio Corradi. Mob. Publications Uichin Lee, Eugenio Magistretti, Biao Zhou, Mario Gerla, Paolo Bellavista, Antonio Corradi. Mob. Eyes: 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. Code. Cast: 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. Code. Torrent: Content Distribution using Network Coding in VANET. Proc. of Mobi. Share, Los Angeles, Sept 2006.

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

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