Content distribution and data retrieval in vehicular networks

Content distribution and data retrieval in vehicular networks Broadnets 2005 Boston, Oct 2005 Mario Gerla Computer Science Dept UCLA

Outline • • • Opportunistic ad hoc networks Car to Car communications Car Torrent Ad Torrent Network games Cars as mobile sensor platforms

What is an opportunistic ad hoc net? • wireless ad hoc extension of the wired/wireless infrastructure • coexists with/bypasses the infrastructure • generally low cost and small scale • Examples – Indoor W-LAN extended coverage – Group of friends networked with Bluetooth to share an expensive resource (eg, 3 G connection) – Peer to peer networking in the urban vehicle grid

Traditional ad hoc nets – Civilian emergency, tactical applications – Typically, large scale – Instant deployment – Infrastructure absent (so, must recreate it) – Very specialized mission/function (eg, UAV scouting behind enemy lines) – Critical: scalability, survivability, Qo. S, jam protection – Not critical: Cost, Standards, Privacy

Opportunistic ad hoc nets – – – Commercial, “commodity” applications Mostly, small scale Cost is a major issue (eg, ad hoc vs 2. 5 G) Connection to Internet often available Need not recreate “infrastructure”, rather “bypass it” whenever it is convenient – Critical: Standards are critical to cut costs and to assure interoperability – Critical: Privacy, security is critical

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

Opportunistic piggy rides in the urban mesh Pedestrian transmits a large file in blocks to passing cars, busses The carriers deliver the blocks to the hot spot

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.

DSRC*/IEEE 802. 11 p : Enabler of Novel Applications • • Car-Car communications at 5. 9 Ghz Derived from 802. 11 a • three types of channels: Vehicle-Vehicle service , a Vehicle-Gateway service a and control broadcast channel. • Ad hoc mode; and infrastructure mode • 802. 11 p: IEEE Task Group that intends to standardize DSRC for Car-Car communications * DSRC: Dedicated Short Range Communications

Hot Spot Vehicular Grid as Opportunistic Ad Hoc Net

Power Blackout Hot Spot Vehicular Grid as Emergency Net

Power Blackout Vehicular Grid as Emergency Net

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 Video preview on the web (10 MB)

Highway Infostation download Internet file

Incentive for “ad hoc networking” Problems: Stopping at gas station to 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 of File from Gateway

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

Experimental Evaluation

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

Peer Selection Strategies Possible selections: • 1) Rarest First: Bit. Torrent-like policy of searching for the rarest bitfield in your peerlist and downloading it • 2) Closest Rarest: download closest missing piece (break ties on rarity) • 3) Rarer vs Closer: weighs the rare pieces based on the distance to the closest peer who has that piece.

Impact of Selection Strategy

Why is the Car-Torrent solution attractive? • Bandwidth at the infostation is limited and “not convenient” – It can become congested if all vehicles stop – It is a nuisance as I must stop and waste time • GPRS and 3 G bandwidth is also limited and expensive • The car to car bandwidth on the freeway is huge and practically unlimited! • Car to car radios already paid for by safe navigation requirement • Car. Torrent transmissions are reliable - they involve only few hops (proximity routing)

Ad. Torrent: Digital Bill. Boards for Vehicular Networks V 2 V COM Workshop Mobiquitous 2005 Alok Nandan, Shirshanka Das Biao Zhou, Giovanni Pau, Mario Gerla

Digital Billboard Safer : Physical billboards can be distracting for drivers Aesthetic : The skyline is not marred by unsightly boards. Efficient : With the presence of a good application on the client (vehicle) side, users will see the Ad only if they actively search for it or are interested in it. Localized : The physical wireless medium automatically induces locality characteristics into the advertisements.

Digital Billboard • Every Access Point (AP) disseminates Ads that are relevant to the proximity of the AP • from simple text-based Ads to trailers of nearby movies, virtual tours of hotels etc • business owners in the vicinity subscribe to this digital billboard service for a fee. • Need a location-aware distributed application to search, rank and deliver content to the end-user (the vehicle)

Ad. Torrent Features • Keyword Set Indexing to reduce Communication Overhead • Epidemic Scoped Query Data Dissemination – optimized for vehicular ad hoc setting • Broadcast medium leveraged for “communication efficiency” of gossip messaging • Torrent Ranking Algorithm • Swarming in actual content delivery • Discourage Selfishness

Hit Rate vs. Hop Count with LRU

Car to car on-line games Claudio Palazzi (UCLA)

Massive Multiplayer Online Games • Exploding market: – Tot Games industry revenues: $40 billion in 2003 – MMOG revenues: $1 billion in 2003, expected $10 billion in Tot MMOG Subscribers MMOG Revenue by Region 2003 8 millions Jan-98 Jan-05

New challenges in car to car on-line games • • • Frequent changes in routing; handoffs Highly variable latency Highly variable bandwidth Intermittent connectivity Packet loss

Proposed Approach: Mirrored Game Server Architecture + Car-networking Scenario

Vehicular Sensor Network (VSN) Uichin Lee, Eugenio Magistretti (UCLA) • Applications – Monitoring road conditions for Navigation Safety or Traffic control – Imaging for accident or crime site investigation Infostation 1. Fixed Infrastructure 2. Processing and storage Car to Infostation 1. On-board “black box” 2. Processing and storage Car-Car multi-hop

VSN Scenario: storage and retrieval • • Private Cars: – Continuously collect images on the street (store data locally) – Process the data and detect an event – Classify the event as Meta-data (Type, Option, Location, Vehicle ID) – Post it on distributed index Police retrieve data from distributed storage Meta-data : Img, Crash, (10, 5), VID 12 Meta-data : Img, -. (10, 10), VID 10 CRASH

Distributed Index options • Info station based index • “Epidemic diffusion” index – Mobile nodes periodically broadcast meta-data of events to their neighbors (via epidemic diffusion) – A mobile agent (the police) queries nodes and harvests events – Data may be dropped when temporally stale and geographically irrelevant

Epidemic: diffusion

VSN: Mobility-Assist Data Harvesting * Relay its Event to Neighbors * Listen and store other’s relayed events

VSN: Mobility-Assist Data Harvesting Data Rep Data Req 1. Agent (Police) harvests situation specific data from its neighbors 2. Nodes return the relevant data they have collected so far

VSN: Mobility-Assist Data Harvesting (cont) • Assumption – N disseminating nodes; each node ni advertises event ei • “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 Δt) • Metrics – Average event “percolation” delay – Average delay until all relevant data is harvested

VSN: Simulation • – – – Simulation Setup Implemented using NS-2 802. 11 a: 11 Mbps, 250 m transmission range Average speed: 10 m/s Network: 2400 m x 2400 m Mobility Models • Random waypoint (RWP) • Road-track model (RT) : Group mobility model with merge and split at intersections – Westwood map is used for a realistic simulation

Road Track Mobility Model

Event diffusion delay: Random Way Point Fraction of Infected Nodes K=2, m=10 K=1, m=10 K=2, m=1 K=1, m=1 1. ‘k’-hop relaying 2. m event sources

Fraction of Infected Nodes Event diffusion delay: Route Tracks 1. ‘k’-hop relaying 2. m event sources

Data harvesting delay with RWP Agent Regular Nodes

Data harvesting results with RT AGENT REGULAR

Vehicular Grid Research Opportunities • Lots of research done on ad hoc nets • Most of it addressed large scale, tactical, civilian emergency problems • New, research (beyond tactical) is critical for “opportunistic” deployment: – Security, privacy – Reward Third Party forwarding; prevent “cheating” – Realistic mobility models (waypoint mobility not enough!) – Delay tolerant networking – P 2 P protocols; proximity routing - epidemic dissemination

The End Thank You