New trends in pervasive computing: vehicular sensor platforms

ISWPC 2006Phuket, Jan 16 2006

Mario Gerla

Computer Science Dept

UCLA

Pervasive Computing: conventional view

• Ubiquitous, “unconscious” user/sensor interaction

• Sensors/actuators are embedded in the environment

• The user (fixed or mobile) interacts with the sensors - reads them, sets them and computes– Sensors on the airport walls drive traveler to the proper gate

– Wall sensors/actuators turn on lights, air conditioning, appliances..

– Firefighters interact with preinstalled sensors/RFIDs to plan next step during disaster recovery

New “pervasive” paradigm: sensors in motion

• Sensors on mobile platforms (eg, cars, people, robots, UAVs)

• The user must extracts the information from a moving set of “observers”

– Car sensor platforms: a posteriori “event” reconstruction in the vehicular grid - eg, car accident , kidnapping, terrorist attack, etc

– Instrumented soldiers, firefighters etc: gaining situation awareness at the command post

Pervasive Computing and Mobility

• Mobility helps:– Cars can see a lot more “situations” because they are moving– Also, as they move, they provide uniform coverage and opportunistic

deployment– They can propagate, “disseminate” the info in the environment more

efficiently– Moving sensors are more difficult to temper with and disable

• However, mobility also creates new challenges:– Difficult to reconstruct space and time correlation– If I am looking for data collected in a specific street, at a specific time, where

do I find it? – Also connectivity to mobiles may break

• Other Car to Car applications exhibit similar behavior

The themes of this talk

• Introduce and compare various car to car scenarios

• Identify the building blocks for efficient car to car application design

• investigate tradeoffs between opportunities and challenges introduced by mobility

Outline

• Opportunistic ad hoc networking– Cars interact with each other and with environment via ad hoc

wireless networking

– Ad hoc networking among cars is “opportunistic”, much less predictable than in traditional large scale systems such as the battlefield

• Car to car Content sharing/delivery – Car torrent

– Ad torrent

• The car sensor application– Disaster recovery (eg, chemical spill, terrorist attack)

– Accident reconstruction

– Traffic condition reporting

Opportunistic ad hoc networking

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, 3G 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, QoS, jam

protection – Non 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– Proximity routing instead of long range routing– Critical: Standards are critical to cut costs and to

assure interoperability– Critical: Privacy, security is critical

Car to Car ad networking is “opportunistic”

• Most Cars applications can in principle connect to the internet (via WiFi, 3G, satellite etc) and download data entirely from it

• In fact direct Internet connection (if available) should always be considered as an option

• However, cost, delay and wireless channel load are the limiting factors

Urban “opportunistic” ad hoc networking

From Wireless toWired networkVia Multihop

Opportunistic piggy rides in the urban mesh

Pedestrian transmits a large file block by block to passing cars, busses

The carriers deliver the blocks to the hot spot

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: 45 mphAcceleration: - 20m/sec^2Coefficient of friction: .65Driver Attention: NoEtc.

Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 75 mphAcceleration: + 20m/sec^2Coefficient of friction: .65Driver Attention: YesEtc.

Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 75 mphAcceleration: + 10m/sec^2Coefficient of friction: .65Driver Attention: YesEtc.

Alert Status: None

Alert Status: Passing Vehicle on left

Alert Status: Inattentive Driver on Right

Alert Status: None

Alert Status: Slowing vehicle aheadAlert Status: Passing vehicle on left

DSRC*/IEEE 802.11p : Enabler of Novel Applications

• 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 that intends to standardize DSRC for Car-Car communications

* DSRC: Dedicated Short Range Communications

Forward radar

Computing platform

Event data recorder (EDR)

Positioning system

Rear radar

Communication facility

Display

Hot Spot

Hot Spot

Vehicular Grid as Opportunistic Ad Hoc Net

Hot Spot

Hot Spot

PowerBlackout

STOP

PowerBlackout

STOP

Vehicular Grid as Emergency Net

PowerBlackout

STOP

PowerBlackout

STOP

Vehicular Grid as Emergency Net

Car to Car Data Sharing Applications

CarTorrent : Opportunistic Ad Hoc networking to download

large multimedia files

Alok Nandan, Shirshanka Das

Giovanni Pau, Mario Gerla

WONS 2005

You are driving to VegasYou 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

Vehicle-Vehicle Communication

Internet

Exchanging Pieces of File Later

Experimental Evaluation

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

Peer Selection Strategies

Possible selections:

• 1) Rarest First: BitTorrent-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 3G 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• CarTorrent transmissions are reliable - they involve only

few hops (proximity routing)

Question: does mobility help or hurt?

AdTorrent: Digital BillBoards for Vehicular Networks

V2V COM WorkshopMobiquitous 2005

WONS 2006 Alok Nandan, Shirshanka

DasBiao Zhou, Giovanni Pau, Mario Gerla

Digital Billboard

Safer : Physical billboards can be distracting to drivers

Less intrusive : The skyline is not marred by unsightly boards.

Efficient : With a “filter” 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)

AdTorrent Features

• Keyword Set Indexing to reduce Communication Overhead

• Epidemic Scoped Query Data Dissemination – optimized for vehicular ad hoc setting

• Torrent Ranking Algorithm• Swarming in actual content delivery• Discourage Selfishness

AdTorrent : Search and Content Delivery

• How is it different from CarTorrent?– Push model for data dissemination as opposed to

pull only (by queries) as in CarTorrent– Search for relevant content in the vehicular

network• Keyword Set Indexing for communication

efficient search • Search query dissemination optimized for

vehicular networks• Models for evaluating impact of search query

hit w.r.t. Scope of epidemic query dissemination

Query Dissemination and Lookup

• Search local cache for Ads/Torrents• Scoped flood

– Scoping level is a key design parameter

– Tradeoff between reliability and communication overhead

• Bloom Filter of cached index entries exchanged• Finally, when the query dissemination and

response is done, the querying node has a list of index entries that match.

Torrent Ranking Algorithm

• Which torrent/ads to pick if multiple query hits?– Proximity – Maximum number of pieces– “Stability” of neighbors– Relevance of keywords to MetaData

queried

Hit Rate vs. Hop Count with LRU

Vehicular Sensor Network (VSN)PerSens 2006

Uichin Lee, Eugenio Magistretti (UCLA)

Infostation

Car-Car multi-hop

1. Fixed Infrastructure2. Processing and storage

1. On-board “black box” 2. Processing and storage

Car to Infostation

Vehicular Sensor Network (VSN) - Applications

• Systems engineering– Road surface diagnosis/maintenance – Traffic pattern analysis

• Environment– Urban environmental pollution monitoring

• Homeland security– Imaging from streets or recording sounds for accident or crime site

investigation (terrorist alerts)

• Commercial applications– “Mobile” bazaar service (large-scale p2p)

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

Crash!Meta-data : Img, -. (10,10), V10

Meta-data : Img, Crash, (10,5), V12

Searching on Mobile Storage- Building an Index

• Major tasks: Post / Harvest• Naïve approach: “Flooding”

– Not scalable to thousands of nodes (network collapse)– Network can be partitioned (data loss)

• Design considerations– Non-intrusive: must not disrupt other critical services such as inter-

vehicle alerts– Scalable: must be scalable to thousands of nodes– Disruption or delay tolerant: even with network partition, must be

able to post & harvest “meta-data”

• Proposed approach: Distributed index

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 - Idea: Mobility-Assist Data Diffusion

Epidemic Diffusion - Idea: Mobility-Assist 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 Data Harvesting

Meta-Data Req

1. Agent (Police) harvestsMeta-Data from its neighbors

2. Nodes return all the meta-datathey have collected so far

Meta-Data Rep

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– 802.11a: 11Mbps, 250m transmission range– Average speed: 10 m/s– Network: 2400m x 2400m– 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

Simulation

• Simulation Setup– Implemented using NS-2

– 802.11a: 11Mbps, 250m tx range

– Average speed: 10 m/s

– Network: 2400m*2400m

– Mobility Models

• Random waypoint (RWP)

• Real-track model:

– Group mobility model

– merge and split at intersections (RT)

• Westwood map is used for simulations

Event diffusion delay: Random Way Point

1. ‘k’-hop relaying2. m event sources

Fra

cti

on

of

Infe

cte

d

Nod

es K=1,m=1

K=1,m=10

K=2,m=10

K=2,m=1

Event diffusion delay: Road Track

1. ‘k’-hop relaying2. m event sources

Fra

cti

on

of

Infe

cte

d

Nod

es

Data harvesting delay with RWP

Agent

Regular Nodes

Harvesting results with Road Tracks

AGENT

REGULAR

Conclusions• Introduced several car to car applications:

– Dynamic content sharing/delivery:• Car Torrent (Pull model)• Ad Torrent (Push+ Pull model)

– Pervasive, mobile sensing (push model)

• New Research Challenges:– “Opportunistic” ad hoc network paradigm:

• Epidemic dissemination• Proximity routing

– Searching massive mobile storage– Realistic mobility models (waypoint mobility not enough!)– Delay tolerant networking– Security, privacy– Reward Third Party forwarding; prevent “cheating”

• Future Goal: define common framework for C2C applications

Related Projects

• UMassDiesel (UMass)– A Bus-based Disruption Tolerant Network (DTN)– http://signl.cs.umass.edu/diesel

• VEDAS (UMBC) – A Mobile and Distributed Data Stream Mining System for Real-Time Vehicle

Monitoring and diagnostics– http://www.cs.umbc.edu/~hillol/vedas.html

• CarTel (MIT)– Vehicular Sensor Network for traffic conditions and car performance– http://cartel.csail.mit.edu

• RecognizingCars (UCSD)– License Plate, Make, and Model Recognition– Video based car surveillance– http://vision.ucsd.edu/car_rec.html

The End

Thank You