Transcript QoS Guarantee in Wirless Network

New trends in pervasive computing:
vehicular sensor platforms
ISWPC 2006
Phuket, 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
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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 to
Wired network
Via 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 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: 65 mph
Acceleration: - 5m/sec^2
Coefficient of friction: .65
Driver Attention: Yes
Etc.
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 XLR
Curb weight: 3,547 lbs
Speed: 75 mph
Acceleration: + 10m/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: - 20m/sec^2
Coefficient of friction: .65
Driver Attention: No
Etc.
DSRC*/IEEE 802.11p : Enabler of Novel
Applications
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Car-Car communications at
5.9Ghz
Derived from 802.11a
Event data recorder (EDR)
Forward radar
• three types of channels:
Vehicle-Vehicle service, a
Vehicle-Gateway service and a
control broadcast channel .
• Ad hoc mode; and
infrastructure mode
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802.11p: IEEE Task Group that
intends to standardize DSRC for
Car-Car communications
* DSRC: Dedicated Short Range
Communications
Positioning system
Communication
facility
Rear radar
Display
Computing platform
Hot Spot
Hot Spot
Vehicular Grid as Opportunistic Ad Hoc Net
STOP
Power
Blackout
Hot Spot
Hot Spot
Vehicular Grid as Emergency Net
STOP
Power
Blackout
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 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
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
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 Workshop
Mobiquitous 2005
WONS 2006
Alok Nandan, Shirshanka Das
Biao 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
1. Fixed Infrastructure
2. Processing and storage
Car to Infostation
1. On-board “black box”
2. Processing and storage
Car-Car multi-hop
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
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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 intervehicle 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
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 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
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
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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
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Simulation Setup
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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
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: Road Track
1. ‘k’-hop relaying
2. m event sources
Data harvesting delay with RWP
Agent
Regular Nodes
Harvesting results with Road Tracks
AGENT
REGULAR
Conclusions
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Introduced several car to car applications:
– Dynamic content sharing/delivery:
• Car Torrent (Pull model)
• Ad Torrent (Push+ Pull model)
– Pervasive, mobile sensing (push model)
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New Research Challenges:
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“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