CarTel
Mark Mucha
University of Central Florida
EEL 6788
Professor: Dr. Lotzi Bölöni
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What is CarTel?
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A distributed sensor computing system
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Important and emerging category of sensor networks
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Driven by a “technology push”
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Mobile
Involves heterogeneous sensor data
Flood of underlying hardware components
Also driven by “application pull”
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Demand for similar applications
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Reusable data management system for querying and collecting data from
intermittently connected devices.
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Distributed, mobile sensor network, and telematics system.
CarTel Goals
S Provide a simple programming interface
S Easy for application developers, easy to write as web applications
S Handle large amounts of heterogeneous sensor data
S Types of sensors isn’t constrained
S Easy to integrate new sensors
S Provide local buffering and processing on mobile nodes
S Handle intermittent connectivity
S Primary mode of network access for mobile CarTel nodes is
opportunistic wireless [Bluetooth, Wi-Fi, etc.]
What does CarTel do?
S Allows applications to
S Collect Data
S Process Data
S Analyze Data
S Visualize Data
S CarTel uses sensors on automobiles and Smartphones
S Uses wireless networks opportunistically
S Wi-Fi, Bluetooth, cellular
Technology Push
S Ubiquitous cheap, embedded, sensor-equipped computers
and mobile phones
S Phones
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iPhone
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Droid
S Other hardware
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Routers (modifiable, running Linux)
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Netbooks
Why not?
S Over 600 million automobiles worldwide
S A lot of potential for sensor data
S Current generation of cars have 100+ sensors
S Resource-rich
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Can support relatively robust computation and communication systems
S Cars would be natural collectors of the following info
S Traffic Monitoring and route planning
S Preventative maintenance and diagnostics of cars
S Civil Infrastructure monitoring
S Monitoring of driver preferences (radio stations, shopping, etc.)
Mobile Sensors on Vehicles
Examples
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Environmental Monitoring
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Civil Infrastructure Monitoring
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Automotive Diagnostics
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Geo-Imaging
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Data muling
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My Ideas
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Rank a Driver
Law enforcement applications
How is CarTel used?
S Commute and Traffic Portal
S See the data @ icartel.net
S Traffic mitigation
S Using predictive delay models and traffic-aware route planning
algos
S iPhone Application
S Pothole Patrol (P2)
How is CarTel used?
S Fleet testbed
S CarTel deployed on 27 car fleet of Boston area limo company.
S Link
S Wi-Fi Monitoring
S Link
S Monitor urban Wi-Fi connectivity
S 290 driving hours found over 13,000 access points in a year’s
time
How is CarTel used?
S On-board automotive diagnostics & notification
S Uses ODB-II interface (standard, made mandatory for all cars sold in the
US in 1996 [source] )
S Monitor and report
S Emissions
S Gas mileage
S RPM
S Long term view of car performance
S Comparison against other cars
How is CarTel used?
S Cars as Mules
S CafNet (“carry and forward network”)
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Data delivery between nodes that aren’t typically connected
S Deliver data to internet servers from mobile sensors with short-
range radio connectivity on the CarTel node
Reinventing the wheel?
S Static sensors
S Can provide the same data the designers of CarTel have
expressed interest in
S Great for a high traffic area, not so for back roads and most
residential areas
S Hard to get coverage over a large area
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Some sensors are very expensive
S Static might not be an optimal use of the asset
Environmental Monitoring
S Mobile chemical and pollution sensors
S Cover a larger geographical area with fewer sensors
compared to static sensors
S Chemical and pollution sensors are costly, so covering a
larger area with fewer sensors would be preferred
Civil Infrastructure Monitoring
S Monitor state of roads & bridges
S Detect vibration, potholes, and black ice
Automotive Diagnostics
S Obtain information from vehicles onboard sensors
S Aid in making preventative maintenance preventative
S Compare diagnostics
Geo-Imaging
S Cameras attached to cars
S Mobile phone cameras (location tagged video/images)
Data Muling
S Cars (and people) = the mules or “delivery networks” for
remote sensornets
S Data sent to Internet servers
Networking
S CafNet (main component, more later)
S Cabernet
S Fast end-to-end connectivity across set of changing Wi-Fi access
points
S Usable network even with short connection times (a few seconds)
S dpipe
S Delay-tolerant pipe
S Allows producer and consumer to transport data across
intermittent connection
CarTel: 3 main software
components
S AutoPortal
S CafNet
S ICEDB
S 2 common abstractions
S Pipes
S Databases
Block Diagram
source
CarTel Architecture
ICEDB
Server
Portal
Clients
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Open Wireless
Access Point
Internet
Ad-hoc
network
User’s Wireless
Access Point
ICEDB Remote
CarTel: AutoPortal
S AutoPortal
S Server software
S Provides
Data management
S Visualization
S Web-based querying
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S Requests data from remote nodes
S Aggregates reports from nodes to get high level view of
conditions, providing visualization of collected data
CarTel: AutoPortal
CarTel: CafNet
S A networking infrastructure for carry-and-forward networks
S Leverages variable and intermittent network connectivity
S Extends reach of traditional networks by the routing of data
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over a wide array of high latency and unreliable links
Mobility of network medium is a strength, not a weakness
Delay-tolerant stack
Mobile data muling
Data transfer across an intermittent network connection
CarTel: CafNet
App 1
…
App N
Transport Layer
•Registers data to be transmitted
•Delivers incoming data
•Request data from the application
•Notifies application of successful delivery
Network Layer
•Notifies transport layer of free buffers
•Schedules data for transmission
•Selects routes
•Buffers data for transmission
Device Driver
Mule Adaptation Layer
•Provides uniform neighbor discovery
Device Driver
CarTel: ICEDB
S Device-level data management infrastructure
S Collects, pre-processes, and prioritizes information on
remote nodes running CarTel software.
S Schema auto-adjusted based on available sensors in the car.
S Stream-processing engine responsible for data aggregation
and processing queries.
S Query selects sensor and rate of data acquisition
CarTel: ICEDB
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Query results are streamed across intermittent connection
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Local prioritization (FIFO, random, threshold, bisect prioritization schemes)
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Summarization queries (global prioritization)
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Built on Postgresql
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Adds continuous queries
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Rate n
Every n
More Info
CarTel: ICEDB
S Example: Continuous query
S SELECT carid, traceid, time, location FROM
gps
WHERE gps.time BETWEEN now()-1 mins and
now() RATE 5 mins
CarTel: ICEDB
S Example: Local Prioritization
S With limited connection times, data must be prioritized locally
S Two added statements: PRIORITY and DELIVERY ORDER
S SELECT carid, traceid, time, location FROM gps
WHERE gps.time BETWEEN now()-1 mins and now()
PRIORITY 2
CarTel: ICEDB
S Example: Global Prioritization
S With limited connection times, data must also be prioritized globally
S Added statement: SUMMARIZE AS
S SELECT …
EVERY …
BUFFER in bufname
SUMMARIZE AS
SELECT f1,f2,…,fn FROM bufname
WHERE pred
GROUP BY f1,f2,…,fn
CarTel: Pothole Patrol
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P2 (Pothole Patrol)
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CarTel + Machine Learning to auto classify road surface conditions
CarTel node with 3-axis acceleration and GPS sensors
Gathers location tagged vibration data @ 400 Hz
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Deployed on 10 taxis in the Boston area
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Analysis algorithms calibrated with human perception of road surface quality
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Able to predict 75% of bad surface conditions as reported by drivers
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One week of driving
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4,800 bad surface locations
CatTel: Pothole Patrol
Road surface issues detected by Pothole Patrol
CarTel :Pothole Patrol
CarTel: Pothole Patrol
Avoid this bridge
Bad surfaces mapped out
iCarTel (iPhone Application)
S “iCartel is a free 3G or 3GS application that will help you
reduce the time you spend stuck in traffic. iCartel, based on
the MIT CarTel ("Car Telecommunications") research
project, builds on a community approach to delivering
reliable traffic information and helping users plan around
it.”
iCarTel
iCarTel
iCarTel
Questions?
Resources
S CarTel website
S CarTel: A Distributed Mobile Sensor Computing System
S Bret Hull, Vladimir Bychkovsky, Yang Zhang, Kevin Chen,
Michel Goraczko,
S Allen Miu, Eugene Shih, Hari Balakrishnan and Samuel
Madden
S MIT Computer Science and Artificial Intelligence Laboratory