When the Sensors Hit the RoadThe CarTel Project

Sam MaddenMIT CSAIL

http://cartel.csail.mit.edu

With Hari Balakrishnan, Vladimir Bychkovsky, Jakob Eriksson, Bret Hull, Yang Zhang, Kevin Chen, Waseem Daher, Michel Goraczko, Hongyi Hu, Sejoon Lim, Allen Miu, Daniela Rus, Eugene Shih, Arvind Thiagarajan,

Sivan Toledo

Stanford Database Group

1st Generation Sensor Networks

Periodic monitoring– Wake up and sense– Sleep for Minutes

Event-based monitoring– Transmit on external event

Low data rates, duty cyles

Static, small area

Extremely underpowered devices “Mote

”

~=

Participatory Sensing

“Crowd-sourced” sensing– Individuals contribute their data– Rather than relying on fixed deployments

Examples:– Personalized Environmental Impact Report– Microsoft SensorMap– Dartmouth BikeNet– MapMyRun, etc. CarTel CarTel

Cars as a Vehicle for Participatory Sensing

Observation:– Static sensing infeasible

over very wide areas– Some apps do not need

high temporal fidelity

Real-world problems:– Civil infrastructure

monitoring– Road-surface conditions– Visual mapping– Commute optimization– Carpool finding– Speed trap mapping

Opportunistic Mobility

Rather than deploy new mobile nodes, take advantage of existing mobility

Example: cellphones w/ sensors– 1.5 billion phones worldwide– High spatial coverage– High-performance processor

Cars equipped with sensors– 650 million cars on the road– Abundance of power and space– Have >100 embedded sensors

What system architecture is best suited for mobile, wide-area sensing?

Data collection servers

Clients

Internet

OpenWi-Fi

GPRSCollect / Process

Deliver

Visualize

CarTel System Overview

Portal

Cabernet / QuickWifi(Carry & Forward Network)

ICEDB(Intermittently Connected & Embedded DB)

Per-node

sensing &

computation

hardware

RoadmapOverview

CarTel Components– Portal– IceDB– Cabernet

Managing missing and uncertain data

Case Studies– Traffic Analysis– WiFi Mapping– Potholes

Portal

Web-based visualization framework

Apps retrieve sensor data by issuing queries to ICEDB

Visualize sensor data using map overlays

Continuous queries to direct sensing– Pushed to remote nodes

using Cabernet

Local ad-hoc queries read streaming results

Visualize / Analyze

Web server

Portal Applications

Traffic Wi-Fi

ICEDB server

CQ

Rel.DB

Cabernet

PortalData Viz.

Streamingsensor data Cont. queries

Visualize / Analyze

DataCollectionDemo

ICEDB: Intermittently Connected Embedded DB

Relational model is a convenient abstraction for data collection

ICEDB:– Queries written in extended SQL– Continuous query processor– Distributed

Bandwidth is variable1. Buffer query and DDL commands2. Buffer query results3. Support “drill down” queries 4. Prioritize results

Collect / Process

SELECT img

FROM camera, gps

WHERE gps.pos in [x,y]

AND camera.time= gps.time

SAMPLE 1s

ICEDB Query Processing

DB

CQ

Ad-hocQuery

Processor

OutputBuffers

Cabern

et

ADAPTERS

sensor

sensor

sensor

Collect / Process

Remote Node

2 data paths to cope with limited BW

ICEDB Server ICEDB Remoteresults

queries

Inter-query Prioritization

Remote node

BISECTPortal SELECT lat, lon

FROM gpsWHERE insert_time

> cqtime – 5EVERY 5 secondsBUFFER IN gpsbufDELIVERY ORDER bisect(gpsbuf)

Portal Portal

Remote node

FIFO

Portal Portal Portal

t=1 t=2 t=3

SELECT lat, lonFROM gpsWHERE insert_time

> cqtime – 5EVERY 5 secondsBUFFER IN gpsbufDELIVERY ORDER fifo(gpsbuf)

Collect / Process

Global Prioritization

Collect / Process

toICEDBServer

toICEDBServer

SELECT lat, lon, image FROM cameraWHERE insert_time > cqtime – 5EVERY 5 secondsBUFFER IN cambufSUMMARIZE AS

SELECT floor(lat/100), floor(lon/100) FROM cambufGROUP BY floor(lat/100), floor(lon/100)

Global Prioritization

Collect / Process

ICEDB Server

1

22

2

1

1

Specifying Priorities

Three SQL language extensions:1. Global prioritization:

SUMMARIZE AS

2. Inter-query prioritization:PRIORITY

3. Intra-query prioritization:DELIVERY ORDER BY

For more details: ICDE ’06

Collect / Process

Cabernet

Disconnection-tolerant transport layer

Buffer data untilconnectivity becomes available– Not connection oriented

QuickWifi: Fast connection establishment

CTP: Unlike TCP, wireless losses ≠ congestion losses

Deliver

QuickWifi

Jakob Eriksson et al – Mobicom 08

Challenge: Connection Establishment is Slow

Process– Scan 11 channels (600 ms average), receive beacon– Authenticate (1 round trip, 3s timeout)– Associate (1 round trip, 3s timeout)– DHCP Discovery (1 round trip, 3s timeout)– DHCP Request (1 round trip, 3s timeout)– ARP Request (1 round trip, 2s timeout)

Many messages, loss rates high, retries often needed

Default Linux stack took 13s to associate, on average (whole connection lasts only 19s avg!)

Deliver

Connection Optimizations

Scan more popular channels first (1,6,11)

Since authentication in open networks always succeeds, do it in parallel with association

Set timeouts to 100 ms

Mean conn. time: 370 ms

DHCP via broadcast addr

Roadmap

Overview

CarTel Components– Portal– Cabernet– Cabernet

Managing missing and uncertain data

Case Studies– Traffic Analysis– WiFi Mapping– Potholes

Noisy Data Challenge

Challenge: how to store and query all of this data?

Discrete points don’t work well

Most apps don’t actually want raw data!– Prefer trajectories, fields, fit functions– Idea: support these as first class objects

inside the DBMS

Model-Based Views

Proposed in MauveDB [SIGMOD 06]– Models can be queried like database views – Basic idea:

• compute model• grid modeled area• use model to compute values at each cell of grid• answer queries using grid

FunctionDB: efficient implementation for an important class of models– Continuous functions of one or more

variables

Benefits

Declarative Queries– No need to write procedures to manipulate

models, or re-implement or re-optimize each new query

View provides intuitive SQL-like interface

If data is already in database, do not need to move data to/from math package like MATLAB

FunctionDB: Key IdeaDatabase system that fits continuous functions to dataMauveDB-like query Interface (SQL + Grids)Algebraic query processor

Raw data (temp readings)

Query: Report when temp crosses threshold

SELECT time WHERE temp = thresh

Regression Function temp(t)

Solve equation temp(t) = thresh

temp

time

FunctionDB: System Architecture

Raw Data“Function Table”

(Storing Piecewise Model)X Y

1 1.1

3 3.1

4 3.9… …

Start

End Slope

Intercept

1 8 1 0

8 12 2 -8

User fits or imports

functions

Algebraic Query Processor(Operates on algebraic

representation)

User Query Query Result

Query Results• Grid semantics: all queries yield discrete

points sampled at user-specified interval (“grid size”)

SELECT x,y WHERE temp < 20 GRID x 8, y 88

8

temp < 20

Algebraic Execution: Overview

• FunctionDB supports efficient algebraic execution for piecewise polynomial functions

• Restriction helps achieve grid semantics even when no closed form solutions are available• E.g., Non-linear polynomials, multi-

variate polynomials, complex constraints

X^2 + Y^3 < 25

SELECT * WHERE Temp < 20 GRID X 8, Y 8 Efficient Algebraic Implementation

User

X:[0,20) , Y:[0,20) , Temp = X+Y

X:[0,20) , Y:[0,20) , Temp = X+Y , Temp < 20

Substitute

Approx

X:[0,20) , Y:[0,20) , Temp = X+Y , X+Y-20 < 0

Grid

Symbolic Filter (Temp < 20)

Function Table Temp = F(X,Y)

Gridded Result Hypercubes+Boundary Points

X+Y < 20

X+Y < 20

Hypercube Approximation• Use technique from graphics rasterization

(Taubin’s test)• Tests if polynomial F(X,Y, …) can have zeros

within a given hypercube H (i.e., F lies on boundary of H)

Hypercube H

Z = F(X,Y)

Z = 0Y

X

Zero of F

• Allows efficient pruning by testing corners of hypercube

Subdivision Algorithm

Predicate

Bounding Box-

-

-

+

Check centers of all grid cells smaller than grid size

++ ++

More Algebraic Operators

• Solver for single-variable equations• Function Inference• E.g., X+Y = 20 X = 20-Y• (Eliminates independent variables)

• Continuous aggregates• E.g., Average Integration

Evaluation: Temperature Data

• Data: <Sensor ID, X, Y, time, temp>• 54 sensors, 10 days of data, 1 million raw data

points• Fit regression model temp = F(X,Y,time)• Degree-2 piecewise polynomial with 22 pieces

• Evaluation queries:• Find regions where temp < threshold (Filter)• Area of region where temp < threshold

(Aggregate)

• Compared algebraic approach to two baselines:• Evaluating all grid points• Pruning search using a precomputed B-Tree

Result 1: Benefits Of Algebraic Execution Depend On Selectivity

Result 2: Algebraic Execution Wins Significantly For Aggregate Queries

Wider grid size is faster, but too wide a grid size

results in low accuracy owing to discretization

error

Roadmap

Overview

CarTel Components– Portal– Cabernet– Cabernet

Managing missing and uncertain data

Case Studies– Traffic Analysis– WiFi Mapping– Potholes

Route Planning

Match traces to map

Compute Gaussian delay for each segment– Assume independence

Minimize 3 Objectives– Distance

• Google Maps

– Expected delay– Pr(missing time goal)

Max. Probability Planning

Travel time of each edge is a Gaussian– If indepdendent, travel time of a path is also Gaussian

Goal: find path with max. probability of reaching destination by deadline

Unlike standard shortest paths, no optimal substructure– If AxCyB is best path from A to B, AxC is not

necessarily the best path from A to C

Implies cannot use A* or Dijkstra

2

A BC

13Lim et al. “Stochastic Motion Planning and Applications to

Traffic.” To Appear, Workshop on Algorithmic Foundations of Robotics, 2008

WiFi As A Sensor

Is WiFi feasible as an uplink from cars?– Study with taxis; 30,000 distinct APs

Answer:– Significant connectivity “in the wild”

• ~ 200 kB / minute• Even at normal driving speeds

– Connections last about 20 seconds

– See an access point about every 20 seconds!

See MOBICOM 2006

Conclusion

Mobile and participatory sensornets can sense at much higher scale over larger areas than static networks

Applications: traffic, fleet management, automotive diagnostics, wireless network monitoring, civil/environmental monitoring, traffic planning,…

CarTel technologies: Portal, IceDB, Cabernet/QuickWifi, FunctionDB

Platform enabled research results: Pothole Patrol, Traffic, WiFi as Uplink

For more info: http://cartel.csail.mit.edu