Wireless Sensing:the Internet's Front-

Tier David Culler Deborah Estrin

Federated Computing Research Conference

June 12, 2007

The Internet

The Internet Front-Tier

0 2 4 6 8 10 12 14 16 18

-1

-0.5

0

0.5

1

Low resolution Sensor, Test4, Increasing frequency

Time (sec)

Acc

eler

atio

n (g

)

sensingmeasurement instruments

(sensors, transducers)

embeddedin the physical environment

(soil, canopy, rivers, groundwater, coastal)

networkedto share information and adapt

function(data, system status, control)

an “internet” of sensorsan “internet” of sensors

Embedded Networked Sensing

Why “Real” Information is so Important?

Improve Productivity

Protect HealthHigh-Confidence Transport

Enhance Safety & Security

Improve Food & H20

Save Resources

Preventing Failures

IncreaseComfort

Enable New Knowledge

Outline

• Introduction

• Technological Foundations

• Unprecedented Information

• Participatory Sensing

• Internet Front-Tier – really

• A Broader Sense

Broad Technology Trends

Today: 1 million transistors per $

Moore’s Law: # transistors on cost-effective chip doubles every 18 months

Mote!years

ComputersPer Person

103:1

1:106

Laptop

PDA

Mainframe

Mini

WorkstationPC

Cell

1:1

1:103

Bell’s Law: a new computer class emerges every 10 years

Same fabrication technology provides CMOS radios for communication and micro-sensors

Enabling Technology

Microcontroller RadioCommunication

FlashStorage

Sensors

IEEE 802.15.4

Network

Enabling Systems Research

PhysicalWorld

SiliconWorld

• Grand challenge visions of microscopic computing everywhere– Lots of Linux/Wince ARM/x86/68k + radio prototypes

• Estrin’s PC104 testbed showed that “idle listening” in 802.11 MAC dominated ALL else

• Huge emergence of interesting papers solving hypothetical problems

Create a platform that would expose the community to real problems

Share a lot of the solutions (and development overhead) Unconstrained by past 40 years of OS and Networking

abstractions

WSN Research Phenomenon…

SmartDustWeC

Rene Mica

Intel/UCBdot

InteliMOTE

XBOWcc-dot

XBOWmica2

Intelrene’

XBOWrene2

Intelcf-mica

Boschcc-mica

Dust Incblue cc-TI

digital sunrain-mica

XBOWmica

zeevo BT

Telos

XBOWmicaZ

IntelMOTE2

EyesBTNode

trio

97

LWIM-III(UCLA)

9998 00 01 0302 04 0605 07

DA

RP

A

SE

NS

IT

LWIM

Exp

edit

ion

NE

ST

NE

TS

/ N

OS

S

CE

NS

S

TC

NS

F

Cyb

er-

Phy

sica

l

WINS(UCLA/ROckwell)

LEAP

Storage ProcessingWireless SensorsWSN mote platform

(Re)discovering the Boundaries

Radio Serial

Flash ADC, Sensor I/F

MCU, Timers, Bus,…

Link

NetworkProtocols Blocks,

Logs, FilesScheduling,

ManagementStreaming

drivers

Over-the-air Programming

Applications and Services

Communication CentricResource-ConstrainedEvent-driven Execution

Tin

yOS

2.0

A worldwide community

StorageWireless Processing

Sensors

SmartDustNEST

Wireless Sensor Networks

Self-Organized Mesh Routing - nutshell

0

112

2

2

22

What we mean by “Low Power”

• 2 AA => 1.5 amp hours (~4 watt hours)• Cell => 1 amp hour (3.5 watt hours)

Cell: 500 -1000 mW => few hours active

WiFi: 300 - 500 mW => several hours

GPS: 50 – 100 mW => couple days

WSN: 50 mW active, 20 uW passive

450 uW => one year

45 uW => ~10 years

Ave Power = fact * Pact + fsleep * Psleep + fwaking * Pwaking

* System design

* Leakage (~RAM)

* Nobody fools mother nature

What WSNs really look like

Field Tools

Client ToolsExternal Tools

Embedded Network

Gateway

Excel, MatlabEnshare, etc.

DeployQueryCommandVisualize

InternetGUI

LegacyData analysis

Towards the Internet Frontier – 6LoWPAN: IPv6 over IEEE 802.15.4IEEE 802.15.4 Frame Format

IETF 6LoWPAN Format

dsp

01 1 Uncompressed IPv6 address [RFC2460]0 40 bytes0 0 0 0

01 010 0 0 0 HC1 Fully compressed: 1 byte

Source address : derived from link addressDestination address : derived from link addressTraffic Class & Flow Label : zeroNext header : UDP, TCP, or ICMP

preamble SF

D

Len

FCF DS

NDst16 Src16

D pan Dst EUID 64 S pan Src EUID 64

Fchk

Network Header Application Data

127 bytes

Unprecedented Information

Science application drivers explore complexspatial variation and heterogeneity

CENS, UCLAP. Davis, UCLA

Dawson, UCB

• Spatial sampling challenges– Difficult to assess spatial variability and model patterns in complex, dynamic media (soil, water, air)– Over-deployment not a general solution: minimum spacing constraints, installation difficulty, settling time – Geometrically-determined locations/metrics don’t capture environment’s complex topology obstacles, inputs (sun, precipitation, currents)– Calls for model and data-informed placement, iterative and adaptive sampling

• Temporal sampling more elastic– Many temporal signals can be fully sampled with existing platforms – Calls for runtime adjustments to live within energy constraints

"Soil microorganisms mediate below- and aboveground processes, but it is difficult to monitor such organisms because of the inherent cryptic nature of the soil. Traditional 'blind' sampling methods yield high sample variance...." [Kliornomos99]

Johnny Appleseed deployment myth

Hansen, Harmon, Schoellhammer, et al.

Lessons from the field...Early themes

Thousands of small devices

Minimize individual node resource needsExploit large numbers

Fully autonomous systemsIn-network and collaborative processing for longevity: optimize communication

New themes

Heterogeneity

Combine in situ and server processing to optimize system

Inevitable under-sampling with static sensing: mobility

Exploit multiple modalities (e.g. imagers), multiple scalesInteractivity

Coupled human-observational systems: tasking, analysis,vis.

In-network processing, system transparency for

responsiveness, data integrity, rapid-iterative deployment

Participatory sensing systems leveraging cellphone,

gps,web.

Coupled Human-Observational Systems

Slope (Spatial Analyst)

Aspect (Spatial Analyst)

Daily Average Temperature(Geostatist

ical Analyst)

Elevation (Calculated from Contour Map)Aerial Photograph (10.16cm/pixels)

Hamilton, Kaiser, Hansen, Kohler, et al.

Transform physical observations from batch to

interactive process

• Rapid deployments are high value.

• Interactive systems take advantage of human observation, actuation, and inference

• Addresses critical issues such as adaptive sampling, topology adjustment, faulty sensor detection

• Requires real time data access, model based analysis, system transparency, visualization in the field

Transform physical observations from batch to

interactive process

• Rapid deployments are high value.

• Interactive systems take advantage of human observation, actuation, and inference

• Addresses critical issues such as adaptive sampling, topology adjustment, faulty sensor detection

• Requires real time data access, model based analysis, system transparency, visualization in the field

Data, Data, Data: Increasing role of statistical models and methods

• Multiple scales: Designing experiments/analyses to match observation of multi-scale phenomena

• Data integrity: Robust procedures for analysis in the presence of sensing and environmental uncertainty

• Opportunistic measures and models: Integrating available measurements with available data sources and models

Hansen, et al

Confidence: Tool for detecting and diagnosing

network faults with an online-variant of K-means clustering to identify outliers in specially crafted feature space

Model-based detection: Hidden-Markov model complete with stochastic descriptions of system fault learned for detection with data from NAMOS

Signatures: Short description of multivariate probability distribution maintained for each sensor or cluster of sensors; likelihood ratio test used to flag readings that appear more faulty than normal

Blind calibration: New approach makes use of projections into function spaces (smoothness classes)

Hansen, Kohler, Ramanathan, Golubcik, Nair, Balzano

Data Integrity focused on maximizing “data return”

Fine scaleEffects of roots, organic particles,and soil structure

Plot-to field scaleEffect of groupof plants, and gradients insoil texture

Large-scaleEffect of vegetationsystems, andtopography

Soil CO2 concentration

Soil respiration

Canopy photosynthesis

Multi-Scale terrestrial carbon fluxes

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0 50 100 150 200 250 300 350DOY

Allen, Graham, Hamilton,et al

If you can’t go to the field with the sensor you want, go with the sensor you have

• Commercially available autonomous devices available for physical and chemical measures only

• System designs need to compensate for lack of sensor specificity, sensitivity, availability…particularly wrt biological response variables

• Leverage proxy sensors and model based signal interpretation

Physical Sensors: Microclimate above and below ground

Chemical Sensors: gross concentrations

Acoustic and Image data samples

Acoustic, Image sensors with on board analysis

Chemical Sensors: trace concentrations

DNA analysis onboard embedded deviceSensor triggered sample

collection

present future

Organism tagging, tracking

abio

tic

bio

tic

Leverage context to apply server-side, and ultimately, on-board processing to infer “interesting behavior” (sensor output)

Ahmadian, Ko, Rahimi, Soatto, Estrin

Blue lines: output of automatic image processing algorithms applied to cyclops images over 5 minute intervals; Red/Green line: temperature.

Imagers as biological sensors: Heartbeat of a Nestbox

Reddy, Burke, Hansen, Parker et al

Merging models and sensing Personalized Environmental Impact Report (PEIR)

• “Footprint” calculators inform long-term choices using coarse-grained models impact• Personalized, real-time assessment to help individuals reduce impact and minimize exposure by viewing their own practices and habits as seen in data and inferred from models • Employ built-in capabilities of mobile handsets to scale without specialized hardware• Leverage model-based analyses with location traces generated using GPS, cell tower, WiFi

System components

GPS-equipped mobile handset.

Custom handset software for automatic location time-series collection, robust upload, over-the-air upgrade/tasking, just-in-time annotation with voice or text.

Server side tools to analyze individual spatio-temporal patterns and calculate corresponding impact and exposure metrics to inform and advise users.

Web-based interfaces informing and advising users, which provide reports, real-time feedback, visualizations and exploratory data analysis tools for non-professional users. (For handsets and workstations.)

+

N80, N95 Campaignr

=

Trace, audio, image

SensorBase

Server-side classifier

Activity type inference

Impact / ExposureModel

Burke, Estrin, Hansen, et al

Participatory Urban Sensing: combines users, mobility, context

Enabled by– Over 2 x 109 users worldwide of cell phones.– Automated geo-coding and pervasive connectivity– Image and acoustic as data and metadata– Bluetooth connected external sensors– Local processing for data quality and triggering– Spatial interface to data and authoring

Applications– Self-administered health diagnostics– Public health/epidemiology: Water and Air– Civic concerns (transportation, safety…)– Personal Environmental Impact Report

Challenges– Mechanisms for selective sharing, verified location– Inference from sensor streams (gps,image,sound)– Campaign framework, data quality, incentives

participatory sensing data promises to make visible

human concerns that

were previously

unobservable…or

unacceptable

A Broader Sense

The Internet Front-Tier

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Time (sec)

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THE Question

RFM,CC10k,…,802.15.4Sonet 802.11Ethernet

XML / RPC / REST / SOAP / OSGI

IP

Web Services

TCP / UDP

HTTP / FTP / SNMP

Serial

Plugs and PeopleSelf-Contained

Enet 10MEnet 100MEnet 1GEnet 10G

802.11a

GPRS802.11b

802.11g

If Wireless Sensor Networks represent a future of “billions of information devices embedded in the physical world,… why don’t they run THE standard internetworking protocol?

Sensor Network “Networking”

RadioMetrixRFM

CC1000Bluetooth 802.15.4

eyesnordic

WooMacSMAC

TMACWiseMAC

FPS

MintRoute

ReORg

PAMAS

CGSR

DBF

MMRP

TBRPF

BMAC

DSDV

ARADSR

TORA

GSR GPSR GRAD

Ascent

SPIN

SPAN

Arrive

AODV

GAFResynch

Yao

Diffusion

Deluge Trickle Drip

RegionsHood

EnviroTrackTinyDB

PC

TTDD

Pico

FTSP

Phy

Link

Topology

Routing

Transport

Appln

Scheduling

The Answer

They should

• Substantially advances the state-of-the-art in both domains.• Implementing IP requires tackling the general case, not just a specific

operational slice– Interoperability with all other potential IP network links

– Potential to name and route to any IP-enabled device within security domain

– Robust operation despite external factors• Coexistence, interference, errant devices, ...

• While meeting the critical embedded wireless requirements– High reliability and adaptability

– Long lifetime on limited energy

– Manageability of many devices

– Within highly constrained resources

Making sensor nets make sense

802.15.4, …802.11Ethernet Sonet

XML / RPC / REST / SOAP / OSGI

IP

IETF 6lowpan

Web Services

TCP / UDP

HTTP / FTP / SNMP Pro

xy

/ G

ate

wa

y

LoWPAN – 802.15.4

• 1% of 802.11 power, easier to embed, as easy to use.

• 8-16 bit MCUs with KBs, not MBs.

• Off 99% of the time

Thinking about the Physical World as “Signals”

• What is the bandwidth of the weather?

• What is the nyquist of the soil?

• What is the placement noise?

• What is the sampling jitter error?

Embeddable device developments• Energy-conserving platforms, radios• Miniaturized, autonomous, sensors• Standardized software interfaces• Self-configuration algorithms• Adaptive, iterative sampling• Cognitive sensors

Embeddable device developments• Energy-conserving platforms, radios• Miniaturized, autonomous, sensors• Standardized software interfaces• Self-configuration algorithms• Adaptive, iterative sampling• Cognitive sensors

Early embedded sensing applications• Biological and Earth Sciences• Environmental, Civil, Bio Engineering• Public health, Medical research• Agriculture, Resource management

Early embedded sensing applications• Biological and Earth Sciences• Environmental, Civil, Bio Engineering• Public health, Medical research• Agriculture, Resource management

Application drivers: From Condition based maintenance to Precision Living…

The maturing technology will transform the business enterprise, environmental resource management, human interaction

– Industrial and civil infrastructure – Individual Health and wellness– Planet health and wellness: water, carbon, pollution, waste

Science is our early adopter because the technology is transformative and research tolerates risk

Important historical precedents- Weather modeling--early computing- Scientific collaboration--Internet- Experimental physics (CERN)--WWW- Computational science--Grid

Research Ecosystem Challenges

• “Early-and-really-to-application”– deployments and resulting data provide feedback to system

innovation… from theory to system architecture

• Multidisciplinary research means taking turns

• Training a generation of Eco-Geeks– Mining for geeks w/diversity: gender, nationality, …– Training for the mundane and the magnificent

• Funding and sustainability

Many critical issues facing science, government, and the public call for high fidelity and real time observations of the physical world

Networks of smart, wireless sensors, forming the Front-Tier of the Internet can help reveal the previously unobservable

An(other) inconvenient truth

But an inconvenient truth is that

the field does not lend itself

to familiar abstractions

and research

practices…

DedicationTo two special people who we would have wanted here above all …

… to give us a dozen pointed criticisms … and two dozen wonderful new ideas.

Richard A. Newton Jim Gray