wireless sensing: the internet's front-tier
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Wireless Sensing: the Internet's Front-Tier. David CullerDeborah Estrin Federated Computing Research Conference June 12, 2007. The Internet. The Internet Front-Tier. Embedded Networked Sensing. embedded in the physical environment (soil, canopy, rivers, groundwater, coastal). networked - PowerPoint PPT PresentationTRANSCRIPT
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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Low resolution Sensor, Test4, Increasing frequency
Time (sec)
Acc
eler
atio
n (g
)
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