1
Chess ReviewMay 11, 2005Berkeley, CA
Wireless Network Embedded Systems:Sensor Nets and Beyond
Edited and Presented byShankar SastryDept of EECS,UC Berkeley
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Moore’s Law – 2x stuff per 1-2 yr
2
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Bell’s Law –new computer class per 10 years
year
log
(peo
ple
per c
ompu
ter)
streaming informationto/from physical world
Number CrunchingData Storage
productivityinteractive
• Enabled by technological opportunities
• Smaller, more numerous and more intimately connected
• Ushers in a new kind of application
• Ultimately used in many ways not previously imagined
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Building Comfort,Smart Alarms
Great Duck Island
Elder Care
Fire Response
Factories
Wind ResponseOf Golden Gate Bridge
Vineyards
Redwoods
Instrumenting the world
Soil monitoring
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The Sensor Network Challenge
applications
service
network
system
architecture
data mgmt
Monitoring & Managing Spaces and Things
technology
MEMSsensing Power
Comm. uRobotsactuate
Miniature, low-power connections to the physical world
ProcStore
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Miniaturization – Pister (SmartDust)
• The Goal: Autonomous millimeter-scale robots
– Sensing– Computation– Communication– Power– Motors– Mechanisms
SENSORS ADC FSMRECEIVER
TRANSMITTER
SOLAR POWER1V 1V 1V 2V3-8V
PHOTO 8-bits
375 kbps
175 bps
1-2V
OPTICAL IN
OPTICAL OUT
Solar Cell Array CCR
CMOSIC
XL
4
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Low Power RF – Rabaey (PicoRadio)
• CMOS– Cheap, Integrated
• mW -> sub mW• Simple• Advantage in
Numbers
RF Filter LNA
fclock
RF Filter EnvDet ∫
fclock
RF Filter EnvDet ∫
RF Filter LNA
fclock
RF Filter EnvDet ∫
fclock
RF Filter EnvDet ∫
RXOn: 3 mWOff: 0 mW
RXOn: 3 mWOff: 0 mW
Receiver
RF Amp Test
LNATest
Diff Osc
PA TestTX1
TX2Env DetTest
Passive Test Structures4 m
m
MatchingNetwork
MOD1
MOD2
OSC1
OSC2 Preamp PAMatchingNetwork
MOD1
MOD2
OSC1
OSC2 Preamp PATX
On: 4 mWStby: 1 mWOff: 0 mW
TXOn: 4 mW
Stby: 1 mWOff: 0 mW
BWRC
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System/Networking/Prog. – Culler (TinyOS)
Small microcontroller
- 8 kb code, - 512 B data
Simple, low-power radio
- 10 kbEEPROM (32 KB)Simple sensors
WeC 99“Smart Rock”
Mica 1/02
NEST open exp. platform
128 KB code, 4 KB data
50 KB radio
512 KB Flash
comm accelerators
- DARPA NEST
Dot 9/01
Demonstrate scale
- Intel
Rene 11/00
Designed for experimentation
-sensor boards
-power boards
DARPA SENSIT, Expeditions
TinyOS www.tinyos.net
Networking
Services
Crossbow
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Traditional Systems
• Well established layers of abstractions
• Strict boundaries• Ample resources• Independent Applications at endpoints communicate pt-pt through routers
• Well attended
User
System
Physical LayerData LinkNetwork
TransportNetwork Stack
Threads
Address Space
Drivers
Files
Application
Application
Routers
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by comparison ...
• Highly Constrained resources– processing, storage, bandwidth, power
• Applications spread over many small nodes– self-organizing Collectives – highly integrated with changing environment and
network– communication is fundamental
• Concurrency intensive in bursts– streams of sensor data and
network traffic• Robust
– inaccessible, critical operation
• Unclear where the boundaries belong
– even HW/SW will move
=> Provide a framework for:
• Resource-constrained concurrency
• Defining boundaries
• Appl’n-specific processing and power management
allow abstractions to emerge
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Vast Networks of Tiny Devices• Past 25 years of internet technology built up
around powerful dedicated devices that are carefully configured and very stable
– local high-power wireless subnets at the edges– 1-1 communication between named computers
• Here, ...
• every little node is potentially a router• work together in application specific ways• collections of data defined by attributes• connectivity is highly variable• must self-organize to manage topology, routing,
etc• and for power savings, radios may be off 99%
of the time
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Mote Evolution
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Berkeley Open Experimental PlatformKey Areas of Progress
• OEP3 Hardware– Telos 802.25.4 mote (& MicaZ)– Mica2 testbed
• TinyOS Advances– Cross-platform 802.15.4 support– EXScal XSM support– Structured Release– BMAC– Large Scale Simulation
• Embedded Networking– Reliable, low-power data collection– Dissemination: trickle and drip– Reliable Bulk Communication– Scheduled Communication
• Network Programming– Deluge (really works at Scale)– Towards a tight lower bound– Incremental Updates
• MacroProgramming– Mate II application specific VM
• Security– 802.15.4
• Localization– Robust methodology - ultrasound– Making RSSI actually work
• Long Lived Applications– Analysis of GDI– Calibration in the redwoods
• Capstone Demo– Management infrastructure– Telos/XSM integration– Complete Simulation– Multiobject Tracking
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OEP3 Platform
• Focused on low power • Sleep - Majority of the time
– Telos: 2.4µA– MicaZ: 30µA
• Wakeup– As quickly as possible to
process and return to sleep– Telos: 290ns typical, 6µs
max– MicaZ: 60µs max internal
oscillator, 4ms external• Process
– Get your work done and get back to sleep
– Telos: 4MHz 16-bit– MicaZ: 8MHz 8-bit
• TI MSP430– Ultra low power
» 1.6µA sleep» 460µA active» 1.8V operation
• Standards Based– IEEE 802.15.4, USB
• IEEE 802.15.4– CC2420 radio– 250kbps– 2.4GHz ISM band
• TinyOS support– New suite of radio stacks– Pushing hardware abstraction– Must conform to std link
• Ease of development and Test– Program over USB– Std connector header
• Interoperability– Telos / MicaZ / ChipCon dev
• 97% Yield on first 200– Remaining reparable
UCB Telos
Xbow MicaZ
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Deluge Network Programming
• Reliable Pipelined Epidemic Distribution of series of pages
– Constrained storage hierarchy– Robust to lossy or asymmetric links– Very low maintenance bandwidth
• Page Advertise, Request/Fix, Xfer– Density-aware suppression and snoop on each
• Packet CRC + Page CRC• 159 Byte memory footprint• Packed image (no 64k xnp limit)• Multiple Program images• Extensive simulation of dissemination Alg.
and many variants• Tested extensively on 77 nodes• Simulated on >1,000 nodes in EXSCAL
configuration with multiple sources• 4 mins for 1-hop 33k image to 33 nodes• Command line host tools
flash
…
Maintain
Request
Transmit
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Simulated Dissemination (10x10)
• Smooth pipelined wavefront of multiple pages
announcement
requests
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Simulation at Larger Scale (16x16)
Wavefront propagates more rapidly along the edges due to lower density
- long linear structures are fast
Behavior exacerbated by conservative interference model in TOSsim
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Localization Methodology
1. Environmental characterization – Measure accuracy and loss rate of many
pairs and range of distances (pseudo-random pattern)
– 45 environments empirically characterized– 660 empirical measurements taken within
.3m of every distance between 0-30 m2. System design
• Design filters, schedule, model, localization alg. against observed error and loss distribution
• Select simplest that works3. Pre-deployment analysis
• Determine a adequate system to deploy• Trace-based simulation make realistic
predictions• Use data from 1.
• Model-based simulation makes optimistic predictions
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Localization with Empirical and Model−based Ranging
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Data Collection Topology
Distances are in meters
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Fit of Model to Raw Data
raw datalinear modeltheoretical model
Lower Signal Strength
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Mean FilterMedianTube FilterConsistency CheckingTheoretical
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Stdev on Placement of Nodes (cm)
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Consistency CheckingTheoretical
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Ultrasound Results
• Non-gaussian error• Probabilistic loss• 50cm median error in deployment,
as predicted– 49 node deployment– 8 hop network
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Distance (m)
Pro
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grassfree spacecarpetpavement
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Error (cm)
Pro
babi
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Dis
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Ranging Error (m)
Pro
babi
lity
Normal Probability Plot of Ranging Error
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Signal Strength Results
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X Axis (m)
GPS errors on a 49 node network
Y A
xis
(m)
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X Axis (m)
Y A
xis
(m)
RSS errors on a 49 node network
1.9m / 7.2m
4.1m / 8.9m
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X Axis (m)
Y A
xis
(m)
RSS errors on a 25 node network
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Y A
xis
(m)
GPS errors on 25 node network
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4.2m / 12.6m
6.1m / 10.1m
•Results vary greatly with each environment•Pre-deployment analysis accurately predicted effects ofenvironment, topology, and algorithmic parameters
•RSSI-based accuracy can be comparable to GPS
GPS RSSI
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Vanderbilt Radio Interferometric RangingChampion to Date
• COTS radio chip (CC1000 on MICA2)– transmit frequency: 400-460 MHz– wave length: 65 cm < λ < 75 cm– adjustable in 64 Hz steps
• Two senders (A and B) transmit simultaneously
– frequency separation: 100-800 Hz– duration of transmission: 32 ms
• Several receivers (C, D and E) measure interference
– sample radio signal strength at 8.9 kHz– beat frequency: 100-800 Hz– samples per beat: 10-80– beats per transmission: 3-25– use time synchronization with 1 µs
precision to correlate phase offsets– result is (dAD-dBD+dBC-dAC ) modulo λ
» dXY is distance between X and Y » λ is wave length of carrier frequency
– expected error is less than 5 cm• Perform multiple measurements with
different frequencies to obtain dAD-dBD+dBC-dAC
© 2005 Vanderbilt University
A B
C D
EdACdBC
dADdBD
relative phase offset of beat frequency= (dAD-dBD+dBC-dAC ) modulo λ
where 65cm < λ < 75cmAkos Ledeczi, Miklos Maroti, et al
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Technology Push matched by Application Pull
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Structural Monitoring – Glaser, Fenves
25 Motes onDamaged sidewall
30 Motes onGlue-lam beam
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-1
-0.5
0
0.5
1
Low resolution Sensor, Test4, Increasing frequency
Time (sec)
Acc
eler
atio
n (g
)
Wind ResponseOf Golden Gate Bridge
• Dense Instrumentation of Full Structure– Cost is all in the wires
• Leads to in situ monitoring• Self-inspection and Diagnosis
Liquifaction, Tokashi Port
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Forest Ecophysiology - Dawson
•• How TREES shape How TREES shape the hydrological the hydrological cycle?cycle?– 2/3 of fresh H2O recycled through forests
• Microclimatic Drivers of Plant Dynamics
• Influence climate2003,
unpublished
Temperature vs. Time
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Date
Humidity vs. Time
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Rel H
um
idity (
%)
101 104 109 110 111
Bottom Top36m
34m30m
20m
10m
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Built Environments - Arens
• 2/3 of US energy used to maintain our bldg environment
– 40% lighting• Inefficient,
unhealthy, uncomfortable due to lack of sensing and control
Lighting, temperature, sound, air quality…
Electricity, gas, water, weather…
Occupancy, comfort, productivity
Building
EnergyPeople
Light ballast
VAV actuator
Reflective vane actuator
Occupancy sensor
Desk climate sensor
Window switch
Climate sensor
Base station
BACnet
Comfort stat
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Shooter Localization Using SensorWebs
Berkeley motes and Vanderbilt algorithm
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And many more
• Sitar - Firebug• Agogino - Occupancy• Varaiya – Traffic• Wright – emergency
personnel• Brewer – ITC4B• Student solar car• Habitat Monitoring• Intel fab app
Condition-BasedMaintenance
Intel Research
Great Duck Island
< 100 m
Intersection
mote13
mote14
receiver
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Protection – Sastry, Culler, Brewer, Wagner
• Detect vehicle entering sensitive area, track using magnetics, pursue and capture by UGV.
• Components– 10x10 array of robust wireless, self-localizing
sensors over 400 m2 area– Low cost, robust ‘mote’ device– Evader: human controlled Rover– Pursuer: autonomous rover with mote, embedded
PC, GPS• Operation
– Nodes inter-range (Ultrasonic) and self localize from few anchors, correct for earth mag, go into low-power ‘sentry’ state
– Detect entry and track evader» Local mag signal processing determines event and
announces to neighbors» Neighborhood aggregates and estimates position» Network routes estimate from leader to tracker
(multihop)– Pursuer enters and navigates to intercede
» Motes detect and estimate multiple events» Route to mobile Pursuer node» Disambiguates events to form map» Closed inner-loop navigation control» Closed information-driven pursuit control» Capture when within one meter
dot
mag ultrasound
acoustic
ultrasoundmain
mag sensepower
battery
reflector
pursuer
evader
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Overall Demo Operation (2):Object Detection
• Evader enters field• Pursuers enter field• Each detecting mote
announces its readings• All readings in a
neighborhood are aggregated
• Aggregate packet is routed through the root mote
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OEP2 Hardware Platform
• Main board: Mica2 dot – ATmega microcontroller, Flash, clock– CC1000 frequency agile FSK radio– Small form-factor– Xbow based on Mica
• Sensor Board: magnetometer– Honeywell mag with 2-stage amplification– Set/reset circuit (5v)– 4-port digital Pot for biasing and filtering
• Ranging boards– Ultrasound– Acoustic
• Enclosure
dot
mag ultrasound
acoustic
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PEG Software Overview
• New routing protocol to relax dependency on localization service
• Remote configuration interface– Solution to a problem, not an original goal
• Network reprogramming• System layer for remotely invoking disparate services• Standard services
– Sleep, ping, RF power, blink, network reprogram• MATLAB command-line interface to network• Strong decoupling between sensor networks and clients
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PEG Demo from July 03
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Mote Drop Experiences:Last 2 of 6 motes are dropped from MAV 29
Palms March 2001
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Bald Camel Drop: China Lake 02/19/04
MIR integrated with Mica-2 Packaging to allow antennas to land pointing up
Explosive DispenserAerial Drop from 500 ft.
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Bald Camel Drop Feb 2004
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Down Range Dispersion [meters]
Clockwise from top left: Actual Sensor Lay Down Pattern, Phoebus Chen walking through sensor field, Tracking of Phoebus through sensor field
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Big July 2005 Demo Philosophy
• Midterm demos focused on building real applications
– “Will this work at all?”• ExScal focuses on scaling real applications
– “How big can it get?”• Capstone will focus on evaluating real
applications– “Can we describe, characterize, and predict how well
it works?” Metrics, Metrics, Metrics– This is our opportunity and need to leverage
experience toward good science– Deployment over a period of time (2-3 weeks) at
Richmond Field Station not just in field but spread out over the station
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Capstone Goals
• Establish metrics to evaluate PEG– Metrics vary at each abstraction
level– Pursuer Algorithm cares about:
» Sensor Accuracy, Latency, Missing Data, False Alarm Rate, etc.
– “Quality” of data stream from the Base Station depends on:
» Network Congestion, Node Density, Power Consumption Constraints, Physical Distance, etc.
• Simulations• Experiments• Characterize and predict
SensorNetwork
(Mobile)Base Station
Pursuer Algorithm
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Towards Final Demo
• Redeployed substantial sensor field to collection data and develop metrics
• Worked with EXSCAL design to ensure can reuse XSM as sensor platform for final demo– Attach Telos as standalone or as bridge
• Replace/ augment acoustic localization with RSSI
• Airborne pursuer, airborne drop simulated• Multiple evaders• Focus on middleware architecture, science,
measurement, ease of deployment, and reliability– Developed a baseline management architecture
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Script for Final Demo: Aug 2005
• 1000+ XSM + Telos motes spread out over urban area (Richmond Field Station: area approximately 3 sq. km. Including several buildings, varying topography, trees, …). Experiment will be up and running for 2 weeks: fully instrumented.
• Base line demo components developed by UCB team. Technology Developers can swap in their components: localization, time synchronization, tracking, network management, network programming.
• Scenario: Evaders are humans/robots. Pursuers are humans/robots: some aerial and some ground robots. Distinguish between red forces, blue forces and green forces in tracking algorithms to minimize capture time or localization
• UAVs with motes used to localize sensor field• Sensor fields to cue higher bandwidth sensors (cameras)• Metrics: time to capture, energy expenditure, ease of
re-programmability
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Features of Final Demo
• Pursuit evasion games with red, blue and green forces, a combination of pre-deployed sensor webs, MAV dropped sensor webs, MAV/UGV camera platforms and other higher bandwidth assets
• Persistence/Reprogrammability• Network Management• MOBILE Sensor Webs• Determination of Pursuit and Evasion policies and tactics
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Implementation in an Instrumented Testbed
• 9 x 5 grid of 45 motes
• 10 ft by 20 ft area, 2.5 ft spacing
• Ethernet backend for monitoring/data collection
• Multiple robots running underneath mesh of sensors
• Camera for “ground truth” position of robots
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Soda Hall Routing Environment
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Conceptual Issues
• Given a certain WSN, can we successfully design a particular application?
• How does the application impose constraints on the network?
• Can we derive important metrics from those constraints?
• How do we measure network parameters?
We need analytical tools and experimental data
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What can you do with a sensor network?
• Literature provides key asymptotic results
• We are interested in answer different semantic questions, e.g.:
• At the algorithmic level:– How much packet loss can a tracking
algorithm tolerate?• At the network level:
– How many objects can a particular sensor network reliably track?
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Expanding the Vision
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Legal / Privacy – Samuelson, Mulligan
• TOWARD A LEGAL FRAMEWORK FOR SENSOR NETWORKS
– existing laws?– new laws or regulations
• Modes of Regulation and Policy• Evolving notions of Privacy• FAIR INFORMATION PRACTICES
– Limitations on collection of data – specific purpose – Notice and choice– Verifiability– Accuracy, Security– Accountability
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Small Technology, Broad Agenda, Unique Confluence
• Social factors– security, privacy, information sharing
• Applications– long lived, self-maintaining, dense instrumentation of
previously unobservable phenomena– interacting with a computational environment
• Programming the Ensemble– describe global behavior, synthesis local rules that have
correct, predictable global behavior• Distributed services
– localization, time synchronization, resilient aggregation• Networking
– self-organizing multihop, resilient, energy efficient routing– despite limited storage and tremendous noise
• Operating system– extensive resource-constrained concurrency, modularity– framework for defining boundaries
• Architecture– rich interfaces and simple primitives allowing cross-layer
optimizationl ADC di i ti ti
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Secure SCADA and beyond
We think that there is a great deal to be done in terms of operationalizing secure versions of SCADA (Supervisory Control And Data Acquisition) and DCS (Digital Control Systems) for the infrastructures considered, especially power, natural gas, chemical and process control, etc. However, the sense was that this infrastructure was going to be gradually replaced by networked embedded devices (possibly wireless) as computing and communication devices become more ubiquitous and prevalent. Thus, the major research recommendations were for an area that we named Secure Networked Embedded Systems (SENSE).
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SCADA of the Future
• Current SCADA– Closed systems, limited coordination, unprotected
cyber-infrastructure– Local, limited adaptation (parametric), manual control– Static, centralized structure
• Future requirements – Decentralized, secure open systems (peer-to-peer,
mutable hierarchies of operation)– Direct support for coordinated control, authority
restriction– Trusted, automated reconfiguration
» Isolate drop-outs, limit cascading failure, manage regions under attack
» Enable re-entry upon recovery to normal operation» Coordinate degraded, recovery modes
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Embedded Software prevalent in all critical infrastructures. Critical to high confidence embedded software are open source techniques for
• Automated Design, Verification and Validation– Verified design in a formal, mathematical sense– Validated design in an engineering sense– Certifiable design to allow for regulatory and certification input
• High Confidence Systems– Narrow waisted middleware
» Trusted abstractions, limited interfaces» Algorithms and protocols for secure, distributed coordination and
control– Security and composable operating systems– Tamper Proof Software
• Generative Programming• Intelligent Microsystems: infrastructure of the future with
security codesign with hardware and software.
Secure Network Embedded Systems
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Adaptive Networked InfrastructureCore partners: Berkeley (lead), Cornell, Vanderbilt
Outreach partners: San Jose State, Smith, Tennessee Tech, UC Davis, UC Merced.Principal investigator: Edward A. Lee, Professor, EECS, UC Berkeley, [email protected]
•Enabling technologies: wireless networked embedded systems with sensors and actuators
• Deliverables: Engineering Methods, Models, and Toolkits for: • design and analysis of systems with embedded computing• computation integrated with the physical world• analysis of control dynamics with software and network
behavior• programming the ensemble, not the computer• computer integrated systems oriented engineering curricula
•Approach: Engineering methods for integrating computer-controlled, networked sensors and actuators in societal-scale infrastructure systems.
•Resource management test beds:• electric power• transportation• water
•Target: efficient, robust, scalable adaptive networked
infrastructure.
The ANI ERC
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Strategic Framewor
k