WSN Wireless Sensor Networks and Applications

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WSNWireless Sensor Networksand Applications
Dwight Borses Member of the Technical
Staff National Semiconductor, Irvine, CA
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Wireless Sensor NetworksDescription

• Consists of a large number of sensor nodes
• Nodes are extremely small, low-cost, low-power
• Nodes communicate over RF or lasers
• Network collect environmental data which they
  forward to infrastructure processing nodes
• Acoustics
• Light
• Humidity
• Temperature
• Imaging
• Seismic, etc

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Deployment and Applications

• WSNs may monitor or control
• Consist of thousands of nodes deployed in very
  high density
• homes, buildings,
• highways, cities,
• infrastructures
• Applications range from
• Monitoring/warning of natural disasters effects
• Protecting homeland security
• Conducting military surveillance

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Making Systems Long-lived

• Consider energy the scarce system resource
• Minimize communication (esp. over long distances)
• Computation costs much less, so
• In-network processing aggregation, summarization
• Adaptivity at fine and coarse granularity
• Maximize lifetime of system, not individual nodes
• Exploit redundancy design for low duty-cycle
  operation
• Exploit non-uniformities when you have them
• Tiered architecture

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Making Systems Long-lived

• Robustness to dynamic conditions
• Make system self-configuring and
  self-reconfiguring
• Avoid manual configuration
• Empirical adaptation (measure and act)
• Localized algorithms prevent single points of
  failure
• Helps to isolate scope of faults
• Also crucial for scaling purposes

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Possible Applications for WSN
Environmental Monitoring
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Mars WSN
Scott Burleigh JPL / Cal Tech 19 Jan 2004
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UCLA Wildlife Habitat Monitoring

• Instrumented with cameras and microphones
• Task is to detect presence of bird and photograph
  it
• One approach
• Use microphones to detect birdcall and estimate
  location
• Then, select a camera that has the bird in field
  of view

Species Detection and Tracking
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Integrated Sensing, Computing, Communication

• WSNs are driven by
• Technological convergence of MEMS
• Microelectromechanical sensors
• Microelectronics
• Signal processing
• Communication
• Enabled by
• Algorithms, network protocols, software, for
  applications conformance (mostly still under
  development)
• Power management technology for operational
  endurance
• Resulting in
• Useful, long-lasting, reliable, survivable,
  programmable systems

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Sensor Technology
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Microsensor Network Technology

• Significant impact on 21 Century lives
• Range in size from mm2 to in2
• Multiple miniaturized sensors for light,
  temperature, humidity, acoustics, imaging, etc
• Considerable processing power
• Positional ability from GPS or local methods
• Short range RF and/or optical communication
• Cheap and Smart
• Deployable in small or very large quantities
• Instrument homes, highways, buildings, bodies,
  cities, infrastructures
• Monitoring and control for security and defense

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Nanotechnology

• Nanosensors
• Extremely small devices with dimensions on the
  order of 10-9 m. (one billionth of a meter)
• Capable of detecting and responding to physical
  stimuli
• Characterized as nanostructured particles,
  nanoparticles, and nanodevices
• Future products
• Based on Nano Electro-Mechanical Systems (NEMS)
• Molecular switches currently under development

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J. Storrs Halls Utility FogA Swarm of Nanobot
Foglets
Foglets can take the shape of virtually anything,
and change shape on the fly.
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Nanosensors Myth or Reality

• Garnered attention of scientists, venture
  capitalists, government officials, industry
  analysts
• Revenues expect to reach 200B by 2006
• Future depends on expenditures and application
  emergence
• Industry Alliance
• NanoBusiness Alliance (www.nanobusiness.org)
• Extensive library of white papers

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NanotechnologyThe Challenges

• Applications interface
• Between nanoscale devices and real world
  microsystems and macrosystems
• Funding to develop and introduce competitive
  products into the marketplace
• US Government spent 2B on nanotechnology
  world-wide over past two years
• Over 1,200 nanotechnology start-up companies
  exist in the United States
• 250-350 nanotechnology start-up companies exist
  in the rest of the world

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Nanosize MachinesNASA Ames Research Center
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Emerging Sensor Network Example Automobiles

• Stringent safety, reliability, and cost
  requirements drive sensor technology
• TREAD Transportation Recall Enhancement,
  Accountability, and Documentation Act
• Tire pressure monitoring
• Electronic Stability Systems
• Vehicle Dynamic control (VDC)
• Airbag Control
• Antilock Breaking Systems (ABS)

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Indirect Tire Monitoring

• E.g., Infineon Technologies AG)
• Wired connectivity, 2-wire with current interface
• Least costly (lt15 per wheel)
• Least precise
• Utilize existing ABS wheel speed sensors
• Underinflated tires increases wheel speed
• Overinflated tire decreases wheel speed
• Separate sensor and uP, packaged together

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Direct Tire Monitoring

• E.g., Motorola Sensor Products
• Wireless connectivity
• Most accurate and reliable
• Most expensive implementation 65 -80 per wheel
• Full system solution solution includes
  microcontroller, radio frequency IC, development
  software

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VDC and ABS

• Hall effect sensors are replacing variable
  reluctance (VR) wheel-speed sensors
• VR sensing mature and less costly
• Hall effect more costly but more benefits
• Sensors integrate signal conditioning
• Provide stable output independent of speed, down
  to DC
• Same sensor types are being applied to numerous
  other automotive applications to measure speed,
  position, and angle

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Advance Chassis ControlMotorola Automotive
Volvo
Volvo S60 R and V70 R Driver selects Comfort,
Sport, or Advance Sport Setting
40 MHz uC continuously samples road-speed
information, position information for each wheel,
and horizontal and lateral acceleration, updating
damper setting every 2 mSec.
Developed by Ohlins Racings and Monroe
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Vehicle and Driver Safety Systems

• Imaging
• Real time for periphery monitoring
• Rear, side view systems
• Lane departure warning
• Automotive blackbox
• Video
• Audio
• Vehicle dynamics
• Collision detection and reporting

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Vehicle and Driver Safety Systems

• Imaging Sensors
• Driver quality monitoring
• Front and rear view

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Vehicle and Driver Safety Systems

• Imaging Sensors
• Machine vision
• Real time
• Lane departure safety warning

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Vehicle and Driver Security Systems

• Remote vehicle monitoring
• OnStar
• Vehicle status reporting
• Remote control of vehicle systems
• LoJack
• Location determination and reporting

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Key Technical ChallengesWSN Networking

• Efficient networking methods
• Enable rapid, ad hoc networking of any number of
  devices
• Support mobile or fixed location devices
• Methods for network programmability
• Collaborative Signal and information processing
  within the network
• Detect, classify, track events and patterns of
  events occurring in geographic area

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Key Technical ChallengesWSN Database Management

• Design of distributed microdatabases of
  information about events of interest
• Over spatio-temporal interval
• Stored in devices and queried by multiple users
• Methods for security and information assurance
  within the network
• Intrusion detection
• Intrusion tolerance
• Survivable operation in event of failure and
  compromise

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Key Technical ChallengesWSN Operational Lifetime

• Effective hardware design for reliability and
  availability
• Effective power management efficiency for maximum
  endurance and network operational lifetime

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Re-creating the Internet

• Sun Microsystems
• The network is the computer
• Intel and MIT
• Extend the internet downward into a
  fine-grained, ubiquitous network of sensors and
  actuators
• Two-Pronged Approach to Retrofit the Internet

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Extending the Internet Above and Below

• PlanetLab
• Extend the Internet upward with an overlay
  network
• Re-create the Internet in the form of a
  distributed, planet-wide parallel processor
• Fine-grained Internet
• Extend the Internet downward
• Nodes consist of miniature hardware (Motes)
• Distributed throughout the natural and urban
  environment
• Based on adaptations to RFID technology

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PlanetLab

• 160 computers
• 65 sites
• 16 countries
• 70 research projects
• Linux as the operating system
• Microsoft OS dominance threatened???
• Initial push for 1,000 nodes connected to all
  Internet regions and long-haul backbones.

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A Dispersed Low-Cost Wireless Sensor and Actuator
Network

• 300 companies have designed Motes (volumes gt 5K
  pieces
• OS TinyOS
• DB Tiny DB
• Intel sponsors projects
• Example Great Duck Island, off the coast of
  Maine
• Network of visual and audio sensors monitors
  island bird population in real-time

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Intels Wireless Vineyard
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Background Sensor Networks

• Array of Sensor Probes (10-1000)
• Collect In-Situ Data about Environment
• Wireless Links
• Relay Data
• Collaboration

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Distributing Queries Over Low Power Sensor
Networks Sam Madden, Robert Szewczyk,
Michael Franklin, Wei Hong, Joe Hellerstein, and
David Culler
Focus Hierarchical Aggregation
Wireless Sensor Networks
Palm DevicesLinux

• Aggregation natural in sensornets
• The big picture typically interesting
• Aggregation can smooth noise and loss
• UDAs to do signal processing
• Provides data reduction
• Power/Network Reduction in-network aggregation
• Hierarchical version of parallel aggregation
• Tricky design space
• Metrics power cost and answer quality
• Variables topology-selection, value-routing
  scheme, other tricks
• Dynamic environment requires adaptive schemes

Smart Dust MotesTinyOS

• A spectrum of devices
• Varying degrees of power and network constraints
• This demo Mica and TinyOS
• Focus on many/tiny
• Toward MEMS Smart Dust
• Off-the-shelf HW for now Berkeley Mica Mote
• Wireless, single-ported, ad-hoc network
• Spanning-tree communication through root

Performance in Tiny SensorNets
A Query Language for Sensors
Aggregation and NW Optimization

• Power consumption
• Communication gtgt Computation
• METRIC radio wake time
• Send gt Receive
• METRIC messages generated
• Bandwidth Constraints
• Internal gtgt External
• Volume gtgt surface area
• Result Quality
• Noisy sensors
• Discrete sampling of continuous phenomena
• Lossy communication channel

• Continuous queries with streaming, periodic
  results
• UDAs and UDFs
• Currently compiled-in
• Mote Virtual Machine (Mate) under development
• Periodic nature allows for
• Scheduling of communication and sleep
• Simple semantics for combining multi-hop readings
• Clearly other alternatives here
• E.g. sequence/timeseries/temporal query languages

• An expanded taxonomy of aggregates
• State
• Duplicate sensitivity
• Montonicity
• Exemplary vs. Summary
• Effects on
• Value Routing
• Snooping and Suppression
• Caching and Presumption
• Hypothesis Testing
• Collapsing of the NW and QP layers!

SELECT ltaggsgt, ltattrsgt WHERE
ltpredsgtGROUP BY ltexprgt HAVING ltpredsgtEPOCH
DURATION ltconstantgt
TinyDB Software On Motes

• 4200 lines of C Code
• Runs on Mica Motes with light and temperature
  sensors, magnetometers and accelerometers
• 4Mhz Atmel Processor
• 4KB RAM, 40kBit radio, 512K EEPROM, 128K Flash
• Ad-hoc queries
• Java UI
• Split-pane display
• Topology visualization
• Applications
• Environmental, military
• NW Monitoring!

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Modern Sensor Nodes
Wireless Integrated Network Sensors (WINS)
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Node Hardware
1Kbps - 1Mbps, 3-100 Meters, Lossy Transmissions
128KB-1MB Limited Storage
Transceiver
Embedded Processor
Memory
8-bit, 10 MHz Slow Computations
Sensors
Battery
66 of Total Cost Requires Supervision
Limited Lifetime
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Desired Operations

• Immediate
• Transmit ID ? Mote health report
• Transmit current readings from one/all sensors
• Send logged data for sensor X
• Calibrate real-time clock
• Reconfiguration
• Start logging data from sensor X sampled every T
  seconds
• Set logging threshold and filter coefficients
• Set ScatterCast interval to T seconds
• Set your wakeup interval to T seconds

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Networking

• Multi-Hop Routing
• Limited Transmission Range
• Routing Issues
• Irregular Topologies Data Transport Aware
• Power Aware Fault Tolerant

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Scientific Value

• Multiple Data Points Time and Position
• Temporal Synchronization
• Hierarchical Schemes
• Position Estimation
• Digital Ranging
• Offline Triangulation

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Sensor Network Initialization
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Simulator Node Layers
Sensor Triggers
Application
Data Fusion Clock Synchronization
Routing
Clustering Algorithms, Reliable Routing
Link
Medium Access, Commercial Chipsets
Node
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Example Election Clustering

• Distributed Algorithm
• Nodes Elect Leaders, Form Groups
• Limited Knowledge

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Example Fixed Leader Clustering

• Predefined Cluster Leaders
• Find Nearest Leader
• Mutiny if Leader too Far Away

Sleep
Undecided
Leader
Member
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Sunrise Synchronization

• Use Sunrise as Synchronization Point
• Earlier Risers are More Eastern
• Smooth with Cluster Values, Neighbor Clusters
• Gross Estimate of East-West Dimension

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Wireless Sensor Nodes Constraints

• Low Data Rates ltlt 10 kbps
• Self-configuring, maintenance-free and robust
• Aggressive networking protocol stack
• Redundancy in deployment
• Low cost lt 1
• Small size lt 1 cm3
• Low power/energy
• Long lifetime of product requires
  energy-scavenging
• Plausible scavenging level lt 100 ?W

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PicoNetwork Specifications

• Density of nodes 1 node every 1 to 20 m2.
• Radio range 3 to 10 m
• Average bit rate per node 100-500 bps
• Peak bit rate per node 10 kbps
• Very low mobility of nodes
• Loose QoS requirements
• Sensor data is redundant, so reliability is not
  required
• Most data is delay insensitive

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Protocol Stack

• Issues at the network layer
• Addressing
• Addressing will be class based
• ltlocation, node type, sub typegt
• Symbolic addressing may be supported
• Routing
• Should route packets to the destination
• Given
• Destination location
• Position of self
• Position of the neighbors

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Data Link Layer

• Maintains position of self and neighbors
• Main radio receiver
• Runs at 10 kbps
• Locally unique channel, globally reused
• Wake-up radio receiver
• Global broadcast channel
• Used to wake-up neighboring nodes

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Routing Protocol Characteristics

• Ensure network survivability
• Low energy (communication and computation)
• Tolerant and robust to topology changes
• Scalable with the number of nodes
• Light weight

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Network Survivability
Network survivability is application-dependent
coverage may also be an issue
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Proactive vs. Reactive Routing

• Proactive routing maintains routes to every other
  node in the network
• Regular routing updates impose large overhead
• Suitable for high traffic networks

• Reactive routing maintains routes to only those
  nodes which are needed
• Cost of finding routes is expensive since
  flooding is involved
• Good for low/medium traffic networks

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Traditional Reactive Protocols
Destination
Source

• Finds the best route and then always uses that!
• But that is NOT the best solution!
• Energy depletion in certain nodes
• Creation of hotspots in the network

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Directed Diffusion
Setting up gradients
Source
Destination

• Destination initiated
• Multiple paths are kept alive

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Energy Aware Routing

• Destination initiated routing
• Do a directional flooding to determine various
  routes (based on location)
• Collect energy metrics along the way
• Every route has a probability of being chosen
• Probability ? 1/energy cost
• The choice of path is made locally at every node
  for every packet

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Setup Phase
Directional flooding
Sensor
Controller
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Data Communication Phase
Each node makes a local decision
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Whats The Advantage?

• Spread traffic over different paths keep paths
  alive without redundancy
• Mitigates the problem of hot-spots in the network
• Has built in tolerance to nodes moving out of
  range or dying
• Continuously check different paths

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Network Lifetime

• Nodes have fixed initial energy 150 mJ
• Measure the network lifetime until the first node
  dies out
• Diffusion 150 minutes
• Energy Aware Routing 216 minutes

44 increase in network lifetime
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Findings on Routing

• Mitigation of hot-spots is crucial in energy
  constrained networks
• Simulation results suggest that probabilistic
  routing increases time until the first node dies
  out
• Analysis is required to show the theoretical
  optimum
• Network performance is application dependent
  need to clearly identify metrics of interest

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Mote Development
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Smart Dust

• COTS Dust commercial-off-the-shelf components
• Daft Dust low power circuit techniques
• Clever Dust low energy microcontroller
• Sapient Dust novel microcontroller

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COTS Dust

• Create a network of sensors
• Explore system design issues
• Provide a platform to test Dust components
• Use off the shelf components

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COTS Dust - RF Motes

• Atmel Microprocessor
• RF Monolithics transceiver
• 916MHz, 20m range, 4800 bps
• 1 week fully active, 2 yr _at_1

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COTS Dust - Optical Motes

• Laser mote
• 650nm laser pointer
• 2 day life full duty

• CCR mote
• 4 corner cubes
• 40 hemisphere

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Smart Dust

• Distributed sensor networks
• Sensor nodes
• Autonomous
• 1mm3
• Interfaces
• Power
• battery, solar, cap.
• Communication
• LOS Optical (CCR, Laser)
• Challenges
• 1 Joule
• 1 kilometer

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Smart Dust Goals

• Autonomous sensor node (mote) in 1mm3
• Multiple sensors temperature, light, vibration,
  etc.
• Thousands of motes
• Demonstrate useful/complex integration in 1mm3

0.25µm CMOS double poly, 5 metals
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Motivation

• CoolRisc 81 µcont. standby power 100nW
• Þ 1J consumed in lt 28 hours
• 1µW/mm2 light incident on 1mm2 solar cell
• Goal Reduce static consumption and minimize
  energy/instruction

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Research Goals

• System integration and miniaturization
• Low-energy microcontroller
• lt 0.1pJ/instruction/bit
• lt 10nW leakage
• 1-100kHz operation
• Novel microcontroller architecture
• Reconfigurable datapath components
• Data-driven operation
• Element-level power cycling

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System Energy Budget Items

• Solar Cell
• Full sun 0.1mW/mm2, 1J/day/mm2
• Indoor 0.1-10µW/mm2, 1-100mJ/day/mm2
• Photodiode Receiver 0.1nJ/bit (projected by
  Leibowitz)
• Accelerometer 0.5nJ/measurement (literature)
• ADC 1nJ/sample (goal for Markow)
• Transmitter 13nJ/bit (measured on charge pump)
• Controller lt 2.8pJ/instruction/bit (CoolRISC 81)

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Smart Dust Components
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Dust Components

• Thick film battery 1mm3, 1 J storage
• Power capacitor 0.25mm3, 1uJ storage
• Solar cell 1x1x0.1mm3, 0.1mW generation
• CMOS controller 1x1x0.1mm3
• Sensor 0.5x0.5x0.1mm3
• Passive CCR communications
• 0.5x0.5x0.1mm3, 10kbps, 1uW, 1km
• Active laser communications
• 1x0.5x0.1mm3, 1Mbps, 10mW, 10km
• Total volume lt 1.5 mm3
• Total mass lt 5 mgm

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High Quality CMOS Mirror

• CMP aluminum surface
• Single crystal silicon mirror body for flatness
• Torsional hinges
• Multi-layer staggered torsional electrostatic
  combdrive (MSTEC) actuation
• Reduced actuation voltage
• More complex actuation mechanism possible

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Optical CommunicationCorner Cube Reflector (CCR)
Imager
Laser
Courtesy of Victor Hsu
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Optical CommunicationCorner Cube Reflector (CCR)
Imager
Laser

• Capacitive actuation
• 118bps w/ 8V actuation
• 670 pJ/bit
• 150m demonstrated range

Courtesy of Victor Hsu
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CCR Interogator
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1 Mbps CMOS Imaging Receiver
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Signal Conversion
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Turbulent Channel
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Optical Communication vs. RF

• Pro
• Low power
• Small aperture
• Spatial division multiplexing
• High data rates
• LPI/LPD
• Baseband coding

• Con
• Line of sight
• Atmospheric turbulence

Scintillation
Low probability of intercept / Low probability of
detection
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Optical Communication Advantages

• Large antenna gain
• Small radiator
• Spatial division multiple access (SDMA)
• Received power µ1/d2
• RF received power µ1/d2?7
• Output efficiency
• Optical
• Laser slope efficiency
• Poverhead 1uW-100mW
• RF
• GMSK slope efficiency 50
• Poverhead 1-100mW

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Dust Delivery

• Floaters
• Autorotators
• solar cells
• Rockets
• thermopiles
• MAVs

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Airborne Dust
Maple seed solar cell MEMS/Hexsil/SOI
1-5 cm
Controlled auto-rotator MEMS/Hexsil/SOI
Rocket dust MEMS/Hexsil/SOI
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Micro Air Vehicle (MAV) Delivery

• 60 mph
• 18 min
• 1 mi comm

Built by MLB Co.
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Project Status
300µm
Courtesy of Victor Hsu
360µm

• 80 mm3
• Circuits 0.25 µm CMOS
• CCR Cronos MUMPS

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Project StatusSecond Attempt
CCR
Photodiode
Charge Pump

• Compact circuit design with photocell, reset
  circuit, and electronics.
• Reduced size to 300µm x 360µm
• Digital circuits placed under ground pad to
  reduce area.

Vdd
GND/ LFSR
Reset
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Project StatusFirst and Second Attempts

• Integrated electronics and CCR on 5mm 1.4V
  battery
• Verified photocell and CCR operation but
  electronics were faulty due to charge
  accumulation during fabrication

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Daft Dust System Architecture
Corner Cube Reflector

• Autonomous platform to demonstrate basic concepts
• Optical signal receiving
• Data processing
• Synchronous information transmission

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Packaging
Daft Dust package by Lixia Zhou
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Daft Dust Device

• 63 mm3
• Circuits 0.25 µm CMOS
• digital circuits underneath ground pad
• metal shields to prevent photogenerated carriers

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Demonstrated Functionality
R
gain
Clock Signal
Laser Input
A
v
i
photo
Transimpedance Amplifier
20 bit Shift Register w/Training Sequence
LFSR Pseudorandom Generator
Charge Pump
- 2 mm2 solar cell power source
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Clever Dust Processor Features

• Laser reprogrammable
• Transmit automatically in case receiver doesnt
  work
• Asynchronous transceiver architecture
• Probe chip to determine baud rate
• May have programmable baud clock
• Store and transmit (fake) sensor data
• Basic processing capabilities
• Princeton architecture

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Novel Architecture Design Method

• Minimize energy through architecture
• Minimum energy Þ ASIC implementation
• Dynamic reconfigurability
• How much is necessary tradeoff with ASIC
  mapping
• Energy driven operation modes
• Typical application scenario to guide design

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Sapient Dust Top-Level Diagram
Sensors
Timer Bank
Setup Memory
Power Supply
ADC
Receiver Front End
Reconfigurable Datapath Components
CCR Driver
Real Time Clock
SRAM
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Power Cycling

• 8 bit comparator
• 2.9nW powered up
• 6.4pW turned off
• Idle gt 33ms, turn off

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Comparison

• Sapient energy estimations from Epic Powermill at
  1kHz

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Findings

• Custom circuits 9x better than standard cell
• Low-energy microcontroller
• Feature laser reprogrammable
• Techniques execution sequencing and
  element-level clock gating
• Novel microcontroller architecture
• Reconfigurable datapath
• Data-driven operation
• Extreme power cycling
• 28x better than CoolRisc

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Power and Energy

• Sources
• Solar cells
• Thermopiles
• Storage
• Batteries 1 J/mm3
• Capacitors 1 mJ/mm3
• Usage
• Digital control nW
• Analog circuitry nJ/sample
• Communication nJ/bit

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UWB Radar

• Ultra Wideband RF Sensors
• UWB radar emission are typically between 100MHz
  and 3 GHz
• Fractional bandwidth is very large 0.2
• Exceptional resolution
• Ability to penetrate common materials
• Walls
• Light foliage
• Detect intrusion by change in impulse response of
  the environment rather than on Doppler dependency
  as in traditional radar systems

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UWB Sensors

• Light weight, battery-powered imaging
  interferometric UWB radars are being utilized by
  police to improve situational awareness during
  hostage and forced entry situations
• The future hold promising potential for UWB based
  sensors providing exceptional intrusion detection
  for protection of critical infrastructures from
  terrorist activities

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WSN Summary
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Sensor Networks Applications

• Military applications
• Environmental applications
• Health applications
• Home applications
• Other commercial applications

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Military Applications

• Monitoring friendly forces, equipment and
  ammunition.
• Battlefield surveillance
• Reconnaissance of opposing forces and terrain
• Targeting
• Battle damage assessment
• Nuclear, biological and chemical attack detection
  and reconnaissance.

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Environmental Applications

• Forest fire detection
• Biocomplexity mapping of the environment
• Flood detection
• Precision agriculture

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Health Applications

• Telemonitoring of human physiological data
• Tracking and monitoring doctors and patients
  inside a hospital
• Drug administration in hospitals

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Home Applications

• Home automation
• Smart environment
• Home security

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Other Commercial Applications

• Environmental control in office buildings
• Interactive museums
• Detecting and monitoring car thefts
• Managing inventory control
• Vehicle tracking and detection

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Factors influencing sensor network design

• Fault tolerance
• Scalability
• Production costs
• Hardware constraints
• Sensor network topology
• Environment
• Transmission media
• Power consumption

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Conclusion

• Rapid advances in WSN technology promises
  ubiquitous deployment in the years ahead
• Significant issues in regard to Orwellian
  consequences of the technology

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Delay Tolerant NetworksWSN References
Thanks, Kwang!
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Thank you!
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