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Title: WSN Wireless Sensor Networks and Applications

WSNWireless Sensor Networksand Applications

Dwight Borses Member of the Technical

Staff National Semiconductor, Irvine, CA

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
  • 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
  • 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
  • Exploit non-uniformities when you have them
  • Tiered architecture
  • Making Systems Long-lived
    • Robustness to dynamic conditions
    • Make system self-configuring and
  • 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
  • Possible Applications for WSN

    Environmental Monitoring

    Mars WSN

    Scott Burleigh JPL / Cal Tech 19 Jan 2004

    UCLA Wildlife Habitat Monitoring
    • Instrumented with cameras and microphones
    • Task is to detect presence of bird and photograph
  • 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 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

  • Power management technology for operational endurance
  • Resulting in
  • Useful, long-lasting, reliable, survivable,

    programmable systems

  • Sensor Technology

    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
  • 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
  • J. Storrs Halls Utility FogA Swarm of Nanobot


    Foglets can take the shape of virtually anything,

    and change shape on the fly.

    Nanosensors Myth or Reality
    • Garnered attention of scientists, venture

    capitalists, government officials, industry

  • Revenues expect to reach 200B by 2006
  • Future depends on expenditures and application emergence
  • Industry Alliance
  • NanoBusiness Alliance (
  • Extensive library of white papers
  • 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

  • Nanosize MachinesNASA Ames Research Center

    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)
  • 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
    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 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

  • 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

    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
    Vehicle and Driver Safety Systems
    • Imaging Sensors
    • Driver quality monitoring
    • Front and rear view
    Vehicle and Driver Safety Systems
    • Imaging Sensors
    • Machine vision
    • Real time
    • Lane departure safety warning
    Vehicle and Driver Security Systems
    • Remote vehicle monitoring
    • OnStar
    • Vehicle status reporting
    • Remote control of vehicle systems
    • LoJack
    • Location determination and reporting
    Key Technical ChallengesWSN Networking
    • Efficient networking methods
    • Enable rapid, ad hoc networking of any number of
  • 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

  • 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


  • Key Technical ChallengesWSN Operational Lifetime
    • Effective hardware design for reliability and
  • Effective power management efficiency for maximum

    endurance and network operational lifetime

  • 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

  • Two-Pronged Approach to Retrofit the Internet
  • Extending the Internet Above and Below
    • PlanetLab
    • Extend the Internet upward with an overlay
  • 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
  • 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.

    A Dispersed Low-Cost Wireless Sensor and Actuator

    • 300 companies have designed Motes (volumes gt 5K
  • 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

  • Intels Wireless Vineyard

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

    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!

    Category: Architecture

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