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					Wireless Sensor Networks and
Real-World Applications
           Nirupama Bulusu
       Portland State University
On Sensor Networks
“One of the 10 technologies that will change the
                  MIT Technology Review, 2003

“More than half a billion sensor nodes will ship
  for wireless sensor applications in 2010 for an
  end-user market worth at least $7 billion”
“Demand growing at 300% between 2004 and
              ON World, a wireless research firm.
Burgeoning Research and
Commercial Activity
NSF Research Centers
  Center for Embedded Networked Sensing

More than 100 Companies (many started after
  Crossbow, Sensoria, Millennial Net, Ember
    Corporation, Dust Networks, Chipcon,
    Arched Rock Corporation, Moteiv
Push: Technology Trends
Moore’s law
Energy capacity
Micro-electro Mechanical
  Systems (MEMS)
System-on-chip Integration
Wireless Sensor Networks
                        • Micro-sensors, on-board
                        processing, wireless
  Sensing   Computing   interfaces feasible at very
                        small scale--can monitor
                        phenomena “up close”

                        • Enables spatially and
                        temporally dense
                        environmental monitoring

Telos Mote
(Source: David Culler, Berkeley)
Pull: Real-world Applications
• Most applications fall into of
  one of three categories*
   – Monitoring Space
   – Monitoring Objects
   – Monitoring Interactions of
     Objects and Space

 *   Classification due to Culler, Estrin, Srivastava
Monitoring Space
• Environmental and Habitat
• Precision Agriculture
• Indoor Climate Control
• Military Surveillance
• Treaty Verification
• Intelligent Alarms
Example: Precision
The “Wireless Vineyard”
  – Sensors monitor
    temperature, moisture
  – Roger the dog collects the

  Source: Richard Beckwith,
  Intel Corporation
Monitoring Objects
• Structural Monitoring
• Eco-physiology
• Condition-based
• Medical Diagnostics
• Urban terrain mapping
Example: Condition-based
Intel fabrication plants
   – Sensors collect vibration
     data, monitor wear and
     tear; report data in real-
   – Reduces need for a team
     of engineers; cutting costs
     by several orders of
Monitoring Interactions
between Objects and Space
•   Wildlife Habitats
•   Disaster Management
•   Emergency Response
•   Ubiquitous Computing
•   Asset Tracking
•   Health Care
•   Manufacturing Process Flows
Example: Habitat Monitoring
The ZebraNet Project:
Collar-mounted sensors
  monitor zebra movement in

  Source: Margaret Martonosi, Princeton University
                                                       Tracking node with
                                                       CPU, FLASH, radio
                        Data                           and GPS
Store-and-forward                      Data
communications                                                 Base station
                                                               (car or plane)

              Data                                Data

 Sensor Network Attributes     ZebraNet           Other Sensor Networks
 Node mobility                 Highly mobile      Static or moderate mobile
 Communication range           Miles              Meters
 Sensing frequency             Constant sensing   Sporadic sensing
 Sensing device power          Hundreds of mW     Tens of mW
The Computing Challenge
Build Robust, Long-lived systems that can be un-
  tethered (wireless) and unattended
    Communication will be the persistent primary
      consumer of scarce energy resources (MICA Mote:
      720nJ/bit xmit, 4nJ/op)
    Autonomy requires robust, adaptive, self-configuring

Leverage data processing inside the network
   Exploit computation near data to reduce
     communication, achieve scalability
   Collaborative signal processing
   Achieve desired global behavior with localized
     algorithms (distributed control)
Some Problems
Calibration =
 correcting                 Networking
 systematic errors in              low-power media
 sensor data                access; power-aware
                            routing of data packets
  Causes: manufacturing,
   environment, age, crud
Localization =              Macro-programming=
  establish spatial         high-level program for a
  coordinates for           sensor network; not
  sensors and target        low-level programs for
  objects                   individual sensors
In-depth: Localization
Mathematically                      10

Given: xi, cij for some i, j €
  {1, …N}                                   C5.11 = 5
Estimate: xs for any s

                                    6                5

1 (0,0,0)
                2 C23 = 5       3
Localization System
                                        This step applies to distributed
          Stitching and                 construction of large-scale
           Refinement                   coordinate systems

                                        This step estimates target
Coordinate System   Coordinate System   coordinates (and often other
    Synthesis           Synthesis       parameters simultaneously)

                                        Parameters might include:
     Filtering            Filtering
                           Filtering    •Range between nodes
     Filtering             Filtering    •Angle between nodes
   Parameter           Parameter        •Psuedo-range to target (TDOA)
    Parameter          Parameter
                        Parameter       •Bearing to target (TDOA)
   Estimation          Estimation
                       Estimation       •Absolute orientation of node
    Estimation          Estimation
                                        •Absolute location of node (GPS)
Example of a Localization
SHM system, developed at Sensoria
                               Each node has 4 speaker/
                               microphone pairs, arranged
Microphone                     along the circumference of the
   Speaker                     enclosure. The node also has a
                               radio system and an absolute
                               orientation sensor that senses
                    12 cm      magnetic north.

   Source: Lewis Girod, UCLA
System Architecture
Ranging between nodes based on
  detection of coded acoustic signals,
  with radio synchronization to
  measure time of flight
Angle of arrival is determined through
  TDOA and is used to estimate
  bearing, referenced from the
  absolute orientation sensor
An onboard temperature sensor is used
  to compensate for the effect of
  environmental conditions on the
  speed of sound
System Architecture
Nodes periodically emit acoustic pulses. Other nodes detect
  these pulses and compute a range and angle of arrival.
Range data, angle data, and absolute orientation are broadcast N
  hops away.
Based on this table of ranges, angles, and orientations, each
  node applies a multi-lateration algorithm with iterative outlier
  rejection to compute a consistent coordinate system.

            Range, Angular Data      Multilat Engine

            Range, Angular Data      Multilat Engine

            Range, Angular Data      Multilat Engine
In-depth: Cane-toad
    Joint work with colleagues at
   University of New South Wales,
Cane Toads’ Distribution in Australia (2003)   Figures of Cane Toad

In-expensive real-time monitoring system (set up
  and maintenance cost) to detect Cane toads and
  their impact (Presence and Area)
 Detecting Frogs by Their
 Acoustic features can be used to distinguish the
 vocalizations of different amphibians.            (call rate, call
 duration, amplitude-time envelope, waveform periodicity, pulse-
 repetition rate, frequency modulation, frequency and spectral

Frog 1

Frog 2

Frog 3
(Cane toad)

               Waveform Figures of Three Different Frogs’ Calls
How Our System Works
•  Input acoustic signal is converted into a spectrogram of time-
frequency pixels by a Fast Fourier Transform (FFT) algorithm.
    • Our system examines each slice of the spectrogram (1 millisecond)
    and tries to estimate frequency local peaks.
    •  Frog species are identified based on the comparisons of these
    frequency local peaks with some classifiers.
• Quinlan’s machine learning system, C4.5 used to build classifiers.

        Frog 1

        Frog 2

        Frog 3
        (Cane toad)

                      Spectrogram Figures of Three Different Frog’s Calls
Application Challenges on
Device Resources
Very High Frequency Sampling (> 10
 KHZ, the rule of double the highest
Machine Learning

Acoustic Signal Processing

Motivation: Increased
sensing coverage at
comparable cost
Design Features

Achieve (Very)
  High Sampling
  Rate in Mica
  motes through
                   Acoustic Signal of a frog’s call
Compression and
                  collected from the field (Top). The
  noise-          same signal after compression and
  reduction.      decompression (Bottom) .
The Future: Participatory
Sensor Networks*
Sensor networks for urban applications will
 form the “next tier of the Internet”+
  - Leverage Cell phone installed base of
    acoustic and image sensors
  - Using internet search, blog, and personal
    feeds, along with automated location tags, to
    achieve context, and in network processing
    for privacy and personal control
  *   Source: Deborah Estrin, UCLA
  + Source: David Culler Berkeley
For more information
“Wireless Sensor Networks: A
  Systems Perspective”,
  Nirupama Bulusu and
  Sanjay Jha (editors),
Artech House, Norwood, MA,
August 2005.