Intelligent Sensing and Sensor Web: Design and Deployment Experiences

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Intelligent Sensing and Sensor Web Design and
Deployment Experiences
IEEE SIMA 2008 Nov. 18, 2008
WenZhan Song Washington State University
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Background - Sensorweb
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Sensor Web Enablement Framework
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Sensor Web Desires

• Quickly discover sensors (secure or public) that
  can meet my needs location, observables,
  quality, ability to task
• Obtain sensor information in a standard encoding
  that is understandable by me and my software
• Readily access sensor observations in a common
  manner, and in a form specific to my needs
• Task sensors, when possible, to meet my specific
  needs
• Subscribe to and receive alerts when a sensor
  measures a particular phenomenon

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OASIS System OverviewOptimized Autonomous Space
In-situ Sensorweb

• OASIS will have two-way communication capability
  between ground and space assets, use both space
  and ground data for optimal allocation of limited
  power and bandwidth resources on the ground, and
  use smart management of competing demands for
  limited space assets.
• 1. In-situ sensor-web autonomously determines
  network topology, bandwidth and power allocation.
  
• 2. Activity level rises causing self-organization
  of in-situ network topology and a request for
  re-tasking of space assets.
• 3. High-resolution remote-sensing data is
  acquired and fed back to the control center.
• 4. In-situ sensor-web ingests remote sensing data
  and re-organizes accordingly. Data are publicly
  available at all stages.

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Why Study Volcano?

• Volcanoes are everywhere - on Earth and beyond
• Magmatism is of fundamental importance to
  planetary evolution and essential to life as we
  know it
• On Earth, volcanic risk is increasing rapidly as
  human population increases
• Volcanic Earthquakes
• Directed Blast
• Tephra
• Volcanic Gases
• Lava Flows
• Debris Avalanches, Landslides, and Tsunamis
• Pyroclastic Surge
• Pyroclastic Flows
• Lahars

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Mount St. Helens an active volcano
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Mount St. Helens an active volcano
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Volcano Crater a harsh environment
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Volcano Crater a harsh environment
Camera and gas sampler spider shown
pre-positioned at Sugar Bowl on 14 January 2005.
Shortly after this picture was taken, spider was
deployed within 100 m of extrusion site.
So we need smarter sensors and networks to ensure
continuous, spatially dense monitoring in
hazardous areas (OASIS)
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Old Stations Technology

• Several types of systems in place
• Dual frequency GPS with digital store and forward
  telemetry when polled not continuous!
• Short period seismic stations with geophones and
  analog telemetry not digital
• Broad band seismic stations with digital
  telemetry cost above 10K and several days to
  deploy
• Microphones for explosion detection added to the
  short period seismic stations

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Application Characteristics

• Challenging environment
• Extreme weathers temperature (baking/freezing),
  wind, snow, rain,
• Dynamic environment rock avalanche, land
  sliding, gas/steam emissions, volcanic eruptions,
  earthquake
• Battery is the only reliable energy source. Solar
  panel is possible in summer, but frequently
  covered by ashes
• Stations are frequently destroyed, some hot spot
  can only be accessed through air drop
• Low signal noise ratio of both communication and
  sampling
• High data rate, and require network synchronized
  sampling
• Seismic sensor 100-200Hz, 16 bit/sample
• Infrasonic sensor 100-200Hz, 16 bit/sample
• Lightning sensor 1Hz, 16 bit/sample
• GPS raw data 200-300 bytes/10 seconds

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Outline

• System Design
• System Deployment
• Lessons

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System Design
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OASIS System Overview
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Ground Segment Overview
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Ground Segment Overview

• In-Situ Sensor Web architecture
• 1. Sensor-nodes are self-organized to form
  network data flow forms a dynamic data diffusion
  tree rooted at gateway.
• 2. Smart bandwidth and power management according
  to environmental changes and mission needs.
• 3. Remote control center manages network and
  data, and interacts with space assets and
  Internet.

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802.15.4 antenna
GPS antenna
old spider design
accelerometer in sandbox as a seismometer
New spider design
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Hardware Design
iMote2
UBlox GPS
MDA320

• Seismic
• Infrasonic
• Lightning

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Hardware Design

• Controller Intel Mote2
• CPU PXA271 13-416MHz with Dynamic Voltage
  Scaling. 13MHz operates at a low voltage (0.85V)
• Storage 256kB SRAM, 32MB SDRAM, 32MB Flash
• 802.15.4 radio CC2420
• Other Hardware Components
• Seismic low noise MEMS accelerometer (Silicon
  Designs Model 1221J-002)
• Infrasonic low range differential pressure
  sensor (All Sensors's Millivolt Output Pressure
  Sensors Model 1 INCH-D-MV)
• Lightning (for ash detection) custom USGS/CVO
  RF pulse detector
• GPS (for deformation measurement) L1 GPS (Ublox
  model LEA-4T)
• Customized SmartAmp 2.4GHz, 250mW, amplify -3dBm
  input to 20dBm output.
• Antenna 12 dB omni, withstand extreme wind
  speeds in excess of 130 MPH
• Battery a bundle of Cegasa air-alkaline
  industrial batteries

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Lab Environment Setup
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Lab Environment Setup
GPS receiver and distributor
DAC for data input
Lab Mini-net
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Ground Segment Software Components

• In-Situ Sensor Web Components Relationship
• 1. Topology Management Routing, Bandwidth
  Management and Power Management runs autonomously
  to meet mission needs and optimize the resource
  usage.
• 2. Network Management module enable network
  status monitoring and external command control
  from Space element, users or scientific agent
  softwares.
• 3. Situation Awareness middleware learns
  environment situations and allocates network
  communication and computation resources
  accordingly..

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Data Management

• Port to USGS existing tools (e.g., EARTHWORM,
  VALVE, SWARM)
• database for real-time data storage
• web tools for seismic, GPS data visualization
• Sensor web services for Ground lt-gt Space Linkage
  and future inter-geo-system data sharing

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Network Management

• Monitor the network status
• Network topology
• Node battery status
• Packet delivery ratio
• Sensor status and sampling rate
• Command Control
• Inject feedback from space asset, user, and
  scientific agent software
• Network adjust resource allocation accordingly
• Programming over the air
• Upgrade software without risky and expensive
  field retrieval

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Node Software Architecture
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Intelligent Sensing Features

• Online Configurable Sensing
• Environment Situation Awareness
• Node Situation Awareness
• Situation-driven Synchronization
• Situation-driven Networking

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Online Configurable Sensing
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Online Configurable Sensing

• Configurable Parameters
• Change sampling rate
• Add/Delete sensor
• Change data priority
• Change node priority
• Parameter will be saved to Flash in case node
  reboots.
• Platform Independent
• Support for different hardware platforms
• Support for different applications

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Online Configurable Sensing

• Configurable Data Processing Tasks

• RSAM calculation
• STA/LTA event
• detection
• Threshold based
• event detection
• Prioritization
• Compression

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Online Configurable Sensing

• Light-weight Remote Procedure Call Mechanism
• Module designers decide which interface or
  command to be allowed to call remotely, by simply
  adding _at_rpc()
• interface SensingConfig _at_rpc()
• It will translated to XML
• ltSmartSensingM.SensingConfig.setSamplingRate
  commandID"23" componentName"SmartSensingM"
  functionName"setSamplingRate" functionType"comma
  nd" interfaceName"SensingConfig"
  interfaceType"SensingConfig" numParams"2"
  provided"1" signature" command result_t
  SmartSensingM.SensingConfig.setSamplingRate (
  uint8_t type, uint16_t samplingRate ) "gt
• ltparamsgt
• ltparam0 name"type"gt
• lttype typeClass"unknown"
  typeDecl"uint8_t" typeName"uint8_t" /gt
• lt/param0gt
• ltparam1 name"samplingRate"gt
• lttype typeClass"unknown"
  typeDecl"uint16_t" typeName"uint16_t" /gt
• lt/param1gt
• lt/paramsgt
• ltreturnType typeClass"unknown"
  typeDecl"result_t" typeName"result_t" /gt
• lt/SmartSensingM.SensingConfig.setSamplingRategt

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Online Configurable Sensing

• Compiling time XML file generation
• Run-time memory address access
• Run-time function call

Marionette, IPSN 2006
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Online Configurable Sensing

• Cascades reliable fast flooding protocol

• Opportunistic broadcast flow
• Parent-children monitoring
• Explicit and implicit ACK
• Retry and request

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Online Configurable Sensing

• 1000 packets injected
• 2 sec interval
• 15 nodes
• 967 vs. 1735

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Online Configurable Sensing

• 1000 packets injected
• 2 sec interval
• 15 nodes
• 303.4 ms vs. 344.1 ms

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Environment Situation Awareness

• Learn environment situations through data
  processing and correlations, with the guide of
  domain sciences
• Changes of volcanic activity will cause sensors
  to adjust sampling autonomously. For instance,
  active seismic activity trigger sampling rate
  increases of gas sensors
• Situation-driven data prioritization
• Priority of sensor types. For instance, during
  eruption, gas sensor reading is more useful than
  seismic reading on the other hand, during
  background activity level, seismic reading is
  more important.
• Priority of node locations. If we can not get
  data from all sensors, we shall be able to
  automatically identify the minimum set of sensors
  that will provide mission critical data.

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Environment Situation Awareness
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Environment Situation Awareness

• Threshold based event detection
• Compare raw data to threshold
• Lightning sensor data
• STA/LTA event detection
• Short-Term Average and Long-Term Average data are
  compared
• Event is triggered when ratio is over threshold

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Environment Situation Awareness

• RSAM value calculation - Real-Time
  Seismic-Amplitude Measurement

• RSAM period 1 sec
• STA window 8 sec
• LTA window 30 sec
• Trigger ratio 2

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Environment Situation Awareness
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Environment Situation Awareness

• Prioritization
• Assigning priorities based on data and event type
• Assigning retransmission opportunities based on
  priorities

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Node Situation Awareness

• Fault Detection
• Sensor board disconnection
• All value are 0xFFFF
• Sensor broken (including GPS)
• Sensing value are much different from other nodes
• System error
• Low battery, low memory space

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Node Situation Awareness

• Watchdog mechanism to restart nodes
• If watchdog timer was not reset for certain time
  periods.
• If radio did not send or receive for 5 minutes
  (when the network data rate is high).
• If some memory buffer is full and never get
  cleared for 5 minutes.
• Those are crucial for a long-term deployed
  system
• Our network has been continuously running after
  deployed, never died till now.

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Situation-driven Time Synchronization

• Design goal
• Synchronize with UTC time
• Synchronized sampling different nodes sample
  channels at same time point, 1ms resolution
• Hybrid Time Synchronization
• Stay synchronized with GPS if GPS is good
• Switch to modified FTSP (Flooding Time
  Synchronization Protocol, Maróti, Sensys 2004)
  when GPS is disconnected

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Situation-driven Time Synchronization

• Modified FTSP
• Dynamic root selection
• Smallest node id with GPS connected
• Smallest node id if none has GPS

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root
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root
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6
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Situation-driven Time Synchronization

• GPS Time Synchronization
• Not trivial due to OS delays

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Situation-driven Time Synchronization
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Situation-driven Networking

• Self-organizing and Self-healing routing
• Link metrics based on cc2420 LQI (Link Quality
  Indicator)
• Fast switch and fast recovery
• Qos-aware packet scheduling
• VWFQ (Variable Weighted Fair Queue)
• Energy-efficient, traffic-adaptive (supports
  high-throughput), and scalable MAC protocol
• TreeMAC localized TDMA for energy-efficient
  high-rate data collection

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Situation-driven Networking TreeMAC

• Most of existing sensor MAC protocol assume low
  duty-cycle applications (e.g., sample every 5
  minutes or so)
• Periodic wakeup, sense and sleep to reduce idle
  listening energy waste
• Until recently, some attention has been paid to
  high data-rate application needs
• Z-MAC (SenSys 2005)
• Funneling-MAC (SenSys 2006)
• In fact, many applications have high data (102
  to 105 Hz sampling rate), including industrial
  monitoring, civil infrastructure, medial
  monitoring, industrial process control,
  structural health monitoring, fluid pipelining
  monitoring, geological environment monitoring.
• Needs an Energy-efficient, traffic-adaptive, and
  scalable MAC protocol for data collection
• Support high-throughput if traffic is heavy
• Conserve energies if traffic is light

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Situation-driven Networking TreeMAC

• TreeMAC is inspired from the following key
  observations
• The majority data flows are from sensors to sink
  or from sink to sensors, not random any-to-any
  communication tree-based routing.

• Existing MAC protocols (including CSMA-based and
  Z-MAC) tend to give every node equal channel
  access opportunity, which is not fair for data
  collection the node closer to the gateway
  forwards more data, and shall gain more channel
  access opportunity.

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Situation-driven Networking TreeMAC

• Time divided to cycles
• Cycle divided to N frames (predefined, e.g., 16
  in the illustration).
• Frame divided to 3 slots
• Slot 0 red Slot 1 green Slot 2 blue
• Each node picks slot according to its tree-level
  only (e.g., level3)
• Each node can send only in picked slot per frame,
  listen/sleep in the other two
• Mitigate vertical 2-hop interference
• Each node assign its childrens frames in the
  beginning of each cycle, such that childrens
  frames does not overlap/conflict with each other.
• Mitigate horizontal 2-hop interference

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Situation-driven Networking TreeMAC
1
0
1
0
0
1
1
1
2
2
2
0
0
0
0
1
Every node get number of slots proportional to
its bandwidth demand
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Situation-driven Networking TreeMAC
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Deployment Experiences

• On Oct. 15, 2008, 5 stations air-dropped into the
  crater of Mount St. Helens

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SEP
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VALT
http//van-database.vancouver.wsu.edu
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10/15/08
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Deployment Experiences

• On Oct. 16, Node 15 disappears

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Node 15 disappears in 18 hours, because
Node 15
15 minutes after the helicopter left, it
disappears again
10/22/08
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Fix it by repairing the voltage regulator circuit
and added more stones to prevent
2 days later, volcano cowboys hike to find the
mystery
10/24/08
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Wind speed peaks at 120 miles/hour
Infrasonic sensor records the unusual gust
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Weather affects link quality
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48-Hour Period Starting 5PM 11/4 Forward 2
Days
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Magnitude 1 Earthquake

• Mount St. Helens
• 3 km depth
• November 4, 2008

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OASIS 2 Hz geophone
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OASIS MEMS accelerometer
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CVO 2 Hz geophoneanalog
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CVO piezo accelerometer
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CVO digital broadband
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Deployment Lessons

• Hardware verification is as important as software
  verification, and shall be done earlier too.
• One month before the deployment, we start to look
  at how to make the transmission range longer than
  300 meters
• CC2420 radio has at most 0 dBm output, some nodes
  only achieve -3dBm after signal attenuation by
  circuit. There is no commercial 802.15.4
  amplifier supports input less than 0 dBm.
• Customized SmartAmp 2.4GHz, 250mW, accept -3dBm
  input.

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Deployment Lessons

• Quantitative measurement is essential, do not
  just trust experiences
• Some articles saying there is a correlation
  between RSSI and LQI it is NOT true!
• After we added RF amplified, the measurement
  device (e.g., Anritsu S332D Site Master) shows it
  has strong RSSI even 400 meters away, but the
  network just does not connect well.
• We decided to write our RSSI and LQI measurement
  program TOSBaseLQI it shows LQI is below 70
  (90-110 means good), though RSSI is -30 dBm (well
  above -90 dBm receiver sensitivity threshold).

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Deployment Lessons

• Open for any possibility need critical thinking
  skills.
• RSSI is high but LQI is low with the
  TOSBaseLQIs help, we tried to change everything,
  almost think nothing is trustable ? Eventually,
  we found that the small L-connector between
  CC2420 radio and amplifier adds sufficient noises
  to make S/N ratio too low before amplification.
• Some iMote2 with 4-digit serial number will die
  after 8 hours running unknown reasons, while
  those with 7-dgit serial number are okay.
• One node (node 10)s signal quality will decrease
  during 1PM-6PM sunny days (when temperature is
  high)
• Unbelievable after we changed the high-quality
  antenna cables (LMR_at_-400-ULTRAFLEX COAXIAL CABLE
  TIMES MICROWAVE SYSTEMS) to some lower quality
  lab cables (BELDEN 8262M17/155-00001 MIL-C-17
  16428 2137 1922 ROHS), the problem is gone. This
  problem does not happen in other nodes.
• During deployment, USGS replaced the cables back
  to the high quality one the problem did not
  repeat in field so far.
• Still do not know exact reasons though it must
  be related to temperature!

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Sensor Data Quality
VALT BHZ
VALT 24 bit broad-band (the gold standard) in a
quiet location. It costs more than 10K and is
deeply buried in the ground, which takes several
days to deploy this station.
SEP EHZ2 Hz geophoneanalogburied, vertical
NED EHZpiezo accelerometerburied, vertical
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Sensor Data Quality
N10 EHZ2Hz geophones, buried, vertical
N14 EHZ MEMS accelerometers in sandbox
N15 EHZMEMS accelerometers, buried, vertical
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Sensor Data Quality
N16 EHZMEMS accelerometers, buried, vertical
N18 EHZ 2Hz geophones, buried, vertical
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Management Lessons
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Management Lessons

• Developing a complete system for 1-year running
  is very different from writing research paper
• Management overheads
• Wiki, SVN, Mailinglist, Google docs and Citeulike
  are useful
• Testbed setup, simulation and debug tools need to
  be invented.
• Not just simulations
• Not just piece by piece when put together,
  things are different
• Black box test and end-to-end performance test.

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Management Lessons

• Benefit from the defined design philosophy for
  the team
• TinyOS 1.x open source sensor network OS.
  Learning curve is sharp, due to few
  documentations
• Programming philosophy and tips need to be
  accumulated and shared
• http//sensorweb.vancouver.wsu.edu/wiki/index.php/
  Tips
• Lacks of test in real deployment lots of bugs
  to fix
• http//sensorweb.vancouver.wsu.edu/wiki/index.php/
  Bugs
• Sanity check is necessary everywhere code
  review among group is helpful
• link layer checksum not working
• byte-alignment problems without _packed
  attribute)
• Array bound check, null pointer check, even you
  are sure it will not happen.
• Backup message header before it sends to radio
  we found sometime radio will corrupt messages
  when sending.
• Not well designed for high-data rate applications

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Acknowledgement

• NASA ESTO AIST and USGS Volcano Hazard Program
• A multidisciplinary team involves
• computer scientists (Washington State University)
• WenZhan Song (Assistant Professor, WSU)
• Behrooz Shirazi (Chair Professor, EECS director,
  WSU)
• Students team
• space scientists (Jet Propulsion Laboratory)
• Steve Chien (Principal Computer Scientist, JPL)
• Sharon Kedar (Geophysicist, JPL)
• Frank Webb (Principal Manager, JPL)
• Danniel Tran (Software Engineer, JPL)
• Joshua Doubleday (Software Engineer, JPL)
• earth scientists (USGS Cascade Volcano
  Observatory)
• Rick LaHusen (Senior Instrumentation Engineer,
  CVO)
• Cynthia Gardener (Science-in-charge, CVO)
• John Pallister (Geologist, CVO)
• Dan Dzurisin (Geophysicist, CVO)
• Seth Moran (Seismologist , CVO)
• Mike Lisowski (Geophysicist , CVO)

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IEEE SIMA 2008 Nov. 18, 2008
Thank You!WenZhan SongEmail songwz_at_wsu.edu
Deployment video http//www.youtube.com/watch?vIb
CpioUlF0I More information, visit http//sensorw
eb.vancouver.wsu.edu
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SEP
NED
VALT
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Ground Segment Overview
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