Sensors on the Grid

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Sensors on the Grid

• M Palaniswami
• Convener, ARC Research Network on Intelligent
  Sensors, Sensor Networks and Information
  Processing
• Dept of Electrical and Electronics Engineering
• The University of Melbourne

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Talk Outline

• Background
• Introduction to Smart Sensors
• SensorGrid
• SensorGrid Application Scenarios
• Global Integration with Smart Sensors
• Defense Sensor Network
• Environmental Sensor Network
• Conclusion

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Focus of ARC RN on ISSNIP Application Driven
Networked Sensors
BISP Bio Inspired Signal Processing NMSO
Non-linear Methods and Stochastic
Optimization CSIP Collaborative Signal and
Information Processing ILSP Inference and
Learning by Signal Processing SBMIP Scale
Based and Multi- dimensional Information
Processing
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Key Disciplines targeted

• Environment
• Environmentally Sustainable Australia
• Defence
• Safeguarding Australia
• Health
• Maintaining Good Health

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Typical Projects

• Wide area surveillance systems
• Vision system for autonomous UAVs Detecting,
  locating and tracking
• Bio-inspired sensor fusion
• Biomimetics for tracking
• Tracking in multi-sensor, multi-target scenarios
• Target location identification by
  self-organising UAVs
• Distributed control through sensor networks

• Distributed communication and computation in
  sensor networks
• Smart Homes for the aged
• E-science
• Ad-hoc wireless networks
• Nano Sensor Vision System
• Quality of service in flow control for sensor
  networks
• Water Catchment's Flow Monitoring

50 Academic Staff 50 Post Docs 50
Students www.sensornetworks.net.au
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Talk Outline

• Background
• Introduction to Smart Sensors
• SensorGrid
• SensorGrid Application Scenarios
• Global Integration with Smart Sensors
• Defense Sensor Network
• Environmental Sensor Network
• Conclusion

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Why Smart Sensors??

• Rated as one of the top 5 technologies which will
  shape the future.
• Interdisciplinary Areas of physics, chemistry,
  advanced materials, molecular biology with
  Electronics and signal processing
• Result of Advances in Nano Technology and
  Wireless Communication
• Advances in Sensor Technologies
• Miniaturization
• New Sensing capabilities
• Wireless, low power and smart
• Enhanced sensitivity, selectivity, speed,
  robustness and fewer false alarms
• Reconfigurable
• Ability to respond to new toxic chemicals,
  explosives and biological agents

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Where is the Market?

• Environmental
• Defence
• Intelligent applications
• Biomedical Informatics and Pharmaceutical
• Transportation (auto, heavy vehicles, off-road
  vehicles, etc.)
• Chemical
• Refinery
• Pump and paper
• Textile, glass, steel and other forging

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Smart Sensors

• Sensing Devices with Microprocessor and
  Transceiver is termed as Smart Sensor
• The ability to function in complex and unusual
  environment
• Research Opportunities Miniaturization, Smart
  materials for actuators and sensors, Efficient
  Data Fusion and archiving, Feature Extraction,
  Wireless Communication, Efficient Power Supply,
  Data handling

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Smart Sensor Architecture
Limited Memory
Limited Lifetime
Communication
Sensing Element
Processing Element
1 kbps 1 Mbps, 3 100 m, Lossy Transmission
S E N S O R S
MEMORY
R A D I O
P S O U W P E P R L Y
A D C
MICRO PROCESSOR
Require Supervision
Courtesy Prof. Vittal Rao
ALGORITHMS
Slow Processing
REAL TIME OS
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A Few Smart Sensor Implementations
Smart ITS Project http//smart-its.teco.edu/
Finger Tip Sensors http//www.emt.uni-linz.ac.at/e
ducation/diplomarbeiten/da_kornsteiner/kornsteiner
.html
TINY HEART Heart of the smart
Sensor http//www.anl.gov/OPA/logos20-3/smartsenso
r01.htm
Worlds smallest Capsule endoscope http//www.rfno
rika.com
JPL Sensor Web
A Bee Tracker
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Distributed Sensor Networks
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Distributed Sensor Networks - Components

• Sources of data Measure data, report them
  somewhere
• Typically equip with different kinds of actual
  sensors
• Sinks of data Interested in receiving data from
  WSN
• May be part of the WSN or external entity, PDA,
  gateway,
• Actuators Control some device based on data,
  usually also a sink

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A few challenges

• Information flow control from multiple sensor to
  single destination
• QOS Requirements Optimization, Flow Control
  Algorithms, Multi-path Networks
• Content based sensor information extraction
• Graph theory, Information Retrieval
• Decentralised Estimation and Distributed
  Processing
• Cryptography and Communication Protocols
• Energy Aware Sensor Networks
• Location Aware Sensor Networks
• Data Aggregation and Reusability

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Talk Outline

• Background
• Introduction to Smart Sensors
• SensorGrid
• SensorGrid Application Scenarios
• Global Integration with Smart Sensors
• Defense Sensor Network
• Environmental Sensor Network
• Conclusion

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Traditional Devices vs. Smart Sensors

• Traditional Devices (Computers and High End
  Resources) are
• Powerful
• Connected to Power Grid so we dont worry too
  much about it power consumption
• Large Storage Space
• Good for archival and large-scale analysis
• Connected by High Bandwidth/Speed Network
• Smart Sensors
• Less powerful
• Scarcity of power (battery operated, or even
  self-power generated)
• Less Storage
• No good for archival
• Connected by Low Bandwidth/Speed Network
• But they can sense/smell a phenomena in the
  physical world.

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Bringing Sensor and Grid together

• Both of them will benefit
• Grids
• Resource sharing coordinated problem solving
  in dynamic, multi-institutional virtual
  organizations
• Get Eye to see the world (so that it can sense
  and assist)
• Sensors
• Off load their processing, storage, archival,
  analysis, etc. requirements to the Grid.
• Sensors Grids SensorGrid

Thanks to Dr.Buyya
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Sensor Grid in Health Care

• Data Management
• Computation Management
• Information Management
• Knowledge Discovery

Source Gayner et. al., IEEE Internet Computing
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Challenges in SensorGrid

• Scalability
• Robustness
• Security
• Quality of Service
• Resource Discovery
• Uniform Access
• Application Construction

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Talk Outline

• Background
• Introduction to Smart Sensors
• SensorGrid
• SensorGrid Application Scenarios
• Global Integration with Smart Sensors
• Defense Sensor Network
• Environmental Sensor Network
• Conclusion

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Radio Frequency Identification Devices (RFID)

• Uses radio-frequency waves to transfer data
  between a reader and a movable item to identify,
  categorize, track...
• Holds a small amount of unique data a serial
  number or other unique attribute of the item
• RFIDs are fast and reliable
• They do not require physical sight or contact
  between reader/scanner and the tagged item
• Require tags, antennas, interrogators and host
  computers
• Of course appropriate software is required

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Talk Outline

• Background
• Introduction to Smart Sensors
• SensorGrid
• SensorGrid Application Scenarios
• Global Integration with Smart Sensors
• Defense Sensor Network
• Environmental Sensor Network
• Conclusion

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RFID System Components
Reader
Antenna
Asset/Tag
Asset
Firmware
TCP/IP

Host
Power
Application Software
Customers MIS
Thanks to Craig K. Harmon
API
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RFID Operations

• Examples
• Supply Chain Management
• Tracking
• Identification and Security

Thanks to Craig K. Harmon
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Basic needs for a company to work

• Manufacturing
• Distribution
• Sales
• Inventory
• Transportation

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To make the company Global

• Sensor Technology (RFID)
• Manufacturing
• Distribution
• Sales
• Inventory
• Transportation
• Appropriate Middleware

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Are there companies with such enabled
technologies?

• Sensor Technology (RFID)
• Manufacturing
• Distribution
• Sales
• Inventory
• Transportation
• Middleware

Answer is Yes
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Supply Chain
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Talk Outline

• Background
• Introduction to Smart Sensors
• SensorGrid
• SensorGrid Application Scenarios
• Global Integration with Smart Sensors
• Defense Sensor Network
• Environmental Sensor Network
• Conclusion

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Network Centric Warfare

• OmniGrid
• This is mother of all grids which has the
  responsibility of collecting, storing, securing
  and processing sensor data from all armed
  divisions
• SensorGrid
• Collection of network sensors which will send the
  aggregated information via distribute processing
  to OmniGrid
• ActuatorGrid
• Acts in response to the command issued by
  OmniGrid in defending from the enemy or
  destroying the enemy.

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Underwater Warfare Data Fusion Pictorial view
http//www.atlantic.drdc-rddc.gc.ca/factsheets/22_
UDF_e.shtml
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Network Centric Warfare
FORMATIONS
HORIZON EXTENSION
LOCATE
MAW
SEAD
Kim Brown 2002
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Talk Outline

• Background
• Introduction to Smart Sensors
• SensorGrid
• SensorGrid Application Scenarios
• Global Integration with Smart Sensors
• Defense Sensor Network
• Environmental Sensor Network
• Conclusion

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Great Barrier Reef

• 3,200 reefs
• 280,000 km2
• Fluctuations range from kilometre oceanic mixing
  to millimetre inter-skeletal currents
• Temperature, Salinity, Light and Oxygen to be
  measured every 30 mins

Courtesy Stuart Kininmonth, AIMS
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Environmental SN Great Barrier Reef

• Effective management of Great Barrier Reef (GBR)
• Study of Environmental Dynamics
• To effectively manage and protect it
• Spread across several hundreds of kilometers
• Large number of networked sensors are required
  for efficient data sampling
• The long term costs to monitor environment due to
  the human involvement is on the rise
• The economic activity associated with the Great
  Barrier Reef is estimated to be over Au 4
  Billion annually.

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Current Status

• Floating buoys are deployed
• Difficult to collect data across the required
  spatial and temporal scales, hence data is often
  poorly sampled
• Long term costs are high

Courtesy Stuart Kininmonth, AIMS
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A SensorGrid Architecture for GBR
Data transfer
Web Server
Regular intervals
Output Salinity Temperature etc. .
Lab Server
Sensors Temperature Salinity Other smart sensors
Digital Library
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Future of Networked Sensors - SLIM
S L I M
Sense To build Intelligent Bio-Sensors
Learn Learning algorithms to model, predict and
classify
Interact Powerful network flow Protocols
Motivate Actuate a process on the basis of
learning Stable network dynamic control
This is the path to powerful thinking machines
that can sense, learn, interact, store and act in
a complex environment
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Collaborators

• Anthony Finn, DSTO
• Ian Atkinson, JCU
• Stuart Kininmonth, AIMS
• Subhash Challa, UTS
• Raj Buyya, UniMelb
• DEST Distributed Sensor Networks Team
• ISSNIP Team

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Thank You