Sensing and Actuation in Pervasive Computing

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Sensing and Actuation in Pervasive Computing

• By
• Abu Zafar Abbasi
• PhD Fellow
• FAST-NU, Karachi
• July 11, 2007

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Overview of Presentation

• Introduction
• Sensors and Sensor Networks
• Connecting the Physical World with Pervasive
  Networks
• Challenges
• A taxonomy of systems
• Distributed Architecture
• IrisNet An example of Sensor Web

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References

• Of Smart Dust and Brilliant Rocks
• M. Satyanarayanan
• IEEE Pervasive Computing, p 2-4, October 2003
• Connecting the Physical World with Pervasive
  Networks
• Deborah Estrin, David Culler, Gaurav Sukhatme
• IEEE Pervasive Computing, p 59-68, January 2002
• IrisNetAn Architecture for a Worldwide Sensor
  Web
• Phillip B. Gibbons, Brad Karp et al
• IEEE Pervasive Computing, p 22-33, October 2003
• Fundamentals of Mobile and Pervasive Computing
• Frank Adelstein, Sandeep Gupta
• Mc-Graw Hill, 2005

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Weisers Vision

• Sensing the state of the physical world
• and influencing it
• seamless continuum between a users
• personal computing environment and his
• physical environment
• technology that disappears

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What is meant by a sensor ?

• Historically, the term sensor has meant dumb
  sensorsomething like a strain gauge or
  thermocouple that generates a signal but involves
  no local computing
• Inputs from many dumb sensors are typically fed
  to a computer that integrates the individual
  signals and triggers actions
• Such designs have been used for process control
  in chemical, petroleum, and nuclear power
  industries for many decades

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Example Sensors

• Thermal
• Magnetic
• Vibration
• Motion
• Chemical
• Biological
• Light
• Acoustic
• Position
• RF IDs
• and many more

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Smart Sensors

• Integration of a dumb sensor with a simple
  microcontroller, a limited amount of memory, a
  short-range wireless transceiver, and a small
  battery

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Smart Dust and Brilliant Rocks

• Smart Dust
• term used for smart sensors-the name connoting
  both small size and disposable nature
• Brilliant Rocks
• legal or social impediments might restrict
  physically embedding sensors in a space.
• The name indicates the much greater computing
  power as well as physical size associated with
  each node in the sensing network
• Whereas smart dust is disposable, brilliant rocks
  are too big and expensive to simply throw away
  after use

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Sensor Networks

• A sensor network is usually wireless network
  consisting of spatially distributed autonomous
  devices using sensors to cooperatively monitor
  physical or environmental conditions, such as
  temperature, sound, pressure, motion or
  pollutants, at different locations

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Sensor Network Applications

• Environmental monitoring
• Habitat monitoring
• Acoustic detection
• Seismic Detection
• Military surveillance
• Inventory tracking
• Medical monitoring
• Smart spaces
• Process Monitoring
• Structural health monitoring

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Applications (Contd)
Scientific eco-physiology
Infrastructure contaminant flow monitoring (and
modeling)
Engineering monitoring (and modeling)
structures
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Pervasive Networks and Sensing

• Systems should fulfill two of Weisers visions
• Ubiquity, by injecting computation into the
  physical world with high spatial density
• Invisibility, by having the nodes and collective
  nodes operate autonomously
• Systems must have reusable building blocks for
  sensing , computing and manipulating the physical
  world
• Systems must not contain specialized
  instrumentation

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Opportunity Ahead

• We need the ability to easily deploy sensors,
  computation and actuation into our world.
• The devices must be general purpose that can
  adapt and organize to support different
  applications and environments.
• Taxonomy of emerging system types for the next
  decade.

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Challenges

• The many distributed system elements, limited
  access to elements, and extreme environmental
  dynamics all combine to force review of
• - Layers of abstraction
• Kinds of hardware acceleration used
• Algorithmic techniques

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Challenges

• Estrin classifies the challenges in three broad
  areas
• Immense Scale
• Limited Access
• Extreme Dynamics

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Immense Scale

• A vast number of small devices will comprise
  these systems
• Devices need to be scale down to extremely small
  volume to achieve dense instrumentation
• In 5 to 10 years device size could be as small as
  a cubic millimeter.
• Measurement fidelity and availability will come
  from the quantity of redundant measurements and
  their correlation.

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Limited Access

• Devices will be embedded where a wired connection
  is impossible or too expensive to use (or
  difficult to reach)
• Communications will have to be wireless and nodes
  will have to rely on renewable or harvested
  energy
• Scale could be so large no one person could ever
  touch all the devices (i.e. operation without
  human attendance)
• Energy sources will limit the amount of activity,
  as in sensor measurements, per unit time.

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Extreme Dynamics

• Since the system is nodal and tied to the
  constantly changing physical world it will have
  extreme dynamics.
• Reaction to environmental changes will directly
  effect the devices performance (e.g. propagation
  characteristics of low power RF)
• Mobility
• Extreme variation in demand
• Most of the time the device senses no change and
  uses low power.
• When an event occurs, then high and low-level
  data, flow from sensors and actuators must be
  managed effectively (minimum latency and
  propagation delays)
• These systems must continuously adapt to resource
  and event activity.

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A taxonomy of systems.

• Applications of physically embedded networks are
  as varied as the physical environment itself
• Yet, even with this heterogeneity, many
  opportunities and resources for exploiting
  commonalities across them exist
• System reuse and evolution is key to
  pervasiveness.
• Taxonomize the systems dimensions like
• Scale
• Variability
• autonomy

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Scale

• Space and time factors, effect the sampling
  interval, overall system coverage, and the total
  number of sensor nodes.
• Sampling What you are trying to measure
  determines the sampling scale. The application
  also effects the sampling scale. If its event
  detection, (lower resolution) vs. event or signal
  reconstruction, (higher resolution).
• Extent Also effects the scale. Environmental
  systems could be 10 kilometers vs. a building or
  room system.
• Density System density is the measure of sensor
  nodes per footprint of input stimuli. High
  density systems can extend the life of sensors
  nodes and reduce noise by redundant measurements.

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Variability

• Takes on many forms and can apply to system
  elements or the phenomena being sensed
• Static systems use design time optimization.
• Dynamic systems use run time optimization.
• Structure Ad hoc vs. engineered. Structure vs.
  bio-complexity monitoring. Also combinations of
  both.
• Task Variability takes the form of how much can
  we tweak the system for single mode operation.
• Space Variability in space equals mobility.
  Applies to nodes and what you are trying to
  measure.

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Autonomy

• Higher system autonomy, indicates less human
  involvement, which requires more complex internal
  processing.
• Modalities Autonomous systems depend on multiple
  sensory modalities. This lowers system noise, and
  helps identify measurement anomalies.
• Complexity Autonomy makes the system model more
  complex. A system that just delivers data for a
  human to process is less complex. A system that
  executes depending on system state, and inputs
  over time, and must execute a programming
  language is much more complex.

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Where are we now? (1)

• Weiser suggested a need for different size
  devices, from the size of a pin to a whole
  building.
• Small packages in the physical world.
• PDAs have had wireless LAN capabilities but this
  requires a large battery pack. Bluetooth (short
  range wireless network) has recently been added
  to PDAs.
• PDAs now have cell phone capabilities, and both
  support GPS.

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Where are we now? (2)
Sensors have been further reduced in size due to
advances in MEMS (microelectromechanical
systems). Small CMOS low-power radios are also
being developed. For example, Crossbow developed
by UC and DARPA. (See picture of UC Berkeley Mote
and a mobile sensor-robomote.)
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Where are we now? (3)

• As device size decreases and complexity
  increases, several new OS have been developed.
• Vxworks (www.windriver.com)
• Geoworks (www.geoworks.com)
• Chorus (www.sun.com/chorusos)
• Small footprints and TCP/IP capabilities have
  been added.
• Windows CE adds a subset of windows to PDAs.
• Unix variants i.e. Linux provide real-time
  multitasking support.
• The TinyOS (tiny operating system environment) is
  component based.
• Traditional scheduling loops are replaced by
  fine-grain multi-threading.
• TinyOS provides fine-grain power management,
  extensive concurrency, with limited processing
  resources.
• Open-source OS.

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Where are we now? (4)

• An effective networked node must have a runtime
  environment that supports
• Scheduling,
• Device Interface,
• Networking,
• Resource management,
• Concurrent data flows from sensor to network to
  controllers.

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Sensing and actuation

• Interacting with the real world involves energy
  exchange in two forms
• Sensing and Actuation.
• Sensing, a sensor, converts (temperature, light
  intensity, etc.) to information.
• nervous system for the environment being sensed
• Actuation lets a node convert information into
  action, An Actuator, moves part of itself,
  relocates itself or moves other items in the
  environment.
• To deal with uncertainty in sensing and actuation
  filtering is used at each node and overlapping
  measurement areas.
• Issue of resolution, approximation, latency

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Localization

• Nodes must know their location.
• Registration between virtual and physical world
• Where am I?
• In relation to a map, other nodes or global
  coordinate system.
• Stereo-processing, utilizes aggregation
• Scale and autonomy play a large role in location
  computation.
• Careful offline calibration and surveying
• Recent trends use algorithms to localize large
  networks autonomously.
• Localization can be seen as a sensor-fusion
  problem.
• One recent example of coarse localization let
  the nodes build a map of their environment.

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Distributed system architecture.

• Constraints imposed by battery power will make or
  break these systems.
• Systems will not be able to constantly stream
  data out to a computer for analysis.
• Computation must be along side the sensors so
  data will be processed locally.
• Two trends are emerging
• Self configuring systems will turn off redundant
  nodes to conserve energy.
• Data-centric network architecture using directed
  diffusion.
• Higher-tier resources will be necessary to
  compliment the lower level data nodes. An example
  could be a roving robot that replaces batteries
  or recharges the batteries of the nodes.

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Where are we headed?

• Thousands of devices embedded in buildings,
  bridges, water ways, highways, and protected
  areas to monitor health and detect critical
  events.
• Advances in miniaturization mean we can now put
  instruments in the experiment, instead of
  conduction the experiment inside an instrument.
• System architecture will have to support
  interrogating, programming , and manipulating the
  real world.
• Embedded systems will need to self-organize,
  spatial reconfiguration is needed.

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IrisNet An Architecture for a Worldwide Sensor
Web
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What is a Sensor Web

• Sensor Web is a type of sensor network that is
  especially well suited for environmental
  monitoring and control
• The term "Sensor Web" is sometimes used to refer
  to sensors connected to the Internet
• It is a technological trend in geospatial data
  collection, fusion and distribution

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IrisNet Architecture
Two components SAs sensor feed processing OAs
distributed database
Web Server for the url
. . .
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Coastal Imaging using IrisNet

• Working with oceanographers at Oregon State
• Process coastline video to detect analyze
  sandbar formation and riptides, etc

Images from IrisNet prototype
Big improvements Process data live, Real-time
actuation, Wide-area