Wireless Sensor Networks Tutorial

1
Wireless Sensor NetworksTutorial

• Katia Obraczka
• Department of Computer Engineering
• University of California, Santa Cruz
• May 2006

2
Introduction
3
Main Goals

• Overview of wireless sensor networks.
• What are sensor networks?
• Unique characteristics/challenges, etc.
• State-of-the-art in sensor networks research.

4
Topics

• Introduction.
• Applications.
• E2E protocols.
• Routing and data dissemination.
• Storage, querying, and aggregation.
• Topology control.

• Deployment issues.
• Localization.
• Time synchronization.
• Medium access control.
• Energy models.

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Introduction

• What are wireless sensor networks?
• Unique characteristics/challenges.
• Basic concepts and terminology.

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What are wireless sensor networks (WSNs)?

• Networks of typically small, battery-powered,
  wireless devices.
• On-board processing,
• Communication, and
• Sensing capabilities.

P O W E R
Sensors
Processor
Storage
Radio
WSN device schematics
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WSN node components

• Low-power processor.
• Limited processing.
• Memory.
• Limited storage.
• Radio.
• Low-power.
• Low data rate.
• Limited range.
• Sensors.
• Scalar sensors temperature, light, etc.
• Cameras, microphones.
• Power.

P O W E R
Sensors
Storage
Processor
Radio
WSN device schematics
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Why Now?

• Use of networked sensors dates back to the 1970s.
• Primarily wired and
• Centralized.
• Today, enabling technological advances in VLSI,
  MEMS, and wireless communications.
• Ubiquitous computing and
• Ubiquitous communications.

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Vision Embed the World

• Embed numerous
• sensing nodes to
• monitor and interact
• with physical world

• Network these devices
• so that they can
• execute more complex task.

Images from UCLA CENS
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Examples of WSN Platforms
PC-104(off-the-shelf)
UCLA TAG (Girod)
UCB Mote (Pister/Culler)
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Berkeley Mote

• Commercially available.
• TinyOS embedded OS running on motes.

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Design Challenges

• Why are WSNs challenging/unique from a research
  point of view?
• Typically, severely energy constrained.
• Limited energy sources (e.g., batteries).
• Trade-off between performance and lifetime.
• Self-organizing and self-healing.
• Remote deployments.
• Scalable.
• Arbitrarily large number of nodes.

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Design Challenges (Contd)

• Heterogeneity.
• Devices with varied capabilities.
• Different sensor modalities.
• Hierarchical deployments.
• Adaptability.
• Adjust to operating conditions and changes in
  application requirements.
• Security and privacy.
• Potentially sensitive information.
• Hostile environments.

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WSN Applications

• Monitoring.
• Scientific, ecological applications.
• Non-intrusiveness.
• Real-time, high spatial-temporal resolution.
• Remote, hard-to-access areas.
• Surveillance and tracking.
• Reconnaissance.
• Perimeter control.
• Smart Environments.
• Agriculture.
• Manufacturing/industrial processes.

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WSN Applications (Contd)

• UCLA Center for Embedded Networked Sensing
  (CENS) http//www.cens.ucla.edu/.
• Berkeley Wireless Embedded Systems (WEBS).

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WSN Applications at UCSC

• SEA-LABS.
• CARNIVORE.
• Meerkats.
• Yellowstone.

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Sensor Exploration Apparatus utilizing
Lowpower Aquatic Broadcasting System
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SEA-LABS

• Joint work with
• Don Potts (Professor, Biology)
• Matt Bromage (PhD student, CE)

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Mission Statement

• SEA-LABS strives to engineer a real-time,
  low-cost, low-power consumption environmental
  monitoring system for use in shallow-water reef
  habitats. Our goal is to measure several
  important physical and chemical variables for use
  in laboratory experiments studying the growth and
  calcification of corals and coralline algae.

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Architecture
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Implementation

• P. O. D.

• Board size 3.0 x 1.5
• One antenna for both transmit and receive
• Transmit receive data packets from base station
• B u o y

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Current Status
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CARNIVORE
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CARNIVORES

• Joint work with
• Terrie Williams (Professor, Biology)
• Dan Costa (Professor, Biology)
• Roberto Manduchi (Professor, CE)
• Vladi Petkov (PhD student, CE)
• Cyrus Bazeghi (PhD student, CE)
• Matt Ruttinshauser (MS student, CE and Biology)

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Motivation

• Need to investigate in more detail the behavior
  of predators.
• Monitoring their location
• More importantly, monitoring their activity
  patterns to draw up in depth energy budgets
  (activities such as walking, trotting, galloping
  and eating will be identified)
• Several questions can be answered
• Can coyotes assimilate food and run
  simultaneously
• Do coyotes conserve their energy when hunting to
  prolong the hunting duration
• What are the human impacts on coyotes with
  respect to the two points above

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Coyote Network Infrastructure
Coyote-tower data exchange
Coyote-coyote data exchange
Coyote-coyote data exchange
Coyote-tower data exchange
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Collar Sensor Package
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Sensor Package Main Board
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Sensor Package Sister-board
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Acceleration Preliminary Tests

• Pippin, a friendly and well trained dog, was used
  to study correlations between behavior and
  acceleration
• Next 4 slides show freeze frames of Pippin
  running at different speeds with acceleration
  graphs overlaid
• Different gaits (walk, trot, gallop) clearly
  affect acceleration graphs
• Higher speeds also identifiable by higher
  amplitudes of acceleration
• Z-axis is the up down axis, and the one used for
  the brief annotations on the graphs

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Pippin Treadmill 3mph walk
Period 360 ms
Amplitude (peak to peak) 800 mg
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Pippin Treadmill 6mph trot
Amplitude (peak to peak) 1750 mg
Period 200 ms
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Pippin Alongside cart 10mph gallop
Period 400 ms
Amplitude (peak to peak) 1750 mg
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Pippin Alongside cart 15mph gallop
Period 400 ms
Amplitude (peak to peak) 2500 mg
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Low Power Considerations

• Texas Instruments MSP430 microcontroller is very
  low-power versatile.
• ZigBee radio was designed for sensor applications
  with low power in mind and will not be on at all
  times.
• GPS module will be turned on only long enough to
  acquire a fix and off interval will be large
  compared to fix-acquisition-interval.
• SD card consumes significant power only during
  read/write operations which happen very quickly
  and as infrequently as possible.
• Virtually all system functions are duty cycled
  allowing peripherals to remain on only as long as
  they are needed.

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Data Handling Considerations

• Non-fully-connected network.
• Not all coyotes guaranteed to come in close
  proximity to base station.
• Collars copy data bundles of other collars in
  proximity to ensure timely transmission to tower
  (messenger coyotes).
• In absence of intelligent routing, all data is
  copied to all collars.
• Better routing decision methods based on metrics
  appropriate to this system are being explored.

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Future Work

• Data analysis algorithm(s) to extract behavior
  information from raw acceleration data.
• More efficient routing algorithm.
• Detailed system power consumption analysis.
• Trial runs in controlled environment.

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Meerkats A Power-Aware, Wireless Camera Network

• Joint work with R. Manduchi, C. Margi, X. Lu, G.
  Zhang, V. Petkov, G. Stanek

Sponsored by NASA, Intelligent Systems Program
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What is Meerkats?

• A small southern Africa mongoose.
• Wireless camera network for surveillance and
  monitoring

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Why camera networks?

• Cameras provide richer information.
• Cameras have wider and longer sensing range.
• BUT
• Consume more power.
• Need more processing and storage.

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Meerkats Goal

• Maximize performance as well as network lifetime.
  
• However, these introduce conflicting
  requirements.
• Approach efficient resource management.
• Complementary to efforts targeting design of
  low-power platforms.

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Resource Management
Bit rate Delay
Activation rate Processing type Duty cycle
design Abstraction level Synchronization
SENSING PROCESSING TRANSMISSION
Performance QoS Lifetime
Power
System parameters
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Meerkats hardware

• Stargate boards
• XScale PXA255 CPU (400MHz)
• 32M flash, 64M DRAM.
• Running Stargate v. 7.3 (embedded Linux).

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Meerkats hardware (contd)

• Orinoco Gold 802.11b wireless network card.
• QuickCam Pro 4000 camera (USB port).
• Used at 320x240 resolution.
• Custom 2-cell Li-Ion 7.4 Volt, 1 Ah battery
• Connected to daughter board.
• DC-DC regulator to 5 Volts.

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Meerkats Node
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Networking

• MAC IEEE 802.11b.
• Dynamic Source Routing (DSR) Johnson et al..
• Source routing data packets carry route
  information.
• Useful for future QoS control.
• Plan is to extend DSR to perform alternate path
  routing for QoS requirements.
• UDP and TCP at the transport layer.
• UDP used to send out alarms.
• TCP used to send out images.

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Node Operation

• Duty cycle based.
• Nodes alternate between sleep, low-power- and
  active states.
• Better energy efficiency.
• But how about performance?

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Event Detection

• Goal
• Capture and transmit at least one image of any
  moving body in any cameras field of view.
• Current scheme
• Periodic image acquisition
• Node-to-node wire-trapping
• Motion analysis highly desirable.

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Foreground Detection

• Background subtraction
• Build model of stationary background.
• Detect pixels unlikely to belong to background.

background
new image
foreground
subimage to be transmitteed
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Power Consumption Characterization

• Goal
• Predict the systems lifetime.
• I.e., how long a node will last if engaged in
  specific activities?
• Representative elementary tasks and duty
  cucles.

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Baseline Duty Cycle
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Wire-tripping Duty Cycle
MASTER
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Whats next?

• Performance analysis.
• Miss rate given arrival rate, trajectory,
  activation rate, etc.
• QoS alternate path routing.
• Synchronization issues.

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Whats next?

• Ongoing work on energy consumption prediction.
• Question
• Given our energy consumption characterization,
  can we predict amount of energy left at a future
  point in time based on past activity?
• Approach probabilistic models of power
  consumption state space and transitions.

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Power Consumption State Space
6. SEND ALERT/ DESCRIPTOR
1. SLEEP
3. TAKE PICTURE
2. LISTEN
5. PROCESS PICTURE
4. COMPRESS/ TRANSMIT
From other nodes
From sink
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Yellowstone Project

• Senior design project.
• Sensor network to monitor volcanic activity in
  Yellowstone National Park.
• Scientists want to observe temperature variations
  spatially and temporally.
• Detect relevant events.
• E.g., geiser eruption.

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Design Considerations

• Low power.
• Visually and environmentally non-intrusive.
• Withstand wildlife and harsh environment.
• Data available readily and in real-time.
• Robust, self-managing, and self-healing.

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System Architecture

• Multi-tier network.
• Sensing and relay nodes.
• Modularity and extensibility.

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Current Status

• System under implementation.
• Semi-functional working prototype.
• Sensing, processing, sending and receiving data.
• Still working on the wireless communications
  capabilities.
• Demonstration scheduled for final project
  presentations in the beginning of June.
• Real deployment scheduled for Summer 2006.