Wireless Sensor Placement for Reliable and Efficient Data Collection

1
Wireless Sensor Placementfor Reliable and
Efficient Data Collection

• Edo Biagioni and Galen Sasaki
• University of Hawaii at Manoa

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Overview

• Wireless Sensor Networks
• Case study an ecological wireless sensor network
• Design Considerations
• Regular Deployments
• Linear and other arrangements

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Sensor Networks are Useful

• Ecological study under what conditions does the
  endangered species thrive?
• Knowing the environment aids in setting goals or
  controlling processes
• Many applications, including ecological,
  industrial, and military

4
Ad-hoc Wireless Networks

• Low-power operation
• Range-limited radios
• Ad-hoc networking each node forwards data for
  other nodes
• Data may be combined en route

5
Wireless Sensor Network Design

• How densely must we sample the environment?
• What is the radio communications range?
• How much reliability do we have, and how does it
  improve if we add more units?
• How many units can we afford?

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The PODS project at the University of Hawaii

• Ecological sensing of Rare Plant environment
• Temperature, sunlight, rainfall, humidity
• High-resolution images
• Kim Bridges, Brian Chee

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Pod placement

• Intensive deployment where the plant does grow
• Interested also in where the plant does not grow
• Connection to the internet is also a line of
  sensors

Sub-region
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Practical Constraints

• Higher radios have more range
• Camouflage
• Plant densities may vary
• Different units may have different sensors
• Ignored in this talk

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Design Goals for Deployment

• We are given a 2-dimensional square region with
  total area A
• Minimize the maximum distance between any point
  in A and the nearest sensor
• Keep the distance between adjacent sensors less
  than r
• Measure point values, compute gradients and
  significant thresholds

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Design Considerations

• Financial and other constraints often limit the
  total number of nodes, N
• Failure of individual nodes should not disable
  the entire network
• Reducing the transmission range improves the
  energy efficiency

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Regular Deployments

• Square, triangular, or hexagonal tiles
• Nodes must be within range r of their neighbors
• Sampling distance d
• Degree 4, 6, or 3 provides redundancy
• Which is best?

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Computing with N, r, d

• Standard formulas for tile area (a) and for
  distance to the center of the tile
• Distance to center lt d
• Distance between nodes lt r
• Each node is part of c (6, 4, or 3) tiles
• N (A/a)/c, where A/a is the number of tiles

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Main Results for Regular Grids

• N is proportional to the surface area of A
• if r lt d, hexagonal deployment minimizes N, and N
  is inversely proportional to r2
• If d lt r, triangular deployment minimizes N, and
  N is inversely proportional to d2
• Triangular, square, or hexagonal are within a
  factor of two of each other

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Sparse Grids

• If r lt d, we can reduce the number of nodes by
  going to sparse grids (sparse meshes)
• Communication distance remains small
• the number of nodes may drop substantially

• 3 nodes per side, s3

S3
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Main Results for Sparse Grids

• Communication radius r, tile side a r s
• N is inversely proportional to a and to r
• The degree of most nodes is two, so reliability
  is reduced the same as for linear deployments

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1-Dimensional Deployment

• Many common applications along streams, roads,
  ridges
• Requires relatively few nodes
• With the least number of nodes for a given r,
  network fails if a single node fails
• How well can we do if we double the number of
  nodes?

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Protection against node failures

• Paired

• Inline

r
r
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Paired and Inline Performance

• For inline, two successive node failures
  disconnect the network
• For paired, failure of the two nodes of a pair
  disconnects the network
• The former is about twice as likely

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Sampling a Gradient

• If we know the gradient, a linear deployment is
  sufficient
• A gradient can be computed from three samples in
  a triangle
• Variable gradients need more and longer
  baselines, as do threshold determinations
• Grids and sparse grids measure gradients well

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Quantifying a gradient
The differences between pairs of samples help
determine the gradient
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Minimizing the number of nodes

• The ultimate sparse grid a circle
• Tolerates single node failures
• Even sampling in all directions

• Lines outward from the center a star
• Center is well covered
• Star-3, Star-4, Star-5, Star-m

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Summary

• Many regular deployments
• Generally, N and r are given, sampling distance
  is allowed to vary
• Tradeoff between N and redundancy sparse grids
  allow large sampling distance
• Lines, circles, stars are optimal when N is
  small, can provide information about gradients

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Acknowledgements

• Kim Bridges
• Brian Chee and many students on the Pods project,
  including Michael Lurvey and Shu Chen
• DARPA (Pods funding)
• Hawaii Volcanoes National Park