Chapter 14 (part I) WSN: Coverage and Energy Conservation

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Chapter 14 (part I)WSN Coverage and Energy
Conservation

• ?????? ?????
• ?????
• Prof. Yu-Chee Tseng

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Research Issues in Sensor Networks

• Hardware (2000)
• CPU, memory, sensors, etc.
• Protocols (2002)
• MAC layers
• Routing and transport protocols
• Applications (2004)
• Localization and positioning applications
• Management (2008)
• Coverage and connectivity problems
• Power management
• etc.

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Coverage Problems

• In general
• Determine how well the sensing field is monitored
  or tracked by sensors.
• Possible Approaches
• Geometric Problems
• Level of Exposure
• Area Coverage
• Coverage
• Coverage and Connectivity
• Coverage-Preserving and Energy-Conserving Problem

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Review Art Gallery Problem

• Place the minimum number of cameras such that
  every point in the art gallery is monitored by at
  least one camera.

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Review Circle Covering Problem

• Given a fixed number of identical circles, the
  goal is to minimize the radius of circles.

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Level of Exposure

• Breach and support paths
• paths on which the distance from any point to the
  closest sensor is maximized and minimized
• Voronoi diagram and Delaunay triangulation
• Exposure paths
• Consider the duration that an object is monitored
  by sensors

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Coverage and Connectivity

• Extending the coverage such that connectivity is
  maintained.
• A region is k-covered, then the sensor network is
  k-connected if RC ? 2RS

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Coverage-Preserving and Energy-Conserving
Protocols

• Sensors' on-duty time should be properly
  scheduled to conserve energy.
• This can be done if some nodes share the common
  sensing region.
• Question Which sensors below can be turned off?

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The Coverage Problems in 2D Spaces

• (ACM MONET, 2005)

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Coverage Problems

• In general
• To determine how well the sensing field is
  monitored or tracked by sensors
• Sensors may be randomly deployed

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Coverage Problems

• We formulate this problem as
• Determine whether every point in the service area
  of the sensor network is covered by at least a
  sensors
• This is called sensor acoverage problem.
• Why a sensors?
• Fault tolerance, quality of service
• applications localization, object tracking,
  video surveillance

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The 2D Coverage Problem
So this area is not 1-covered!
This region is not covered by any sensor!
Is this area 1-covered?
This area is not only 1-covered, but also
2-covered!
What is the coverage level of this area?
1-covered means that every point in this area is
covered by at least 1 sensor
2-covered means that every point in this area is
covered by at least 2 sensors
Coverage level a means that this area is
a-covered
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Sensing and Communication Ranges
1Honghai Zhang and Jennifer C. Hou, On deriving
the upper bound of a-lifetime for large sensor
networks,'' Proc. ACM Mobihoc 2004, June 2004
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Assumptions

• Each sensor is aware of its geographic location
  and sensing radius.
• Each sensor can communicate with its neighbors.
• Difficulties
• There are an infinite number of points in any
  small field.
• A better way O(n2) regions can be divided by n
  circles
• How to determine all these regions?

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The Proposed Solution

• We try to look at how the perimeter of each
  sensors sensing range is covered.
• How a perimeter is covered implies how an area is
  covered
• by the continuity of coverage of a region
• By collecting perimeter coverage of each sensor,
  the level of coverage of an area can be
  determined.
• a distributed solution

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How to calculate the perimeter cover of a sensor?
Si is 2-perimeter-covered
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Relationship between k-covered and
k-perimeter-covered

• THEOREM. Suppose that no two sensors are located
  in the same location. The whole network area A is
  k-covered iff each sensor in the network is
  k-perimeter-covered.

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Detailed Algorithm

• Each sensor independently calculates its
  perimeter-covered.
• k mineach sensors perimeter coverage
• Time complexity nd log(d)
• n number of sensors
• d number of neighbors of a sensor

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Simulation Results
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A Toolkit
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Summary

• An important multi-level coverage problem!
• We have proposed efficient polynomial-time
  solutions.
• Simulation results and a toolkit based on
  proposed solutions are presented.

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The Coverage Problem in 3D Spaces

• (IEEE Globecom 2004)

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The 3D Coverage Problem
What is the coverage level of this 3D area?
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The 3D Coverage Problem

• Problem Definition
• Given a set of sensors in a 3D sensing field, is
  every point in this field covered by at least a
  sensors?
• Assumptions
• Each sensor is aware of its own location as well
  as its neighbors locations.
• The sensing range of each sensor is modeled by a
  3D ball.
• The sensing ranges of sensors can be non-uniform.

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Overview of Our Solution

• The Proposed Solution
• We reduce the geometric problem
• from a 3D space to one in a 2D space,
• and then from a 2D space to one in a 1D space.

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Reduction Technique (I)

• 3D gt 2D
• To determine whether the whole sensing field is
  sufficiently covered, we look at the spheres of
  all sensors
• Theorem 1 If each sphere is a-sphere-covered,
  then the sensing field is a-covered.
• Sensor si is a-sphere-covered if all points on
  its sphere are sphere-covered by at least a
  sensors, i.e., on or within the spheres of at
  least a sensors.

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Reduction Technique (II)

• 2D gt 1D
• To determine whether each sensors sphere is
  sufficiently covered, we look at how each
  spherical cap and how each circle of the
  intersection of two spheres is covered.
• (refer to the next page)
• Corollary 1 Consider any sensor si. If each
  point on Si is a-cap-covered, then sphere Si is
  a-sphere-covered.
• A point p is a-cap-covered if it is on at least a
  caps.

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Cap and Circle
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k-cap-covered

• p is 2-cap-covered (by Cap(i, j) and Cap(i, k)).

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Reduction Technique (III)

• 2D gt 1D
• Theorem 2 Consider any sensor si and each of its
  neighboring sensor sj. If each circle Cir(i, j)
  is a-circle-covered, then the sphere Si is
  a-cap-covered.
• A circle is a-circle-covered if every point on
  this circle is covered by at least a caps.

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k-circle-covered

• Cir(i, j) is 1-circle-covered (by Cap(i, k)).

Cap(i, k)
Cir(i, j)
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Reduction Technique (IV)

• 2D gt 1D
• By stretching each circle on a 1D line, the level
  of circle coverage can be easily determined.
• This can be done by our 2-D coverage solution.

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Reduction Example
gt
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Reduction Example
gt
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Calculating the Circle Coverage
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Calculating the Circle Coverage
gt
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Calculating the Circle Coverage
gt
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Calculating the Circle Coverage
gt
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The Complete Algorithm

• Each sensor si independently calculates the
  circle coverage of each of the circle on its
  sphere.
• sphere coverage of si
• min circle coverage of all circles on Si
• overall coverage min sphere coverage of all
  sensors

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Complexity

• To calculate the sphere coverage of one sensor
  O(d2logd)
• d is the maximum number of neighbors of a sensor
• Overall O(nd2logd)
• n is the number of sensors in this field

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Short Summary

• We define the coverage problem in a 3D space.
• Proposed solution
• 3D gt 2D gt 1D
• Network Coverage gt Sphere Coverage gt Circle
  Coverage
• Applications
• Deploying sensors
• Reducing on-duty time of sensors

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A Decentralized Energy-Conserving,
Coverage-Preserving Protocol

• (IEEE ISCAS 2005)

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Overview

• Goal prolong the network lifetime
• Schedule sensors on-duty time
• Put as many sensors into sleeping mode as
  possible
• Meanwhile active nodes should maintain sufficient
  coverage
• Two protocols are proposed
• basic scheme (by Yan, He, and Stankovic, in ACM
  SenSys 2003)
• energy-based scheme (by Tseng, IEEE ISCAS 2005)

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Basic Scheme

• Two phases
• Initialization phase
• Message exchange
• Calculate each sensors working schedule in the
  next phase
• Sensing phase
• This phase is divided into multiple rounds.
• In each round, a sensor has its own working
  schedule.
• Reference time
• Each sensor will randomly generate a number in
  the range 0, cycle_length as its reference
  time.

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Structure of Sensors Working Cycles

• Theorem
• If each intersection point between any two
  sensors boundaries is always covered, then the
  whole sensing field is always covered.
• Basic Idea
• Each sensor i and its neighbors will share the
  responsibility, in a time division manner, to
  cover each intersection point.

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An Example (to calculate sensor as working
schedule)

Round 1
Round 2
Round n
Sensing phase
Initial phase
Initial phase
Round i
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more details

• The above will also be done by sensors b, c, and
  d.
• This will guarantee that all intersection points
  of sensors boundaries will be covered over the
  time domain.

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Energy-Based Scheme

• goal based on remaining energy of sensors
• Nodes with more remaining energies should work
  longer.
• Each round is logically separated into two zones
• larger zone 3T/4
• smaller zone T/4.
• Reference time selection
• If a nodes remaining energy is larger than ½ of
  its neighbors, randomly choose a reference time
  in the larger zone.
• Otherwise, choose a reference time in the smaller
  zone.
• Work schedule selection
• based on energy (refer to the next page)

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Energy-Based Scheme (cont.)

• Frontp,i and Backp,i are also selected based on
  remaining energies.

richer
rich
poor
Round i
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Two Enhancements

• k-Coverage-Preserving Protocol
• (omitted)
• active time optimization
• Longest Schedule First (LSF)
• Shortest Lifetime First (SLF)

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Simulation Results
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Simulation Results (cont.)
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Summary

• A distributed node-scheduling protocol
• Conserve energy
• Preserve coverage
• Handle k-coverage problem
• Advantage
• Distribute energy consumption among nodes

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Conclusions

• a survey of solutions to the coverage problems
• Both in 2D and 3D spaces
• a survey of solutions to coverage-preserving,
  energy-conserving problems
• Fairly distribute sensors energy expenditure

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References

1. C.-F. Huang and Y.-C. Tseng, The Coverage
   Problem in a Wireless Sensor Network, ACM Mobile
   Networking and Applications (MONET), Special
   Issue on Wireless Sensor Networks.
2. C.-F. Huang and Y.-C. Tseng, A Survey of
   Solutions to the Coverage Problems in Wireless
   Sensor Networks, Journal of Internet Technology,
   Special Issue on Wireless Ad Hoc and Sensor
   Networks.
3. C.-F. Huang and Y.-C. Tseng, The Coverage
   Problem in a Wireless Sensor Network, ACM Intl
   Workshop on Wireless Sensor Networks and
   Applications (WSNA) (in conjunction with ACM
   MobiCom), 2003.
4. C.-F. Huang, Y.-C. Tseng, and Li-Chu Lo, The
   Coverage Problem in Three-Dimensional Wireless
   Sensor Networks, IEEE GLOBECOM, 2004.
5. C.-F. Huang, L.-C. Lo, Y.-C. Tseng, and W.-T.
   Chen, Decentralized Energy-Conserving and
   Coverage-Preserving Protocols for Wireless Sensor
   Networks, Intl Symp. on Circuits and Systems
   (ISCAS), 2005.