Load Balancing in Periodic Wireless Sensor Networks for Lifetime - - PowerPoint PPT Presentation

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Load Balancing in Periodic Wireless Sensor Networks for Lifetime - - PowerPoint PPT Presentation

Aug 09, 2023 380 likes •707 views

Load Balancing in Periodic Wireless Sensor Networks for Lifetime Maximisation Anthony Kleerekoper 2 nd year PhD Multi-Service Networks 2011 Combining the strengths of UMIST and The Victoria University of Manchester The Energy Hole Problem

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Combining the strengths of UMIST and The Victoria University of Manchester

Load Balancing in Periodic Wireless Sensor Networks for Lifetime Maximisation

Anthony Kleerekoper

2nd year PhD Multi-Service Networks 2011

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Combining the strengths of UMIST and The Victoria University of Manchester

The Energy Hole Problem

  • Uniform distribution of motes
  • Regular, periodic reporting
  • eg. Habitat monitoring
  • Many-to-one traffic flow
  • Multi-hop communication
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Combining the strengths of UMIST and The Victoria University of Manchester

The Energy Hole Problem

  • Uniform distribution of motes
  • Regular, periodic reporting
  • eg. Habitat monitoring
  • Many-to-one traffic flow
  • Mutli-hop communication
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Combining the strengths of UMIST and The Victoria University of Manchester

The Energy Hole Problem

  • Non-uniform distribution of

work

  • Central motes die first
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Combining the strengths of UMIST and The Victoria University of Manchester

The Energy Hole Problem

  • Energy hole appears
  • No packets get to sink
  • Uniform distribution of location

and non-uniform distribution of work

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Combining the strengths of UMIST and The Victoria University of Manchester

Existing Solutions

Avoidance

  • Non-uniform distribution
  • Power control
  • Mobile sink
  • Clustering
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Combining the strengths of UMIST and The Victoria University of Manchester

Existing Solutions

Avoidance

  • Non-uniform distribution
  • Power control
  • Mobile sink
  • Clustering
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Combining the strengths of UMIST and The Victoria University of Manchester

Existing Solutions

Avoidance

  • Non-uniform distribution
  • Power control
  • Mobile sink
  • Clustering
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Combining the strengths of UMIST and The Victoria University of Manchester

Existing Solutions

Avoidance

  • Non-uniform distribution
  • Power control
  • Mobile sink
  • Clustering
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Combining the strengths of UMIST and The Victoria University of Manchester

Existing Solutions

Mitigation

  • Focus on same level balance
  • Dynamically switch parents
  • Create top load-balanced tree
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Combining the strengths of UMIST and The Victoria University of Manchester

Existing Solutions

Mitigation

  • Focus on same level balance
  • Dynamically switch parents
  • Create top load-balanced tree
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Combining the strengths of UMIST and The Victoria University of Manchester

DECOR Proposal

DEgree COnstrained Routing

  • Construct degree-constrained minimum spanning tree
  • Distributed
  • Static routes
  • Balanced
  • No need for location information
  • Designed for periodic applications

Trade-off connectivity and latency for extra lifetime

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Combining the strengths of UMIST and The Victoria University of Manchester

Assumptions

  • Uniform distribution of motes in a circular network
  • Single, central sink
  • Every mote produces 1 new packet per “round”
  • Perfect MAC – no collisions, no interference
  • All motes transmit the same distance
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Combining the strengths of UMIST and The Victoria University of Manchester

DECOR Preliminaries

Average number of children per parent: Ratio of motes in level n to motes in level 1: Level Ratio 1 1 2 3 3 5 4 7 Level Avg Children 1 3 2 1.66667 3 1.4 4 1.286

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Combining the strengths of UMIST and The Victoria University of Manchester

DECOR Theory I

  • Limit the number of children per parent during tree construction
  • All motes have same number of children = balance
  • Average number of children per parent usually not a whole number
  • Round down to nearest whole number

i.e. 1 for most levels Very few motes connected to tree

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Combining the strengths of UMIST and The Victoria University of Manchester

DECOR Theory II

  • Level 1 motes can have 3 children each
  • Find levels when ratio to level 1 motes is
  • Have 3 children per parent in those levels

In practice delay by one level because of imperfect uniformity

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Combining the strengths of UMIST and The Victoria University of Manchester

DECOR Algorithm

Phase One

  • Start with sink
  • Leaf motes broadcast “advert” (incl hop count and subtree number)
  • Unconnected motes gather all adverts
  • Send offer to “best” parent
  • Parents gather all offers respond to “best” child
  • Rejected motes reevaluate and send new offers
  • Wait until all child motes have finished
  • Parent signal children to start next round
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Combining the strengths of UMIST and The Victoria University of Manchester

Example Subtree After Phase One

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Combining the strengths of UMIST and The Victoria University of Manchester

DECOR Algorithm

Phase Two

  • Basic distributed minimum spanning tree algorithm
  • Motes may only become children of parents in the same original subtree
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Combining the strengths of UMIST and The Victoria University of Manchester

Example Subtree After Phase Two

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Combining the strengths of UMIST and The Victoria University of Manchester

DECOR Choices

Best Parent

  • Maintain network topological shape
  • Choose most distant parent
  • Use RSSI to indicate distance

Best Child

  • Maintain network topological shape
  • Not deny children only option
  • Choose child with fewest parent options
  • Distance as tie-breaker
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Combining the strengths of UMIST and The Victoria University of Manchester

Simulation Set-up

  • Radius of network defined in terms of transmission range
  • Constant density (10 motes per unit area)
  • Sink is unconstrained
  • Fixed initial energy values (50J)
  • Fixed packet size (50 bytes)
  • Average results from 200 runs
  • Compare basic minimum spanning tree, dynamic scheme and DECOR
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Time to First Mote Death

5 10 15 20 0.5 1 1.5 2 2.5 3 3.5

Normalised Time to First Node Death

Basic Dynamic DECOR Radius Normalised Time

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Balance

5 10 15 20 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Balance

Basic Dynamic DECOR Radius Balance Ratio

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Connectivity

5 10 15 20 90 91 92 93 94 95 96 97 98 99 100

Percentage of Motes Connected to Sink

Basic Dynamic DECOR Radius Percentage

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Average Latency

5 10 15 20 0.9 0.95 1 1.05 1.1 1.15

Normalised Average Mote Latency

Basic Dynamic DECOR Radius Normalised Average Mote Level

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Worst Case Latency

5 10 15 20 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Normalised Worst Case Latency

Basic Dynamic DECOR Radius Normalised Maximum Mote Level

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Combining the strengths of UMIST and The Victoria University of Manchester

Discussion

  • DECOR provides a large increase in time to first mote death
  • Trade-off for lower connectivity and higher latency
  • Improvement by much larger factor than trade-offs
  • Implicit use of global information
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Further Work

Investigate the effects of:

  • Imperfect uniform distribution
  • Non-central sink
  • In-network aggregation
  • Mobility
  • Density
  • Shadowing / Random events
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Conclusion

  • Energy hole problem has many existing solutions
  • DECOR tailored for periodic applications
  • Introduces new trade-offs
  • Large increase in lifetime for small loss of connectivity and latency
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Thanks for Listening

Any Questions?