Minimizing Energy Consumption with Probabilistic Distance Models in Wireless Sensor Networks

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Minimizing Energy Consumption with Probabilistic
Distance Models in Wireless Sensor Networks

• Yanyan Zhuang, Jianping Pan, Lin Cai
• University of Victoria, Canada

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Background Related Work

• Clustering Schemes
• Cluster Head (CH) cluster nodes
• two-tier hierarchical structure simple node
  coordination
• Multi-hop avoid long-range transmissions

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Background Related Work (cont.)?

• Grid-Based Clustering
• Partition equal-sized squares
• Facilitate data dissemination sensors can
  transmit data without route setup in advance

Manhattan Walk
Diagonal-First Routing
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Background Related Work (cont.)?

• Variable-size Clustering
• traffic volume highly skewed ? bottleneck
• consume their energy much faster than other
  nodes ? earlier breakdown of the network
• Existing Work
• time synchronization/frequent message exchanges
• linear network, or quasi-two-dimensional

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Distance Distribution Model

• Wireless Transmitter
• data transmission rate
• a constant related to the environment
• path loss exponent 2,6

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Distance Distribution Model

• Energy consumption ? node distance ? average
  distance (?) ? Average Distance Model
• Grid structure geometric property ?
  probabilistic distance distribution ? Distance
  Distribution Model

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Coordinate Distributions

• Two nodes in same grid (AB) U0,1
• Two nodes in diagonal grids (PQ)?
• X1, Y1 U0,1 and X2, Y2 U-1,0
• Two nodes in parallel grids (RS)?
• X1, Y1, Y2 U0,1 and X2 U-1,0

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Distance Distributions

• Node distance
• Goal
• Four step derivation
• Difference --gt Square --gt Sum --gt Square Root

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Distance Distributions

• Node distance
• Goal
• Four step derivation
• Difference --gt Square --gt Sum --gt Square Root

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• (1) Difference distribution
• Example P
  and Q

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• (2) Square distribution
• Example P and Q

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• (3) Sum distribution
• (4) Square-root distribution

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• Example P and Q

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PDF within a Unit Grid Polyfit
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PDF between Parallel/Diagonal Grids

• Parallel Diagonal

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Probabilistic Energy Optimization

• Simulation Setup Friis Free Space Two-Ray
  Ground
• cross-over distance
• system loss factor
• rx/tx antenna height
• wavelength of the carrier signal

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Distance Verification

• CDF vs. Simulation
  One-hop Energy Consumption

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Total Energy Consumption Distance Distribution
vs. Average Model
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Improvement Variable Size Griding

• P and Q
• X1, Y1 U0,1-q
• X2, Y2 U-q(1-q),0
• R
• X1 U-q,0, Y1 U0,1-q
• S
• X2 -q, -q(1-q), Y2 U-q(1-q),0

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Distance Verification

• CDF vs. Simulation
  One-hop Energy Consumption
• CDF with q0.4 and 0.7
  One-Hop Energy Consumption with q0.5

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Per-Grid/Total Energy Consumption vs. Size Ratio
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Conclusions

• Energy consumption model based on distance
  distributions
• Nonuniform grid-based clustering both data
  traffic and energy consumption balanced
• The importance of grid-based clustering and the
  optimal grid size ratio that can balance the
  overall energy consumption

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• Thanks!
• QA

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Coordinate Distributions

• Two nodes in same grid (AB) U0,1
• Two nodes in diagonal grids (PQ)?
• X1, Y1 U0,1 and X2, Y2 U-1,0
• Two nodes in parallel grids (RS)?
• X1, Y1, Y2 U0,1 and X2 U-1,0

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• X1, Y1 U0,1
• X2, Y2 U-1,0

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Improvement Variable Size Griding

• PQ X1, X2 U0,1-q and Y1, Y2 U-q(1-q),0
• R X1 U-q,0, Y1 U0,1-q
• S X2 -q, -q(1-q), Y2 U-q(1-q),0

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• Wireless Channel Model
• the data transmission rate
• a constant related to the environment
• path loss exponent 2,6
• distance distribution function (poly
  fit appx)?