minimizing energy consumption with probabilistic distance models in wireless sensor networks yanyan...
TRANSCRIPT
Minimizing Energy Consumption with Probabilistic Distance Models in
Wireless Sensor Networks
Yanyan Zhuang, Jianping Pan, Lin Cai
University of Victoria, Canada
2
Background & Related Work
Clustering Schemes Cluster Head (CH) + cluster nodes
two-tier hierarchical structure: simple node coordination
Multi-hop: avoid long-range transmissions
3
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
4
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
5
Distance Distribution Model
Wireless Transmitter
: data transmission rate
: a constant related to the environment
: path loss exponent [2,6]
6
Distance Distribution Model
Energy consumption → node distance → average distance (?) → Average Distance Model
Grid structure & geometric property →
probabilistic distance distribution → Distance Distribution Model
7
Coordinate Distributions
Two nodes in same grid (AB): U[0,1]
Two nodes in diagonal grids (PQ)
X1, Y1 ~ U[0,1] and X2, Y2 ~ U[-1,0]
Two nodes in parallel grids (RS)
X1, Y1, Y2 ~ U[0,1] and X2 ~ U[-1,0]
8
Distance Distributions
Node distance:
Goal:
Four step derivation
Difference --> Square --> Sum --> Square Root
9
Distance Distributions
Node distance:
Goal:
Four step derivation
Difference --> Square --> Sum --> Square Root
10
(1) Difference distribution
Example: P and Q
11
(2) Square distribution
Example: P and Q
12
(3) Sum distribution
(4) Square-root distribution
13
Example: P and Q
14
PDF within a Unit Grid & Polyfit
15
PDF between Parallel/Diagonal Grids
Parallel Diagonal
16
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
17
Distance Verification
CDF vs. Simulation One-hop Energy Consumption
18
Total Energy Consumption: Distance Distribution vs. Average Model
19
Improvement: Variable Size Griding
P and Q
X1, Y1 ~ U[0,1-q]
X2, Y2 ~ U[-q(1-q),0]
R
X1 ~ U[-q,0], Y1 ~ U[0,1-q]
S
X2 ~ [-q, -q(1-q)], Y2 ~ U[-q(1-q),0]
20
Distance Verification
CDF vs. Simulation One-hop Energy Consumption
CDF with q=0.4 and 0.7 One-Hop Energy Consumption with q=0.5
21
Per-Grid/Total Energy Consumption vs. Size Ratio
22
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
23
Thanks!
Q&A
24
Coordinate Distributions
Two nodes in same grid (AB): U[0,1]
Two nodes in diagonal grids (PQ)
X1, Y1: U[0,1] and X2, Y2: U[-1,0]
Two nodes in parallel grids (RS)
X1, Y1, Y2: U[0,1] and X2: U[-1,0]
25
X1, Y1 ~ U[0,1]
X2, Y2 ~ U[-1,0]
26
Improvement: Variable Size Griding
PQ: X1, X2 ~ U[0,1-q] and Y1, Y2 ~ U[-q(1-q),0]
R: X1 ~ U[-q,0], Y1 ~ U[0,1-q]
S: X2 ~ [-q, -q(1-q)], Y2 ~ U[-q(1-q),0]
27
Wireless Channel Model
: the data transmission rate
: a constant related to the environment
: path loss exponent [2,6]
: distance distribution function (poly fit appx)