WSN Optimization - created from Mind Map

Seite 1

Coverage

Connectivity

Increasing

While reducing

By considering

Time

Energy

# of nodes

By considering

Termination Condition

Dynamic sensing range

Active/sleep schedule

Taking into account

Clustering

We need to evaluate

Threshold

moving back and forth

We then need to evaluate

PAC

ADT

Energy used

Time

# of connected nodes

U_a

U_n

Metrics of performance evaluation

Comm. noise

Implementation

Multipath fading

Shadowing

Doppler effect

Localization uncertainty

Sensing Coverage Prediction for Wireless Sensor Networks in shadowed and multipath environment

Which affect

which affect

add error rate

use probabilistic model

Fuzzy logic with anchors

2D/3D

with/without obstacles

Virtual Force algorithm

Voronoi Polyhedron

Coverage and connectivity in 3D WSN

Node Deployment

Node Reallocation

Bio-Inspired Voronoi Based Algorithm

Rs value

Randomly

Deterministic

Hierarchical

Choose Algorithm

Bio-Inspired

Physics Inspired

Classical

choice affects

Voronoi diagram

ACO

PSO

GA

Movement is based on

Centroid

Furthest vertex

midpoint between vertices

midpoint between edges

single

dual

Bio-Inspired Voronoi based algorithm

Potential Field

Virtual Force

Linear Programming

non-linear

Newton's 2nd law

choose

binary model

probabilistic model

Probabilistic Coverage in WSN

While Reducing

By considering

Rs/Rc relation

Nodes spacing

sqrt(3)*RS

2Rs

Rs