1 1.0 Introduction Modelling, Simulation & Computing Laboratory (mscLab) School of Engineering and Information Technology, Universiti Malaysia Sabah, Malaysia CICSyN 2012 4 th International Conference on Computational Intelligence, Communication Systems and Networks Phuket, Thailand, 24-26 July 2012 • Wireless sensor networks : distributed computing devices (sensor nodes) that interact with the environment. •A wireless sensor node -Microcontroller -Radio device -Sensors -Power supply •Environmental monitoring, -Months, a year -Impractical to change the battery •Energy aware wireless sensor networks protocols.
Transcript
1
1.0 Introduction
Modelling, Simulation & Computing Laboratory (mscLab)School of Engineering and Information Technology, Universiti Malaysia Sabah, Malaysia
CICSyN 20124th International Conference on Computational Intelligence, Communication Systems and Networks
Phuket, Thailand, 24-26 July 2012
• Wireless sensor networks : distributed computing devices (sensor nodes)that interact with the environment.
•Environmental monitoring,-Months, a year-Impractical to change the battery
•Energy aware wireless sensor networks protocols.
2
1.0 Introduction
Modelling, Simulation & Computing Laboratory (mscLab)School of Engineering and Information Technology, Universiti Malaysia Sabah, Malaysia
CICSyN 20124th International Conference on Computational Intelligence, Communication Systems and Networks
Phuket, Thailand, 24-26 July 2012
•Cluster based hierarchical routing protocol (environmental monitoring)-data aggregation
• Heavy work load concentrated on cluster head.-load balancing among each sensor node
•Select 5 suitable cluster heads from 100 sensor nodes-How many possible combination of 5 cluster heads from a set of100 sensor nodes???
3
2.0 Objective
Modelling, Simulation & Computing Laboratory (mscLab)School of Engineering and Information Technology, Universiti Malaysia Sabah, Malaysia
CICSyN 20124th International Conference on Computational Intelligence, Communication Systems and Networks
Phuket, Thailand, 24-26 July 2012
•To find the optimum set of cluster heads among sensor nodesso that it can reduce network energy consumption per roundand prolong the first node die (FND) cycle.
4
3.0 Methodology
Modelling, Simulation & Computing Laboratory (mscLab)School of Engineering and Information Technology, Universiti Malaysia Sabah, Malaysia
CICSyN 20124th International Conference on Computational Intelligence, Communication Systems and Networks
Phuket, Thailand, 24-26 July 2012
Low Energy Adaptive Clustering Hierarchy (LEACH)• A cluster based hierarchical routing protocol which randomly rotates the cluster-head among sensor nodes.
• LEACH consists of two phases:•(i) Set-up Phase
1 ) Advertisement Phase2 ) Cluster Set-up Phase
•(ii) Steady Phase1 ) Schedule Creation2 ) Data Transmission
5
3.0 Methodology
Modelling, Simulation & Computing Laboratory (mscLab)School of Engineering and Information Technology, Universiti Malaysia Sabah, Malaysia
CICSyN 20124th International Conference on Computational Intelligence, Communication Systems and Networks
Phuket, Thailand, 24-26 July 2012
Particle Swarm Optimization• Inspired by observing behavior of bird flocking or fish school.
• Begins with a group of random particles.
•Each particle interacts with one another while learning from their best experience.
•Update particle velocity and position.Cognitive component-relative to past performance (Local)
Social component-relative to group of particle (Global)
•Termination criteria.
6
3.0 Methodology
Modelling, Simulation & Computing Laboratory (mscLab)School of Engineering and Information Technology, Universiti Malaysia Sabah, Malaysia
CICSyN 20124th International Conference on Computational Intelligence, Communication Systems and Networks
Phuket, Thailand, 24-26 July 2012
Adaptive Particle Swarm Optimization• Inertia Weight, Cognitive Learning Factor and Social Learning Factor.
• Inertia Weight, w-Momentum of the particle-Decreasing linearly over time
• Adaptive Social and Cognitive Learning Factors, c-Particles local best and local best fitness ratio.-Decreasing over time
7
4.0 Simulation
Modelling, Simulation & Computing Laboratory (mscLab)School of Engineering and Information Technology, Universiti Malaysia Sabah, Malaysia
CICSyN 20124th International Conference on Computational Intelligence, Communication Systems and Networks
Phuket, Thailand, 24-26 July 2012
Parameter Value
Network size 100 x 100 m2
Base station location x = 50 m, y = 200 m
Simulation round 200
Number of node, n 100
Cluster head probability, p 0.05
Initial energy, E0 0.05 J
Packet Size, k 4000 bit
PSO
Number of particle 20
Iteration (PSO) 50
Number of Cluster head 5
• Network topology
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100
X
Y
• Simulation parameters
•100 m x 100 m sensing field•Base station is located at 50 m and 200 m
Modelling, Simulation & Computing Laboratory (mscLab)School of Engineering and Information Technology, Universiti Malaysia Sabah, Malaysia
CICSyN 20124th International Conference on Computational Intelligence, Communication Systems and Networks
Phuket, Thailand, 24-26 July 2012
0 20 40 60 80 100 120 140 160 180 2000
10
20
30
40
50
60
70
80
90
100
Simulation Time (round)
Num
ber o
f Aliv
e N
odes
proposedLEACH
Figure 1 : Network lifetime comparison
9
5.0 Results and Discussions
Modelling, Simulation & Computing Laboratory (mscLab)School of Engineering and Information Technology, Universiti Malaysia Sabah, Malaysia
CICSyN 20124th International Conference on Computational Intelligence, Communication Systems and Networks
Phuket, Thailand, 24-26 July 2012
0 20 40 60 80 100 120 140 160 180 2000
0.01
0.02
0.03
0.04
0.05
0.06
Ene
rgy
Con
sum
e pe
r Rou
nd (J
)
Simulation Time (round)
proposedLEACH
0 0.5 1 1.5 2 2.5
x 106
0
10
20
30
40
50
60
70
80
90
100
Data Received by Base Station (bits)
Num
ber o
f Aliv
e N
odes
proposedLEACH
Figure 2 : Energy consumption pre round Figure 3 : Data received by base station (bits)
-improvement of 60.3%
10
6.0 Conclusions
• Proposed method outperforms LEACH protocol in terms of networklifetime, energy use per round and total data received by base stationbefore first node die cycle.
• Adaptive learning factor and particles re-select mechanism can improvesPSO average fitness value compare to standard PSO.
• For further improvement, number of member nodes in a cluster may varybase on the distance between cluster head and base station.
Modelling, Simulation & Computing Laboratory (mscLab)School of Engineering and Information Technology, Universiti Malaysia Sabah, Malaysia
CICSyN 20124th International Conference on Computational Intelligence, Communication Systems and Networks
