Maximum Lifetime Routing in Wireless Sensor Networks
by
Collins Adetu
Nicole Powell
Course: EEL 5784
Instructor: Dr. Ming Yu
Overview
What are Wireless Sensor Network (WSN) Applications of WSN The Energy Efficiency Problem Solution: Flow Augmentation Algorithm Simulation Results Conclusions Questions
What is a Wireless Sensor Network?
A wireless sensor network is an ad hoc network of sensors and gateways communicating wireless amongst each other.
GatewaySensor
Fig. 1. Diagram Illustrating a Wireless Sensor Network
Applications of Wireless Sensor Networks
Used in military applications for battlefield surveillance
Used for detecting seismic activity in earthquake and volcanic prone regions
Used in ecosystem monitoring e.g. firetower sensors for monitoring forest fires
Used in weather forecasting and hurricane prediction
The Energy Efficiency Problem
Because of the compact nature of wireless sensors they are normally equipped with small-sized batteries e.g. AA batteries
Smaller batteries mean less power available for communication
Therefore, optimizing sensor battery lifetime is a matter of utmost concern
Collectively optimizing battery lifetime for all nodes in the network increases the network’s lifetime
Flow Augmentation Algorithm
The paper we researched provides a solution to the energy efficiency problem by using the flow augmentation algorithm
The algorithm uses a relative energy metric, which it dynamically updates, to compute the most energy efficient communication path
Data flows through the most energy efficient path at all times, hence maximizing network lifetime
Flow Augmentation Algorithm
Compute Energy
Cost Metric
Calculate MostEnergy-Efficient
Path
ComputeResidual Energy
Continue until first node dies
Calculated using Dijkstra,Bellman-Ford etc.
Computes residual energyonly on nodes traveled
on shortest path.
Uses energy-cost metric formula
Flow Augmentation Algorithm
The cost metric is computed using the formula:
where Energy expended transmitting data (J/bits)
Energy used in processing received data (J/bits)
Ei and Ej = Initial energy of the transmitting and receiving node (J)
and = Residual energy of the transmitting and receiving node (J)
x1, x2, x3 = positive weights
Residual Energy is computed as follows:
Note: This applies for both transmitting and receiving nodes, lambda = packet size
ijte
ijre
iE jE
ijoldnew eEE
Simulation Results The objective of our simulation was to obtain
similar results as proposed in the paper In our simulation,
20 nodes were distributed randomly over a 50m by 50m area
Sensors were initialized with 10J of energy Source and destination nodes were randomly
selected 50 instances were simulated with different
source and destination nodes in order to compute an average network lifetime
The input parameters to the Flow Augmentation algorithm were the weights, x1, x2, and x3 i.e. FA(1,1,1) means x1=x2=x3=1 are passed as input parameters to the algorithm
All simulations were done using MATLAB
Simulation Results
Average and Worst Case Performance of FA(x 1,x 2,x 3)
0.4
0.5
0.6
0.7
0.8
0.9
1
1 5 10 15 20 25 30R, n
orm
aliza
ed n
etw
ork
lifeti
me
FA(1,x,x) AVERAGE
FA(1,x,x) WORST
FA(1,x,0) AVERAGE
FA(1,x,0) WORST
Average and Worst Case Performance of FA(x 1,x 2,x 3)
00.10.20.30.40.50.60.70.80.9
1
1 5 10 15 20 25 30
R, n
orm
alize
d ne
twor
k life
time
FA(1,x,x) AVERAGE
FA(1,x,x) WORST
FA(0,x,x) AVERAGE
FA(0,x,x) WORST
Performance of FA(1,x,x) compared with FA(1,x,0)
Simulation ResultsPerformance of FA(1,x,x) for various λ = Data Packet Size
Worst Performance of FA(x 1,x 2,x 3)
0.7
0.75
0.8
0.85
0.9
0.95
1
1 10 20 30R, n
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aliza
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λ=5000
λ=10000
λ=20000
Average Performance of FA(x 1,x 2,x 3)
0.90.910.920.930.940.950.960.970.980.99
1
1 10 20 30
R, n
orm
aliza
ed n
etw
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lifeti
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λ=5000
λ=10000
λ=20000
Conclusion
The network lifetime increases as the sensor energy weights are increased when using the Flow Augmentation algorithm
An increase in data packet size (lambda), produces an adverse effect on the network lifetime
Our simulation results match closely to those obtained in the paper
The paper went further to compare the flow augmentation algorithm with other energy-efficient routing algorithm. The flow augmentation algorithm out performed the other algorithms
References
Chang, J.-H., and L. Tassiulas. "Maximum Lifetime Routing in Wireless Sensor Networks." IEEE ACM TRANSACTIONS ON NETWORKING. 12 (2004): 609-619.
Tanenbaum, Andrew S. Computer Networks. 4th ed. New Jersey: Prentice Hall PTR, 2003.
Questions