Wireless sensor network is energy constrain network. Lifetime of network is defined by life of first certain percentage of dying nodes. Load balancing is a method to make energy consumption of all nodes equal and this way all nodes will die together. By load balancing, lifetime of network does not depend only on life of weak node but depends on life all nodes in network which helpful to increase life of network. In this paper two methods are proposed for load balancing which are also called data aggregation methods. In first method, nodes which are far from the sink consume more energy and load balancing is achieved by increasing the interval of communication based on residual energy of these nodes. In second method, load balancing is achieved by tolerating the quality of data. Nodes which have less energy send data only when data is sufficient deviate from past data. Quality of data is based on deviation control function and this deviation control function is based on residual energy of nodes. Simulation results show proposed methods significantly increases the lifetime of wireless sensor network.

1. Load Balancing to Extend Life of
Wireless Sensor Network
Presented By: Gaurang Rathod
M.E.-E.C.
Gujarat Technological
University
India
rathodgaurang@hotmail.com ICCUBEA 2015 Paper Id. 580

2. Outline
 Introduction
 Precision Allocation Method
 Experimental Work
 Conclusion
 References
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3. Introduction
 Wireless sensor network is made of sensor
nodes and base station[1]
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Figure 1 Wireless Sensor Network

4. Continue…
 Lifetime of network[2]:
time duration until the
first node is out of energy
 Load balancing: make energy consumption of
all nodes equal
 Load balancing can be achieved by routing,
mobile base station, data aggregation[3]
, etc.
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5. Precision Allocation Method
 Precision of data is given by error bound(e)
 Example
Desired reading : d
Readings in the range [d-e,d+e] are accepted.
 Nodes communicate with sink only when
new sense reading(xt+1) significantly
deviates from last sense reading(xt) and
out of interval [xt-e, xt+e]
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6. Same Error Bound Problem
 Problem with same error bound allocation to all
node :
1. Data captured by different nodes change at
different magnitudes and frequencies.
2. Energy consumption of nodes is not same
because of parameter like distance between
node and sink.
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7. Precision Partitions (solution!!!)
 Give different error bounds to different
nodes and derive benefit of energy in
sensor network.
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8. Precision Guarantee Example
Real (Actual sense value): 20.16
Approximate (Data aggregation) :20.15
Total Error Bound of Network E: 3 ˚C
Error Bound allocated to each Node e : 0.5˚C
Here E = No. of Nodes * e
= 6 * 0.5
=3
18 20 22
3
+ +
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9. Constrain for Error Bound
 Total error bound allocated to nodes cannot
exceed total network error bound(E).

1
n
i
ei E
=
≤∑
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10. Precision Guarantee Example
Real (Actual sense value): 20.16
Approximate (Data aggregation) :20.15
Total Error Bound of Network E: 3 ˚C
Error Bound allocated to each Node e : 0.5˚C
Here E = No. of Nodes * e
= 6 * 0.5
=3
18 20 22
3
+ +
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11. Continue…
 Example:
Let 10 temperature sensor nodes in network and total
network error bound (E) is 10 C.
e1= e2=e3=e4=e5=e6=e7=e8=e9=e10= 1 C OR
e1=0.25 e2=0.75 e3=0.3 e4=0.4 e5=0.6
e6=0.7 e7=1.00 e8=1.25 e9=1.75 e10=3
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12. Optimal Precision Allocation
Let u : Frequency of communication between
node and sink
e : Error Bound allocate to node
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13. Example for Candidate
Precision Allocation
 Let 3 Nodes: n1, n2, & n3
 Error Bound :
e1<e2<e3 and e1+e2+e3=E
 let energy consumption rate of node r1, r2 & r3
r1<r2<r3
 e1 assigned to n1, e2 assigned to n2 and e3
assigned to n3
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14. Continue..
 Let the energy cost due to communication
between node and sink= Si
 Let residual energy of node= P
 Expected lifetime ratio of node
( ). i
P
l
u e S
=
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15. Flow Chart for Load Balancing
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Start
Allocate same error bound (e) to all nodes
Input: network error bound, adjustment period
Network error bound
=
Totalnumber of nodesin network
e
Increment update count
New sensed data
1x [x , x ]t t te e+ ∉ − +
No
No change in
update count
Yes
15

16. April 22, 2015 ICCUBEA 2015 16
Adjustment period over
Restart adjustment timer and find the node with highest expected
lifetime ratio and lowest expected life time ratio
error boundof thenode which hashighest expectedlifetimeratio
delta =
number of nodesin network
Update error bound of node which has highest expected lifetime ratio by
subtracting delta from previous error bound also update error bound of node which
has lowest expected lifetime ratio by adding delta to previous error bound
Any node died?
End
Yes
Yes
No
No

17. Experiment Work A (MATLAB)
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Parameter Value
Network Area 500m X 500m
Number of nodes 25
Initial energy 100 J
Data packet size 200 bytes
Electronics energy 50 nJ/bit
Free space energy 10 pJ/bit/m2
Simulation Parameters

18. Network Topology
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19. Data (Temperature) Profile for
Node
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20. Initial Error Bound
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21. Error Bound at Simulation End
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22. Node With Highest Expected Lifetime
Ratio After Every Adjustment Period
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23. Node With Lowest Expected Lifetime
Ratio After Every Adjustment Period
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24. Value of Delta after Every
Adjustment Period
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25. Number of Times Node
Communicate with Base Station
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26. Residual Energy of Node at
Simulation End
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27. Experiment Work B (NS2)
Simulation Cases :
1. With same error bound to all nodes
2. With different random error bound to all nodes
3. Error bound with respect to distance between
node and base station to all nodes
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28. Simulation Parameters
Parameter Value
Channel Type Wireless
Propagation Type Two Ray Ground
MAC protocol MAC – 802.15.4
Queue Type Drop tail
Antenna Omni Antenna
Number of nodes 25
Queue Length 50
Routing protocol AODV
Network area 500 m x 500 m
Packet size 200 bytes
Initial Energy 2 joules
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29. Network Topology
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30. Case 1 :
Same Error Bound
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31. Case 2 :
Random Error Bound
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32. Case 3 :
Error Bound Based on Distance
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33. Case 1 :
Energy Left at Simulation End
Node Energy Node Energy Node Energy
0 1.5216 8 1.5206 16 1.3288
1 1.5066 9 1.5210 17 1.3287
2 1.5212 10 1.5211 18 1.5213
3 1.4236 11 1.4175 19 1.5219
4 1.5216 12 1.4927 20 1.5080
5 1.5077 13 1.5207 21 1.5215
6 1.5215 14 1.4755 22 1.5204
7 1.5218 15 1.5209 23 1.6813

34. Case 2 :
Energy Left at Simulation End
Node Energy Node Energy Node Energy
0 1.6736 8 1.6847 16 1.6849
1 1.6383 9 1.6450 17 1.6714
2 1.6823 10 1.6851 18 1.7172
3 1.6827 11 1.6812 19 1.6004
4 1.6968 12 1.6838 20 1.6808
5 1.6346 13 1.6857 21 1.6819
6 1.6686 14 1.6608 22 1.6600
7 1.6786 15 1.6441 23 1.6813

35. Case 3 :
Energy Left at Simulation End
Node Energy Node Energy Node Energy
0 1.8562 8 1.8585 16 1.8534
1 1.8582 9 1.8563 17 1.8561
2 1.8592 10 1.8588 18 1.8572
3 1.8586 11 1.8576 19 1.8586
4 1.8566 12 1.8592 20 1.8527
5 1.8455 13 1.8589 21 1.8580
6 1.8592 14 1.8424 22 1.8554
7 1.8480 15 1.8597 23 1.8505

36. Comparison of Residual Energy
of Nodes at Simulation End
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37. Packet Delivery Ratio
Case Send Packets
Received
Packets
Ratio
Same Error
Bound
1250 1250 1.0000
Random Error
Bound
575 574 0.9983
Error Bound
Based on
Location of Node
450 450 1.0000
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38. Conclusion
 Wireless sensor network is energy constrain network. By
load balancing life can be significantly increased.
 By precision allocation method, tolerating just a small
degree of inaccuracy in data collection prolongs network
lifetime substantially.
 By simulating the network of 25 nodes, with different
precision allocation based on communication cost
between nodes to sink, the life time of network is more
compare to the case of same precision allocation and
random precision allocation
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39. References
1. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A survey
on sensor networks,” IEEE Commun. Mag., vol. 40, no. 8, pp. 102–
114, Aug. 2002.
2. E. Fasolo, M. Rossi, “In-network aggregation techniques for wireless
sensor networks: a survey”, IEEE Wireless Communications , pp. 70-
87, April 2007.
3. K. Maraiya, K. Kant and N. Gupta, "Architectural based data
aggregation techniques in wireless sensor network: a comparative
study", International Journal on Computer Science and Engineering
(IJCSE), vol. 3, no. 3, March 2011.
4. R. Rajagopalan and P. Varshney, “Data-aggregation techniques in
sensor networks: a survey”, IEEE Communications Surveys &
Tutorials, vol. 8, no. 4, pp. 48-63, 2006.
5. J. Sen, “A robust and secure aggregation protocol for wireless sensor
networks”, IEEE International Symposium on Electronic Design, pp.
222-227, 2011.
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40. Continue…
6. C. Sha, R. Wang, H. Huang, L. Sun, “Energy efficient clustering
algorithm for data aggregation in wireless sensor networks”, The
Journal of China Universities of Posts and Telecommunications, Volume
17, Supplement 2, Pages 104-109,122, December 2010.
7. X. Tang, J. Xu, “Optimizing lifetime for continuous data aggregation
with precision guarantees in wireless sensor networks”, IEEE/ACM
transactions on networking, vol. 16, no. 4, August 2008.
8. Network Simulator -2
http://www.isi.edu/nsnam/ns/doc/index.html
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41. Thank You