Load balancing to extend life of wireless sensor network

Load Balancing to Extend Life of Wireless Sensor Network

Presented By: Gaurang RathodM.E.-E.C.Gujarat Technological

UniversityIndia

[email protected] ICCUBEA 2015 Paper Id. 580

Outline

Introduction Precision Allocation Method Experimental Work Conclusion References

April 15, 2023 ICCUBEA 2015 2

Introduction Wireless sensor network is made of sensor

nodes and base station[1]


Figure 1 Wireless Sensor Network

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.


Precision Allocation Method Precision of data is given by error bound(e)

ExampleDesired reading : dReadings 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]


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.


Precision Partitions (solution!!!)

Give different error bounds to different nodes and derive benefit of energy in sensor network.


Precision Guarantee Example

Real (Actual sense value): 20.16Approximate (Data aggregation) :20.15

Total Error Bound of Network E: 3 ˚CError Bound allocated to each Node e : 0.5˚CHere E = No. of Nodes * e

= 6 * 0.5=3

18 20 22

3


Constrain for Error Bound

Total error bound allocated to nodes cannot exceed total network error bound(E).

1

n

i

ei E


Precision Guarantee Example

Real (Actual sense value): 20.16Approximate (Data aggregation) :20.15

Total Error Bound of Network E: 3 ˚CError Bound allocated to each Node e : 0.5˚CHere E = No. of Nodes * e

= 6 * 0.5=3

18 20 22

3


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


Optimal Precision AllocationLet u : Frequency of communication between node and sinke : Error Bound allocate to node


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


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

Pl

u e S


Flow Chart for Load Balancing

April 15, 2023 ICCUBEA 2015

Start

Allocate same error bound (e) to all nodes

Input: network error bound, adjustment period

Network error bound=

Total number of nodes in networke

Increment update count

New sensed data1x [x , x ]t t te e

No

No change in update count

Yes

15


Adjustment period over

Restart adjustment timer and find the node with highest expected lifetime ratio and lowest expected life time ratio

error bound of the node which has highest expected lifetime ratiodelta =

number of nodes in 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

Experiment Work A (MATLAB)


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

Network Topology


Data (Temperature) Profile for Node


Initial Error Bound


Error Bound at Simulation End


Node With Highest Expected Lifetime Ratio After Every Adjustment Period


Node With Lowest Expected Lifetime Ratio After Every Adjustment Period


Value of Delta after Every Adjustment Period


Number of Times Node Communicate with Base Station


Residual Energy of Node at Simulation End


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


Simulation ParametersParameter 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


Network Topology


Case 1 : Same Error Bound


Case 2 : Random Error Bound


Case 3 : Error Bound Based on Distance


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

Case 2 : Energy Left at Simulation End


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

Case 3 :Energy Left at Simulation End


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

Comparison of Residual Energy of Nodes at Simulation End


Packet Delivery Ratio

Case Send PacketsReceived Packets

Ratio

Same Error Bound

1250 1250 1.0000

Random Error Bound

575 574 0.9983

Error Bound Based on

Location of Node450 450 1.0000


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


References1. 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.


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


Thank You

Load balancing to extend life of wireless sensor network

Engineering

Transcript of Load balancing to extend life of wireless sensor network