Precision Based Data Aggregation to Extend Life of WSN

Prepared By : Gaurang Rathod120420704005ME EC IISCET India

Guided By : Niteen Patel Associate Professor SCET

rathodgaurang@hotmail.com

Outline

Motivation Introduction Precision Allocation Method Experimental Work Conclusion References

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Motivation Wireless Sensor Network is Energy Constrained

Network

Energy Consumption of Node[1]

1. Data transmission2. Signal processing 3. Hardware operation

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Data Aggregation[2]

Global process of gathering and routing information through a network, processing data at intermediate nodes with objective of reducing resource energy consumption

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Without Data Aggregation With Data Aggregation

Benefits of Aggregation[3]

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Precision Allocation Data Aggregation[9]

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]

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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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Precision Partitions (solution!!!)

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

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Constrain for Error Bound

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

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1

n

i

ei E=

≤∑

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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Three Steps of Algorithm

1. Finding optimum error bound for a single node in terms of energy consumption

2. Candidate-Based Precision Allocation:Effective allocation of error bounds to all nodes

3. Adaptive Precision Allocation:Adjusting error bounds periodically

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Optimal Precision Allocation

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Let u : Frequency of communication between node and sinke : Error Bound allocate to node

Continue.. Let the energy cost due to communication

between node and sink= Si

Let residual energy of node= P

Expected life time of node

Error Bound

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( ). i

Plu e S

=

1

.

Pe u

l Si− = ÷

Candidate Precision Allocation Allocate the highest error bound (e) to the

node that have highest energy consumption rate and likewise allocate energy bound according to node’s energy consumption rate.

Let E : total network error bound of data aggregation,e : candidate error bound for a node,r : energy consumption rate

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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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Adaptive Precision Allocation Adjusting the error bounds of the sensor nodes

periodically

Interval between two successive adjustments is called an adjustment period.

Node keeps track of the update counts in adjustment period for a given node error bound.

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Continue... At the end of adjustment period, node

calculate:

1. Update Rate : u =count/period

2. Energy Consumption Rate for Error Bound e:

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.u Sir

P=

Continue... Node sends a candidate report message

including error bound and energy consumption rate to sink

After receiving messages from all sensor nodes, base station computes optimum precision allocation using algorithm -1.

The leftover error bound = Et-Et+1 = e is simply added to the node with highest energy consumption rate

Finally base station sends this new error bounds to nodes

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Experiment Work A (MATLAB) After every adjustment period, error bounds of

nodes which have highest and lowest residual energy are updated.

Change in Error Bound : Add or Subtract delta from error bound

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error bound of node which has highest residual enerydelta

Number of nodes in network=

Network Topology

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Data (Temperature) Profile for Node

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Initial Error Bound

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Error Bound at Simulation End

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Highest Residual Energy Node After Every Adjustment Period

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Lowest Residual Energy Node After Every Adjustment Period

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Value of Delta after Every Adjustment Period

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Number of Times Node Communicate with Base Station

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Residual Energy of Node at Simulation End

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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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Simulation ParametersParameter Value

Channel Type Wireless 802.11

Propagation Type Two Ray Ground

MAC protocol MAC – 802.11

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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Network Topology

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Continue...

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Case 1 : Same Error Bound

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Case 2 : Random Error Bound

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Case 3 : Error Bound Based on Distance

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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

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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

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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

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Comparison of Residual Energy of Nodes at Simulation End

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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

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Data (Temperature) Profile for Node

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Data (Radiation) Profile for Node

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Initial Error Bound for Radiation

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Error Bound for Temperature at Simulation End

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Error Bound for Radiation at Simulation End

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Number of Times Node Send Temperature Data to Sink

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Number of Times Node Send Radiation Data to Sink

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Residual Energy of Node at Simulation End

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Conclusion The purpose of precision allocation is to

differentiate the quality of data collected from different sensor nodes, thereby balancing energy consumption of sensor nodes.

Energy of node can be saved by assigning different precision to each node. And by that precision, we control and reduced the frequency of communication between node and sink.

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References1. N. Pantazis, S. Nikolidakis and D. Vergados, “Energy- efficient routing

protocols in wireless sensor networks: a survey”, IEEE Communications Surveys & Tutorials, vol.15, no.2, pp.551,591, Second Quarter 2013.

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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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. Y. Chen and A. Liestman, “A hierarchical energy-efficient framework for data aggregation in wireless sensor network”, IEEE/Vehical Technology, vol 55, no. 3, pp. 789-795, May 2006.

8. Y. Yu, B. Krishnamachari and V. Prasanna, “Data gathering with tunable compression in sensor networks”, IEEE transaction on Parallel and Distributed Systems, vol. 19, no. 2, pp. 276-286,February 2008.

9. 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.

10. Network Simulator -2 http://www.isi.edu/nsnam/ns/doc/index.html

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Thanking You

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