An mmse based weighted aggregation scheme for event detection using wireless sensor network
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AN MMSE BASED WEIGHTED AGGREGATION SCHEME FOR EVENT DETECTION USING WIRELESS SENSOR NETWORK. Bhushan Jagyasi (Presenting) Prof. Bikash K. Dey Prof. S. N. Merchant Prof. U. B. Desai. Overview of Wireless Sensor network (WSN).

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AN MMSE BASED WEIGHTED AGGREGATION SCHEME FOR EVENT DETECTION USING WIRELESS SENSOR NETWORK

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An mmse based weighted aggregation scheme for event detection using wireless sensor network

AN MMSE BASED WEIGHTED AGGREGATION SCHEMEFOREVENT DETECTION USING WIRELESS SENSOR NETWORK

Bhushan Jagyasi (Presenting)

Prof. Bikash K. Dey

Prof. S. N. Merchant

Prof. U. B. Desai


Overview of wireless sensor network wsn

Overview of Wireless Sensor network (WSN)

  • Wireless Sensor Network is a network formed by densely deploying tiny and low power sensor nodes in an application area.

  • Application:

    • Military application

    • Smart home

    • Agriculture

    • Event detection (May be disaster event)

      • For eg. Landslide Detection


Aggregation schemes m1 and m2

Aggregation Schemes M1 and M2

  • M1:Aggregation using majority rule

1

H={0,1}

P(H=0)=P(H=1)=0.5

p Precision of sensor

1

0

0

1

0

1

1

1

1

1

1

1

1

1

0

0

1

1

1

Yi  Information transmitted

Yi Majority decision of children.


Aggregation schemes m1 and m21

Aggregation Schemes M1 and M2

  • M2: Infinite precision aggregation scheme

H={0,1}

P(H=0)=P(H=1)=0.5

p Precision of sensor

1

0,1

1,0

0

1

1,1

1

0,1

0,1

1, 4

1

1

2,7

1

1,2

1,0

0

1

0,1

<Zi,Oi> : Information Transmitted

Zi No. of zero’s in subtree.

Oi No. of one’s in a subtree.

1


Link metric for routing c1 and c2

Link metric for Routing C1 and C2

  • Routing : Bellman-Ford Routing Algorithm

  • Link cost C1

    • C1=Ij/Bi

      Where, Bi  Battery level of node Si.

      Ij Number of nodes that can transmit to node Sj.

  • Link cost C2

    • C2=Pij/Bi

      Where, Pij Power required to transmit a bit from node Si to node Sj.

Sj

Si


Steven s results

Steven’s results

  • Steven Claims that:-C1 results in balanced tree-Thus M1-C1 is better aggregation-routing pair for event detection application as compared to M2-C2(traditional).


Motivation behind was

Motivation behind WAS

  • We observe

    • The Spanning obtained by Bellman-Ford routing algorithm using link cost C1=Ij/Bi is far from balanced.

    • So majority rule may not be the optimum way of aggregating the data.


Spanning tree

Spanning tree

Spanning tree as a result of Bellman ford routing algorithm

with link cost C1


Development of weighted aggregation scheme

Development of Weighted Aggregation Scheme

Local view of a Network


Weighted aggregation scheme

Weighted Aggregation Scheme

  • Assumption

    • Transmission of one bit from a node to its parent.

    • Every node Si knows number of descendent their children have.


Weighted aggregation scheme1

Weighted Aggregation Scheme

  • Xi One bit decision made by Si

  • Ni Number of descendants of node Si

  • ni Number of descendants of node Si deciding in favor of event.

  • Information available with node So:

  • Decisions made by its children

    • Xi for i=1,2,…,k

  • Decision made by itself, Xo

  • Number of descendants its each child have

    • Ni for i=1,2,…,k


Probability mass function

Probability Mass Function


Mmse estimate

MMSE Estimate


Final decision by so

Final decision by So


Was applicability

WAS Applicability

  • Static Network

  • Dynamic Network


Overhead on was

Overhead on WAS

  • Extra transmission and reception required for descendant update.


Simulation results

Simulation Results

Comparison of accuracy for M1, M2 and WAS


Simulation results1

Simulation Results

Comparison of lifetime for M1, M2 and WAS


Conclusion

Conclusion

  • Weighted Aggregation Scheme (WAS) has equivalent network lifetime as compared to M1 (majority rule aggregation scheme).

  • Both WAS and M1 outscores infinite precision aggregation scheme M2 in terms of network lifetime.

  • WAS outscores M1 in terms of accuracy.


References

References

  • [1] Bhushan G. Jagyasi, Bikash K. Dey, S. N. Merchant, U. B. Desai, “An MMSE based Weighted Aggergation Scheme for Event Detection using Wireless Sensor Network,” European Signal Processing Conference, 4-8 September 2006, EUSIPCO 2006.

  • [2] A. Sheth, K. Tejaswi, P. Mehta, C. Parekh, R. Bansal, S.N.Merchant, U.B.Desai, C.Thekkhath, K. Toyama and, T.Singh, “Poster Abstract-Senslide: A Sensor network Based Landslide Prediction System,” in ACM Sensys, November 2005.

  • [3] Steven A. Borbash, “Design considerations in wireless sensor networks, ” Doctoral thesis submitted to University of Maryland, 2004.

  • [4] R. Niu and P. K. Varshney, “Distributed detection and fusion in a large wireless sensor network of random size, ”EURASIP Journal on Wireless Communication and Networking 2005, pp. 462-472.

  • [5] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A survey on sensor networks, ” in IEEE Comm. Mag., Vol. 40, No. 8, August 2002, pp. 102-116.

  • [6] R. Madan and S. Lall, “Distributed algorithms for maximum lifetime routing in wireless sensor networks, ” in Globecom’04, Volume 2, 29 Nov- 3 Dec 2004, pp.748 -753.

  • [7] R. Viswanathan and P. K. Varshney, “Distributeddetection with multiple sensors: part Ifundamentals,” Proceedings of the IEEE, Vol. 85, Issue1, Jan 1997, pp. 54-63.


Many thanks

Many Thanks


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