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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

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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

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Aggregation Schemes M1 and M2 M1:Aggregation using majority rule Yi Information transmitted Yi Majority decision of children H={0,1} P(H=0)=P(H=1)=0.5 p Precision of sensor

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Aggregation Schemes M1 and M2 M2: Infinite precision aggregation scheme ,0 1,1 0,1 1, 4 1,2 2,7 0, ,0 1 0,1 : Information Transmitted Zi No. of zero’s in subtree. Oi No. of one’s in a subtree. H={0,1} P(H=0)=P(H=1)=0.5 p Precision of sensor

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Link metric for Routing C1 and C2 Routing : Bellman-Ford Routing Algorithm Link cost C1 –C1=I j /B i Where, B i Battery level of node Si. I j Number of nodes that can transmit to node Sj. Link cost C2 –C2=P ij /B i Where, P ij Power required to transmit a bit from node Si to node Sj. SiSj

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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).

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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.

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Spanning tree Spanning tree as a result of Bellman ford routing algorithm with link cost C1

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Development of Weighted Aggregation Scheme Local view of a Network

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Weighted Aggregation Scheme Assumption –Transmission of one bit from a node to its parent. –Every node Si knows number of descendent their children have.

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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

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Probability Mass Function

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MMSE Estimate

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Final decision by So

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WAS Applicability Static Network Dynamic Network

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Overhead on WAS Extra transmission and reception required for descendant update.

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Simulation Results Comparison of accuracy for M1, M2 and WAS

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Simulation Results Comparison of lifetime for M1, M2 and WAS

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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.

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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 [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 [3] Steven A. Borbash, “Design considerations in wireless sensor networks, ” Doctoral thesis submitted to University of Maryland, [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 [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 [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 [7] R. Viswanathan and P. K. Varshney, “Distributeddetection with multiple sensors: part Ifundamentals,” Proceedings of the IEEE, Vol. 85, Issue1, Jan 1997, pp

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Many Thanks

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