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POT : An Efficient Top-k Monitoring Method for SpatiallyCorrelated Sensor Readings

YongHyun Cho, Jihoon Son and Yon Dohn Chung

Data management for sensor networks 2008

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Outline

Introduction Partial Ordered Tree Simulation Conclusion

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Introduction

Since sensor readings are usually correlated with location, top-k nodes are clustered at some areas

This paper propose a novel tree structure to efficiently maintain clusters of the highest readings

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Temperature of Kawah Ijen crater lake

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POT : Partial Ordered Tree

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Partial Ordered Trees

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Initial construction of POTs

(50,49,47)

(48,45,39)

TOP 3

(50,49,48,47,45,39)

(48+47)/2=47.5

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Local Top-k evaluation

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Global Top-k evaluation

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Updating the POTs

If the size of GR is k, the new threshold is the same to the previous one.

Otherwise, the new threshold is set as the average of the and the

In each POT, only the nodes that have reported their readings are required to update their thresholds.

KGR 1KGR

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Skewed distribution

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AverageEnergyConsumption(1/3)

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AverageEnergyConsumption(2/3)

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AverageEnergyConsumption(3/3)

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Network Lifetime(1/3)

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Network Lifetime(2/3)

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Network Lifetime(3/3)

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Monitoring accuracy

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Conclusion

In sensor networks, sensor readings are usually correlated with their locations

In this paper proposed a novel structure to manage the highest readings and process top-k query