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Collaborative Sampling in Wireless Sensor

Networks

Minglei Huang

Yu Hen Hu2010 IEEE Global Telecommunications Conference

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Introduction

Sensor consumes a lot of energy when it communicates with others.

With prior knowledge of correlation between two sensor nodes, the amount of communication can be greatly reduced.1) Select a representative node

2) Divide the field into Voronoi cells and approximate the underlying function

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Problem Formulation(1/3)

Assumptions:1) Broadcast links are symmetric.

2) The broadcast range and energy cost of all sensors are the same.

3) The broadcast time is negligible compared to the backoff time.

4) Fusion Center(FC) has no power constraints.

5) No handshaking before each broadcast.

6) All broadcasts are heard and decoded correctly by the FC.

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Problem Formulation(2/3)

N sensor nodes locate at (i=1,…,N) having instantaneous reading at time k

Using all the reported readings() to estimate the next reading

Error function

g(.) : An estimator that approximate the underlying function

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Problem Formulation(3/3)

Define as a set of sensor nodes that has broadcasted by the report

The optimal node to report at

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Using Distributed Delay(1/2)

Delay due to data aggregation

When to clock out data as it is processed by nodes have significant performance impact in terms of data accuracy and freshness

A back off delay that is inversely proportional to the prediction error at each node

[12] I. Solis and K. Obraczka, “The impact of timing in data aggregation for sensor networks,” in Proceedings of the IEEE ICC, Paris, France, Jun. 2004.

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Using Distributed Delay(2/2)

Packets arrive at FC can be modeled as Poisson Process

=1-

c=

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Algorithm

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Complexity

Goal : Select k N samplesAssume the broadcast range is rN, 0Compare 3 sampling method

1) Oracle – the best possible performance

2) Greedy Oracle

3) Collaborative Sampling

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Experim

ent R

esu

lts

Experim

ent R

esu

lts

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We could save communication and computations by using the stopping criteria to let the algorithm finish sooner.

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Conclusion

The proposed method actively samples the data in network by scheduling the broadcasts in a distributed fashion.

Apply the idea of performing quick approximation of a underlying function in WSN.

The error bound can be tightened and performance of real life data has to be tested.