Collaborative Sampling in Wireless Sensor Networks Minglei Huang Yu Hen Hu 2010 IEEE Global Telecommunications Conference 1

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 2

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. 3

Problem Formulation(2/3) 4 g(.) : An estimator that approximate the underlying function

Problem Formulation(3/3) 5

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 6 [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

Using Distributed Delay(2/2) Packets arrive at FC can be modeled as Poisson Process 7

Algorithm 8

Complexity 9

Experiment Results 10

Experiment Results 11 We could save communication and computations by using the stopping criteria to let the algorithm finish sooner.

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. 12