Abstract
The data space collected by a wireless sensor network (WSN) is the basis of data mining and data visualization. In the process of monitoring physical quantities with large time and space correlations, incomplete acquisition strategy with data interpolation can be adopted to reduce the deployment cost. To improve the performance of data interpolation in such a scenario, we proposed a robust data interpolation based on a back propagation artificial neural network operator. In this paper, a neural network learning operator is proposed based on the strong fault tolerance of artificial neural networks. The learning operator is trained by using the historical data of the data acquisition nodes of WSN and is transferred to estimate the value of physical quantities at the locations where sensors are not deployed. The experimental results show that our proposed method yields smaller interpolation error than the traditional inverse-distance-weighted interpolation (IDWI) method.
Highlights
The purpose of a wireless sensor network (WSN) is to obtain the data field or data space of the physical world as accurate and complete as possible through acquisition technology
On the basis of analyzing the demand of network models, this paper proposes a robust data interpolation based on a back propagation artificial neural network operator for incomplete acquisition in wireless sensor networks
We proposed a robust data interpolation based on a back propagation artificial neural network operator for incomplete acquisition in a wireless sensor network
Summary
The purpose of a wireless sensor network (WSN) is to obtain the data field or data space of the physical world as accurate and complete as possible through acquisition technology. It is an important part of forecasting, simulation, and prediction to obtain the spatial-temporal distribution information of the monitored object accurately. Because the constraints of interpolation are relatively small, it is more appropriate to use the interpolation algorithm to complete or refine the entire data space in the case of spatial incomplete acquisition. In [2], Alvear et al applied interpolation techniques for creating detailed pollution maps
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