## 1 Introduction
One distinct example is presented in research for image quality assessment [4], where larger MSE dose not show lower structure level on the test images. As mentioned before, the over-deployed sensor nodes are high correlated or structured, and thus meaning that the sampling data collected by spatial correlated nodes have strong dependencies carrying important structure information. We attempt to exploit the dependencies to reduce the number of nodes required to work for sampling and data transmission. Such reduction is bound to save energy and prolong the network lifetime of WSNs. In this paper, we propose a novel approach to implement sensor scheduling by maintaining the continuous sampling data fidelity that is defined by the structural similarity successful applied to image quality assessment methods. The goal of image quality assessment research is to provide a direct approach that quantifies the similarity or difference between the test and reference images. The simplest implementation to achieve the goal is the error sensitivity approach, which determines a better quality image based on lower MSE. Some limitations on the traditional approach have been highlighted by many previous researchers from image processing area [5, 6, 7], e.g., quality definition problem, decorrelation problem and cognitive interaction problem. These problems also exist in the quality assessment of the continuous sampling applications for WSNs. A new philosophy for the assessment of image quality is the Structural SIMilarity (SSIM) proposed in [4,8,9], which calculate the structural similarity index of the degraded images by incorporating known human visual system (HVS) properties. With the method, the structural information of image signals consisting of abundant pixels exhibiting high spatial correlation can be extracted. Despite a big MSE relative to the reference image, the tested image still obtains a very high fidelity value as long as the most of structural information of the reference image can be preserved. Using the structural similarity to process large scale data in WSNs, especially when data is continuous sampled, it is unquestionable that we can get benefits in energy saving. There are two strong motivations for this:  
* The extracted structural information is just the main concern in the continuous data sampling applications for WSNs. Such as the application domain that need to capture the data structural changes is highly adapted with the SSIM approach, which is inapplicable in event detection because of the character of error sensitivity. * The limitations from the error sensitivity approaches are avoided when the SSIM index is used to be a data fidelity assessment criterion. In this way, we refer the data collected by sensor nodes at any time as a tested image to judge the node's contribution to the whole data collection. So that, only a part of sensor nodes need to work and sample while the senor nodes with a negligible contribution will enter a sleep mode. Inspired by the above ideas, we propose a structure fidelity data collection (SFDC) framework leveraging the spatial correlations between nodes to reduce the number of the active sensor nodes while maintaining the low structural distortion of the collected data. The ultimate goal of this paper is to save energy in the continuous data sampling applications for WSNs by designing a novel node scheduling method, which considers the data fidelity in term of spatial structure instead of the traditional MSE. To get the most out of spatial correlation, our framework is based on the spatial clustering algorithm and splitted into two phases: the learning phase and data collection phase. During the learning phase, all sensor nodes report their values to the sink. The sink assigns the sensor nodes with close distance and similar trend of the historical data to the same cluster. Within each cluster, the one with the least mean deviation on the readings is selected as the cluster head (CH).