## 2 Related Work  
Many data collection approaches aimed to reduce energy consumption have been proposed for wireless sensor networks recently. LEACH (Low-Energy Adaptive Clustering Hierarchy) [10] is the most famous clustering-based data collection protocol that utilizes randomized rotation of local cluster heads to evenly distribute the energy load among the sensors in the network. Since LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol, it is not surprising that the amount of information that must be transmitted to the base station would be greatly reduced. Thereafter, a large number of data collection approaches based on clustering emerged as required. A centralized version of LEACH is LEACH-C proposed in [11]. Unlike LEACH, where nodes self-configure themselves into clusters, LEACH-C utilizes the base station for cluster formation. By using a central control algorithm, the base station selects the nodes with residual energy above the average node energy throughout the network as the cluster head nodes. A modification to LEACH is presented in [12], in which a two-level hierarchy clustering protocol (TL-LEACH) is designed for sensor networks where end user wants to remotely monitor the environment. TL-LEACH permits to better distribute the energy load among the sensors in the network especially when the density of network is higher. The impact of heterogeneity of nodes in terms of their energy in WSNs that are hierarchically clustered is studied in [13]. Adapting this approach, an energy efficient heterogeneous clustered (EEHC) scheme for WSNs based on weighted election probabilities of each node to become a CH according to the residual energy in each node is proposed. Clustering techniques have an added benefit that provide an architectural framework for exploring data correlation in sensor networks. Some recent research on spatial clustering investigates the correlation measurement on sensor data in a highly correlated region. By using the spatial distance of sensors as the judgment of correlation, an approach presented in [14] defines a weight for each sensor's data which depends on the distance from the sample position to the target position. The Clustered AGgregation (CAG) proposed in [15] provides a mechanism that reduces the number of transmissions by calculating the deviation between different sensor readings to measure the spatial correlation. The cluster construction behind this concept is that a node is included in the cluster only when the deviation between the readings of it and the cluster head is less than the a user-provided error threshold, otherwise this node becomes a new cluster head. In the response phase, CAG transmits a single value per cluster, where only the cluster heads contribute to the data aggregation. By focusing on a few representative values rather than a large number of redundant data, CAG achieves a significant energy saving at the expense of a negligible quality. Another judgment method proposed in [16] is that a probability distribution function and correlation coefficients of nodes readings are used as a means of showing the representation of spatial correlation in the sensor network data model. A common feature in the approaches described above is that the cluster head nodes are selected stochastically. This raises the obvious problem that the selected cluster head is not best one for performing the high quality data collection in a specified application. In our SFDC framwork, the cluster head is the one that achieve the maximization of structure fidelity in the cluster data model. Thus overcoming the shortcoming of the random selection of cluster heads. Both the spatial distance and the correlation coefficient are considered in the design of our cluster construction such that the clustering algorithm provides a better foundation for obtaining the high structure fidelity.