## 2 Related Work
The cluster head selection approach based on the dominating set theory is proposed in [17], the authors of which define a weight to calculate the spatial correlation between sensor data. When the clusters are constructed, the data sampled in each cluster head have very high correlation with the data sampled in its cluster members. Consequently, only the cluster heads that can represent the data features of its members need to do the data sampling work, which means the data transmitted in the sensor network are reduced remarkably without any extra data aggregation algorithm. However, the biggest problem for large-scaled WSNs is a massive amount of valuable information from member nodes cannot be found by the end users. In [18], an adaptive sampling approach (ASAP) is developed for energy efficient periodic data collection in WSNs. In ASAP, the samplers is composed of a dynamically changing subset of the nodes in which the readings are directly collected, whereas the readings of non-samplers are predicted through the use of probabilistic models that are locally and periodically constructed. ASAP can be effectively used to increase the network lifetime while avoiding the data loss from non-sampler nodes such that keeping the high quality of the collected data. Another approach of energy-efficient data collection in WSNs is data prediction technique leveraging the spatial or temporal correlation between sensor data. The data prediction applying in WSNs has a tradeoff between reducing communication cost and limiting prediction cost. Based the analysis for the performance of the tradeoff in [19, 20], an energy-efficient framework for clustering-based data collection is proposed in [21], where the benefit of adaptive scheme to enable/disable prediction operations is exploited. To improve prediction accuracy, authors of [22, 23] perform prediction of data based on the multivariate correlation and multiple linear regression methods, respectively. Our purpose of organizing sensor nodes into clusters is to realize the energy-efficient wake-up/sleep scheduling scheme by which some active nodes to provide sensing services, while the others are inactive to conserve their energy. For nodes scheduling problem, many attempts have mainly focused on getting complete coverage of the monitored area, where a target of interest is within the sensing range of at least one sensor. A survey on target coverage problem in WSNs is presented in [24, 25]. In [26] authors propose a target coverage scheduling scheme based on a genetic algorithm that can find the optimal cover sets to extend the network lifetime. In each cover set, a sufficient number of sensor nodes cover the specified targets by using the evolutionary global search technique. The problem of proper scheduling and putting unnecessary sensor nodes into sleep mode was also explored in [27]. The proposed scheme is a kind of an adaptive/periodic on-off scheduling scheme in which sensor nodes use only local information to make scheduling decisions. A sensing topology management strategy proposed in [28] have similar characteristics with our approach in terms of exploiting redundancy for continuous data sampling applications and no a-priori statistical assumptions on the underlying phenomenon need to be made. By introducing the finite-dimensional Hilbert space framework of sensors as random variables, sensor locations map onto vectors in this Hilbert space, and inner products between them are defined by the correlation structure of the sensed physical process. As a consequence of adopting the methodology, the number of disjoint sets of sensors can be maximized (or equivalently, the average number of sensors in each set is minimized), while ensuring that each one can provide no more than the user specified distortion in the data sampling period. However, the proposed algorithms do not consider the cluster-based networking architecture that proved to have more advantages than other network model such as energy-saving, scalability, ease of data fusion and robustness.