## 1 Introduction  
As the rapid advances recently in wireless communications and sensor technologies, wireless sensor networks (WSNs) have emerged as the promising solution for various applications due to their low cost and easy deployment. A wireless sensor network consists of sensor nodes spatially deployed over a geographical area for monitoring physical environment. These sensor nodes are mainly composed of four components: power unit for power supply, sensing unit for data acquisition (sampling), processing unit for data processing, and radio unit for wireless communication. Typically, their limited power capability, _i.e._, battery powered, make WSNs encounter the challenge of extension of network lifetime. Many interesting literatures [1; 2; 3; 4; 5] have proposed how to address this kind of problem. Therefore, it is a key issue in the design of systems based on WSNs that how to reduce the energy consumption and further prolong the network lifetime. With regard to the energy conservation, Anastasi _et al._[4] identify three main enabling techniques, namely, duty cycling, data driven, and mobility-based approaches. The first category exploits the power management (including mechanisms on MAC layer, network layer and cross layers) for energy saving by considering duty cycling [6; 7; 8; 9]. To this end, various criterions can be used to decide which/when sensor nodes should be active. It is a well-known fact that the radio unit is the dominant part of energy consumption in a sensor node [10]. Hence, it has been shown in previous works [10; 11] that an idle sensor node should turn its radio off to avoid unneeded energy waste, namely avoid idle listening. In [12], the authors adapt the scheduled rendezvous scheme for duty cycling. The basic idea is that each node should wake up based on a wakeup schedule. They utilize the data gathering tree structure to achieve both energy efficiency and low packet delivery latency. The scheme staggers the active/sleep schedule of the nodes in the data gathering tree according to its depth in the tree. The second category focuses on sensor readings and can be further be subdivided into two main classifications: data reduction and energy-efficient data acquisition. The former adopts in-network processing, such as aggregation [13; 14], compression [15] or prediction [16; 17], in order to reduce the amount of data readings that need to be transmitted. Clearly, it can perform energy conservation that is benefitting from cutting down the transmissions. However, it has to pay more attention to keep the sensing accuracy within an acceptable level for the application, since data reduction is designed to reduce the amount of communication. The other proposes adaptive sampling schemes [18] for energy saving while especially taking energy hungry devices into consideration. Furthermore, unneeded and reducible sampling results in useless energy consumption because they bring about unneeded communications. The last category considers the specific schemes that the mobility of sensors nodes or sink is available. By introducing mobility in WSNs, routing schemes for the collection of data reading should take the mobile pattern of sensors nodes into consideration. As mentioned above, a lot of studies on the WSNs have been dedicated to energy conservation. WSNs using hierarchical protocols are partitioned into clusters with different concerns [19]. Many hierarchical protocols show their better ability of energy efficiency, stability, and scalability [20; 21] in WSNs. The cluster head can not only be one of the coordinators among the inter-cluster communication, but also be a key role for in-network processing within the intra-cluster. Besides, data reduction based on the correlation of sensed readings can efficiently reduce the amount of required transmissions. Therefore, we use a sub-clustering procedure based on spatial data correlation to further separatethe hierarchical (clustered) architecture of a WSN.