## 1 Introduction  
Recent advances in wireless communication technology, computing equipment manufacturing technology and sensor technology have led to the development of low cost, low power, multifunctional sensor nodes. Sensor nodes are able to gather physical environmental data and also to process data. Sensor nodes equipped with various sensors, such as temperature, humidity, acceleration and gases, allow monitoring of different environments. They are also able to network with other sensor nodes and exchange data with each other and external users. Sensor networks can be used in various applications, including wireless data acquisition, machine monitoring and maintenance, smart buildings and highways, environmental monitoring, site security, automated on-site tracking of expensive materials, safety management, and in many other areas [1,2]. Applications based on sensor network require various types of query processing. For example, we can deploy a sensor network to monitor air pollution of a certain city. The sensor network consists of sensor nodes that sense poisonous gases like CO\({}_{2}\) and SO\({}_{2}\). Users may search the geographic area in which are within a certain range or specific value. This type of query can be divided into range queries or exact match queries. Users may also need to find out the most or least polluted area in terms of CO\({}_{2}\) and SO\({}_{2}\). The places with high concentrations of either CO\({}_{2}\) or SO\({}_{2}\) are regarded to suffer serious air pollution. This type of query is called a skyline query. A skyline query on the sensor network can identify such places for environmental monitoring purposes. Skyline queries are popular in modern databases for multi-criteria decision making that has been received much attention recently among the database community. For a past decade, various research topics on data query processing techniques in sensor networks have been proposed [3-11]. The proposed method takes carefully into account the resource constraints of sensor nodes when designing data processing algorithms. Especially, they consider the low battery power constraint as the most important factor because when some sensor nodes in a sensor network run out of energy, the sensor network may not work anymore. Accordingly, researchers have put great effort on increasing the sensor network lifetime of their data processing algorithms  
Even though a sensor network is required to process various types of queries, to our knowledge existing methods only focus on one type of query. Data centric storage (DCS) is a well-known data storage technique in sensor networks that efficiently supports multi-dimensional range queries and multi-dimensional exact match queries. DCS stores data in a sensor network by its values. Each sensor reading (event) is mapped to an owner sensor node by a hashing function based on the values of the event's attributes. The event is routed to the owner node from the original sensor node according to some routing protocols, such as greedy perimeter stateless routing (GPSR) [12]. Therefore, all events with the same value are stored at the same owner node. In some of existing DCSs such as DIM [3], KDDCS [4] and GDCS [7], similar data is stored in geographically adjacent sensor nodes. On these DCSs, data are ordered by their values. This property enables DCSs to process multidimensional range queries efficiently. For example, in Figure 1, there are 25 sensor nodes that sense CO\({}_{2}\) and SO\({}_{2}\). In this example, each sensor node has its geographical location, and each sensor reading is mapped to geographical location by its value. The sensor reading is transmitted to a sensor node that is nearest from the mapped location. In the figure, sensor node 1 stores CO\({}_{2}\) values within 0 and 4, and SO\({}_{2}\) values within 4 and 8. The gray circles store some sensing values while white circles do not have any sensing values. As mentioned above, this kind of DCS are built to process multidimensional range queries efficiently.