## 2 Related Works  
Information retrieval in sensor networks can be broadly categorised into two types: proactive and on-demand. In proactive information retrieval, sensor nodes periodically report data with a predefined data rate and data delivery infrastructures. Meanwhile, in on-demand information retrieval, a query is delivered to the sensor network and data are collected according to the query's requirements. Existing approaches for proactive information retrieval mainly focus on the energy-efficient collection of sensor data [7-11,14,15]. To aggregate sensing data, sensor nodes are organised into infrastructures, such as clusters, chains or optimised trees. For example, LEACH (Low-Energy Adaptive Clustering Hierarchy) is a prevailing scheme of proactive data collection based on a dynamic clustering approach [7]. In LEACH, data can be efficiently collected and aggregated in a hierarchical way. EEDC (Energy Efficient Data Collection) is another proactive data collection approach based on a cluster infrastructure [9]. It introduces an energy-efficient data collection approach by exploiting spatiotemporal correlation of sensing data. Energy efficiency is achieved by reducing the spatial sampling rate of sensor nodes. PEGASIS (Power-Efficient Gathering in Sensor Information Systems) organises sensor nodes into a chain structure rather than clusters [15]. The network structures of data aggregation, such as clusters and chains, significantly improve the energy efficiency of data collection. However, generating and maintaining a network structure entail much additional cost. In addition, a fixed network structure for data collection creates an energy imbalance. That is, the nodes near the sink node or cluster head generally consume much more energy than other nodes do. Unlike proactive data collection, query-based on-demand information retrieval generally has a temporary organization of sensor nodes to collect data. A straightforward approach for on-demand information retrieval generally utilises a broadcast-reverse tree based model. A query is broadcast from the sink to the network and then a tree structure is constructed together with the task allocation of query. Sensors report their data back to the sink using the tree structure. Collected data can be aggregated on the tree. Most on-demand information retrieval approaches follow this model, but they differ in the mechanisms they use for query propagation and route selection for data collection [16-19]. Efficient query propagation is considered in [16]. The target is to propagate query message to a limited number of nodes so as to save energy. The ideal case is the minimized tree that includes no redundant broadcast of query message. However, this tree-based query requires repetition of sink queries and is not suitable to the arbitrary queries from users. If a query specifies a query area, a spatial query broadcast can be archived based on geocasting or multicasting, which reduce redundant traffic overhead and achieve energy efficiency [5,20]. A typical approach Location based multicast (LBM) can be used to efficiently broadcast query messages in on-demand information retrieval in the case that there is a predefined region as the query destination [20]. LBM uses a multicast zone that is rectangular in shape and contains both the sender and all destination nodes. LBM assumes a roughly predefined region in which destination nodes reside. But the predefined region of destined sensor nodes might be either large or not available to a user who initiates on-demand information retrieval. Although geocasting can be used to localise a query dissemination area, these operations rely on the assumptions that the user specifies a region as a query designated area. In fact, a user-defined region of destined sensor nodes may not always be available to a user who initiates on-demand information retrieval, or it may differ from the area that contains the sensor nodes corresponding to the query.