There has been much interest in a spatial query which acquires sensor readings from sensor nodes inside specified geographical area of interests. A centralized approach performs the spatial query at a server after acquiring all sensor readings. However, it incurs high wireless transmission cost in accessing all sensor nodes. Therefore, various in-network spatial search methods have been proposed, which focus on reducing the wireless transmission cost. However, the in-network methods sometimes incur unnecessary wireless transmissions because of dead space, which is spatially indexed but does not contain real data. In this paper, we propose a hybrid spatial query processing algorithm which removes the unnecessary wireless transmissions. The main idea of the hybrid algorithm is to find results of a spatial query at a server in advance and use the results in removing the unnecessary wireless transmissions at a sensor network. We compare the in-network method through several experiments and clarify our algorithm's remarkable features.

In the area of wireless sensor networks, the efficient spatial query processing based on the locations of sensor nodes is required. Especially, spatial queries on two sensor networks need a distributed spatial join processing among the sensor networks. Because the distributed spatial join processing causes lots of wireless transmissions in accessing sensor nodes of two sensor networks, our goal of this paper is to reduce the wireless transmissions for the energy efficiency of sensor nodes. In this paper, we propose an energy-efficient distributed spatial join algorithm on two heterogeneous sensor networks, which performs in-network spatial join processing. To optimize the in-network processing, we also propose a Grid-based Rectangle tree (GR-tree) and a grid-based approximation function. The GR-tree reduces the wireless transmissions by supporting a distributed spatial search for sensor nodes. The grid-based approximation function reduces the wireless transmissions by reducing the volume of spatial query objects which should be pushed down to sensor nodes. Finally, we compare naive and existing approaches through extensive experiments and clarify our approach's distinguished features.