Abstract
Database applications in wireless sensor networks very often demand data collection from sensor nodes of specific target regions. Design and development of spatial query expressions and energy-efficient query processing strategy are important issues for sensor network database systems. The existing sensor network database systems lack the needed sophistication for the space calculation of the target sensor nodes; hence, unnecessary query/data transmissions are required between the sensor nodes and the server. This paper describes our spatial operations and energy-efficient query processing methods that are designed and implemented in our sensor network database system called [Formula: see text]. With a set of spatial operators based on geometric parameters, such as Envelope, NearBy, Distance, Direction, and set theoretic operators, [Formula: see text] allows sensor network applications to easily specify the target space of interest. Our energy-efficient query processing strategy implements an in-network query management based on the lowest common ancestor (LCA) algorithm, so that the query processing cost for calculating the target spaces is greatly reduced by avoiding the need of heavy query/data transmissions between the base-station and target nodes. Performance evaluation shows that our proposed design and implementation of spatial query expressions and processing strategy achieve improved energy efficiency for database operations in the wireless sensor network.
Highlights
Sensor networks are increasingly used in a wide range of modern applications, including disaster management, precision agriculture, healthcare, traffic management, and ecological monitoring as one of the most useful emerging information management systems [1,2,3,4,5]
(i) We have extended our previous work on sensor network query language Sensor Network Query Language (SNQL) [8, 14] by incorporating various spatial operators, so that more sophisticated expressions of target space are made possible in our new system called SNQL+s
The efficiency of lowest common ancestor (LCA)-based spatial operations is evaluated by two experiments for two typical spatial operations of multiple targeting: operation for identifying intermediate target region and operation for targeting multiple regions, respectively
Summary
Sensor networks are increasingly used in a wide range of modern applications, including disaster management, precision agriculture, healthcare, traffic management, and ecological monitoring as one of the most useful emerging information management systems [1,2,3,4,5]. In many earlier developments of sensor networks, sensor nodes were viewed as network components; they were designed mainly to propagate stream data over a network to the base-station using preinstalled data aggregation instructions from the network perspectives [6, 7]. By grafting the traditional database management perspectives to the sensor network, sensor networks have become more functional in data processing so as to be able to serve a wider range of modern sensor network applications. The typical procedure of query processing in a sensor network database system can be summarized as follows: (1) an application issues a query over to the sensor network; (2) the designated sensor nodes execute the query; and (3) data are aggregated along the topological alignment of networked sensor nodes back to the base-station, where the application processes them on the fly or stores them in a back-end database. A few works on sensor network database systems have been reported [8,9,10,11,12]
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