Abstract
Recently as efficient processing of aggregate queries for fetching desired data from sensors has been recognized as a crucial part, in-network aggregate query processing techniques are studied intensively in wireless sensor networks. Existing representative in-network aggregate query processing techniques propose routing algorithms and data structures for processing aggregate queries. However, these aggregate query processing techniques have problems such as high energy consumption in sensor nodes, low accuracy of query processing results, and long query processing time. In order to solve these problems and to enhance the efficiency of aggregate query processing in wireless sensor networks, this paper proposes Bucket-based Parallel Aggregation (BPA). BPA divides a query region into several cells according to the distribution of sensor nodes and builds a quadtree, and then processes aggregate queries in parallel for each cell region according to routing. It sends data in duplicate by removing redundant data, which, in turn, enhances the accuracy of query processing results. Also, BPA uses a bucket-based data structure in aggregate query processing, and divides and conquers the bucket data structure adaptively according to the number of data in the bucket. In addition, BPA compresses data in order to reduce the size of data in the bucket and performs data transmission filtering when each sensor node sends data. Finally, in this paper, we prove its superiority through various experiments using sensor data.
Highlights
With the rapid advance of sensing technologies for capturing various types of data such as temperature, humidity, and pressure as well as the development of wireless communication technologies, research is being made actively for utilizing wireless sensor network technologies in diverse application areas including military, medicine, meteorology, environment, transportation, home, and business [1, 2].Generally sensor nodes do not use unicasting whsen they regularly send the sensed data
In order to solve these problems in existing aggregate query processing techniques and to enhance the efficiency of aggregate query processing in wireless sensor networks, this study proposed aggregate query processing technique based Parallel Aggregation (BPA)
BPA uses bucket-based data structure and the variable bit compression coding technique in order to reduce energy consumption by sensor nodes in processing aggregate queries such as MEDIAN and HISTOGRAM
Summary
With the rapid advance of sensing technologies for capturing various types of data such as temperature, humidity, and pressure as well as the development of wireless communication technologies, research is being made actively for utilizing wireless sensor network technologies in diverse application areas including military, medicine, meteorology, environment, transportation, home, and business [1, 2]. In order to reduce the energy consumption of sensor nodes, aggregate query processing in network is being studied actively, which processes aggregate queries on sensed data in the sensor nodes and sends the results to the server [5,6,7]. TAG, and IWQE propose routing algorithm for efficient aggregate query processing, they have problems such as high energy consumption by the sensor nodes, low accuracy of query processing results, and long query processing time. Q-digest is an approximate aggregate query processing technique using tree data structure for aggregate operations MEDIAN and HISTOGRAM [10, 12], and SMC is an approximate aggregate query processing technique using bucket data structure for aggregate operations MEDIAN and HISTOGRAM [9] In this way, q-digest, and SMC suggest data structures for efficient aggregate query processing but they still have problems such as high energy consumption by the sensor nodes, low accuracy of query processing results, and long query processing time. BPA performs data transmission filtering, which sets a filtering range in each sensor node and sends data only when the data are outside the range, and this reduces the energy consumption of sensor nodes
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