In-Su Shin

Aggregate Queries in Wireless Sensor Networks

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.

Spatial TinyDB: A Spatial Sensor Database System for the USN Environment

For the Ubiquitous Sensor Network (USN) environment, which generally uses spatial as well as aspatial sensor data, a sensor database system to manage these data is essential. For this reason, sensor database systems such as TinyDB and Cougar are being developed by researchers. However, as most of these systems do not support spatial data types and spatial operators for managing spatial sensor data, they are not suitable for the USN environment. Therefore, in this paper, we design and implement Spatial TinyDB which is a spatial sensor database system that extends TinyDB to support spatial data types and spatial operators for the efficient management of spatial sensor data. In particular, Spatial TinyDB provides memory management and filtering functions to reduce system overload caused by sensor data streams. Finally, we prove that Spatial TinyDB is superior by comparing its actual performance, in terms of execution time, accuracy, and memory usage, with that of TinyDB.

NUNS: A Nonuniform Network Split Method for Data-Centric Storage Sensor Networks

If data have the same value frequently in a data-centric storage sensor network, then the load is concentrated on a specific sensor node and the node consumes energy rapidly. In addition, if the sensor network is expanded, the routing distance to the target sensor node becomes longer in data storing and query processing, and this increases the communication cost of the sensor network. This paper proposes a nonuniform network split(NUNS) method that distributes the load among sensor nodes in data-centric storage sensor networks and efficiently reduces the communication cost of expanding sensor networks. NUNS splits a sensor network into partitions of nonuniform sizes in a way of minimizing the difference in the number of sensor nodes and in the size of partitions, and it stores data occurring in each partition in the sensor nodes of the partition. In addition, NUNS splits each partition into zones of nonuniform sizes as many as the number of sensor nodes in the partition in a way of minimizing the difference in the size of the split zones and assigns each zone to the processing area of each sensor node. Finally, we performed various performance evaluations and proved the superiority of NUNS to existing methods.