Skyline queries on sensor networks have attracted much attention from the database research community due to their wide applications related to multi-criteria decision making. The existing methods use filters that are based on the data locality of sensor nodes and routing paths. However, they have two serious problems: i) unnecessary data transmission is still to frequent. ii) the processing cost of a continuous skyline query on high-dimensional data is very high. In this paper, we propose a new method that uses competitive mechanisms for processing continuous skyline queries. The proposed method dramatically reduces the data transmissions of sensors and quickly processes a continuous skyline query on high-dimensional data. An extensive performance study verifies the merits of our new method.

Clustering the sensor nodes is one of the most popular and effective approaches for applications that must support hundreds or thousands of nodes. The conventional algorithms consider various parameters to evenly distribute the energy load. However, energy consumption problem of the cluster head still remains. In this paper, we propose a novel clustering approach that periodically elects cluster heads with assistant nodes. The assistant nodes substitute for each cluster head to transmit sensor readings to the base station. Performance evaluations show that our proposed clustering algorithm achieves about 10-40% better performance than the existing clustering algorithms in terms of lifetime.

In this paper, we propose a continuous range query processing method over moving objects. To efficiently process continuous range queries, we design a main-memory-based query index that uses smaller storage and significantly reduces the query processing time. We show through performance evaluation that the proposed method outperforms the existing methods.

Recently, environmental monitoring applications or home automation systems often deal with wireless sensor data. These applications deal with multi-dimensional sensing data and most processing operations involves skyline queries. In this paper, we focus on skyline queries in WSNs. However, as the number of data dimensions increases, the results of skyline queries become unmanageably large which reduces the lifetime of the sensor network. To solve these problems, we propose a novel k-dominant skyline query processing method using filtering mechanisms. The filter is designed by considering the data property and the data transmission cost. Extensive experiments show that our proposed method results in significant performance improvements over the existing method.

With rapid increase of information requirements from various application areas, there has been much research on the efficient information retrieval. A signature is an abstraction of information, and has been applied in many proposals of information retrieval systems. In this paper we evaluate the performance of various signature-based information retrieval methods and provide guidelines for the most effective usage to a given operational environment. We derive analytic performance evaluation models of these access methods based on retrieval time, storage overhead and insertion time. The relationships between various performance parameters are thoroughly investigated. We also perform simulation experiments by using wide range of parameter values and show that the performance experiments agree with those analytic models.

In this paper, we propose a novel filtering method for processing continuous skyline queries in wireless sensor network environments. The existing filtering methods on such environments use filters that are based on router paths. However, these methods do not have a major effect on reducing data for sensor nodes to transmit to the base station, because the filters are applied to not the whole area but a partial area. Therefore, we propose a novel and efficient method to dramatically reduce the data transmissions of sensors through applying an effective filter with low costs to all sensor nodes. The proposed effective filter is generated by using characteristics such as the data locality and the clustering of sensors. An extensive performance study verifies the merits of our new method.

In this paper, a novel cache conscious indexing technique based on space partitioning trees is proposed. Many researchers investigated efficient cache conscious indexing techniques which improve retrieval performance of in-memory database management system recently. However, most studies considered data partitioning and targeted fast information retrieval. Existing data partitioning-based index structures significantly degrade performance due to the redundant accesses of overlapped spaces. Specially, R-tree-based index structures suffer from the propagation of MBR (Minimum Bounding Rectangle) information by updating data frequently. In this paper, we propose an in-memory space partitioning index structure for optimal cache utilization. The proposed index structure is compared with the existing index structures in terms of update performance, insertion performance and cache-utilization rate in a variety of environments. The results demonstrate that the proposed index structure offers better performance than existing index structures.

Most large-scale distributed file systems decouple a metadata operation from read and write operations for a file. In the distributed file systems, a certain server named a metadata server (MDS) is responsible for maintaining the metadata information of the file systems. In this paper, we propose a new metadata management scheme in order to provide the high metadata throughput and scalability for a cluster of MDSs. First, we derive a new metadata distribution technique. Then, we present a load balancing technique based on the distribution technique. Several experiments show that our scheme outperforms existing metadata management scheme in terms of scalability and load balancing.

In this paper, a new access method is proposed for current positions of moving objects on road networks in order to efficiently update their positions. In the existing index structures, the connectivity of edges is lost because the intersection points in which three or more edges are split. The proposed index structure preserves the network connectivity, which uses intersection oriented network model by not splitting intersection nodes that three or more edges meet for preserving the connectivity of adjacent road segments. The data node stores not only the positions of moving object but also the connectivity of networks.