Energy Efficient Rectangular Indexing for Mobile Peer-to-Peer Environment

Now a days in wireless environment there are many challenges. One of them which is need to be addressed in mobile Peer-to-Peer environment is getting the information of interest quickly and efficiently. Wherein whenever the node tries to get the desired data it has to wait too long or have to contact to unnecessary nodes which are not having their data of interest. This causes the node to waste the limited power resources and incurs more cost in terms of energy wastage. Here we proposed an energy efficient rectangular indexing called PMBR (Peer-to-Peer Minimum Bounding Rectangle) which allows the user to get the information of interest in energy efficient manner. We proposed algorithms namely PMBR_DSS, PMBR_HB and PMBR_CP and processed Nearest Neighbor & Range type queries. The experimental results carried out shows that the proposed algorithm PMBR_CP provides the efficient, quick and assured access to information of interest by saving the scarce power resources

Skyline Query Processing in Sensor Network Based on Data Centric Storage

Data centric storages for sensor networks have been proposed to efficiently process multi-dimensional range queries as well as exact matches. Usually, a sensor network does not process only one type of the query, but processes various types of queries such as range queries, exact matches and skyline queries. Therefore, a sensor network based on a data centric storage for range queries and exact matches should process skyline queries efficiently. However, existing algorithms for skyline queries have not considered the features of data centric storages. Some of the data centric storages store similar data in sensor nodes that are placed on geographically similar locations. Consequently, all data are ordered in a sensor network. In this paper, we propose a new skyline query processing algorithm that exploits the above features of data centric storages

Progressive skyline query evaluation and maintenance in wireless sensor networks

ABSTRACT Skyline query has been received much attention due to its wide application backgrounds for multi-preference and decision making. In this paper we consider skyline query evaluation and maintenance in wireless sensor networks. We devise an evaluation algorithm for finding skyline points progressively and a maintenance algorithm for skyline maintenance incrementally. We also conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms on various datasets. The experimental results show that the proposed algorithms significantly outperform existing algorithms in terms of network lifetime prolongation

Privacy Aware Parallel Computation of Skyline Sets Queries from Distributed Databases

A skyline query finds objects that are not dominated by another object from a given set of objects. Skyline queries help us to filter unnecessary information efficiently and provide us clues for various decision making tasks. However, we cannot use skyline queries in privacy aware environment, since we have to hide individual's records values even though there is no ID information. Therefore, we considered skyline sets queries. The skyline set query returns skyline sets from all possible sets, each of which is composed of some objects in a database. With the growth of network infrastructure data are stored in distributed databases. In this paper, we expand the idea to compute skyline sets queries in parallel fashion from distributed databases without disclosing individual records to others. The proposed method utilizes an agent-based parallel computing framework that can efficiently compute skyline sets queries and can solve the privacy problems of skyline queries in distributed environment. The computation of skyline sets is performed simultaneously in all databases which increases parallelism and reduces the computation time

Progressive Skyline Query Processing in Wireless Sensor Networks

Abstract—With the further development of sensor techniques in wireless sensor networks (WSNs), it is becoming urgent that they should be able to support complicated queries like skyline query for multi-preference and decision making. In this paper, we consider skyline query evaluation in WSNs by devising evaluation algorithms for finding skyline points on a dataset progressively. The core techniques adopted are to partition the dataset into several disjoint subsets and output the skyline points by examining each subsequent subset progressively, using some of the skyline points obtained so far to filter out those unlikely skyline points in the current processing subset from transmission. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms on synthetic and real datasets. The experimental results show that the proposed algorithms outperform existing algorithms significantly in network lifetime prolongation

An Energy-Efficient Skyline Query for Massively Multidimensional Sensing Data

Cyber physical systems (CPS) sense the environment based on wireless sensor networks. The sensing data of such systems present the characteristics of massiveness and multi-dimensionality. As one of the major monitoring methods used in in safe production monitoring and disaster early-warning applications, skyline query algorithms are extensively adopted for multiple-objective decision analysis of these sensing data. With the expansion of network sizes, the amount of sensing data increases sharply. Then, how to improve the query efficiency of skyline query algorithms and reduce the transmission energy consumption become pressing and difficult to accomplish issues. Therefore, this paper proposes a new energy-efficient skyline query method for massively multidimensional sensing data. First, the method uses a node cut strategy to dynamically generate filtering tuples with little computational overhead when collecting query results instead of issuing queries with filters. It can judge the domination relationship among different nodes, remove the detected data sets of dominated nodes that are irrelevant to the query, modify the query path dynamically, and reduce the data comparison and computational overhead. The efficient dynamic filter generated by this strategy uses little non-skyline data transmission in the network, and the transmission distance is very short. Second, our method also employs the tuple-cutting strategy inside the node and generates the local cutting tuples by the sub-tree with the node itself as the root node, which will be used to cut the detected data within the nodes of the sub-tree. Therefore, it can further control the non-skyline data uploading. A large number of experimental results show that our method can quickly return an overview of the monitored area and reduce the communication overhead. Additionally, it can shorten the response time and improve the efficiency of the query

Optimization of Dominance Testing in Skyline Queries Using Decision Trees

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Skyline queries identify skyline points, the minimal set of data points that dominate all other data points in a large dataset. The main challenge with skyline queries is executing the skyline query in the shortest possible time. To address and solve skyline query performance issues, we propose a decision tree-based method known as the decision tree-based comparator (DC). This method minimizes unnecessary dominance tests (i.e., pairwise comparisons) by constructing a decision tree based on the dominance testing. DC uses dominance relations that can be obtained from the decision rules of the decision tree to determine incomparability between data points. DC can also be easily applied to improve the performance of various existing skyline query methods. After describing the theoretical background of DC and applying it to existing skyline queries, we present the results of various experiments showing that DC can improve skyline query performance by up to 23.15 times.Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT)Industrial Strategic Technology Development Program funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea)