Recent sensor networks research has produced a class of data storage and query processing techniques called <i>DataCentric Storage</i> that leverages localitypreserving distributed indexes like DIM, DIFS, and GHT to efficiently answer multidimensional range and rangeaggregate queries.
We investigate the innetwork processing of an iceberg join query in wireless sensor networks (WSNs). An iceberg join is a special type of join where only those joined tuples whose cardinality exceeds a certain threshold (called iceberg threshold) are qualified for the result.
As the detected data tuples are distributively stored in storage nodes in the sensor network, an efficient skyline query processing algorithm is needed to retrieve skyline tuples from the sensor network. By "efficient" we mean that the algorithm should be able to (1) prune as many storage nodes as possible that do not contain any tuple of
Nov 20, 2012 · A density query is one of query types for object monitoring appliions. A density query finds out an area spread by density that a target object requires in the whole sensing field. In this paper, we propose a novel homogeneous networkbased innetwork density query processing scheme that significantly reduces query processing costs and
CiteSeerX Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract — This paper demonstrates an energy efficient query processing technique for wireless sensor networks which achieves energy efficiency through centralized caching and variable frequency sensing. I. INTRODUCTION AND RELATED WORK In Wireless Sensor Networks (WSNs), energy efficient query processing
International Journals  H.M. Park, "Efficient Iceberg Query Processing in Sensor Networks," The Computer Journal, Vol. 57, Issue 12, and S.J. Chun, "Effective Processing of Continuous GroupBy Aggregate Queries in Sensor Networks," Journal of Systems and Software, Vol. 83, Issue 12, December 2010,
Abstract. Abstract — This paper studies the issue of energyefficient query optimization for wireless sensor networks. Different from existing query optimization techniques that consider only query plans for extracting data from sensors at individual nodes, our approach takes into account both of the sensing and communiion cost in query plans.
Mohamed, MMA, Khokar, A & Trajcevski, GP 2011, '' Energy Efficient Data Indexing and Query Processing for Static and Mobile Wireless Sensor Networks '' International Journal of NextGeneration Computing, vol. 1, no. 2.
In this paper,we address the problem of processing join query among different regions progressively and energyefficiently in sensor networks. The proposed algorithm PEJA(Progressive Energyefficient Join Algorithm) adopts an eventdriven strategy to output the joining results as soon as possible,and alleviates the storage shortage problem in
Heejung Yang''s 6 research works with 13 citations and 60 reads, including: Erratum to ''An efficient topk query processing framework in mobile sensor networks'' [Data Knowl. Eng. 102 (2016) 78
In clusterbased sensor networks, when a user queries a sensitive data, the adversaries can monitor original node or gain the data in cluster node. To deal with this problem, we propose a secure and efficient scheme for clusterbased query processing in wireless sensor networks.
As one of the commonly used queries in modern databases, skyline query has received extensive attention from database research community. The uncertainty of the data in wireless sensor networks makes the corresponding skyline uncertain and not unique. This paper investigates the PrSkyline problem, i.e., how to compute the skyline with the highest existence probability in a computational and
Abstract—The architecture of twotiered sensor networks, where storage nodes serve as an intermediate tier between sensors and a sink for storing data and processing queries, has been widely adopted because of the beneﬁts of power and storage saving for sensors as well as the efﬁciency of query processing.
communiion technologies, wireless sensor networks (WSNs) become important data sources and have been widely used in many appliions . In the database view, a WSN can be regarded as a distributed database, and efficient query processing methods for various types of queries in WSN become a hot topic in research community .
In this paper, we study the problem of iceberg join processing in wireless sensor networks. The iceberg join query only includes a small fraction of data in its result set, yet, still contains the most ''interesting'' and useful data relationships and linkages of the sensing data.
Many appliions compute aggregate functions (such as COUNT, SUM) over an attribute (or set of attributes) to find aggregate values above some specified threshold. We call such queries iceberg queries because the number of abovethreshold results is often very small (the tip of an iceberg), relative to the large amount of input data (the iceberg).
A wireless sensor network (WSNET) can support various types of queries. The energy resource of sensors constrains the total number of query responses, called query capacity, received by the sink. There are four problems in the existing approaches for energyefficient query processing in WSNETs:
We investigate the innetwork processing of an iceberg join query in wireless sensor networks (WSNs). An iceberg join is a special type of join where only those joined tuples whose cardinality
spatial range aggregation query processing in wireless sensor networks Liang Liu, Zhenhai Hu and Lisong Wang Abstract The existing privacypreserving aggregation query processing methods in sensor networks rely on preestablished network topology and require all nodes in the network to participate in query processing. Maintaining the topology
Therefore, in this paper, we propose and describe an Incremental Join Algorithm (IJA) in Sensor Networks to reduce the overhead caused by moving a join pair to the final join node or to minimize the communiion cost that is the main consumer of the battery when processing the distributed queries in sensor networks environments.
Efficient Iceberg Query Processing in Sensor Networks. Heejung Yang ChinWan Chung. The Computer Journal, Volume 57, Issue An EnergyEfficient Distributed Cut Vertex Detection Algorithm for Wireless Sensor Networks. Orhan Dagdeviren Vahid Khalilpour Akram. The Computer Journal, Volume 57, Issue 12, December 2014, Pages 1852–1869, https
Most of existing spatiotemporal query processing algorithms organized all the nodes in the whole network or the nodes in the query area into a single routing tree guided by which the sensor readings of the nodes in the query area are sent back to the sink.
We investigate the innetwork processing of an iceberg join query in wireless sensor networks (WSNs). An iceberg join is a special type of join where only those joined tuples whose cardinality exceeds a certain threshold (called iceberg threshold) are qualified for the result. Processing such a join involves the value matching for the join predie as well as the checking of the cardinality
Technological advances have enabled the deployment of several largescale sensor networks for environmental monitoring and surveillance purposes, efficient processing of topk query in such networks poses great challenges due to the unique characteristics of sensor nodes and a vast amount of data generated by sensor networks.
EnergyEfficient Skycube Query Processing in Wireless Sensor Networks (Zhiqiong Wang) 6242 3. SCAES Algorithm 3.1. SCAESBasic Algorithm In wireless sensor network, the basic algorithm of culculating skycube is to calculate it in the nodes of sensor and then to reduce the data quantity of convey in the middle process by innet combination.
The large volume of data generated by sensors needs to be processed to respond to the users queries. However, efficient processing of queries in sensor networks poses great challenges due to the unique characteristics imposed on sensor networks including slow processing capability, limited storage, and energylimited batteries, etc.
An Efficient top k Query Processing in Distributed Wireless Sensor Networks II. RELATED WORK In recent years, many works have been done. Here we review representative work in the areas of 1) top
Efficient Event Detection in sensor networks that addresses this limitation, enabling the deployment of sensor networks for the types of appliions described above. REED is based on TinyDB, but extends it with the ability to support joins between sensor data and static tables built outside the sensor network.
Copyright © 2019.GXmachine All rights reserved.Sitemap