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An Energy-Efficient Skyline Query for Massively Multidimensional Sensing Data

by 1,2, 3, 1,*, 1 and 4
School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
School of Information, Liaoning University, Shenyang 110036, China
School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
Department of Electrical and Computer Engineering, West Virginia University, Montgomery, WV 25136, USA
Author to whom correspondence should be addressed.
Academic Editor: Albert M. K. Cheng
Received: 29 September 2015 / Revised: 7 December 2015 / Accepted: 6 January 2016 / Published: 9 January 2016
(This article belongs to the Special Issue Cyber-Physical Systems)
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. View Full-Text
Keywords: CPS; WSN; skyline query; energy-efficient; node cut CPS; WSN; skyline query; energy-efficient; node cut
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MDPI and ACS Style

Wang, Y.; Wei, W.; Deng, Q.; Liu, W.; Song, H. An Energy-Efficient Skyline Query for Massively Multidimensional Sensing Data. Sensors 2016, 16, 83.

AMA Style

Wang Y, Wei W, Deng Q, Liu W, Song H. An Energy-Efficient Skyline Query for Massively Multidimensional Sensing Data. Sensors. 2016; 16(1):83.

Chicago/Turabian Style

Wang, Yan, Wei Wei, Qingxu Deng, Wei Liu, and Houbing Song. 2016. "An Energy-Efficient Skyline Query for Massively Multidimensional Sensing Data" Sensors 16, no. 1: 83.

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