DocumentCode :
1468521
Title :
Energy-Efficient Reverse Skyline Query Processing over Wireless Sensor Networks
Author :
Wang, Guoren ; Xin, Junchang ; Chen, Lei ; Liu, Yunhao
Author_Institution :
Coll. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China
Volume :
24
Issue :
7
fYear :
2012
fDate :
7/1/2012 12:00:00 AM
Firstpage :
1259
Lastpage :
1275
Abstract :
Reverse skyline query plays an important role in many sensing applications, such as environmental monitoring, habitat monitoring, and battlefield monitoring. Due to the limited power supplies of wireless sensor nodes, the existing centralized approaches, which do not consider energy efficiency, cannot be directly applied to the distributed sensor environment. In this paper, we investigate how to process reverse skyline queries energy efficiently in wireless sensor networks. Initially, we theoretically analyzed the properties of reverse skyline query and proposed a skyband-based approach to tackle the problem of reverse skyline query answering over wireless sensor networks. Then, an energy-efficient approach is proposed to minimize the communication cost among sensor nodes of evaluating range reverse skyline query. Moreover, optimization mechanisms to improve the performance of multiple reverse skylines are also discussed. Extensive experiments on both real-world data and synthetic data have demonstrated the efficiency and effectiveness of our proposed approaches with various experimental settings.
Keywords :
optimisation; query processing; wireless sensor networks; battlefield monitoring; distributed sensor environment; energy-efficient approach; environmental monitoring; habitat monitoring; multiple reverse skylines; optimization mechanism; reverse skyline query processing; skyband-based approach; wireless sensor network; Base stations; Birds; Humidity; Monitoring; Query processing; Technical Activities Guide - TAG; Wireless sensor networks; Reverse skyline; multiple queries optimization.; query processing; wireless sensor network;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2011.64
Filename :
5728813
Link To Document :
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