An Energy-Efficient Skyline Query for Massively Multidimensional Sensing Data
AbstractCyber 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
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Wang, Y.; Wei, W.; Deng, Q.; Liu, W.; Song, H. An Energy-Efficient Skyline Query for Massively Multidimensional Sensing Data. Sensors 2016, 16, 83.
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; Deng, Qingxu; Liu, Wei; Song, Houbing. 2016. "An Energy-Efficient Skyline Query for Massively Multidimensional Sensing Data." Sensors 16, no. 1: 83.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.