Efficiently computing Pareto optimal G-skyline query in wireless sensor network

Leigang Dong, Guohua Liu, Xiaowei Cui and Quan Yu

International Journal of Distributed Sensor Networks, 2021, vol. 17, issue 12, 15501477211060673

Abstract: There are much data transmitted from sensors in wireless sensor network. How to mine vital information from these large amount of data is very important for decision-making. Aiming at mining more interesting information for users, the skyline technology has attracted more attention due to its widespread use for multi-criteria decision-making. The point which is not dominated by any other points can be called skyline point. The skyline consists of all these points which are candidates for users. However, traditional skyline which consists of individual points is not suitable for combinations. To address this gap, we focus on the group skyline query and propose efficient algorithm to computing the Pareto optimal group-based skyline (G-skyline). We propose multiple query windows to compute key skyline layers, then optimize the method to compute directed skyline graph, finally introduce primary points definition and propose a fast algorithm based on it to compute G-skyline groups directly and efficiently. The experiments on the real-world sensor data set and the synthetic data set show that our algorithm performs more efficiently than the existing algorithms.

Keywords: Wireless sensor network; skyline; multi-criteria; group-based skyline; multiple query windows; primary points (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:17:y:2021:i:12:p:15501477211060673

DOI: 10.1177/15501477211060673

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