G-skyline query over data stream in wireless sensor network

Dong, Leigang; Li, Guohua; Cui, Xiaowei; Li, Tianyu

doi:10.1007/s11276-018-1784-2

Cited by 7 publications

(6 citation statements)

References 31 publications

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“…In recent years, group-based skyline query has been focused on by researchers increasingly. 3,4,28,[33][34][35][36][37][38][39][40][41] Previous articles [33][34][35][36] computed top-k composition skyline. Few studies [37][38][39] did combination skyline query based on composition function, which computes the value of the same attribute of k points.…”

Section: Related Workmentioning

confidence: 99%

“…Guo et al 40 proposed skyline group algorithms with composition function in data stream based on the algorithms in articles. 37,38 Dong et al 41 proposed G-skyline query over data stream in WSN. Zhu et al 4 evaluated most existing algorithms based on two types of definitions in terms of time and space on various synthetic and real data sets.…”

Section: Related Workmentioning

confidence: 99%

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

Dong

Cui

et al. 2021

International Journal of Distributed Sensor Networks

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Dong

Cui

et al. 2021

International Journal of Distributed Sensor Networks

Self Cite

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“…The most common one is K-means algorithm [17]. The K-means algorithm is mainly to introduce the K-means algorithm into the wireless sensor network and improve and optimize it, thereby further improving the operating performance of the wireless sensor network [18]. In literature, a fuzzy clustering algorithm was proposed to be used in the LEACH protocol [19], and it was proved that the improved algorithm is superior to the LEACH protocol in the first node death time and half the node death time.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Network Coverage Optimization Based on Feedback K-Means Clustering and Artificial Fish Swarm Algorithm

2020

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There is a certain energy loss in the process of wireless sensor network information collection. Moreover, the current network protocols and network coverage methods are not sufficient to effectively reduce system energy consumption. In order to improve the operating efficiency and service life of wireless sensor networks, this study analyzes the classic LEACH protocol, summarizes the advantages and disadvantages, and proposes a targeted clustering method based on the K-means algorithm. At the same time, in order to maximize the network coverage and minimize the energy consumption on the basis of ensuring the quality of service, a wireless sensor network coverage optimization method based on an improved artificial fish swarm algorithm was proposed. In addition, a controlled experiment is designed to analyze the effectiveness and practical effects of the proposed algorithm. The experimental results show that the method proposed in this paper has certain advantages over traditional methods and can provide theoretical references for subsequent related research. INDEX TERMS Feedback, K-means clustering, artificial fish swarm algorithm, network coverage, algorithm optimization.

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“…Roh et al [18] propose a filter-based method for two-dimensional skyline query processing in WSNs, which provides an enhanced efficiency by reduction of the total wireless communication between sensor nodes. Furthermore, more aspects of skyline computation in WSNs, such as G-skyline query [19] and Geometry-Based Distributed Spatial Skyline Query [20], have been studied. However, none of these methods described above provides support for continuous skyline queries.…”

Section: Related Workmentioning

confidence: 99%

Efficient Continuous Skyline Query Processing in Wireless Sensor Networks

Xiao

Wang

et al. 2019

Sensors

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Owing to the rapid advent of wireless technology and proliferation of smart sensors, wireless sensor networks (WSNs) have been widely used to monitor and query the physical world in many applications based on the Internet of Things (IoT), such as environmental monitoring and event surveillance. A WSN can be treated as a distributed database to respond to user queries. Skyline query, as one of the popular queries for multi-criteria decision making, has received considerable attention due to its numerous applications. In this paper, we study how to process a continuous skyline query over a sensor data stream in WSNs. We present an energy-efficient continuous skyline query method called EECS. EECS can avoid the transmission of invalid sensor data and prolong the lifetime of WSNs. Extensive experiments are conducted, and the experimental results demonstrate the effectiveness of the proposed method.

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G-skyline query over data stream in wireless sensor network

Cited by 7 publications

References 31 publications

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

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

Analysis of Network Coverage Optimization Based on Feedback K-Means Clustering and Artificial Fish Swarm Algorithm

Efficient Continuous Skyline Query Processing in Wireless Sensor Networks

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