Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks

Ahmed, Khandakar; Nafi, Nazmus Shaker; Gregory, Mark A

doi:10.3390/jsan5010002

Cited by 8 publications

(7 citation statements)

References 29 publications

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“…However, they only considered the mobile distributed data sites over MANETs but did not consider the moving objects. Ahmed et al [19] proposed an approach, Enhanced Distributed Dynamic Skyline (EDDS), to handle skyline queries over the IoMT. EDDS used disc track and sector to map the data locations.…”

Section: Related Workmentioning

confidence: 99%

Distributed Continuous Range-Skyline Query Monitoring Over the Internet of Mobile Things

Lai

Akbar

Liu

et al. 2019

IEEE Internet Things J.

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A Range-Skyline Query (RSQ) is the combination of range query and skyline query. It is one of the practical query types in multi-criteria decision services, which may include the spatial and non-spatial information as well as make the resulting information more useful than skyline search when the location is concerned. Furthermore, Continuous Range-Skyline Query (CRSQ) is an extension of Range-Skyline Query (RSQ) that the system continuously reports the skyline results to a query within a given search range. This work focuses on the RSQ and CRSQ within a specific range on Internet of Mobile Things (IoMT) applications. Many server-client approaches for CRSQ have been proposed but are sensitive to the number of moving objects. We propose an effective and non-centralized approach, Distributed Continuous Range-Skyline Query process (DCRSQ process), for supporting RSQ and CRSQ in mobile environments. By considering the mobility, the proposed approach can predict the time when an object falls in the query range and ignore more irrelevant information when deriving the results, thus saving the computation overhead. The proposed approach, DCRSQ process, is analyzed on cost and validated with extensive simulated experiments. The results show that DCRSQ process outperforms the existing approaches in different scenarios and aspects.

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Section: Related Workmentioning

confidence: 99%

Distributed Continuous Range-Skyline Query Monitoring Over the Internet of Mobile Things

Lai

Akbar

Liu

et al. 2019

IEEE Internet Things J.

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“…Paper [42] proposed the skyline algorithms over data stream, but they focus on individual subjects rather than groups. [33][34][35][36][37][38][39][40] discussed different skyline applications in wireless sensor networks, such as continuous reverse skyline, spatial skyline, distributed dynamic skyline and probabilistic skyline query in the wireless sensor networks.…”

Section: Related Workmentioning

confidence: 99%

G-skyline query over data stream in wireless sensor network

Dong

Cui

et al. 2018

Wireless Netw

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There are much data sampled continuously by sensors in the wireless sensor network. Storing and mining these data can find more potential information and provide help for decision making. As an important technology for data mining and multi-criteria decision, skyline computation can identify the interesting single points for user. In order to analyze the groups of points, the group-based skyline is proposed to query all the Pareto Optimal groups which are not g-dominated by other groups with the same number of points. Existing algorithms about g-skyline can just compute static data. However, data stream is very common in many applications, and it is very important to design algorithm go query g-skyline over data stream. In this paper, we propose new algorithms to compute g-skyline over a data stream. We present sharing strategy and then present two efficient algorithms: point-arriving algorithm and point-expiring algorithm. The experimental results on three kinds of synthetic data and a real stock data show that our algorithms perform efficiently over a data stream.

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“…Several researchers have proposed processing skyline queries in a distributed way [ 51 , 52 , 53 ]. Afrati et al [ 52 ] investigated parallel skyline processing based on a massively parallel (MP) model that requires the data to be perfectly load-balanced.…”

Section: Related Workmentioning

confidence: 99%

“…Afrati et al [ 52 ] investigated parallel skyline processing based on a massively parallel (MP) model that requires the data to be perfectly load-balanced. A novel, enhanced distributed dynamic skyline (EDDS) technique [ 51 ] was proposed and implemented for wireless sensor networks. Zhou et al [ 53 ] investigated probabilistic skyline queries over uncertain data in distributed environments.…”

Section: Related Workmentioning

confidence: 99%

DISPAQ: Distributed Profitable-Area Query from Big Taxi Trip Data

Putri

Song

Kwon

et al. 2017

Sensors

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One of the crucial problems for taxi drivers is to efficiently locate passengers in order to increase profits. The rapid advancement and ubiquitous penetration of Internet of Things (IoT) technology into transportation industries enables us to provide taxi drivers with locations that have more potential passengers (more profitable areas) by analyzing and querying taxi trip data. In this paper, we propose a query processing system, called Distributed Profitable-Area Query (DISPAQ) which efficiently identifies profitable areas by exploiting the Apache Software Foundation’s Spark framework and a MongoDB database. DISPAQ first maintains a profitable-area query index (PQ-index) by extracting area summaries and route summaries from raw taxi trip data. It then identifies candidate profitable areas by searching the PQ-index during query processing. Then, it exploits a Z-Skyline algorithm, which is an extension of skyline processing with a Z-order space filling curve, to quickly refine the candidate profitable areas. To improve the performance of distributed query processing, we also propose local Z-Skyline optimization, which reduces the number of dominant tests by distributing killer profitable areas to each cluster node. Through extensive evaluation with real datasets, we demonstrate that our DISPAQ system provides a scalable and efficient solution for processing profitable-area queries from huge amounts of big taxi trip data.

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Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks

Cited by 8 publications

References 29 publications

Distributed Continuous Range-Skyline Query Monitoring Over the Internet of Mobile Things

Distributed Continuous Range-Skyline Query Monitoring Over the Internet of Mobile Things

G-skyline query over data stream in wireless sensor network

DISPAQ: Distributed Profitable-Area Query from Big Taxi Trip Data

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