Energy-efficient skyline query optimization in wireless sensor networks

Chen, Baichen; Liang, Weifa; Yu, Jeffrey Xu

doi:10.1007/s11276-012-0446-z

Cited by 9 publications

(21 citation statements)

References 19 publications

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“…We model per-message energy consumption by the following model used in [5]: energy = m × message size + b, where m and b are device-specific constants, and message size denotes the size of message in bytes. As in [5], when sending a message, m and b are set to 0.0144 mJ and 0.4608 mJ, respectively; when receiving a message, m and b are set to 0.00576 mJ and 0.1152 mJ, respectively.…”

Section: Discussionmentioning

confidence: 99%

Efficient In-Network Processing of Continuous Grouped Aggregation Queries in Sensor Networks

Song

Roh

Kim

2015

IEICE Trans. Inf. & Syst.

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SUMMARYIn this letter, we propose an energy-efficient in-network processing method for continuous grouped aggregation queries in wireless sensor networks. As in previous work, in our method sensor nodes partially compute aggregates as data flow through them to reduce data transferred. Different from other methods, our method considers group information of partial aggregates when sensor nodes forward them to next-hop nodes in order to maximize data reduction by same-group partial aggregation. Through experimental evaluation, we show that our method outperforms the existing methods in terms of energy efficiency.

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Section: Discussionmentioning

confidence: 99%

Efficient In-Network Processing of Continuous Grouped Aggregation Queries in Sensor Networks

Song

Roh

Kim

2015

IEICE Trans. Inf. & Syst.

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“…Pei et al [11] introduced notions of skyline group and decisive subspace by combining the semantics of skyline, and provided information for subspace skyline query using skyline group and decisive subspace. According to extended skyline theory, Xin Junchang [5] proposed a multisubspace skyline query algorithm based on efficient energy.…”

Section: Related Workmentioning

confidence: 99%

“…The skycube structure of above-mentioned greenhouse system is shown in table 4 (e.g. the first row means, t 4 is in the cuboid <Temperature>; the last row means t 1 , t 2 , t 4 , t 5 , t 7 is in the cuboid <Temperature, Humidity, Moisture, PH>, which is skyline result of full-space).…”

Section: Compressed Skycube Structurementioning

confidence: 99%

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Change-aware Skyline Query and Update in Wireless Sensor Networks

Wang¹,

Wang²,

Wu³

et al. 2015

Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference

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Abstract. In energy-limited wireless sensor networks, multi-subspace skyline query is widely used for all kinds of applications. In this paper, we propose a change-aware skyline query model based on Compressed Skycube (CSC) and Extended Skyline (ES), which reduces the traffic to save the energy of sensor in wireless sensor network on the premise of meeting user needs. We analyze the updating possibilities of different sensors according to the variously updating frequency in monitoring crop growth environment. We introduce an UpdateCSC algorithm based on CSC structure and an UpdateES algorithm based on Extended Skyline. Experiments on simulated environment show that the proposed model and algorithms can deal with mufti-subspace skyline query, which improve the computational efficiency and effectively reduce the updating energy.

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“…Skyline and its variants such as traditional skyline [5] and dynamic skyline [6][7][8] have been applied in many multiple criteria decision making applications. Traditional skyline (TS) retrieves all of the points, which are not dominated by others, from a set of points [9]. Given a dataset X, a point x 1 dominates x 2 , if x 1 is not worse than x 2 for each dimension i P l (i.e., x 1 [i] ď x 2 [i]), and x 1 is better than x 2 for at least one dimension m P l (x 1 [m] < x 2 [m]).…”

Section: Introductionmentioning

confidence: 99%

Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks

Ahmed

Nafi

Gregory

2016

JSAN

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Dynamic skyline query is one of the most popular and significant variants of skyline query in the field of multi-criteria decision-making. However, designing a distributed dynamic skyline query possesses greater challenge, especially for the distributed data centric storage within wireless sensor networks (WSNs). In this paper, a novel Enhanced Distributed Dynamic Skyline (EDDS) approach is proposed and implemented in Disk Based Data Centric Storage (DBDCS) architecture. DBDCS is an adaptation of magnetic disk storage platter consisting tracks and sectors. In DBDCS, the disc track and sector analogy is used to map data locations. A distance based indexing method is used for storing and querying multi-dimensional similar data. EDDS applies a threshold based hierarchical approach, which uses temporal correlation among sectors and sector segments to calculate a dynamic skyline. The efficiency and effectiveness of EDDS has been evaluated in terms of latency, energy consumption and accuracy through a simulation model developed in Castalia.

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Energy-efficient skyline query optimization in wireless sensor networks

Cited by 9 publications

References 19 publications

Efficient In-Network Processing of Continuous Grouped Aggregation Queries in Sensor Networks

Efficient In-Network Processing of Continuous Grouped Aggregation Queries in Sensor Networks

Change-aware Skyline Query and Update in Wireless Sensor Networks

Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks

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