research-article

A cooperative method for processing range-skyline queries in mobile wireless sensor networks

Authors:

Zulhaydar Fairozal Akbar, and

Chuan-Ming LiuAuthors Info & Claims

EDB '16: Proceedings of the Sixth International Conference on Emerging Databases: Technologies, Applications, and Theory

October 2016

Pages 1 - 8

https://doi.org/10.1145/3007818.3007820

Published: 17 October 2016 Publication History

Abstract

A Range-Skyline Query (RSQ) is the combination of range query and skyline query. It is one of the practical query types in multi-criterion decision services, which may include the spatial and non-spatial information. Due to the multi-criterion decision, a skyline search may return some results of which the locations are far away from the query node, thus making the information useless to the user. We there-fore consider the range-skyline query, which will produce the skyline within a given range in Mobile Wireless Sensor Networks (MWSNs). By considering the mobility information of nodes in WSNs, we propose a reasonably distributed query processing approach which makes each mobile node derive partial possible candidates of the result with its neighboring information. Thus, the query node collects the partial possible candidate sets from the neighboring mobile nodes and computes the final results of a range-skyline query. Such a cooperative process combining the data collecting phase with the query processing phase can effectively prune most of the unnecessary data objects and reduce the overhead of computing on the query node. In the simulation, the proposed approach, Cooperative Range-Skyline Query (C-RSQ), can outperform the traditional centralized approach in different scenarios.

References

[1]

S. Borzsony, D. Kossmann, and K. Stocker. The skyline operator. In Proceedings of 17th International Conference on Data Engineering, pages 421--430, 2001.

Digital Library

[2]

M. A. Cheema, X. Lin, W. Zhang, and Y. Zhang. A safe zone based approach for monitoring moving skyline queries. In Proceedings of the 16th International Conference on Extending Database Technology, EDBT '13, pages 275--286, New York, NY, USA, 2013. ACM.

Digital Library

[3]

L. Chen, B. Cui, and H. Lu. Constrained skyline query processing against distributed data sites. IEEE Transactions on Knowledge and Data Engineering, 23(2):204--217, 2011.

Digital Library

[4]

G. R. Hjaltason and H. Samet. Distance browsing in spatial databases. ACM Trans. Database Syst., 24(2):265--318, June 1999.

Digital Library

[5]

K. Hose and A. Vlachou. A survey of skyline processing in highly distributed environments. The VLDB Journal, 21(3):359--384, 2012.

Digital Library

[6]

Z. Huang, C. S. Jensen, H. Lu, and B. C. Ooi. Skyline queries against mobile lightweight devices in manets. In 22nd International Conference on Data Engineering (ICDE'06), pages 66--66, April 2006.

Digital Library

[7]

S. Im, M. Song, S.-W. K. Jongwan Kim, C.-S. Hwang, and S. Lee. Cell-based distributed index for range query processing in wireless data broadcast systems. In Knowledge-Based Intelligent Information and Engineering Systems Lecture Notes in Computer Science, volume 4251, pages 1139--1146. Springer Berlin Heidelberg, 2006.

Digital Library

[8]

C. Li, Y. Gu, J. Qi, R. Zhang, and G. Yu. A safe region based approach to moving knn queries in obstructed space. Knowledge and Information Systems, 45(2):417--451, 2015.

Digital Library

[9]

X. Lin, J. Xu, and H. Hu. Range-based skyline queries in mobile environments. IEEE Transactions on Knowledge and Data Engineering, 25(4):835--849, April 2013.

Digital Library

[10]

C.-M. Liu and S.-Y. Fu. Effective protocols for knn search on broadcast multi-dimensional index trees. Information Systems, 33:18--35, 2008.

Digital Library

[11]

D. Papadias, Y. Tao, G. Fu, and B. Seeger. Progressive skyline computation in database systems. In ACM Transactions on Database Systems, TODS, pages 41--82, New York, NY, USA, 2005. ACM.

Digital Library

[12]

S. Rahul and R. Janardan. Algorithms for range-skyline queries. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems, SIGSPATIAL '12, pages 526--529, New York, NY, USA, 2012. ACM.

Digital Library

[13]

S. Ratnasamy, P. Francis, M. Handley, R. Karp, and S. Shenker. A scalable content-addressable network. In Proceedings of the 2001 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, SIGCOMM '01, pages 161--172, New York, NY, USA, 2001. ACM.

Digital Library

[14]

L. Tian, L. Wang, P. Zou, Y. Jia, and A. Li. Continuous monitoring of skyline query over highly dynamic moving objects. In Proceedings of the 6th ACM International Workshop on Data Engineering for Wireless and Mobile Access, MobiDE '07, pages 59--66, New York, NY, USA, 2007. ACM.

Digital Library

[15]

P. Wu, C. Zhang, Y. Feng, B. Y. Zhao, D. Agrawal, and A. El Abbadi. Parallelizing skyline queries for scalable distribution. In Proceedings of the 10th International Conference on Advances in Database Technology, EDBT'06, pages 112--130, Berlin, Heidelberg, 2006. Springer-Verlag.

Digital Library

[16]

J. Zhang and L. Gruenwald. Optimizing data placement over wireless broadcast channel for multi-dimensional range query processing. In Mobile Data Management, pages 256--265. IEEE, 2004.

[17]

B. Zheng, K. C. K. Lee, and W. C. Lee. Location-dependent skyline query. In The Ninth International Conference on Mobile Data Management (mdm 2008), pages 148--155, April 2008.

Digital Library

Cited By

Tzouramanis TTiakas EPapadopoulos AManolopoulos YCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)The Range Skyline QueryProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271693(47-56)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3271693

Index Terms

A cooperative method for processing range-skyline queries in mobile wireless sensor networks
1. Information systems
  1. Information systems applications
    1. Mobile information processing systems
    2. Spatial-temporal systems
      1. Location based services
      2. Sensor networks

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cover image ACM Other conferences

EDB '16: Proceedings of the Sixth International Conference on Emerging Databases: Technologies, Applications, and Theory

October 2016

183 pages

ISBN:9781450347549

DOI:10.1145/3007818

General Chairs:
Jinho Kim,
Young-Kuk Kim,
James Geller,
Program Chairs:
Wonik Choi,
Carson K. Leung,
Young-Ho Park

Copyright © 2016 ACM.

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KoDB: Korea Database Agency
Nara System: Nara System
2e consulting: 2e consulting

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2016

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KoDB
Nara System
2e consulting

EDB: 2016 International Conference on Emerging Databases

October 17 - 19, 2016

Jeju, Republic of Korea

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Cited By

Tzouramanis TTiakas EPapadopoulos AManolopoulos YCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)The Range Skyline QueryProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271693(47-56)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3271693

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