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Approximate Holistic Aggregation in Wireless Sensor Networks

Authors:

Yingshu LiAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 13, Issue 2

Article No.: 11, Pages 1 - 24

https://doi.org/10.1145/3027488

Published: 19 April 2017 Publication History

Abstract

Holistic aggregations are popular queries for users to obtain detailed summary information from Wireless Sensor Networks. An aggregation operation is holistic if there is no constant bound on the size of the storage needed to describe a sub-aggregation. Since holistic aggregation cannot be distributable, it requires that all the sensory data should be sent to the sink in order to obtain the exact holistic aggregation results, which costs lots of energy. However, in most applications, exact holistic aggregation results are not necessary; instead, approximate results are acceptable. To save energy as much as possible, we study the approximated holistic aggregation algorithms based on uniform sampling. In this article, four holistic aggregation operations, frequency, distinct-count, rank, and quantile, are investigated. The mathematical methods to construct their estimators and determine optional sample size are proposed, and the correctness of these methods are proved. Four corresponding distributed holistic algorithms to derive (ϵ, δ)-approximate aggregation results are given. The solid theoretical analysis and extensive simulation results show that all the proposed algorithms have high performance on the aspects of accuracy and energy consumption.

References

[1]

Giuseppe Anastasi, Alessio Falchi, Andrea Passarella, Marco Conti, and Enrico Gregori. 2004. Performance measurements of motes sensor networks. In Proceedings of the 7th ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems. ACM, 174--181.

Digital Library

[2]

Ziv Bar-Yossef, Ravi Kumar, and D Sivakumar. 2001. Sampling algorithms: Lower bounds and applications. In Proceedings of the 33rd Annual ACM Symposium on Theory of Computing. ACM, 266--275.

Digital Library

[3]

Kevin Beyer, Peter J. Haas, Berthold Reinwald, Yannis Sismanis, and Rainer Gemulla. 2007. On synopses for distinct-value estimation under multiset operations. In Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data. ACM, 199--210.

Digital Library

[4]

Athanassios Boulis, Saurabh Ganeriwal, and Mani B Srivastava. 2003. Aggregation in sensor networks: An energy--accuracy trade-off. Ad Hoc Netw. 1, 2 (2003), 317--331.

[5]

Siyao Cheng, Zhipeng Cai, and Jianzhong Li. 2015. Curve query processing in wireless sensor networks. IEEE Trans. Vehic. Technol. 64, 11 (2015), 5198--5209.

[6]

Siyao Cheng, Zhipeng Cai, and Jianzhong Li. 2016. Digging kernel component from big sensory data in wireless sensor networks. IEEE Trans. Knowl. Data Eng. (2016).

Digital Library

[7]

Siyao Cheng, Zhipeng Cai, Jianzhong Li, and Xiaolin Fang. 2015. Drawing dominant dataset from big sensory data in wireless sensor networks. In INFOCOM.

[8]

Siyao Cheng and Jianzhong Li. 2009. Sampling based (epsilon, delta)-approximate aggregation algorithm in sensor networks. In Proceedings of the 29th IEEE International Conference on Distributed Computing Systems (ICDCS’09). IEEE, 273--280.

Digital Library

[9]

Siyao Cheng, Jianzhong Li, Qianqian Ren, and Lei Yu. 2010. Bernoulli sampling based (ϵ, Δ)-approximate aggregation in large-scale sensor networks. In Proceedings of the 29th Conference on Information Communications. IEEE Press, 1181--1189.

Digital Library

[10]

David Chu, Amol Deshpande, Joseph M Hellerstein, and Wei Hong. 2006. Approximate data collection in sensor networks using probabilistic models. In Proceedings of the 22nd International Conference on Data Engineering (ICDE’06). IEEE, 48--48.

Digital Library

[11]

Jeffrey Considine, Feifei Li, George Kollios, and John Byers. 2004. Approximate aggregation techniques for sensor databases. In Proceedings of the 20th International Conference on Data Engineering, 2004. IEEE, 449--460.

Digital Library

[12]

Graham Cormode, Minos Garofalakis, S. Muthukrishnan, and Rajeev Rastogi. 2005. Holistic aggregates in a networked world: Distributed tracking of approximate quantiles. In Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data. ACM, 25--36.

Digital Library

[13]

Crossbrow Inc. 2003. MPR-Mote Processor Radio Board User’s Manual. Crossbrow Inc.

[14]

Morris H. DeGroot and Mark J. Schervish. 2012. Probability and Statistics. Addison-Wesley.

[15]

Jim Gray, Surajit Chaudhuri, Adam Bosworth, Andrew Layman, Don Reichart, Murali Venkatrao, Frank Pellow, and Hamid Pirahesh. 1997. Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Data Min. Knowl. Discov. 1, 1 (1997), 29--53.

Digital Library

[16]

Gregory Hartl and Baochun Li. 2005. Infer: A bayesian inference approach towards energy efficient data collection in dense sensor networks. In Proceedings of the 25th IEEE International Conference on Distributed Computing Systems (ICDCS’05). IEEE, 371--380.

Digital Library

[17]

Zaobo He, Zhipeng Cai, Siyao Cheng, and Xiaoming Wang. 2014. Approximate aggregation for tracking quantiles in wireless sensor networks. In Proceedings of the 8th Annual International Conference on Combinatorial Optimization and Applications. 161--172.

[18]

Zaobo He, Zhipeng Cai, Siyao Cheng, and Xiaoming Wang. 2015. Approximate aggregation for tracking quantiles and range countings in wireless sensor networks. Theoret. Comput. Sci. 607 (2015), 381--390.

Digital Library

[19]

Wendi Rabiner Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan. 2000. Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, 2000. IEEE, 10--pp.

Digital Library

[20]

Zengfeng Huang, Lu Wang, Ke Yi, and Yunhao Liu. 2011. Sampling based algorithms for quantile computation in sensor networks. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data. ACM, 745--756.

Digital Library

[21]

Rosana Lachowski, Marcelo E. Pellenz, Manoel C. Penna, Edgard Jamhour, and Richard D. Souza. 2015. An efficient distributed algorithm for constructing spanning trees in wireless sensor networks. Sensors 15, 1 (2015), 1518--1536.

[22]

Ji Li, Siyao Cheng, Zhipeng Cai, Qilong Han, and Hong Gao. 2015. Bernoulli sampling based (epsilon, delta)-approximate frequency query in mobile ad hoc networks. In Proceedings of the 10th International Conference on Wireless Algorithms, Systems, and Applications. 315--324.

[23]

Kebin Liu, Lei Chen, Minglu Li, and Yunhao Liu. 2008. Continuous answering holistic queries over sensor networks. In Proceedings of the 2008 IEEE International Symposium on Parallel and Distributed Processing (IPDPS’08). IEEE, 1--11.

[24]

Michael Mitzenmacher and Eli Upfal. 2005. Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Cambridge University Press.

Digital Library

[25]

Suman Nath, Phillip B. Gibbons, Srinivasan Seshan, and Zachary R. Anderson. 2004. Synopsis diffusion for robust aggregation in sensor networks. In Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems. ACM, 250--262.

Digital Library

[26]

John Rice. 2006. Mathematical Statistics and Data Analysis. Nelson Education.

[27]

Sankardas Roy, Mauro Conti, Sanjeev Setia, and Sushil Jajodia. 2008. Securely computing an approximate median in wireless sensor networks. In Proceedings of the 4th International Conference on Security and Privacy in Communication Netowrks. ACM, 6.

Digital Library

[28]

Nisheeth Shrivastava, Chiranjeeb Buragohain, Divyakant Agrawal, and Subhash Suri. 2004. Medians and beyond: New aggregation techniques for sensor networks. In Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems. ACM, 239--249.

Digital Library

[29]

Andrew S. Tanenbaum. 1996. Computer Networks. Vol. 4. Prentice Hall, Upper Saddle River, NJ.

[30]

Xueyan Tang and Jianliang Xu. 2006. Extending network lifetime for precision-constrained data aggregation in wireless sensor networks. In Proceedings of the 25th IEEE International Conference on Computer Communications (IEEE INFOCOM 2006). 1--12.

[31]

Dow-Yung Yang, Akshay Johar, Ananth Grama, and Wojciech Szpankowski. 2000. Summary structures for frequency queries on large transaction sets. In Proceedings of the Data Compression Conference, 2000 (DCC 2000). IEEE, 420--429.

Digital Library

[32]

Tongxin Zhu, Xinrui Wang, Siyao Cheng, Jianzhong Li, and Zhipeng Cai. 2015. Critical points aware adaptive data acquisition algorithm in sensor networks. In Proceedings of the 10th International Conference on Wireless Algorithms, Systems, and Applications. 798--808.

Cited By

Bomnale AMore A(2024)Node-Alive Index Driven Redundancy Elimination for Energy-Efficient Wireless Sensor NetworksICST Transactions on Scalable Information Systems10.4108/eetsis.739711Online publication date: 6-Dec-2024
https://doi.org/10.4108/eetsis.7397
He ZCai Z(2024)Trading Aggregate Statistics Over Private Internet of Things DataIEEE Transactions on Computers10.1109/TC.2023.333181673:2(394-407)Online publication date: Feb-2024
https://doi.org/10.1109/TC.2023.3331816
Fan XWang YHuo YTian Z(2023)1-Bit Compressive Sensing for Efficient Federated Learning Over the AirIEEE Transactions on Wireless Communications10.1109/TWC.2022.320919022:3(2139-2155)Online publication date: Mar-2023
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Index Terms

Approximate Holistic Aggregation in Wireless Sensor Networks
1. Networks
  1. Network types
    1. Cyber-physical networks
      1. Sensor networks

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 13, Issue 2

May 2017

235 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3081318

Editor:
Chenyang Lu
Department of Computer Science and Engineering / School of Engineering and Applied Sciences / Washington University / 1 Brookings Drive / St. Louis, MO 63130

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Copyright © 2017 ACM.

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Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 19 April 2017

Accepted: 01 December 2016

Revised: 01 December 2016

Received: 01 May 2015

Published in TOSN Volume 13, Issue 2

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Key Program of the National Natural Science Foundation of China
Research Fund for the Doctoral Program of Higher Education of China
National Science Foundation (NSF)
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities

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Cited By

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https://doi.org/10.4108/eetsis.7397
He ZCai Z(2024)Trading Aggregate Statistics Over Private Internet of Things DataIEEE Transactions on Computers10.1109/TC.2023.333181673:2(394-407)Online publication date: Feb-2024
https://doi.org/10.1109/TC.2023.3331816
Fan XWang YHuo YTian Z(2023)1-Bit Compressive Sensing for Efficient Federated Learning Over the AirIEEE Transactions on Wireless Communications10.1109/TWC.2022.320919022:3(2139-2155)Online publication date: Mar-2023
https://doi.org/10.1109/TWC.2022.3209190
Zheng XTian LCai Z(2023)A Fair and Rational Data Sharing Strategy Toward Two-Stage Industrial Internet of ThingsIEEE Transactions on Industrial Informatics10.1109/TII.2022.317936119:1(1088-1096)Online publication date: Jan-2023
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Yang XGuan XWang NLiu YWu HZhang Y(2023)Cloud-Edge-End Intelligence for Fault-Tolerant Renewable Energy Accommodation in Smart GridIEEE Transactions on Cloud Computing10.1109/TCC.2021.313354011:2(1144-1156)Online publication date: 1-Apr-2023
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Cai ZChen QShi TZhu TChen KLi Y(2023)Battery-Free Wireless Sensor Networks: A Comprehensive SurveyIEEE Internet of Things Journal10.1109/JIOT.2022.322238610:6(5543-5570)Online publication date: 15-Mar-2023
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Chen ZWang JXu BXiao LLong J(2023)A Density Routing Algorithm in Wireless Sensor Networks for Dangerous Chemicals Monitoring2023 12th International Conference of Information and Communication Technology (ICTech)10.1109/ICTech58362.2023.00105(537-544)Online publication date: Apr-2023
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Sheeja Rani SSankar K(2023)Improved buffalo optimized deep feed forward neural learning based multipath routing for energy efficient data aggregation in WSNMeasurement: Sensors10.1016/j.measen.2022.10066227(100662)Online publication date: Jun-2023
https://doi.org/10.1016/j.measen.2022.100662
Gotfryd KCichoń J(2023)On distributed data aggregation and the precision of approximate histogramsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2023.104722180:COnline publication date: 1-Oct-2023
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Bomnale AMore A(2023)Node utilization index-based data routing and aggregation protocol for energy-efficient wireless sensor networksThe Journal of Supercomputing10.1007/s11227-023-05800-480:7(9220-9252)Online publication date: 6-Dec-2023
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