Approximate Data Aggregation in Sensor Equipped IoT Networks

Ji Li; Madhuri Siddula; Xiuzhen Cheng; Wei Cheng; Zhi Tian; Yingshu Li

doi:10.26599/TST.2019.9010023

Tsinghua Science and Technology 2020, 25(1): 44-55 https://doi.org/10.26599/TST.2019.9010023

Open Access | Issue | Published: 22 July 2019

Approximate Data Aggregation in Sensor Equipped IoT Networks

Show Author's Information Hide Author's Information Ji Li, Madhuri Siddula, Xiuzhen Cheng, Wei Cheng, Zhi Tian, Yingshu Li(

)

Kennesaw State University, Marietta, GA 30060, USA.

Georgia State University, Atlanta, GA 30303, USA.

George Washington University, Washington, DC 20052, USA.

Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA.

Department of Electrical & Computer Engineering, George Mason University, Fairfax, VA 22030, USA.

Keywords:

data aggregation, sampling, Internet-of-Things (IoT) networks

Cite this article:

Li J, Siddula M, Cheng X, et al. Approximate Data Aggregation in Sensor Equipped IoT Networks. Tsinghua Science and Technology, 2020, 25(1): 44-55. https://doi.org/10.26599/TST.2019.9010023

Download citation

EndNote(RIS)

BibTeX

556

Views

Downloads

Citations

Crossref

N/A

WoS

Scopus

CSCD

Abstract Full text About this article

Abstract

As Internet-of-Things (IoT) networks provide efficient ways to transfer data, they are used widely in data sensing applications. These applications can further include wireless sensor networks. One of the critical problems in sensor-equipped IoT networks is to design energy efficient data aggregation algorithms that address the issues of maximum value and distinct set query. In this paper, we propose an algorithm based on uniform sampling and Bernoulli sampling to address these issues. We have provided logical proofs to show that the proposed algorithms return accurate results with a given probability. Simulation results show that these algorithms have high performance compared with a simple distributed algorithm in terms of energy consumption.

Full text

Abstract

Full text

Outline

About this article

Approximate Data Aggregation in Sensor Equipped IoT Networks

Show Author's information Hide Author's Information Ji Li, Madhuri Siddula, Xiuzhen Cheng, Wei Cheng, Zhi Tian, Yingshu Li(

)

Kennesaw State University, Marietta, GA 30060, USA.

Georgia State University, Atlanta, GA 30303, USA.

George Washington University, Washington, DC 20052, USA.

Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA.

Department of Electrical & Computer Engineering, George Mason University, Fairfax, VA 22030, USA.

Abstract

Keywords: data aggregation, sampling, Internet-of-Things (IoT) networks

References(37)

[1]

Z. P. Cai, X. Zheng, and J. G. Yu, A differential-private framework for urban traffic flows estimation via taxi companies, IEEE Trans. Ind. Inf., .

DOI Google Scholar

[2]

Z. P. Cai and X. Zheng, A private and efficient mechanism for data uploading in smart cyber-physical systems, IEEE Trans. Netw. Sci. Eng., .

DOI Google Scholar

[3]

X. Zheng, Z. P. Cai, and Y. S. Li, Data linkage in smart internet of things systems: A consideration from a privacy perspective, IEEE Commun. Mag., vol. 56, no. 9, pp. 55-61, 2018.

DOI Google Scholar

[4]

Y. Liang, Z. P. Cai, J. G. Yu, Q. L. Han, and Y. S. Li, Deep learning based inference of private information using embedded sensors in smart devices, IEEE Netw. Mag., vol. 32, no. 4, pp. 8-14, 2018.

DOI Google Scholar

[5]

Y. Huo, C. Q. Hu, X. W. Qi, and T. Jing, LoDPD: A location difference-based proximity detection protocol for fog computing, IEEE Internet Things J., vol. 4, no. 5, pp. 1117-1124, 2017.

DOI Google Scholar

[6]

Y. Huo, C. T. Yong, and Y. F. Lu, Re-ADP: Real-time data aggregation with adaptive ω-event differential privacy for fog computing, Wirel. Commun. Mobile Comput., vol. 2018, pp. 1-13, 2018.

DOI Google Scholar

[7]

Y. K. Wen, Y. Huo, L. R. Ma, T. Jing, and Q. H. Gao, A scheme for trustworthy friendly jammer selection in cooperative cognitive radio networks, IEEE Trans. Veh. Technol., vol. 68, no. 4, pp. 3500-3512, 2019.

DOI Google Scholar

[8]

Y. Huo, M. Xu, X. Fan, and T. Jing, A novel secure relay selection strategy for energy-harvesting-enabled internet of things, EURASIP J. Wirel. Comm., vol. 2018, p. 264, 2018.

DOI Google Scholar

[9]

Y. Q. Jia, Y. Chen, X. S. Dong, P. Saxena, J. Mao, and Z. K. Liang, Man-in-the-browser-cache: Persisting https attacks via browser cache poisoning, Comput. Secur., vol. 55, pp. 62-80, 2015.

DOI Google Scholar

[10]

J. Mao, S. S. Zhu, J. D. Bian, Q. X. Lin, and J. W. Liu, Anomalous power-usage behavior detection from smart home wireless communications, J. Commun. Inf. Netw., vol. 4, no. 1, pp. 13-23, 2019.

DOI Google Scholar

[11]

C. Schurgers and M. B. Srivastava, Energy efficient routing in wireless sensor networks, in 2001 MILCOM Proc. Communications for Network-Centric Operations: Creating the Information Force, McLean, VA, USA, 2001, pp. 357-361.

[12]

S. Y. Cheng, Z. P. Cai, J. Z. Li, and H. Gao, Extracting kernel dataset from big sensory data in wireless sensor networks, IEEE Trans. Knowl. Data Eng., vol. 29, no. 4, pp. 813-827, 2017.

DOI Google Scholar

[13]

S. Y. Cheng, Z. P. Cai, J. Z. Li, and X. L. Fang, Drawing dominant dataset from big sensory data in wireless sensor networks, in Proc. 2015 IEEE Conf. Computer Communications, Kowloon, China, 2015, pp. 531-539.

DOI

[14]

S. Y. Cheng, Z. P. Cai, and J. Z. Li, Curve query processing in wireless sensor networks, IEEE Trans. Veh. Technol., vol. 64, no. 11, pp. 5198-5209, 2015.

DOI Google Scholar

[15]

S. Y. Cheng, J. Z. Li, and Z. P. Cai, O(ε)-approximation to physical world by sensor networks, in Proc. 32nd Ann. IEEE Int. Conf. Computer Communications, Turin, Italy, 2013, pp. 3084-3092.

DOI

[16]

Z. B. He, Z. P. Cai, S. Y. Cheng, and X. M. Wang, Approximate aggregation for tracking quantiles and range countings in wireless sensor networks, Theor. Comput. Sci., vol. 607, pp. 381-390, 2015.

DOI Google Scholar

[17]

X. Zheng and Z. P. Cai, Real-time big data delivery in wireless networks: A case study on video delivery, IEEE Trans. Ind. Inf., vol. 13, no. 4, pp. 2048-2057, 2017.

DOI Google Scholar

[18]

X. Zheng, Z. P. Cai, J. Z. Li, and H. Gao, A study on application-aware scheduling in wireless networks, IEEE Trans. Mobile Comput., vol. 16, no. 7, pp. 1787-1801, 2017.

DOI Google Scholar

[19]

J. G. Yu, Q. B. Zhang, D. X. Yu, C. C. Chen, and G. H. Wang, Domatic partition in homogeneous wireless sensor networks, J. Netw. Comput. Appl., vol. 37, pp. 186-193, 2014.

DOI Google Scholar

[20]

J. G. Yu, X. L. Ning, Y. C. Sun, S. L. Wang, and Y. W. Wang, Constructing a self-stabilizing CDS with bounded diameter in wireless networks under SINR, in Proc. IEEE INFOCOM 2017-IEEE Conf. Computer Communications, Atlanta, GA, USA, 2017, pp. 1-9.

DOI

[21]

J. G. Yu, B. G. Huang, X. Z. Cheng, and M. Atiquzzaman, Shortest link scheduling algorithms in wireless networks under the SINR model, IEEE Trans. Veh. Technol., vol. 66, no. 3, pp. 2643-2657, 2017.

DOI Google Scholar

[22]

S. L. Wang, X. Wang, X. Z. Cheng, J. H. Huang, R. F. Bie, and F. Zhao, Fundamental analysis on data dissemination in mobile opportunistic networks with levy mobility, IEEE Trans. Veh. Technol., vol. 66, no. 5, pp. 4173-4187, 2017.

Google Scholar

[23]

Y. Wang, Topology control for wireless sensor networks, in Wireless Sensor Networks and Applications, Y. S. Li, M. T. Thai, and W. L. Wu, eds. Springer, 2008, pp. 113–147.

DOI

[24]

J. Elson and D. Estrin, Time synchronization for wireless sensor networks, in Proc. 15th Int. Parallel and Distributed Processing Symp., San Francisco, CA, USA, 2001.

[25]

J. B. Li and J. Z. Li, Data sampling control, compression and query in sensor networks, Int. J. Sens. Netw., vol. 2, nos. 1&2, pp. 53-61, 2007.

DOI Google Scholar

[26]

J. Considine, F. Li, G. Kollios, and J. Byers, Approximate aggregation techniques for sensor databases, in Proc. 20th Int. Conf. Data Engineering, Boston, MA, USA, 2004, pp. 449-460.

[27]

G. Hartl and B. C. Li, infer: A Bayesian inference approach towards energy efficient data collection in dense sensor networks, in Proc. 25th IEEE Int. Conf. Distributed Computing Systems, Columbus, OH, USA, 2005, pp. 371-380.

[28]

R. Lachowski, M. E. Pellenz, M. C. Penna, E. Jamhour, and R. D. Souza, An efficient distributed algorithm for constructing spanning trees in wireless sensor networks, Sensors, vol. 15, no. 1, pp. 1518-1536, 2015.

DOI Google Scholar

[29]

S. Y. Cheng and J. Z. Li, Sampling based (epsilon, delta)-approximate aggregation algorithm in sensor networks, in Proc. 29th IEEE Int. Conf. Distributed Computing Systems, Montreal, Canada, 2009, pp. 273-280.

DOI

[30]

Crossbow, MPR-Mote Processor Radio Board User’s Manual. San Jose, CA, USA: Crossbow Technology Inc, 2003.

[31]

G. Anastasi, A. Falchi, A. Passarella, M. Conti, and E. Gregori, Performance measurements of motes sensor networks, in Proc. 7th ACM Int. Symp. Modeling, Analysis and Simulation of Wireless and Mobile Systems, Venice, Italy, 2004, pp. 174-181.

DOI

[32]

Z. F. Huang, L. Wang, K. Yi, and Y. H. Liu, Sampling based algorithms for quantile computation in sensor networks, in Proc. 2011 ACM SIGMOD Int. Conf. Management of Data, Athens, Greece, 2011, pp. 745-756.

DOI

[33]

B. Gedik, L. Liu, and P. S. Yu, ASAP: An adaptive sampling approach to data collection in sensor networks, IEEE Trans. Parallel Distrib. Syst., vol. 18, no. 12, pp. 1766-1783, 2007.

DOI Google Scholar

[34]

A. S. Silberstein, R. Braynard, C. Ellis, K. Munagala, and J. Yang, A sampling-based approach to optimizing top-k queries in sensor networks, in Proc. 22nd Int. Conf. Data Engineering, Atlanta, GA, USA, 2006, p. 68.

DOI

[35]

J. Li, S. Y. Cheng, Z. P. Cai, J. G. Yu, C. K. Wang, and Y. S. Li, Approximate holistic aggregation in wireless sensor networks, ACM Trans. Sens. Netw., vol. 13, no. 2, p. 11, 2017.

DOI Google Scholar

[36]

K. Beyer, P. J. Haas, B. Reinwald, Y. Sismanis, and R. Gemulla, On synopses for distinct-value estimation under multiset operations, in Proc. 2007 ACM SIGMOD Int. Conf. Management of Data, Beijing, China, 2007, pp. 199-210.

DOI

[37]

Z. Cai and Z. He, Trading private range counting over big iot data. in Proc. 39th IEEE Int. Conf. Distributed Computing Systems, Dallas, TX, USA, 2019.

DOI

About this article

Publication history

Acknowledgements

Rights and permissions

Publication history

Received: 22 May 2019

Accepted: 27 May 2019

Published: 22 July 2019

Issue date: February 2020

Copyright

Acknowledgements

This work was partly supported by the National Science Foundation (NSF) (Nos. 1741277, 1741287, 1741279, 1851197, and 1741338).

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).