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Open Access

Approximate Data Aggregation in Sensor Equipped IoT Networks

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.
Show Author Information

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.

Keywords

data aggregationsamplingInternet-of-Things (IoT) networks

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Tsinghua Science and Technology
Volume 25 Issue 1,
February 2020
Pages 44-55
DOI: 10.26599/TST.2019.9010023
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

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Received: 22 May 2019
Accepted: 27 May 2019
Published: 22 July 2019
© The author(s) 2020

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/).
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