research-article

A Reduced Network Traffic Method for IoT Data Clustering

Authors:

Ricardo De Azevedo,

Gabriel Resende Machado,

Ronaldo Ribeiro Goldschmidt,

Ricardo ChorenAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 1

Article No.: 13, Pages 1 - 23

https://doi.org/10.1145/3423139

Published: 07 December 2020 Publication History

Abstract

Internet of Things (IoT) systems usually involve interconnected, low processing capacity, and low memory sensor nodes (devices) that collect data in several sorts of applications that interconnect people and things. In this scenario, mining tasks, such as clustering, have been commonly deployed to detect behavioral patterns from the collected data. The centralized clustering of IoT data demands high network traffic to transmit the data from the devices to a central node, where a clustering algorithm must be applied. This approach does not scale as the number of devices increases, and the amount of data grows. However, distributing the clustering process through the devices may not be a feasible approach as well, since the devices are usually simple and may not have the ability to execute complex procedures. This work proposes a centralized IoT data clustering method that demands reduced network traffic and low processing power in the devices. The proposed method uses a data grid to summarize the information at the devices before transmitting it to the central node, reducing network traffic. After the data transfer, the proposed method applies a clustering algorithm that was developed to process data in the summarized representation. Tests with seven datasets provided experimental evidence that the proposed method reduces network traffic and produces results comparable to the ones generated by DBSCAN and HDBSCAN, two robust centralized clustering algorithms.

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Cited By

Liu ZChen YXia FBian JZhu BShen GKong X(2023)TAP: Traffic Accident Profiling via Multi-Task Spatio-Temporal Graph Representation LearningACM Transactions on Knowledge Discovery from Data10.1145/356459417:4(1-25)Online publication date: 24-Feb-2023
https://dl.acm.org/doi/10.1145/3564594
Hosseinzadeh MHemmati ARahmani A(2022)Clustering for smart cities in the internet of things: a reviewCluster Computing10.1007/s10586-022-03646-825:6(4097-4127)Online publication date: 27-Jun-2022
https://doi.org/10.1007/s10586-022-03646-8
Li S(2022) PSO ‐based traffic scheduling mechanism in information‐centric networking Internet Technology Letters10.1002/itl2.3625:5Online publication date: 8-Mar-2022
https://doi.org/10.1002/itl2.362

Index Terms

A Reduced Network Traffic Method for IoT Data Clustering
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
  2. Embedded and cyber-physical systems
    1. Embedded systems
      1. Embedded software
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 15, Issue 1

February 2021

361 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3441647

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

Issue’s Table of Contents

Copyright © 2020 ACM.

© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 December 2020

Accepted: 01 September 2020

Revised: 01 July 2020

Received: 01 January 2020

Published in TKDD Volume 15, Issue 1

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Cited By

Liu ZChen YXia FBian JZhu BShen GKong X(2023)TAP: Traffic Accident Profiling via Multi-Task Spatio-Temporal Graph Representation LearningACM Transactions on Knowledge Discovery from Data10.1145/356459417:4(1-25)Online publication date: 24-Feb-2023
https://dl.acm.org/doi/10.1145/3564594
Hosseinzadeh MHemmati ARahmani A(2022)Clustering for smart cities in the internet of things: a reviewCluster Computing10.1007/s10586-022-03646-825:6(4097-4127)Online publication date: 27-Jun-2022
https://doi.org/10.1007/s10586-022-03646-8
Li S(2022) PSO ‐based traffic scheduling mechanism in information‐centric networking Internet Technology Letters10.1002/itl2.3625:5Online publication date: 8-Mar-2022
https://doi.org/10.1002/itl2.362

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