Association of IoT Devices Using Fuzzy C-Means Clustering and Apriori Algorithms

Haesik Kim

doi:10.1109/MASS56207.2022.00012

Association of IoT Devices Using Fuzzy C-Means Clustering and Apriori Algorithms

^*Corresponding author for this work

BA6402 Future communication networks

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

Abstract

The IoT systems enable us to create various new services and offer new solutions. They play an important role in 5G and 6G systems. In order to operate IoT systems efficiently and support scalability, they should be grouped properly. There are many clustering and association ways for IoT systems. Clustering algorithms of machine learning are techniques for finding similarity groups in data. In this paper, we propose two-steps IoT device association method by combining Fuzzy C-Means clustering algorithm and Apriori algorithm. Fuzzy C-Means clustering as a soft clustering approach assigns data points to probability score and allocates them to clusters. Apriori algorithm is used in finding frequent sets and relevant association rules. As combining the clustering technique and the association rule mining technique, we can obtain multiple benefits including handover cost reduction. The performances of the proposed method are evaluated and compared with K mean clustering algorithm in terms of energy efficiency and handover frequency.

Original language	English
Title of host publication	2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	27-32
ISBN (Electronic)	978-1-6654-7180-0
ISBN (Print)	978-1-6654-7181-7
DOIs	https://doi.org/10.1109/MASS56207.2022.00012
Publication status	Published - 2022
MoE publication type	A4 Article in a conference publication
Event	19th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022 - Denver, United States Duration: 20 Oct 2022 → 22 Oct 2022

Publication series

Series	IEEE International Conference on Mobile Ad-hoc and Sensor Systems
Volume	19
ISSN	2155-6806

Conference

Conference	19th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022
Country/Territory	United States
City	Denver
Period	20/10/22 → 22/10/22

Keywords

5G and 6G systems, etc.
Apriori algorithm
Association
Clustering
Energy efficiency
Fuzzy C-Means Clustering
Handover
IoT

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/MASS56207.2022.00012

Cite this

@inproceedings{881f144a717b44a9be90e96ce733b5a3,

title = "Association of IoT Devices Using Fuzzy C-Means Clustering and Apriori Algorithms",

abstract = "The IoT systems enable us to create various new services and offer new solutions. They play an important role in 5G and 6G systems. In order to operate IoT systems efficiently and support scalability, they should be grouped properly. There are many clustering and association ways for IoT systems. Clustering algorithms of machine learning are techniques for finding similarity groups in data. In this paper, we propose two-steps IoT device association method by combining Fuzzy C-Means clustering algorithm and Apriori algorithm. Fuzzy C-Means clustering as a soft clustering approach assigns data points to probability score and allocates them to clusters. Apriori algorithm is used in finding frequent sets and relevant association rules. As combining the clustering technique and the association rule mining technique, we can obtain multiple benefits including handover cost reduction. The performances of the proposed method are evaluated and compared with K mean clustering algorithm in terms of energy efficiency and handover frequency.",

keywords = "5G and 6G systems, etc., Apriori algorithm, Association, Clustering, Energy efficiency, Fuzzy C-Means Clustering, Handover, IoT",

author = "Haesik Kim",

note = "Funding Information: This work was supported by the European Commission in the framework of the H2020-ICT-19-2019 project 5G-HEART (Grant agreement no. 857034). Publisher Copyright: {\textcopyright} 2022 IEEE.; 19th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022 ; Conference date: 20-10-2022 Through 22-10-2022",

year = "2022",

doi = "10.1109/MASS56207.2022.00012",

language = "English",

isbn = "978-1-6654-7181-7",

series = " IEEE International Conference on Mobile Ad-hoc and Sensor Systems",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

pages = "27--32",

booktitle = "2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022",

address = "United States",

}

Kim, H 2022, Association of IoT Devices Using Fuzzy C-Means Clustering and Apriori Algorithms. in 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022. IEEE Institute of Electrical and Electronic Engineers, IEEE International Conference on Mobile Ad-hoc and Sensor Systems, vol. 19, pp. 27-32, 19th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022, Denver, Colorado, United States, 20/10/22. https://doi.org/10.1109/MASS56207.2022.00012

Association of IoT Devices Using Fuzzy C-Means Clustering and Apriori Algorithms. / Kim, Haesik.
2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022. IEEE Institute of Electrical and Electronic Engineers, 2022. p. 27-32 ( IEEE International Conference on Mobile Ad-hoc and Sensor Systems, Vol. 19).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Association of IoT Devices Using Fuzzy C-Means Clustering and Apriori Algorithms

AU - Kim, Haesik

PY - 2022

Y1 - 2022

N2 - The IoT systems enable us to create various new services and offer new solutions. They play an important role in 5G and 6G systems. In order to operate IoT systems efficiently and support scalability, they should be grouped properly. There are many clustering and association ways for IoT systems. Clustering algorithms of machine learning are techniques for finding similarity groups in data. In this paper, we propose two-steps IoT device association method by combining Fuzzy C-Means clustering algorithm and Apriori algorithm. Fuzzy C-Means clustering as a soft clustering approach assigns data points to probability score and allocates them to clusters. Apriori algorithm is used in finding frequent sets and relevant association rules. As combining the clustering technique and the association rule mining technique, we can obtain multiple benefits including handover cost reduction. The performances of the proposed method are evaluated and compared with K mean clustering algorithm in terms of energy efficiency and handover frequency.

AB - The IoT systems enable us to create various new services and offer new solutions. They play an important role in 5G and 6G systems. In order to operate IoT systems efficiently and support scalability, they should be grouped properly. There are many clustering and association ways for IoT systems. Clustering algorithms of machine learning are techniques for finding similarity groups in data. In this paper, we propose two-steps IoT device association method by combining Fuzzy C-Means clustering algorithm and Apriori algorithm. Fuzzy C-Means clustering as a soft clustering approach assigns data points to probability score and allocates them to clusters. Apriori algorithm is used in finding frequent sets and relevant association rules. As combining the clustering technique and the association rule mining technique, we can obtain multiple benefits including handover cost reduction. The performances of the proposed method are evaluated and compared with K mean clustering algorithm in terms of energy efficiency and handover frequency.

KW - 5G and 6G systems, etc.

KW - Apriori algorithm

KW - Association

KW - Clustering

KW - Energy efficiency

KW - Fuzzy C-Means Clustering

KW - Handover

KW - IoT

UR - http://www.scopus.com/inward/record.url?scp=85146121999&partnerID=8YFLogxK

U2 - 10.1109/MASS56207.2022.00012

DO - 10.1109/MASS56207.2022.00012

M3 - Conference article in proceedings

AN - SCOPUS:85146121999

SN - 978-1-6654-7181-7

T3 - IEEE International Conference on Mobile Ad-hoc and Sensor Systems

SP - 27

EP - 32

BT - 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - 19th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022

Y2 - 20 October 2022 through 22 October 2022

ER -

Association of IoT Devices Using Fuzzy C-Means Clustering and Apriori Algorithms

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this