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Comparative study on ant colony optimization (ACO) and K-Means clustering approaches for jobs scheduling and energy optimization model in Internet of Things (IoT)

The concept of Internet of Things (IoT) was proposed by Professor Kevin Ashton of the Massachusetts Institute of Technology (MIT) in 1999. IoT is an environment that people understand in many different ways depending on their requirement, point of view and purpose. When transmitting data in IoT envi...

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Main Authors:	Kumar, Sumit, Solanki, Vijender Kumar, Choudhary, Saket Kumar, Selamat, Ali, Crespo, Ruben Gonzalez
Format:	Article
Published:	UNIR - Universidad Internacional de La Rioja 2020
Subjects:	QA75 Electronic computers. Computer science T58.5-58.64 Information technology
Online Access:	http://eprints.utm.my/id/eprint/89956/ http://dx.doi.org/10.9781/ijimai.2020.01.003
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id	my.utm.89956
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spelling	my.utm.899562021-05-18T07:13:05Z http://eprints.utm.my/id/eprint/89956/ Comparative study on ant colony optimization (ACO) and K-Means clustering approaches for jobs scheduling and energy optimization model in Internet of Things (IoT) Kumar, Sumit Solanki, Vijender Kumar Choudhary, Saket Kumar Selamat, Ali Crespo, Ruben Gonzalez QA75 Electronic computers. Computer science T58.5-58.64 Information technology The concept of Internet of Things (IoT) was proposed by Professor Kevin Ashton of the Massachusetts Institute of Technology (MIT) in 1999. IoT is an environment that people understand in many different ways depending on their requirement, point of view and purpose. When transmitting data in IoT environment, distribution of network traffic fluctuates frequently. If links of the network or nodes fail randomly, then automatically new nodes get added frequently. Heavy network traffic affects the response time of all system and it consumes more energy continuously. Minimization the network traffic/ by finding the shortest path from source to destination minimizes the response time of all system and also reduces the energy consumption cost. The ant colony optimization (ACO) and K-Means clustering algorithms characteristics conform to the auto-activator and optimistic response mechanism of the shortest route searching from source to destination. In this article, ACO and K-Means clustering algorithms are studied to search the shortest route path from source to destination by optimizing the Quality of Service (QoS) constraints. Resources are assumed in the active and varied IoT network atmosphere for these two algorithms. This work includes the study and comparison between ant colony optimization (ACO) and K-Means algorithms to plan a response time aware scheduling model for IoT. It is proposed to divide the IoT environment into various areas and a various number of clusters depending on the types of networks. It is noticed that this model is more efficient for the suggested routing algorithm in terms of response time, point-to-point delay, throughput and overhead of control bits. UNIR - Universidad Internacional de La Rioja 2020-03 Article PeerReviewed Kumar, Sumit and Solanki, Vijender Kumar and Choudhary, Saket Kumar and Selamat, Ali and Crespo, Ruben Gonzalez (2020) Comparative study on ant colony optimization (ACO) and K-Means clustering approaches for jobs scheduling and energy optimization model in Internet of Things (IoT). International Journal of Interactive Multimedia and Artificial Intelligence, 6 (1). pp. 107-116. ISSN 1989-1660 http://dx.doi.org/10.9781/ijimai.2020.01.003 DOI:10.9781/ijimai.2020.01.003
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	QA75 Electronic computers. Computer science T58.5-58.64 Information technology
spellingShingle	QA75 Electronic computers. Computer science T58.5-58.64 Information technology Kumar, Sumit Solanki, Vijender Kumar Choudhary, Saket Kumar Selamat, Ali Crespo, Ruben Gonzalez Comparative study on ant colony optimization (ACO) and K-Means clustering approaches for jobs scheduling and energy optimization model in Internet of Things (IoT)
description	The concept of Internet of Things (IoT) was proposed by Professor Kevin Ashton of the Massachusetts Institute of Technology (MIT) in 1999. IoT is an environment that people understand in many different ways depending on their requirement, point of view and purpose. When transmitting data in IoT environment, distribution of network traffic fluctuates frequently. If links of the network or nodes fail randomly, then automatically new nodes get added frequently. Heavy network traffic affects the response time of all system and it consumes more energy continuously. Minimization the network traffic/ by finding the shortest path from source to destination minimizes the response time of all system and also reduces the energy consumption cost. The ant colony optimization (ACO) and K-Means clustering algorithms characteristics conform to the auto-activator and optimistic response mechanism of the shortest route searching from source to destination. In this article, ACO and K-Means clustering algorithms are studied to search the shortest route path from source to destination by optimizing the Quality of Service (QoS) constraints. Resources are assumed in the active and varied IoT network atmosphere for these two algorithms. This work includes the study and comparison between ant colony optimization (ACO) and K-Means algorithms to plan a response time aware scheduling model for IoT. It is proposed to divide the IoT environment into various areas and a various number of clusters depending on the types of networks. It is noticed that this model is more efficient for the suggested routing algorithm in terms of response time, point-to-point delay, throughput and overhead of control bits.
format	Article
author	Kumar, Sumit Solanki, Vijender Kumar Choudhary, Saket Kumar Selamat, Ali Crespo, Ruben Gonzalez
author_facet	Kumar, Sumit Solanki, Vijender Kumar Choudhary, Saket Kumar Selamat, Ali Crespo, Ruben Gonzalez
author_sort	Kumar, Sumit
title	Comparative study on ant colony optimization (ACO) and K-Means clustering approaches for jobs scheduling and energy optimization model in Internet of Things (IoT)
title_short	Comparative study on ant colony optimization (ACO) and K-Means clustering approaches for jobs scheduling and energy optimization model in Internet of Things (IoT)
title_full	Comparative study on ant colony optimization (ACO) and K-Means clustering approaches for jobs scheduling and energy optimization model in Internet of Things (IoT)
title_fullStr	Comparative study on ant colony optimization (ACO) and K-Means clustering approaches for jobs scheduling and energy optimization model in Internet of Things (IoT)
title_full_unstemmed	Comparative study on ant colony optimization (ACO) and K-Means clustering approaches for jobs scheduling and energy optimization model in Internet of Things (IoT)
title_sort	comparative study on ant colony optimization (aco) and k-means clustering approaches for jobs scheduling and energy optimization model in internet of things (iot)
publisher	UNIR - Universidad Internacional de La Rioja
publishDate	2020
url	http://eprints.utm.my/id/eprint/89956/ http://dx.doi.org/10.9781/ijimai.2020.01.003
_version_	1701162974624874496
score	13.211869

Comparative study on ant colony optimization (ACO) and K-Means clustering approaches for jobs scheduling and energy optimization model in Internet of Things (IoT)

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