LAPSE:2023.8094
Published Article

LAPSE:2023.8094
Multi-Controller Model for Improving the Performance of IoT Networks
February 24, 2023
Abstract
Internet of Things (IoT), a strong integration of radio frequency identifier (RFID), wireless devices, and sensors, has provided a difficult yet strong chance to shape existing systems into intelligent ones. Many new applications have been created in the last few years. As many as a million objects are anticipated to be linked together to form a network that can infer meaningful conclusions based on raw data. This means any IoT system is heterogeneous when it comes to the types of devices that are used in the system and how they communicate with each other. In most cases, an IoT network can be described as a layered network, with multiple tiers stacked on top of each other. IoT network performance improvement typically focuses on a single layer. As a result, effectiveness in one layer may rise while that of another may fall. Ultimately, the achievement issue must be addressed by considering improvements in all layers of an IoT network, or at the very least, by considering contiguous hierarchical levels. Using a parallel and clustered architecture in the device layer, this paper examines how to improve the performance of an IoT network’s controller layer. A particular clustered architecture at the device level has been shown to increase the performance of an IoT network by 16% percent. Using a clustered architecture at the device layer in conjunction with a parallel architecture at the controller layer boosts performance by 24% overall.
Internet of Things (IoT), a strong integration of radio frequency identifier (RFID), wireless devices, and sensors, has provided a difficult yet strong chance to shape existing systems into intelligent ones. Many new applications have been created in the last few years. As many as a million objects are anticipated to be linked together to form a network that can infer meaningful conclusions based on raw data. This means any IoT system is heterogeneous when it comes to the types of devices that are used in the system and how they communicate with each other. In most cases, an IoT network can be described as a layered network, with multiple tiers stacked on top of each other. IoT network performance improvement typically focuses on a single layer. As a result, effectiveness in one layer may rise while that of another may fall. Ultimately, the achievement issue must be addressed by considering improvements in all layers of an IoT network, or at the very least, by considering contiguous hierarchical levels. Using a parallel and clustered architecture in the device layer, this paper examines how to improve the performance of an IoT network’s controller layer. A particular clustered architecture at the device level has been shown to increase the performance of an IoT network by 16% percent. Using a clustered architecture at the device layer in conjunction with a parallel architecture at the controller layer boosts performance by 24% overall.
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Keywords
device level clustering, internet of things, layered networking, parallel architectures, performance optimization, radio-frequency identification, topology binding
Subject
Suggested Citation
Davanam G, Kallam S, Singh N, Gunjan VK, Roy S, Rahebi J, Farzamnia A, Saad I. Multi-Controller Model for Improving the Performance of IoT Networks. (2023). LAPSE:2023.8094
Author Affiliations
Davanam G: Department of CSE, Sree Vidyanikethan Engineering College (Autonomous), Tirupati 517102, Andhra Pradesh, India
Kallam S: Department of CSE, Sree Vidyanikethan Engineering College (Autonomous), Tirupati 517102, Andhra Pradesh, India
Singh N: Department of Computer Science & Engineering, CMR Institute of Technology, Hyderabad 501401, Telengana, India
Gunjan VK: Department of Computer Science & Engineering, CMR Institute of Technology, Hyderabad 501401, Telengana, India [ORCID]
Roy S: Department of Artificial Intelligence & Data Science, Jio Institute, Navi Mumbai 410206, Maharastra, India [ORCID]
Rahebi J: Software Engineering Department, Istanbul Topkapi University, 34087 Istanbul, Turkey
Farzamnia A: Faculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia [ORCID]
Saad I: Faculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia
Kallam S: Department of CSE, Sree Vidyanikethan Engineering College (Autonomous), Tirupati 517102, Andhra Pradesh, India
Singh N: Department of Computer Science & Engineering, CMR Institute of Technology, Hyderabad 501401, Telengana, India
Gunjan VK: Department of Computer Science & Engineering, CMR Institute of Technology, Hyderabad 501401, Telengana, India [ORCID]
Roy S: Department of Artificial Intelligence & Data Science, Jio Institute, Navi Mumbai 410206, Maharastra, India [ORCID]
Rahebi J: Software Engineering Department, Istanbul Topkapi University, 34087 Istanbul, Turkey
Farzamnia A: Faculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia [ORCID]
Saad I: Faculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia
Journal Name
Energies
Volume
15
Issue
22
First Page
8738
Year
2022
Publication Date
2022-11-21
ISSN
1996-1073
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Original Submission
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PII: en15228738, Publication Type: Journal Article
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LAPSE:2023.8094
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https://doi.org/10.3390/en15228738
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Feb 24, 2023
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