Intelligent Network Selection Mechanisms in the Internet of Everything System

In the Internet of Everything (IoE) ecosystem, it is necessary for connected devices to move between different access points seamlessly. As IoE is deployed in diverse environments, such as smart cities, industrial settings, and vehicular networks, ensuring reliable connectivity and uninterrupted services across different networks becomes important. As part of the IoE framework, this research paper focuses on developing and evaluating intelligent handoff decision mechanisms. So, in this work, the optimal handoff decision technique is proposed, which transmits the information in a fast manner. The Fuzzy Analytical Hierarchical Process (FAHP) and Analytical Hierarchical Process (AHP) two-user preference calculation algorithms are used. Then, the user preference is included in the prospect algorithm and the optimal network is selected. After that, the result of the prospect algorithm is compared with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Grey Rational Analysis (GRA), and Multiplicative Exponent Weighting (MEW) algorithms in terms of network scores and the number of handoffs. The advantage of network selection using a prospect algorithm is that it will decrease the failure rate and increase the data transfer and user experience. These advantages deliver seamless connectivity for a diverse range of applications and devices using an IoE infrastructure that is more robust and reliable. The network selected by combining different techniques is the optimal network because it selects the most efficient or effective network.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3528099
Citations
2
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
2.97

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article

Intelligent Network Selection Mechanisms in the Internet of Everything System

Meenakshi Munjal, Indrasen Singh
2 citations
IEEE Access
IoT and Edge/Fog Computing
2.97
article

Intelligent Network Selection Mechanisms in the Internet of Everything System

Meenakshi Munjal, Indrasen Singh
article en
2 citations

Abstract

In the Internet of Everything (IoE) ecosystem, it is necessary for connected devices to move between different access points seamlessly. As IoE is deployed in diverse environments, such as smart cities, industrial settings, and vehicular networks, ensuring reliable connectivity and uninterrupted services across different networks becomes important. As part of the IoE framework, this research paper focuses on developing and evaluating intelligent handoff decision mechanisms. So, in this work, the optimal handoff decision technique is proposed, which transmits the information in a fast manner. The Fuzzy Analytical Hierarchical Process (FAHP) and Analytical Hierarchical Process (AHP) two-user preference calculation algorithms are used. Then, the user preference is included in the prospect algorithm and the optimal network is selected. After that, the result of the prospect algorithm is compared with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Grey Rational Analysis (GRA), and Multiplicative Exponent Weighting (MEW) algorithms in terms of network scores and the number of handoffs. The advantage of network selection using a prospect algorithm is that it will decrease the failure rate and increase the data transfer and user experience. These advantages deliver seamless connectivity for a diverse range of applications and devices using an IoE infrastructure that is more robust and reliable. The network selected by combining different techniques is the optimal network because it selects the most efficient or effective network.

IEEE AccessVol. 13
Chandigarh University (IN), Vellore Institute of Technology University (IN)
VIT University
Industry, innovation and infrastructure
Openalex Percentile: Top 11%
IoT and Edge/Fog Computing
2.97
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Intelligent Network Selection Mechanisms in the Internet of Everything System — Meenakshi Munjal, Indrasen Singh · IEEE Access (2025) | TGRS Research Map | TGRS