A new clustering‐based optimised energy approach for fog‐enabled IoT networks

Essalhi, Salah Eddine; Fenni, Mohammed Raiss El; Chafnaji, Houda

doi:10.1049/ntw2.12082

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…A three-dimensional network can have a different structure, in particular, it can be a regular structure in the form of a different kind of lattice or an arbitrary structure. However, in most cases, the structure of such a network is related to the structure of the environment in which it is created [15][16][17].…”

Section: Fig 4 Examples Of High-density Network Structure Modelingmentioning

confidence: 99%

Study of the Internet of Things Network Construction Tasks

Mozhaiev,

Kuchuk,

Shtepa

et al. 2024

CNCS

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The emergence and development of the Internet of Things stimulates the development of telecommunications and computing technologies. The current state of this process and its prospects allow the inclusion of a large number of devices connected to communication networks. This leads to the need to select an adequate model and methodological apparatus that allows working with such a quantity. The article discusses the concept of the IoT, which determines that any devices or things can now interact with each other at any time at any point in space.

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Section: Fig 4 Examples Of High-density Network Structure Modelingmentioning

confidence: 99%

Study of the Internet of Things Network Construction Tasks

Mozhaiev,

Kuchuk,

Shtepa

et al. 2024

CNCS

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“…The key difference between the fog concept and the cloud concept is the ability to dynamically transfer the computing load from the cloud to the periphery of the network infrastructure using fog layer devices, as well as partial placement of the load in the fog layer. This allows you to significantly reduce the load on the communication infrastructure of the network [16].…”

Section: Introductionmentioning

confidence: 99%

Fog Computing Technology in Distributed Systems

Kolesnyk,

Mezhenskyi,

Davykoza

et al. 2024

CNCS

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Topicality. The concept of fog computing is an evolutionary stage in the development of the cloud concept. It occupies a leading position among the general trends in the development of information technology. The emergence of this concept is closely related to the origin and development of the concept of the Internet of Things. The results. The subject area was analyzed. It includes an analysis of current trends in the field of organizing distributed computing, an analysis of the use of population algorithms and ontology models for solving optimization problems in distributed systems, an analysis of models, methods and algorithms for solving the problem of transferring the computational load in distributed systems implemented on the basis of fog computing. Conclusion. It has been revealed that the concept of fog computing makes it possible to solve most of the problems associated with the load on the communication infrastructure and the latency of information exchange. But they do not resolve issues related to the high dynamism of the foggy environment and the concomitant decrease in the efficiency of the distributed system.

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ClusFC‐IoT: A clustering‐based approach for data reduction in fog‐cloud‐enabled IoT

Hemmati,

Rahmani

2024

Concurrency and Computation

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SummaryThe Internet of Things (IoT) is an ever‐expanding network technology that connects diverse objects and devices, generating vast amounts of heterogeneous data at the network edge. These vast volumes of data present significant challenges in data management, transmission, and storage. In fog‐cloud‐enabled IoT, where data are processed at the edge (fog) and in the cloud, efficient data reduction strategies become imperative. One such method is clustering, which groups similar data points together to reduce redundancy and facilitate more efficient data management. In this paper, we introduce ClusFC‐IoT, a novel two‐phase clustering‐based approach designed to optimize the management of IoT‐generated data. In the first phase, which is performed in the fog layer, we used the K‐means clustering algorithm to group the received data from the IoT layer based on similarity. This initial clustering creates distinct clusters, with a central data point representing each cluster. Incoming data from the IoT side is assigned to these existing clusters if they have similar characteristics, which reduces data redundancy and transfers to the cloud layer. In a second phase performed in the cloud layer, we performed additional K‐means clustering on the data obtained from the fog layer. In this secondary clustering phase, we stabilized the similarities between the clusters created in the fog layer further optimized the data display, and reduced the redundancy. To verify the effectiveness of ClusFC‐IoT, we implemented it using four different IoT data sets in Python 3. The implementation results show a reduction in data transmission compared to other methods, which makes ClusFC‐IoT very suitable for resource‐constrained IoT environments.

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A new clustering‐based optimised energy approach for fog‐enabled IoT networks

Cited by 4 publications

References 42 publications

Study of the Internet of Things Network Construction Tasks

Study of the Internet of Things Network Construction Tasks

Fog Computing Technology in Distributed Systems

ClusFC‐IoT: A clustering‐based approach for data reduction in fog‐cloud‐enabled IoT

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