K-Means Based on Resource Clustering for Smart Farming Problem in Fog Computing

Mtshali, Mxolisi; Dlamini, Sabelo; Adigun, Matthew O.; Mudali, Pragasen

doi:10.1109/africon46755.2019.9133992

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…The proposed algorithm is compared with Symbiotic Organism Search (SOS) (Hosseinioun et al, 2022) and k-means (Mtshali et al, 2019) algorithm. Symbiotic Organism Search (SOS) is a metaheuristic algorithm inspired by the symbiotic relationships between organisms in an ecosystem.…”

Section: Literature Surveymentioning

confidence: 99%

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Sheikh,

Enam,

Qureshi

2023

Front. Comput. Sci.

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Fog Computing has emerged as a pivotal technology for enabling low-latency, context-aware, and efficient computing at the edge of the network. Effective task scheduling plays a vital role in optimizing the performance of fog computing systems. Traditional task scheduling algorithms, primarily designed for centralized cloud environments, often fail to cater to the dynamic, heterogeneous, and resource-constrained nature of Fog nodes. To overcome these limitations, we introduce a sophisticated machine learning-driven methodology that adapts task allocation to the ever-changing Fog environment's conditions. Our approach amalgamates K-Means clustering algorithm enhanced with fuzzy logic, a robust unsupervised learning technique, to efficiently group Fog nodes based on their resource characteristics and workload patterns. The proposed method combines the clustering capabilities of K-means with the adaptability of fuzzy logic to dynamically allocate tasks to fog nodes. By leveraging machine learning techniques, we demonstrate how tasks can be intelligently allocated to fog nodes, resulting in reducing execution time, response time and network usage. Through extensive experiments, we showcase the effectiveness and adaptability of our proposed approach in dynamic fog environments. Clustering proves to be a time-effective method for identifying groups of jobs per virtual machine (VM) efficiently. To model and evaluate our proposed approach, we have utilized iFogSim. The simulation results affirm the effectiveness of our scheduling technique, showcasing significant enhancements in execution time reduction, minimized network utilization, and improved response time when compared to existing machine learning and non-machine learning based scheduling methods within the iFogSim framework.

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Section: Literature Surveymentioning

confidence: 99%

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Sheikh,

Enam,

Qureshi

2023

Front. Comput. Sci.

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“…Another study used the K-means to cluster energy consumption for agriculture to control water due to minimal rain and boost food security in South Africa [12]. Moreover, using spatial parameters, Janrao, Mishra, and Bharadi [13] clustered sugarcane zones to plant management and fertilization.…”

Section: Introductionmentioning

confidence: 99%

K-Means Algorithm Analysis for Election Cluster Prediction

Wahyuni¹,

Khanom

Astuti³

2023

JOIV : Int. J. Inform. Visualization

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The general election is a democratic process that is carried out in every country whose system of government is presidential, including Indonesia, which conducts it every five years. In fact, some people abstain, leading to budget wasting and missing target. Thus, it is very important to identify clusters of general election districts and map the number of voters to map the budget for the upcoming election. This process needs prediction to help reduce budgeting risk as an early warning. Based on the latest election data taken from Margokaton, Yogyakarta, Indonesia, many people voted in 2021, but the number of abstainers is high. In this case, cluster prediction is important to identify the election participants in each area. The K-Means algorithm could also predict abstainer areas in election activities to facilitate early mitigation in drafting election budgeting. Therefore, this study aimed to identify the pattern of voters in the election using the K-means algorithm. The data parameters comprised the list of voters, Unused ballot papers, and the sum of abstainers. This study is important because it contributes to reducing the election budget of each area. The data obtained from the Indonesia Ministry of Internal Affairs official website in 2021 were processed using the RapidMiner tool. The results showed more than 11% of the non-voters in cluster 1, 16% in Cluster 2, and 8% in cluster 3. The evaluation of clusters value is 2.04, indicating that the clustering using K-means is suitable, as shown by the DBI value close to 0. The results indicate that testing the cluster optimization of the K-Means algorithm using DBI is highly recommended. Based on this prediction result, the government needs special attention to clusters with many abstainers to decrease the number of abstainers and prevent overbudgeting. These results indicate the need to review the election participant data in 2024. Furthermore, there is a need for continuous socialization and education about election activities to reduce the number of abstainers and prevent overbudgeting.

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