A Comprehensive Performance Evaluation of Proactive, Reactive and Hybrid Routing in Wireless Sensor Network for Real Time Monitoring System

Mukti, Fransiska Sisilia; Lorenzo, Juan Enrico; Zuhdianto, Roid; Junikhah, Allin; Soetedjo, Aryuanto; Krismanto, Awan Uji

doi:10.1109/ic2se52832.2021.9791992

Cited by 5 publications

(5 citation statements)

References 22 publications

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“…The k-means algorithm is effortless to enforce and execute, reasonably quick, flexible, and widely used in practice. K-Means has historically been one of the most considerable data mining algorithms [17]. K-Means is a non-hierarchical approach to statistics clustering that attempts to divide current data into one or more clusters or organizations [18].…”

Section: K-means Classifiermentioning

confidence: 99%

Decision Support System to Determine Feasibility of Rice Aid Recipient Group Based on K-Means

Firdaus,

Putra,

Pamungkas

et al. 2024

IIAI Letters on Informatics and Interdisciplinary Research

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Poverty is a crucial social problem in many countries. High economic inequality can lead to social instability. The Indonesian government seeks to alleviate poverty through the Rice for the Poor program. However, aid distribution is often off-target. This study proposes a decision support system based on the K-Means method to determine the group of poor rice recipients. The system was developed by following the user's functional requirements, in this case, the Banjarmasin City Social Service. This study involved 50 respondents as a test sample to evaluate the performance of the proposed system. In addition, this study conducted accuracy tests to assess the accuracy of system processing results. The test results show that the system's accuracy in distinguishing qualified and non-qualified candidates further strengthens its potential use in facilitating the allocation of social assistance to those in need.

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Section: K-means Classifiermentioning

confidence: 99%

Decision Support System to Determine Feasibility of Rice Aid Recipient Group Based on K-Means

Firdaus,

Putra,

Pamungkas

et al. 2024

IIAI Letters on Informatics and Interdisciplinary Research

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“…In reactive nature, data is dispatched when sensed data has reached a predefined threshold. The term hybrid refers to the combination of proactive and reactive nature according to the requirements of specific applications [38].…”

Section: Uneven Clustering Characteristicsmentioning

confidence: 99%

An In-depth Analysis of Uneven Clustering Techniques in Wireless Sensor Networks

Zhang¹

2023

IJACSA

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The low-cost and convenient feature of Wireless Sensor Networks (WSNs) has made them popular in many sectors over the last decade. The WSNs are now widely used as a result of recent advancements in low-power communication and being energy-efficient. The WSNs typically use batteries to power sensor nodes. The finite stored energy in batteries and the hassle of battery replacement have led to a critical focus on energy efficiency for WSNs. Clustering and data aggregation are the most efficient methods to address the energy concerns of WSNs. This paper comprehensively reviews several uneven clustering methods and compares the various uneven clustering algorithms. The methods are described in terms of their goals, attributes, categories, advantages and disadvantages. Probabilistic clustering is used when there is a need of simplicity and speed. As a result, this study compared all these types of protocols based on their clustering properties, CHs properties, and on the type of clustering process; and current research gap effective techniques are also addressed.

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“…The K-means method can group similar data into data with the same label [34]. The calculation of the K-means method can be shown as [31], [38].…”

Section: K-means Clusteringmentioning

confidence: 99%

“…Rahman and Selvaperumal [30] combined the K-means method with the neuro fuzzy method for brain segmentation. Mukti et al [31] said that the performance of the K-means method was better than other clustering methods. Kim et al [32] also noted that the K-means method efficiently labeled clusters.…”

mentioning

confidence: 99%

Success factors for citizen-based government decision making using K-means fuzzy learning vector quantization

Utomo,

Adi,

Nurhayati

2023

IJEECS

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<span>The Indonesian government often needs assistance in making citizen-based decisions, for example selecting work program plans. Residents have their criteria in the forum to choose a work program plan. This study proposes the K-means fuzzy learning vector quantization (FLVQ) methods to select citizen-based government decision-making criteria. The K-means FLVQ method has never been used to assist government decision-making. However, citizen criteria can be a success factor for government decision-making. The selection of criteria begins with data collection from forum participants. The results of data collection get 11 criteria. Then, the K-means FLVQ method carries out labeling and classification. The addition of the K-means process in the selection criteria can provide optimal results. Citizens can give assessment criteria freely. Then the assessment of citizens is classified by FLVQ. The classification results obtained seven criteria, namely: i) urgency, ii) sustainability, iii) priority, vi) usability, v) prosperity, vi) comfortability, and vii) artistic. Governments can use these criteria to make decisions about planned work programs. The criteria selection algorithm was also evaluated using the confusion matrix method with an accuracy of 88% and an error of 12%.</span>

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A Comprehensive Performance Evaluation of Proactive, Reactive and Hybrid Routing in Wireless Sensor Network for Real Time Monitoring System

Cited by 5 publications

References 22 publications

Decision Support System to Determine Feasibility of Rice Aid Recipient Group Based on K-Means

Decision Support System to Determine Feasibility of Rice Aid Recipient Group Based on K-Means

An In-depth Analysis of Uneven Clustering Techniques in Wireless Sensor Networks

Success factors for citizen-based government decision making using K-means fuzzy learning vector quantization

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