Wireless Sensor Network Lifetime Extension via K-Medoids and MCDM Techniques in Uncertain Environment

Sen, Supriyan; Sahoo, Laxminarayan; Tiwary, Kalishankar; Šimić, Vladimir; Senapati, Tapan

doi:10.3390/app13053196

Cited by 7 publications

(3 citation statements)

References 37 publications

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“…The computation of entropy weight involves analyzing the choice matrix. One can consult Sen et al [30] for further information regarding the entropy weighted technique.…”

Section: Entropy Weighted Methodsmentioning

confidence: 99%

Lifetime Extension of Wireless Sensor Networks by Perceptive Selection of Cluster Head Using K-Means and Einstein Weighted Averaging Aggregation Operator under Uncertainty

Sen,

Sahoo,

Ghosh

2024

JII

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“…The computation of entropy weight involves analyzing the choice matrix. One can consult Sen et al [30] for further information regarding the entropy weighted technique.…”

Section: Entropy Weighted Methodsmentioning

confidence: 99%

Lifetime Extension of Wireless Sensor Networks by Perceptive Selection of Cluster Head Using K-Means and Einstein Weighted Averaging Aggregation Operator under Uncertainty

Sen,

Sahoo,

Ghosh

2024

JII

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“…The more clusters there are, the smaller the SSE value will be. When the SSE value is close to 0, this indicates that the amount of random error in the model is so small that the model can be used to predict future data [7] [32]. The SSE formula is like the following equation :…”

Section: Sum Of Squared Errors (Sse)mentioning

confidence: 99%

Integration K-Means clustering and AHP for recommendations batik MSMEs

Kustiyahningsih,

Khozaimi,

Khotimah

et al. 2024

E3S Web Conf.

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Batik MSME industry is a creative industry sector in Indonesia which contributes quite a lot to Gross Domestic Product. Batik products have been recognized worldwide as one of creative products from Indonesia by UNESCO which confirmed batik as an intangible Cultural Heritage of Humanity. There are around 250 batik makers in Madura Indonesia. The problem is that the large number of batik craftsmen makes it difficult for cooperatives to determine MSME priorities and the Cooperative Work Program. Some batik indicator data is not all filled and there is still categorical and numerical data. The aim is to group batik based on the number of workers, number of products, age, education, business license, turnover, and number of batik motifs. The method used is data preprocessing using Min-Max normalization to convert categorical data into numerical and averages to overcome imputation of empty data. The data grouping method uses K-Means Clustering. AHP is used to determine indicators that have most influence on clustering and ranking of Batik MSMEs. The research contribution is integration of K-Means with AHP and preprocessing techniques. The most optimal cluster evaluation technique uses SSE. Based on the test results, the optimal cluster is K=3, with an SSE value = 0.287, Cluster 1 (Low) = 28%, Cluster 2 (medium) = 33%, and cluster 3 (High) = 39%. The results of recommendations for four highest weighting criteria using AHP are number of customers 24%, employee training 18.8%, product branding 17%, market place 16.3%.

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“…MCDM and TOPSIS [32] are highly useful in WSN applications. MCDM [33] enables informed decision-making by assessing multiple criteria, such as energy efficiency, coverage, and cost [34]. Meanwhile, TOPSIS ranks alternative WSN solutions based on their similarity to an ideal solution.…”

Section: Background and Related Workmentioning

confidence: 99%

Entropy Weighted TOPSIS Based Cluster Head Selection in Wireless Sensor Networks under Uncertainty

Sen,

Sahoo,

Tiwary

et al. 2023

Telecom

Self Cite

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In recent decades, wireless sensor networks (WSNs) have become a popular ambient sensing and model-based solution for various applications. WSNs are now achievable due to the developments of micro electro mechanical and semiconductors logic circuits with rising computational power and wireless communication technology. The most difficult issues concerning WSNs are related to their energy consumption. Since communication typically requires a significant amount of energy, there are some techniques/ways to reduce energy consumption during the operation of the sensor’s communication systems. The topology control technique is one such effective method for reducing WSNs’ energy usage. A cluster head (CH) is usually selected using a topology control technique known as clustering to control the entire network. A single factor is inadequate for CH selection. Additionally, with the traditional clustering method, each round exhibits a new batch of head nodes. As a result, when using conventional techniques, nodes decay faster and require more energy. Furthermore, the inceptive energy of nodes, the range between sensor nodes and base stations, the size of data packets, voltage and transmission energy measurements, and other factors linked to sensor nodes are also completely unexpected due to irregular or hazardous natural circumstances. Here, unpredictability represented by Triangular Fuzzy Numbers (TFNs). The associated parameters of nodes were converted into crisp ones via the defuzzification of fuzzy numbers. The fuzzy number has been defuzzified using the well-known signed distance approach. Here, we have employed a multi-criteria decision-making (MCDM) approach to choosing the CHs depending on a bunch of characteristics of each node (i) residual energy, (ii) the number of neighbors, (iii) distance from the sink, (iv) average distance of cluster node, (v) distance ratio, and (vi) reliability. This study used the entropy-weighted Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) approach to select the CH in WSNs. For experiments, we have used the NSG2.1 simulator, and based on six characteristics comprising residual energy, number of neighbor nodes, distance from the sink or base station (BS), average distance of cluster nodes, distance ratio, and reliability, optimal CHs have been selected. Finally, experimental results have been presented and compared graphically with the existing literature. A statistical hypothesis test has also been conducted to verify the results that have been provided.

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Wireless Sensor Network Lifetime Extension via K-Medoids and MCDM Techniques in Uncertain Environment

Cited by 7 publications

References 37 publications

Lifetime Extension of Wireless Sensor Networks by Perceptive Selection of Cluster Head Using K-Means and Einstein Weighted Averaging Aggregation Operator under Uncertainty

Lifetime Extension of Wireless Sensor Networks by Perceptive Selection of Cluster Head Using K-Means and Einstein Weighted Averaging Aggregation Operator under Uncertainty

Integration K-Means clustering and AHP for recommendations batik MSMEs

Entropy Weighted TOPSIS Based Cluster Head Selection in Wireless Sensor Networks under Uncertainty

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