Multi-Domain Feature Extraction for Improved Clustering of Smart Meter Data

Shamim, Gulezar; Rihan, Mohd

doi:10.1007/s40866-020-00080-w

Cited by 18 publications

(8 citation statements)

References 19 publications

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“…The deficiency of energy sources in BANs can also be improved by integrating energy harvesting solutions, that transform body scale clean energies into electrical energy [31,32], to supply node batteries.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Analysing the Impact of Mutual Interference in Body Area Networks

Boumaiz

Ghazi

Fattah

et al. 2021

Technol Econ Smart Grids Sustain Energy

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The development of electronics and short-range communications gave rise to smart healthcare systems known as body area networks (BAN), to record the vital signs of patients for diagnosis. However, these systems may be located in dense areas, where several networks operate next to each other. Hence, interference occurs, which can adversely affect their proper functioning. Before proceeding with interference reduction or avoidance techniques, it is very important to first analyze this problem under different contexts and scenarios, to better understand it. The aim of this paper is to study the impact of inter-ban interference in body area networks on overall energy consumption, under two different contexts: when the inter-ban distance is varying, and when the amount of nodes in surrounding BANs increases. For this purpose, the two following scenarios are considered: In the first scenario, the distance between two adjacent BANs is decreasing from 6 to 2 m, and the overall energy consumption of the BAN of interest is evaluated. On the other hand, the second scenario highlights the impact of the amount of nodes in an interfering BAN on the overall energy consumption. Simulations were conducted using Castalia 3.3 software. The obtained results showed that when two BANs are located next to each other, if the distance between them becomes smaller, or the amount of nodes in the interfering BAN exceeds that of the reference one, the energy consumption of the BAN of interest will increase, due to the highly experienced interference levels, especially at high data rates.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Analysing the Impact of Mutual Interference in Body Area Networks

Boumaiz

Ghazi

Fattah

et al. 2021

Technol Econ Smart Grids Sustain Energy

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“…More specifically, SC indicates whether the data samples were correctly clustered and separated (clusters coherence and goodness). The SC for data (i) is computed as [20]:…”

Section: Discussionmentioning

confidence: 99%

An Evolving Fuzzy Model to Determine an Optimal Number of Data Stream Clusters

Al-Khamees¹,

Al-A’araji²,

Al-Shamery³

2022

IJFIS

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Data streams are a modern type of data that differ from traditional data in various characteristics: their indefinite size, high access, and concept drift due to their origin in non-stationary environments. Data stream clustering aims to split these data samples into significant clusters, depending on their similarity. The main drawback of data stream clustering algorithms is the large number of clusters they produce. Therefore, determining an optimal number of clusters is an important challenge for these algorithms. In practice, evolving models can change their general structure by implementing different mechanisms. This paper presents a fuzzy model that mainly consists of an evolving Cauchy clustering algorithm which is updated through a specific membership function and determines the optimal number of clusters by implementing two evolving mechanisms: adding and splitting clusters. The proposed model was tested on six different streaming datasets, namely, power supply, sensor, HuGaDB, UCI-HAR, Luxembourg, and keystrokes. The results demonstrated that the efficiency of the proposed model in producing an optimal number of clusters for each dataset outperforms that of previous models.

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“…Clustering analysis and technology network analysis were used to identify topics in nuclear waste management over time. Shamim and Rihan ( 2020 ) compared using k-means clustering and k-means clustering with feature extraction in smart metering electricity. Results of their experiments showed that clustering using features from raw data obtained better performance than direct raw data.…”

Section: Applications Of Clusteringmentioning

confidence: 99%

Data clustering: application and trends

Oyewole

Thopil

2022

Artif Intell Rev

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Clustering has primarily been used as an analytical technique to group unlabeled data for extracting meaningful information. The fact that no clustering algorithm can solve all clustering problems has resulted in the development of several clustering algorithms with diverse applications. We review data clustering, intending to underscore recent applications in selected industrial sectors and other notable concepts. In this paper, we begin by highlighting clustering components and discussing classification terminologies. Furthermore, specific, and general applications of clustering are discussed. Notable concepts on clustering algorithms, emerging variants, measures of similarities/dissimilarities, issues surrounding clustering optimization, validation and data types are outlined. Suggestions are made to emphasize the continued interest in clustering techniques both by scholars and Industry practitioners. Key findings in this review show the size of data as a classification criterion and as data sizes for clustering become larger and varied, the determination of the optimal number of clusters will require new feature extracting methods, validation indices and clustering techniques. In addition, clustering techniques have found growing use in key industry sectors linked to the sustainable development goals such as manufacturing, transportation and logistics, energy, and healthcare, where the use of clustering is more integrated with other analytical techniques than a stand-alone clustering technique.

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Multi-Domain Feature Extraction for Improved Clustering of Smart Meter Data

Cited by 18 publications

References 19 publications

Analysing the Impact of Mutual Interference in Body Area Networks

Analysing the Impact of Mutual Interference in Body Area Networks

An Evolving Fuzzy Model to Determine an Optimal Number of Data Stream Clusters

Data clustering: application and trends

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