Energy Efficiency and Quality of Data Reconstruction Through Data-Coupled Clustering for Self-Organized Large-Scale WSNs

Chidean, Mihaela I.; Morgado, Eduardo; Sanromán‐Junquera, Margarita; Ramiro, Josefa; Ramos, J. I.; Caamaño, Antonio J.

doi:10.1109/jsen.2016.2551466

Cited by 35 publications

(8 citation statements)

References 33 publications

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“…In addition, there are some strategies combining with the mathematics method to improve the Energy Efficiency. For instance, by means of decoupling the whole network into clusters with the help of SCoReI [245] framework which is integrated with the Principal Component Analysis (PCA), the data collection is conducted in CS data acquisition mode in each cluster [246] . An Unbalanced Expander Graph Based Compressed Data Gathering algorithm (UEGCD) which theoretically indicates that a proper sparse measurement matrix is equivalent to an adjacency matrix of an unbalanced expansion graph was proposed [247] .…”

Section: Acm T S Nmentioning

confidence: 99%

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

Lin

Wang

Min

et al. 2020

ACM Trans. Sen. Netw.

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A comprehensive analysis on the energy-efficient strategy in static Wireless Sensor Networks (WSNs) which are not equipped with any energy harvesting modules was conducted in this paper. Firstly, a novel generic mathematical definition of Energy Efficiency was proposed, which took the acquisition rate of valid data, the total energy consumption, and the network lifetime of WSNs into consideration simultaneously. To the best of our knowledge, it is for the first time that the Energy Efficiency of WSNs is mathematically defined. The energy consumption characteristics of each individual sensor node and the whole network were expounded at length. Accordingly, the concepts concerning the Energy Efficiency, namely the Energy-efficient Means, the Energy-efficient Tier, and the Energy-efficient Perspective, were proposed. Subsequently, the relevant energy-efficient strategies proposed from 2002 to 2019 were tracked and reviewed. Specifically, they were classified into five categories, i.e., the Energy-Efficient Media Access Control (EEMAC) protocol, the Mobile Node Assistance Scheme (MNAS), the Energy-Efficient Clustering Scheme (EECS), the Energy-Efficient Routing Scheme (EERS), and the Compress Sensing based Scheme (CSS) respectively. A detailed elaboration on both the basic principle and the evolution of them was made. Finally, further analysis on the categories was made and the related conclusion was drawn. To be specific, the interdependence among them, the relationships between each of them and the Energy-efficient Means, the Energy-efficient Tier as well as and the Energy-efficient Perspective were analyzed in detail. Besides, the specific applicable scenarios for each of them and the relevant statistics analysis were detailed. The proportion and the number of citations of each category were illustrated by the statistical chart. In addition, the existing opportunities and challenges facing WSNs in the context of the new computing paradigm, and the feasible direction concerning the energy efficiency in the future were pointed out.

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Section: Acm T S Nmentioning

confidence: 99%

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

Lin

Wang

Min

et al. 2020

ACM Trans. Sen. Netw.

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“…Generally, which are based on clustering, hierarchical clustering, density-based clustering, grid-based clustering and model-based clustering. 4…”

Section: Classification Of Clustering Algorithmmentioning

confidence: 99%

“…Formulas (3) to (7) are used to adjust the weights for all nodes in the winning neighborhood N j *(t). 4 Wherein, the function (t, N) represents the distance between the jth neuron in the neighborhood and the winning neuron…”

Section: Clustering Steps For Marine Big Data Based On Som Algorithmmentioning

confidence: 99%

Research on the clustering algorithm of ocean big data based on self‐organizing neural network

Yang

Han

2020

Computational Intelligence

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In the construction of a smart marine, marine big data mining has a significant impact on the growing maritime industry in the Beibu Gulf. Clustering is the key technology of marine big data mining, but the conventional clustering algorithm cannot achieve the efficient clustering of marine data. According to the characteristics of marine big data, a marine big data clustering scheme based on self-organizing neural network (SOM) algorithm is proposed. First, the working principle of SOM algorithm is analyzed, and the algorithm's two-dimensional network model, similarity model and competitive learning model are focused. Secondly, combining with the working principle of algorithm, the marine big data clustering process and algorithm achievement based on SOM algorithm are developed; finally, experiments show that all vectors in marine big data clustering are stable, and the neurons in the output layer of clustering result have obvious consistency with the data itself, which shows the effectiveness of SOM algorithm in marine big data clustering.

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“…As the cluster-head node has knowledge of all the cluster members, a TDMA schedule is created that tells the time at which every node needs to transmit the data. Furthermore, by utilizing TDMA schedule for transmitting the data by circumventing the intra-cluster collisions [10].…”

Section: Leach (Low Energy Adaptive Clusteringmentioning

confidence: 99%

“…Enabled less data transmission as compare to random Clustering method formerly used for HCDG Achieved PRD (Percentage Root-mean squared Distortion) of less than 5% Mihaela I. Chidean [10] In-network processing methods and clustering algorithms were jointly used Controlled number of transmissions with high data qualityby usage of aggregation function and an energy efficient network partition.…”

Section: Related Workmentioning

confidence: 99%

An Analytical Review on Wireless Sensor Clustering for the Prolong Network Life Time

Chopra¹

2017

ijarcs

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Wireless sensor network is the group of small units called nodes which has gained a lot of light due to its variant applications in various sectors. The tiny mechanical devices referred as sensor nodes are deployed in the very rough area and they are left unattended to uninterruptedly report about the environment parameters .Nodes in rough areas rapidly diminish energy resources, which lead to disruption in network services. This is the main problem of data collection scenarios in which CH's have a huge burden of collecting and relaying statistics. The relay load on heads particularly intensifies as the sink distance decreases. For balancing the load of traffic and the energy consumption in WSN, the CH role must be switched among all the network nodes and the sizes of cluster must be carefully determined at every portion of the network. These devices have energy which is consumed during a long time at processing, gathering, aggregation and transmission of data. So, clustering approach is applied to reduce the consumption of data and give prolonged life to the network. This paper surveyed about the clustering mechanism, its protocols and various issues regarding it. The various parameters of the clustering in WSN are discussed.

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Energy Efficiency and Quality of Data Reconstruction Through Data-Coupled Clustering for Self-Organized Large-Scale WSNs

Cited by 35 publications

References 33 publications

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

Research on the clustering algorithm of ocean big data based on self‐organizing neural network

An Analytical Review on Wireless Sensor Clustering for the Prolong Network Life Time

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