A cluster prediction model-based data collection for energy efficient wireless sensor network

Diwakaran, S.; Perumal, B.; Devi, K. Vimala

doi:10.1007/s11227-018-2437-z

Cited by 51 publications

(42 citation statements)

References 19 publications

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“…If the error between the predicted and actual readings is less than the predefined threshold error, the predicted reading will not be sent to the CHs or sink and vice-versa. Some of the most recent techniques in [18,19,[35][36][37][38][39][40][41] are utilized to predict data for in-network data reduction in WSNs, which are simpler, easy to implement and provide acceptable accuracy. Since, the model-based techniques predict data based on historical data, these techniques may not be capable of providing correct prediction when the historical data are noisy and highly inconsistent.…”

Section: Data Clustering Techniquesmentioning

confidence: 99%

“…Data clustering is mostly utilized to reduce correlated data for achieving energy conservation in WSNs [6][7][8][9]. In particular, several data clustering techniques have been explored including principal component analysis based aggregation (PCAg) [10], multiple-PCA [11], candid covariance-free incremental PCA (CCIPCA) [5], data aggregative window function (DAWF) [12], projection basis PCA [13], distributed PCA [14], K-means [15], enhanced K-means [9], K-medoids [16], singular value decomposition (SVD) [17], auto-regressive moving average (ARMA) [18], and least mean square (LMS) [19]. Various applications of these techniques are available in existing literature [20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks

Alam

Aziz

Latif

et al. 2020

Sensors

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A wireless sensor network (WSN) deploys hundreds or thousands of nodes that may introduce large-scale data over time. Dealing with such an amount of collected data is a real challenge for energy-constraint sensor nodes. Therefore, numerous research works have been carried out to design efficient data clustering techniques in WSNs to eliminate the amount of redundant data before transmitting them to the sink while preserving their fundamental properties. This paper develops a new error-aware data clustering (EDC) technique at the cluster-heads (CHs) for in-network data reduction. The proposed EDC consists of three adaptive modules that allow users to choose the module that suits their requirements and the quality of the data. The histogram-based data clustering (HDC) module groups temporal correlated data into clusters and eliminates correlated data from each cluster. Recursive outlier detection and smoothing (RODS) with HDC module provides error-aware data clustering, which detects random outliers using temporal correlation of data to maintain data reduction errors within a predefined threshold. Verification of RODS (V-RODS) with HDC module detects not only random outliers but also frequent outliers simultaneously based on both the temporal and spatial correlations of the data. The simulation results show that the proposed EDC is computationally cheap, able to reduce a significant amount of redundant data with minimum error, and provides efficient error-aware data clustering solutions for remote monitoring environmental applications.

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Section: Data Clustering Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks

Alam

Aziz

Latif

et al. 2020

Sensors

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show abstract

“…The efficiency of the recovery method is also demonstrated. Diwakaran et al [19] proposed a data-aware energy conservation prediction model for wireless sensor network to reduce the redundant data transmission. To avoid an increase in the memory usage of the algorithm when the matching algorithm is applied due to a large number of transfer edges, Sun et al [20] proposed an improved method of the concatenation of transfer edges using a range of characters, with several consecutive characters represented by character intervals.…”

Section: Communication Networkmentioning

confidence: 99%

Machine learning on big data for future computing

2019

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IntroductionRecently, the integration of big data and machine learning technologies has emerged as an exciting new paradigm for industry and life. These technologies are fast-growing areas of research that cover the fields of Information and Communication Technologies (ICT) for future computing (FC). It becomes a preferable solution for solving complex problems in different fields, such as artificial intelligence, large-scale analysis systems, natural language processing, pattern recognition, video analysis, and telematics. For instance, AlphaGo, developed by Google DeepMind, changed the current paradigm of machine learning on big data after its steady win in the Google DeepMind Challenge. Machine learning is the art and science of designing computer algorithms from the dataset, and AlphaGo learns and improves performance by relying on massive data associated with huge games played by humans to improve artificial intelligence. Likewise, there are many issues to realize and provide effective services and applications with enormous attentions, and efforts have been focused on machine learning on big data for FC. Contributions to theoretical research presenting advanced technologies and concepts, analysis and reporting of experiences, and experiences related to the implementation and application of theories, as well as tutorials on emerging trends are required for these fields of research. As such, the purpose of this special issue is to provide an overview of the state-ofthe-art technologies and solution guidelines for these areas of research.This special issue covers pure research and applications within novel scopes related to machine learning on big data for future computing. In addition, it deals

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“…Em [Diwakaran et al 2019], usando o fato de que sensores adjacentes mostram alta correlação espacial e temporal,é desenvolvido um modelo preditivo para conservação de energia centrado nos dados já coletados, para reduzir a transmissão de dados redundantes. Agrupamentos são formados e modelos de regressão são contruídos para representar o agrupamento.…”

Section: Trabalhos Relacionadosunclassified

Continuous Monitoring in Wireless Sensor Networks: A Fuzzy-Probabilistic Approach

Nunes¹,

Maia²

2019

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2019)

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This work presents and evaluates a fuzzy-probabilistic strategy to save energy in Wireless Sensor Networks (WSNs). The energy savings are obtained with the sensor nodes, no longer sensing and transmitting measurements. In this simple strategy, in each epoch each sensor node transmits its measurement with probability p, and does not transmit with probability (1 􀀀 p), does not correlate with that of any other sensor node. The task at the sink node, which is to estimate the sensor field at non-sensed points, is solved using fuzzy inference to impute the non-transmitted data followed by regression or interpolation of the sensed scalar field. In this, Nadaraya-Watson regression, regression with Fuzzy Inference and Radial Base Functions Interpolation are compared. The compromise curve between the value of p and the accuracy of the sensor field estimation measured by root mean square error (RMSE) is investigated. When compared to a published linear prediction strategy of the literature, the results show a small loss of performance versus the great simplification of the procedure in the sensor node, making it advantageous in applications that require extremely simple network nodes.

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A cluster prediction model-based data collection for energy efficient wireless sensor network

Cited by 51 publications

References 19 publications

Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks

Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks

Machine learning on big data for future computing

Continuous Monitoring in Wireless Sensor Networks: A Fuzzy-Probabilistic Approach

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