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

Alam, M. K.; Aziz, Azrina Abd; Latif, Suhaimi Abd; Awang, Azlan

doi:10.3390/s20041011

Cited by 20 publications

(8 citation statements)

References 53 publications

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“…The authors have developed histogram-based data clustering (HDC) method that groups the homogeneous data at the cluster head into cluster and then selecting the mid value of each cluster to drastically reduce the redundant data in an energy constrained WSNs. Further, Alam et al 50 extended this study in their recent work and developed new error-aware data clustering (EDC) technique which composes of recursive outlier detection and smoothing (RODS) with HDC and verification of RODS (V-RODS) with HDC. The aim of their work was to make data reduction error within an user defined threshold value.…”

Section: Performance Matrixmentioning

confidence: 99%

Data reduction and recovery in wireless communication system: An extensive experimental evaluation using PSK and QAM modulations

Panem

Vetrekar

Gad

2022

Int J Communication

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Summary The evolution of technologies in data communication system has resulted in generation of large data. With the availability of limited bandwidth and to maintain the energy efficiency of the wireless communication system, data reduction plays a significant amount of role in preserving the lifetime of wireless communication system. Considering the significance of this problem in wireless communication system, in this work, we present data reduction and recovery or reconstruction on multivariate data using principal component analysis (PCA). In general, we send the eigen coefficients from the source and use these coefficients at the destination to recover or reconstruct the original data back in wireless communication system. We employ in our work two different digital modulation techniques such as phase‐shift keying (PSK) and quadrature amplitude modulation (QAM) with seven different bit rate to demonstrate our approach. We carry the experiments independently using five different signal ensemble and four different multiple‐input, multiple‐output (MIMO) configuration along with Rayleigh fading. This work presents an extensive simulated benchmark results based on performance matrix such as root mean square error (RMSE) and bit error rate (BER) at different values of signal‐to‐noise ratio (SNR). We further validate our approach by presenting synthesis of our proposed model using Xilinx target device Virtex‐6 LX240T Field Programmable Gate Array (FPGA). The experimental results reported based on simulation, synthesis, and validation of model demonstrate the applicability of our proposed approach in real‐time scenario.

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Section: Performance Matrixmentioning

confidence: 99%

Data reduction and recovery in wireless communication system: An extensive experimental evaluation using PSK and QAM modulations

Panem

Vetrekar

Gad

2022

Int J Communication

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“…Several data reduction algorithms based on clustering techniques have been proposed in the past few years 25–33 . The PFF strategy is used in the sensor and aggregator devices 25 .…”

Section: Related Workmentioning

confidence: 99%

“…This model contributes to reducing the transmitted data and increasing the lifetime of the network. Alam et al 30 proposed an in‐network data lowering approach at the cluster head using an error‐aware data clustering scheme. This approach allows the user to select the suitable model that satisfies their requirements and quality of data.…”

Section: Related Workmentioning

confidence: 99%

Distributed energy‐efficient data reduction approach based on prediction and compression to reduce data transmission in IoT networks

Hussein

Idrees

Couturier

2022

Int J Communication

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Summary In the modern world, it will be necessary to deploy a large number of sensor devices to sense everything around us in order to detect changes, risks, and hazards and to mitigate them. This increasing number of sensor devices represents an essential data provider in the Internet of Things (IoT). The devices generate and transmit a huge amounts of data which requires a large amount of storage and high processing power to come real‐time processing and speed up the network. It also leads to an increase in high energy consumption. Thus, it is important to remove redundant data to reduce the data transmission before sending it to the gateway while maintaining a good level of data quality. In this paper, a distributed energy‐efficient data reduction (DEDaR) approach based on prediction and compression to minimize the data transmission in IoT Networks is proposed. The DEDaR is used in periods to make decision. In each period, the autoregressive prediction (ARP) is used to predict the data of the next period and make a decision on whether to send the data of the current period to the gateway or not. In the case of data transmission, the redundant data are eliminated using an efficient compression approach based on adaptive piecewise constant approximation (APCA), symbolic aggregate approximation (SAX), and finally fixed code dictionary (FCD) based on Huffman encoding. The simulation results based on real‐sensed data show that the proposed DEDaR approach outperforms the other recent methods in terms of data reduction percentage, transmitted data size, energy consumption, and data accuracy.

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“…Afterward, we choose the number of desired data samples from each bin adaptively by eliminating data redundancy. The concept of histogram-based adaptive data sampling is adopted from the work in [ 19 ]. The second module is an unsupervised clustering technique named bivariate K-Means clustering which groups bivariate data into clusters to classify the relationship among different groups of data of a particular dependent variable with respect to the groups of data of another independent variable.…”

Section: Proposed Partitioning-based Adaptive Data Sampling and Clustmentioning

confidence: 99%

Unsupervised bivariate data clustering for damage assessment of carbon fiber composite laminates

et al. 2020

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Damage assessment is a key element in structural health monitoring of various industrial applications to understand well and predict the response of the material. The big uncertainty in carbon fiber composite materials response is because of variability in the initiation and propagation of damage. Developing advanced tools to design with composite materials, methods for characterizing several damage modes during operation are required. While there is a significant amount of work on the analysis of acoustic emission (AE) from different composite materials and many loading cases, this research focuses on applying an unsupervised clustering method for separating AE data into several groups with distinct evolution. In this paper, we develop an adaptive sampling and unsupervised bivariate data clustering techniques to characterize the several damage initiations of a composite structure in different lay-ups. An adaptive sampling technique pre-processes the AE features and eliminates redundant AE data samples. The reduction of unnecessary AE data depends on the requirements of the proposed bivariate data clustering technique. The bivariate data clustering technique groups the AE data (dependent variable) with respect to the mechanical data (independent variable) to assess the damage of the composite structure. Tensile experiments on carbon fiber reinforced composite laminates (CFRP) in different orientations are carried out to collect mechanical and AE data and demonstrate the damage modes. Based on the mechanical stress-strain data, the results show the dominant damage regions in different lay-ups of specimens and the definition of the different states of damage. In addition, the states of the damage are observed using Scanning Electron Microscope (SEM) analysis. Based on the AE data, the results show that the strong linear correlation between AE and mechanical energy, and the classification of various modes of damage in all lay-ups of specimens forming clusters of AE energy with respect to the mechanical energy. Furthermore, the validation of the cluster-based characterization and improvement of the sensitivity of the damage modes classification are observed by the combined knowledge of AE and mechanical energy and time-frequency spectrum analysis.

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

Cited by 20 publications

References 53 publications

Data reduction and recovery in wireless communication system: An extensive experimental evaluation using PSK and QAM modulations

Data reduction and recovery in wireless communication system: An extensive experimental evaluation using PSK and QAM modulations

Distributed energy‐efficient data reduction approach based on prediction and compression to reduce data transmission in IoT networks

Unsupervised bivariate data clustering for damage assessment of carbon fiber composite laminates

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