Energy-efficient cluster-based protocol using an adaptive data aggregative window function (A-DAWF) for wireless sensor networks

Kandukuri, Somasekhar; Lebreton, Jean; Murad, Nour; Lorion, Richard; Lan-Sun-Luk, Jean-Daniel

doi:10.1109/wowmom.2016.7523529

Cited by 3 publications

(3 citation statements)

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“…The investigation on the minimization of the exceeded MAD has been covered in the subsequent analysis. In Figure 11a,b, the percentage of accuracy over time is presented based on two different K values and is computed from the obtained MAD in Figure 10 using Equations (21) and (22). This computation is carried out due to the inversely proportional relation between accuracy and the obtained MAD as mentioned earlier.…”

Section: Performance Evaluation Of the Proposed Hdc On Data Reductionmentioning

confidence: 99%

“…The Intel Berkeley Research Lab (IBRL) dataset [57] has been utilized to evaluate the performance of our proposed EDC technique. This dataset was commonly used to evaluate the performance of many existing data clustering techniques in order to gain in-network data reduction in WSNs [5,13,15,22,23,32,64]. The IBRL dataset was collected using a WSN deployed in Intel Laboratory at the University of Berkeley.…”

Section: Datasetmentioning

confidence: 99%

“…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]. However, current data clustering techniques lead to a myriad of problems including error-control for in-network data reduction, time-intensiveness and complex computation.…”

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: Performance Evaluation Of the Proposed Hdc On Data Reductionmentioning

confidence: 99%