Abstract
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.
Highlights
Wireless sensor networks (WSNs) have been utilized for various applications such as facility monitoring, environmental monitoring, and military surveillance
Compute Histogram based on the limits of each cluster interval; Discretize the data of Xi into different bins SCk based on the given intervals; Find the empty SCk if any and replace them by median Mi of Xi ; Find the centers of each SCk based on μk or Mk ; Calculate the mean absolute deviation MADk of each SCk ; end Aggregate the discretized data stream X, where X < Xi ; 4.1.1
The K-means and K-medoids have been implemented based on the following basic steps
Summary
Wireless sensor networks (WSNs) have been utilized for various applications such as facility monitoring, environmental monitoring, and military surveillance. The second module is recursive outlier detection and smoothing (RODS) with HDC module, which provides the error-bound guaranteed data clustering This module is capable of detecting random outliers (sudden changes and those that lie outside of the regular data pattern) based on the temporal correlation and replaces them by the normal data. With HDC module, which detects the random outliers and frequent outliers (frequent changes and those that lie outside of the regular data pattern of the neighbour nodes) simultaneously This module considers both the temporal and spatial correlations to detect outliers and replace the outliers with the normal data in order to provide more robust error-aware data clustering.
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