Abstract
This paper investigates the effect of data reduction methods in the performance of Wireless Sensor Network (WSN) using a variety of real-time datasets. The simulation tests are carried out in MATLAB for several methods of reducing the quantity of sent data. These approaches are Data Reduction based - Neural Network Fitting (NNF), Neural Network Time Series (NNTS), Linear Regression with Multiple Variables (LRMV), Data Reduction based – “An Efficient Data Collection and Dissemination (EDCD2)” and Data Reduction based – Fast Independent Component Analysis (FICA). The selected algorithms NNF, NNST, EDCD2, LRMV, and FICA are evaluated using real-time datasets. The performance indicators included are energy consumption, data accuracy, and data reduction percentage. The research results show that the selected algorithm helps to reduce the amount of data transferred and consumed energy, but each algorithm performs differently depending on the dataset used.
Highlights
In this paper, Wireless Sensor Network (WSN) is a network that collects data from spatially isolated sensors
Spatial-temporal correlation is used in dual prediction (DP) and data compression (DC) techniques to reduce the number of transmissions to save energy and bandwidth
The EDCD2 algorithm has the best accuracy compared to the other algorithms Fast Independent Component Analysis (FICA), Neural Network Fitting (NNF), Neural Network Time Series (NNTS), and Linear Regression with Multiple Variables (LRMV)
Summary
Wireless Sensor Network (WSN) is a network that collects data from spatially isolated sensors. In [9], the authors have evaluated the performance of several methods based on computational intelligence to decrease the amount of the payload of every packets sent from the sensor node to the base station. These approaches are data reduction based on “artificial neural networks (DR-ANN)”; independent component analysis (DR-ICA) and deep learning regression methods called DR-GDMLR”. It uses “Candid Covariance-free Incremental PCA (CCIPCA)” with an adaptive threshold and to reach a high reduction ratio the number of Principal Components (PCs) assigned to “1” Another method to decrease the amount of payload sensed data is named MLR-B, which it using multiple linear regression (MLR) model.
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