Data Reduction Algorithms based on Computational Intelligence for Wireless Sensor Networks Applications

Abdullah, Jiwa; Hussien, Mostafa; Alduais, Nayef Abdulwahab Mohammed; Husni, Muhammed Ihsan; Jamil, Ansar

doi:10.1109/iscaie.2019.8743665

Cited by 9 publications

(7 citation statements)

References 12 publications

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“…This comparison is based on the prediction accuracy and the amount of sent data. We used a MATLAB simulator with a meteorological dataset for the 8 th , 9 th and the 10 th of April 2020 from two sensor nodes deployed in Lille city, France (Lille airport and Lille city centre) from Weather Underground website which gathers data from a sensor network of different weather stations deployed around the globe 1 . In the remaining of the simulations we consider S1 as the airport node and S2 as the city centre node.…”

Section: Resultsmentioning

confidence: 99%

“…Other machine learning based methods have been proposed in the literature for data prediction for data reduction. A lot of approaches were interested in data correlation for this purpose, mainly using the Pearson correlation technique and its derivatives [4], [10], [2], the Auto Regression model [16], [5] and the convolutional long short-term memory (LSTM) networks techniques [17], [1]. The authors in [4] proposed a methodology to analyse data streams, based on spatio-temporal correlations using Pearson correlation.…”

Section: Background and Related Workmentioning

confidence: 99%

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On-Node Correlation Based Data Reduction in WSN for Smart Agriculture

Salim

Mitton

2020

2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)

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Nowadays, climate change is one of the numerous factors affecting the agricultural sector. Optimizing the usage of natural resources is one of the challenges this sector faces. For this reason, it could be necessary to locally monitor weather data and soil conditions to make faster and better decisions locally adapted to the crop. Wireless sensor networks (WSNs) can serve as a monitoring system for these types of parameters. However, in WSNs, sensor nodes suffer from limited energy resources. The process of sending a large amount of data from the nodes to the sink results in high energy consumption at the sensor node and significant use of network bandwidth, which reduces the lifetime of the overall network. In this paper, for data reduction, a data correlation and prediction technique is proposed both at the sensor node level and at the sink level. The aim of this approach is to reduce the amount of transmitted data to the sink, depending on the degree of correlation between different parameters. In this work we propose the Pearson Data Correlation and Prediction (PDCP) algorithm to detect this correlation. This data reduction maintains the accuracy of the information while reducing the amount of data sent from the nodes to the sink. This approach is validated through simulations on MATLAB using real meteorological data-sets from Weather-Underground sensor network. The results show the validity of our approach by reducing the amount of data by a percentage up to 69% while maintaining the accuracy of the information. The humidity values prediction based on the temperature parameter is accurate and the deviation from the real value does not surpass 7% of humidity.

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Section: Resultsmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

On-Node Correlation Based Data Reduction in WSN for Smart Agriculture

Salim

Mitton

2020

2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)

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“…Other machine learning based methods have been proposed in the literature for data prediction for data reduction. A lot of approaches were interested in data correlation for this purpose, mainly using the Pearson correlation technique and its derivatives [6,[18][19][20], the Auto Regression model [21][22][23], the long short-term memory (LSTM) networks [24,25] and the convolutional networks techniques [26][27][28][29]. The authors in [6] proposed a methodology to analyse data streams, based on spatio-temporal correlations using Pearson correlation.…”

Section: Background and Related Workmentioning

confidence: 99%

K-predictions based data reduction approach in WSN for smart agriculture

Salim

Mitton

2020

Computing

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Nowadays, climate change is one of the numerous factors affecting the agricultural sector. Optimising the usage of natural resources is one of the challenges this sector faces. For this reason, it could be necessary to locally monitor weather data and soil conditions to make faster and better decisions locally adapted to the crop. Wireless sensor networks (WSNs) can serve as a monitoring system for these types of parameters. However, in WSNs, sensor nodes suffer from limited energy resources. The process of sending a large amount of data from the nodes to the sink results in high energy consumption at the sensor node and significant use of network bandwidth, which reduces the lifetime of the overall network and increases the number of costly interference. Data reduction is one of the solutions for this kind of challenges. In this paper, data correlation is investigated and combined with a data prediction technique in order to avoid sending data that could be retrieved mathematically in the objective to reduce the energy consumed by sensor nodes and the bandwidth occupation. This data reduction technique relies on the observation of the variation of every monitored parameter as well as the degree of correlation between different parameters. This approach is validated through simulations on MATLAB using real meteorological data-sets from Weather-Underground sensor network. The results show the validity of our approach which reduces the amount of data by a percentage up to 88% while maintaining the accuracy of the information having a standard deviation of 2 degrees for the temperature and 7% for the humidity.

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“…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".…”

Section: Introductionmentioning

confidence: 99%

Performance of Data Reduction Algorithms for Wireless Sensor Network (WSN) using Different Real-Time Datasets: Analysis Study

Hussein¹,

Marghescu²,

Alduais³

2022

IJACSA

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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.

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Data Reduction Algorithms based on Computational Intelligence for Wireless Sensor Networks Applications

Cited by 9 publications

References 12 publications

On-Node Correlation Based Data Reduction in WSN for Smart Agriculture

On-Node Correlation Based Data Reduction in WSN for Smart Agriculture

K-predictions based data reduction approach in WSN for smart agriculture

Performance of Data Reduction Algorithms for Wireless Sensor Network (WSN) using Different Real-Time Datasets: Analysis Study

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