A reliable and energy efficient dual prediction data reduction approach for WSNs based on Kalman filter

Wang, Haibin; Yemeni, Zaid; Ismael, Waleed M.; Hawbani, Ammar; Alsamhi, Saeed Hamood

doi:10.1049/cmu2.12262

Cited by 21 publications

(22 citation statements)

References 34 publications

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“…e second phase is the data prediction phase (DPP), in which nontransmitted data are predicted at the base station itself. Data predictions are done on the basis of the Kalman filter [27].…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid

et al. 2022

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The stability of the power grid is concernment due to the high demand and supply to smart cities, homes, factories, and so on. Different machine learning (ML) and deep learning (DL) models can be used to tackle the problem of stability prediction for the energy grid. This study elaborates on the necessity of IoT technology to make energy grid networks smart. Different prediction models, namely, logistic regression, naïve Bayes, decision tree, support vector machine, random forest, XGBoost, k-nearest neighbor, and optimized artificial neural network (ANN), have been applied on openly available smart energy grid datasets to predict their stability. The present article uses metrics such as accuracy, precision, recall, f1-score, and ROC curve to compare different predictive models. Data augmentation and feature scaling have been applied to the dataset to get better results. The augmented dataset provides better results as compared with the normal dataset. This study concludes that the deep learning predictive model ANN optimized with Adam optimizer provides better results than other predictive models. The ANN model provides 97.27% accuracy, 96.79% precision, 95.67% recall, and 96.22% F1 score.

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“…e second phase is the data prediction phase (DPP), in which nontransmitted data are predicted at the base station itself. Data predictions are done on the basis of the Kalman filter [27].…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid

et al. 2022

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“…Wang et al 8 suggest two phases of dual prediction to decrease the amount of data which are (DPP) which refer to the data prediction phase and (DRP) which refer to the data reduction phase which decrease the volume of data sent to the gateway, and save power in the network, while DPP processed on gateway in synchronization with previous level (SN) to predicted non‐transmitted data in previous level. Jarwan et al, 9 producing two variant algorithms: data compression (DC) which use to minimize the traffic between gateway and based station and gateway and dual prediction (DP) which utilize to decrease transform of data to the gateway, they proposed NN network and long short‐term memory networks (LSTMs) for runs the prediction.…”

Section: Related Workmentioning

confidence: 99%

“…">A custom simulator based on the Python programming language and based on real observed data from sensor nodes put at the Intel Berkeley Research Lab 7 is used to run several simulation experiments. The suggested DEDaR technique is compared against DPDR, 8 DP LSTM, 9 DRR‐IoT, 10 and LMS, 11 which are all recent existing methodologies. The DEDaR technique outperforms other methods in terms of data reduction, data accuracy, transferred data size, and consumption of energy, according to the comparison results.…”

Section: Introductionmentioning

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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“…It is the transformation of digital numerical or alphabetical information derived empirically or experimentally into a corrected, ordered and simplified form [49]. Data reduction is shown with the help of Figure 5.…”

Section: A Data Reductionmentioning

confidence: 99%

A Survey of Preprocessing Methods Used for Analysis of Big Data Originated From Smart Grids

Alghamdi

Javaid

2022

IEEE Access

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In this paper, a brief survey of data preprocessing methods is presented. Specifically, the data preprocessing methods used in the smart grid (SG) domain are surveyed. Also, with the advent of SG, data collection on a large scale became possible. The data is essential for electricity demand, generation and price forecasting, which plays an important role in making energy efficient decisions, and long and short term predictions regarding energy generation, consumption and storage. However, the forecasting accuracy decreases when data is used in raw form. Hence, data preprocessing is considered essential. This paper provides an overview of the data preprocessing methods and a detailed discussion of the methods used in the existing literature. A comparison of the methods is also given. A survey of closely related survey papers is also presented and the papers are compared based on their contributions. Moreover, based on the discussion of the data preprocessing methods, a narrative is built with a critical analysis. Finally, future research directions are discussed to guide the readers.

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A reliable and energy efficient dual prediction data reduction approach for WSNs based on Kalman filter

Cited by 21 publications

References 34 publications

Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid

Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid

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

A Survey of Preprocessing Methods Used for Analysis of Big Data Originated From Smart Grids

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