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Data prediction model in wireless sensor networks based on bidirectional LSTM
2019
EURASIP Journal on Wireless Communications and Networking
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This paper has proposed a new data prediction method multi-node multi-feature (MNMF) based on bidirectional long short-term memory (LSTM) network. ...
The data collected by the wireless sensor nodes often has some spatial or temporal redundancy, and the redundant data impose unnecessary burdens on both the nodes and networks. ...
[6] proposed a wireless sensor network data prediction model PLB based on periodicity and linear relationship. ...
doi:10.1186/s13638-019-1511-4
fatcat:gk35rwhgfndklbvkvq7jh4owoi
Multi-step Data Prediction in Wireless Sensor Networks Based on One-Dimensional CNN and Bidirectional LSTM
2019
IEEE Access
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This paper proposes a novel model for multi-step sensory data prediction in wireless sensor network. ...
Firstly, we introduce the artificial neural networks based on 1-D CNN (One-Dimensional Convolutional Neural Network) and Bi-LSTM (Bidirectional Long and Short-Term Memory) to get the abstract features ...
The neural network model based on CNN and LSTM can be used to extract the correlation in data, which is suitable for the prediction problem of sensing data in wireless sensor networks. ...
doi:10.1109/access.2019.2937098
fatcat:lr2iqjvt4rcb3cfzc7ar2lmika
Improved Soil Moisture and Electrical Conductivity Prediction of Citrus Orchards Based on IoT Using Deep Bidirectional LSTM
2021
Agriculture
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With the environmental information data, deep bidirectional long short-term memory (Bid-LSTM) networks are proposed to improve soil moisture (SM) and soil electrical conductivity (SEC) predictions, providing ...
In order to create an irrigation scheduling plan for use in large-area citrus orchards, an environmental information collection system of citrus orchards was established based on the Internet of Things ...
We are thankful to the citrus orchard in Yangcun Town in Huizhou, China. ...
doi:10.3390/agriculture11070635
fatcat:ywrvjhzx75hvlbfy4bmntebgbq
Data Imputation in Wireless Sensor Networks using Regression Models
2020
International Journal of Advanced Trends in Computer Science and Engineering
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This work deals with regression model in selecting the nearest sensor based upon nature of dependant variable. ...
Wireless sensor nodes and its inconsistency in reporting sensory data information tend to inaccurate processing at sink. ...
The SSIM model consists of bidirectional LSTM encoder and unidirectional LSTM decoder. ...
doi:10.30534/ijatcse/2020/252952020
fatcat:iyu5xtnb2vemhpunmnsmyes3ya
AESGRU: An Attention-based Temporal Correlation Approach for End-to-End Machine Health Perception
2019
IEEE Access
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Then, we deploy a single-layer bidirectional GRU network, which is enhanced by attention mechanism, to capture the long-term dependency of sensor segments and focus limited attention resources on those ...
Therefore, we present a novel model, named Attention-based Equitable Segmentation Gated Recurrent Unit Networks (AESGRU), to improve diagnostic accuracy and model-building efficiency. ...
In [19] , a convolutional bidirectional LSTM network was proposed to predict the actual wear of a high-speed CNC machine. ...
doi:10.1109/access.2019.2943381
fatcat:ecokwkzyijgqldx6m7nri7qj4m
Wearable Device-Based Smart Football Athlete Health Prediction Algorithm Based on Recurrent Neural Networks
2021
Journal of Healthcare Engineering
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As a result, this article proposes a novel wearable device-based smart football player health prediction algorithm based on recurrent neural networks. ...
The time step data are then fed into a recurrent neural network to extract deep features, followed by the health prediction results. ...
proposes a wearable device smart football player health prediction algorithm [10, 11] based on the recurrent neural network (RNN). ...
doi:10.1155/2021/2613300
pmid:34373774
pmcid:PMC8349259
fatcat:tk66rwxa2feyhblcjajo5gszdy
Combined experimental and numerical study of uniaxial compression failure of hardened cement paste at micrometre length scale
2019
Cement and Concrete Research
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Then, we deploy a single-layer bidirectional GRU network, which is enhanced by attention mechanism, to capture the long-term dependency of sensor segments and focus limited attention resources on those ...
Therefore, we present a novel model, named Attention-based Equitable Segmentation Gated Recurrent Unit Networks (AESGRU), to improve diagnostic accuracy and model-building efficiency. ...
In [19] , a convolutional bidirectional LSTM network was proposed to predict the actual wear of a high-speed CNC machine. ...
doi:10.1016/j.cemconres.2019.105925
fatcat:biu4eqcjcfeirn3y4qpjxy77mq
IoT-Based Wearable Sensors and Bidirectional LSTM Network for Action Recognition of Aerobics Athletes
2021
Journal of Healthcare Engineering
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In this paper, a motion prediction model of aerobics athletes is built based on the wearable inertial sensor of the Internet of Things and the bidirectional long short term memory (BiLSTM) network. ...
Firstly, a wireless sensor network based on ZigBee was designed and implemented to collect the posture data of aerobics athletes. ...
Firstly, a wireless sensor network based on ZigBee was designed and implemented to collect the posture data of aerobics athletes through inertial sensors. ...
doi:10.1155/2021/9601420
pmid:34349892
pmcid:PMC8328736
fatcat:gunu2zngqfe7dbx3xv5454w2by
Table of Contents
2021
2021 3rd International Conference on Electronics Representation and Algorithm (ICERA)
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Neural Networks
EEG-based Happy and Sad Emotions Classification using LSTM and Bidirectional
LSTM
Effect of Bidirectional Reflector Technology on the Non-line-of-sight propagation of Light
Fidelity ...
Prediction Based on Academic Performances Utilizing Neural Network Algorithm The Broiler Chicken Coop Temperature Monitoring Use Fuzzy Logic Mamdani and LoRAWAN Approach The effect of atmospheric turbulence ...
doi:10.1109/icera53111.2021.9538797
fatcat:6vt5h6q64bcntdxorhgzbran24
Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors' Data
2021
Information
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The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. ...
In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to ...
to predict the energy load in the next time window, based on the collected sensor data. ...
doi:10.3390/info12120499
fatcat:mm353zhvxnelllykgispcnughi
NO2 pollutant concentration forecasting for air quality monitoring by using an optimised deep learning bidirectional GRU model
2021
International Journal of Computational Science and Engineering (IJCSE)
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A novel deep learning bidirectional gated recurrent units (GRUs) model is proposed. ...
The model is evaluated and optimised for the number of features, number of neurons, number of look backs and epochs. It is implemented on real-time dataset of Pune city in India. ...
Wireless sensor networks (WSN) (Zhao et al., 2018) can be used for integrating sensor information, data preprocessing and packet formation. ...
doi:10.1504/ijcse.2021.113652
fatcat:mdckus27unfavdzq44dw7iedoa
A Deep Learning Approach for Human Activities Recognition from Multimodal Sensing Devices
2020
IEEE Access
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[30] applied Bidirectional LSTM network to predict missing words based on contexts, Graves and Schmidhuber [31] to perform phoneme classifications and Graves et al. ...
These efforts so far have focused on the use of video [1] , wearable sensors and wireless sensor networks [2] , [3] to capture simple human activities. ...
and data analytic. ...
doi:10.1109/access.2020.3027979
fatcat:pjsjhlcdijhynka3kuddckm35a
Deep Recurrent Neural Networks for Human Activity Recognition
2017
Sensors
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We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. ...
Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. ...
Bidirectional LSTM-Based DRNN Model The second model architecture is built by using a bidirectional LSTM-based DRNN, as shown in Figure 5 . ...
doi:10.3390/s17112556
pmid:29113103
pmcid:PMC5712979
fatcat:r3w7zqihh5cebcwi2d2tjq7pqe
Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN
2021
Sensors
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One of the most important challenges in Wireless Sensor Networks (WSN) is the extension of the sensors lifetime, which are battery-powered devices, through a reduction in energy consumption. ...
Using data prediction to decrease the amount of transmitted data is one of the approaches to solve this problem. ...
A prediction model based on Bidirectional LSTMs, in what the authors called the multi-node multi-feature model (MNMF), was proposed in [44] . ...
doi:10.3390/s21217375
pmid:34770681
pmcid:PMC8588308
fatcat:wtkgq4o55ngnln7mmtk34ury4u
Fusing RGB and Thermal Imagery with Channel State Information for Abnormal Activity Detection Using Multimodal Bidirectional LSTM
[chapter]
2021
Lecture Notes in Computer Science
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AbstractIn this paper, we present a multimodal deep model for detection of abnormal activity, based on bidirectional Long Short-Term Memory neural networks (LSTM). ...
We also present a Bayesian optimization framework that fine-tunes the Bidirectional LSTM parameters in an optimal manner. ...
In this work, we propose a model based on a Bayesian optimized multimodal bidirectional LSTM neural network for abnormal activity detection. ...
doi:10.1007/978-3-030-69781-5_6
fatcat:j3e75gsacjcvtjwfq5fca425w4
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