Periodicity-and-Linear-Based Data Suppression Mechanism for WSN

Song, Yanchao; Luo, Juan; Liu, Chang; He, Wenfeng

doi:10.1109/trustcom.2015.516

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Song et al [6] proposed a wireless sensor network data prediction model PLB based on periodicity and linear relationship. The model used the large amount of redundancy in the data to predict future data and reduce the transmission of predictable data.…”

Section: Related Workmentioning

confidence: 99%

“…Using data prediction methods to reduce unnecessary data transmission is an effective way to improve the quality of data collection and increase the network lifetime. The current methods usually use the periodicity and redundancy to predict the specific sensory data based on historical data, which often results in low prediction stability and biased predictions [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data prediction model in wireless sensor networks based on bidirectional LSTM

Cheng

Xie

et al. 2019

J Wireless Com Network

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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. Data prediction is helpful to improve data quality and reduce the unnecessary data transmission. However, the current data prediction methods of wireless sensor networks seldom consider how to utilize the spatial-temporal correlation among the sensory data. This paper has proposed a new data prediction method multi-node multi-feature (MNMF) based on bidirectional long short-term memory (LSTM) network. Firstly, the data quality is improved by quartile method and wavelet threshold denoising. Then, the bidirectional LSTM network is used to extract and learn the abstract features of sensory data. Finally, the abstract features are used in the data prediction by adopting the merge layer of the neural network. The experimental results show that the proposed MNMF model has better performance compared with the other methods in many evaluation indicators.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data prediction model in wireless sensor networks based on bidirectional LSTM

Cheng

Xie

et al. 2019

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…Considering a well-behaved sensor node that is allowed to sleep in a deep low-power mode between measurements, approximately 80% of energy is consumed in transmission and the rest in sensing and processing [4]. In a WSN, data prediction stands out as a possible solution to this problem by predicting part of the sensed data without triggering transmission, thereby saving energy and reducing congestion traffic in the wireless network [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN

Morales

Sousa

Brusamarello

et al. 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. This paper provides a comparison of deep learning methods in a dual prediction scheme to reduce transmission. The structures of the models are presented along with their parameters. A comparison of the models is provided using different performance metrics, together with the percent of points transmitted per threshold, and the errors between the final data received by Base Station (BS) and the measured values. The results show that the model with better performance in the dataset was the model with Attention, saving a considerable amount of data in transmission and still maintaining a good representation of the measured data.

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Process Modelling and Creating Predictive Models of Sensory Networks Using Fuzzy Petri Nets

Fodor

Balogh

2021

Procedia Computer Science

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Periodicity-and-Linear-Based Data Suppression Mechanism for WSN

Cited by 5 publications

References 9 publications

Data prediction model in wireless sensor networks based on bidirectional LSTM

Data prediction model in wireless sensor networks based on bidirectional LSTM

Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN

Process Modelling and Creating Predictive Models of Sensory Networks Using Fuzzy Petri Nets

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