A hierarchical fractional LMS prediction method for data reduction in a wireless sensor network

Ganjewar, Pramod D.; Barani, S.; Wagh, Sanjeev J.

doi:10.1016/j.adhoc.2018.10.028

Cited by 22 publications

(11 citation statements)

References 15 publications

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“…Pramod Ganjewar proposed a hierarchical minimum mean square prediction algorithm for reducing data transmission in wireless sensor networks [9]. In this paper, a predictive model based on hierarchical fractional least mean squares (HFLMS) was proposed, which attempts to predict the sensory data by error estimation and only sends the required data to the receiving node by using the proposed adaptive filter to reduce the energy consumption in the wireless sensor networks.…”

Section: Related Workmentioning

confidence: 99%

“…The former tries to optimize the data collection process, and the latter focuses on mining relevant information in the collected data to improve data quality. First, in wireless sensor networks, redundant data transmission can be avoided by prediction, which makes wireless sensor networks improve in energy efficiency and data transmission quality [9]- [12]. Second, predicting the state of equipment or area that monitored by wireless sensor networks can increase the lifetime of the equipment or avoid unnecessary accidents [13]- [17].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Step Data Prediction in Wireless Sensor Networks Based on One-Dimensional CNN and Bidirectional LSTM

et al. 2019

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The increase of network size and sensory data leads to many serious problems to the wireless sensor networks due to the limited energy. Data prediction method is helpful to reduce network traffic and increase the network lifetime accordingly, especially by exploring data correlation among the sensory data. Data prediction can also be used to recover abnormal/lost data in case these sensor nodes fail to work. The current prediction methods in wireless sensor networks do not make full usage of the spatialtemporal correlation between wireless sensor nodes, and thus leads to higher prediction error relatively. 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 of different attributes via the pre-processed sensory data. Then, these abstract features are used to obtain one-step prediction. Finally, the multi-step prediction is introduced by using historical data and the prediction results of the previous step iteratively. Experiment results show that after selecting suitable node combinations in which the spatial-temporal correlation is highlighted, the proposed multi-step predictive model can predict multi-step (short and medium term) sensory data, and its performance is better compared with other related methods.INDEX TERMS Neural networks, predictive models, wireless sensor networks.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Step Data Prediction in Wireless Sensor Networks Based on One-Dimensional CNN and Bidirectional LSTM

et al. 2019

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“…This method is inefficient when there isn't sufficient data for prediction. As an attempt to improve the lifetime of WSNs, the hierarchical fractional least mean-square filter is developed in [22] to accurately predict the sensing data, based on weight coefficient matrices of two layers sub-filters. Taking the energy consumption and prediction error as metrics of performance measurement, the experimental results of the work illustrate a decrease in the quantity of energy consumed and an increase in the compression rate of the data.…”

Section: Non-machine Learning Approachesmentioning

confidence: 99%

Data Prediction Based Encoder-Decoder Learning in Wireless Sensor Networks

et al. 2022

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Wireless sensor networks are typically characterized by large network size and sensor nodes with low energy capacity and a limited bandwidth for data transmission. Over-activity of the sensor nodes will therefore cause many issues to the network, such as an increase in the network depletion rate and poor data transmission. Data prediction methods that exploit the inter-relationship between sensor nodes can be used to reduce data traffic across wireless networks. Several related work in data prediction do not consider the time-series distribution of the sensing data. However, exploiting sequential features of the historical observations can improve prediction accuracy, and increase the number of sensing data predicted per sequence. This work propose a sequence to sequence data prediction model, using a one dimensional layer convolutional neural network to extract spatial features from the pre-processed sensing data, and an encoderdecoder model to predict the next two outputs in the sequence by exploiting the temporal distribution of the data. The aforementioned approach has the capacity of generating more accurate information, which can reduce network traffic and energy expenditure in WSNs. Furthermore, the experimental results reveals that, based on a suitable choice of nodes, our proposed model perform accurate predictions, with reduced root mean squared error as compared to related work. We also propose an approach to regulate and control the data traffic toward the base station.

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“…Prediction-based approach enables selective transmission of the data collected by sensor nodes [ 28 , 29 , 30 ]. According to this approach, only a subset of the collected data is delivered to the sink node.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring

Lewandowski

Płaczek

Bernaś

2020

Sensors

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The recent development of wireless wearable sensor networks offers a spectrum of new applications in fields of healthcare, medicine, activity monitoring, sport, safety, human-machine interfacing, and beyond. Successful use of this technology depends on lifetime of the battery-powered sensor nodes. This paper presents a new method for extending the lifetime of the wearable sensor networks by avoiding unnecessary data transmissions. The introduced method is based on embedded classifiers that allow sensor nodes to decide if current sensor readings have to be transmitted to cluster head or not. In order to train the classifiers, a procedure was elaborated, which takes into account the impact of data selection on accuracy of a recognition system. This approach was implemented in a prototype of wearable sensor network for human activity monitoring. Real-world experiments were conducted to evaluate the new method in terms of network lifetime, energy consumption, and accuracy of human activity recognition. Results of the experimental evaluation have confirmed that, the proposed method enables significant prolongation of the network lifetime, while preserving high accuracy of the activity recognition. The experiments have also revealed advantages of the method in comparison with state-of-the-art algorithms for data transmission reduction.

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A hierarchical fractional LMS prediction method for data reduction in a wireless sensor network

Cited by 22 publications

References 15 publications

Multi-Step Data Prediction in Wireless Sensor Networks Based on One-Dimensional CNN and Bidirectional LSTM

Multi-Step Data Prediction in Wireless Sensor Networks Based on One-Dimensional CNN and Bidirectional LSTM

Data Prediction Based Encoder-Decoder Learning in Wireless Sensor Networks

Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring

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