Fault Prediction Based on the Kernel Function for Ribbon Wireless Sensor Networks

Yue, Yinggao; Li, Jianqing; Fan, Hong-Yi; Qin, Qin; Gu, Le; Du, Li

doi:10.1007/s11277-017-4361-6

Cited by 10 publications

(7 citation statements)

References 14 publications

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“…Table 3 shows the experiment results under various parameter combinations. The error in the table is the average error of 1000 prediction processes, in which the batch size is set to 200, and epoch is 15.…”

Section: Simulation Results a Parameters Selectionmentioning

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: Simulation Results a Parameters Selectionmentioning

confidence: 99%

“…Yinggao Yue proposed a fault predictive model for strip wireless sensor networks [15]. Based on the theory of kernel function, this paper proposes a fault prediction method, and chooses the radial basis function as its kernel function to predict the fault from two aspects: node hardware fault and network fault.…”

Section: Related Workmentioning

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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“…In 2017, Yue et al [13] used kernel function for fault prediction in ribbon wireless sensor networks. Results yield that the proposed approach has higher accuracy in predicting data.…”

Section: Literature Surveymentioning

confidence: 99%

“…(2) computes it. (13) where represents the number of bits in the sensed data packet after conversion to the digital signal, V represents the voltage supply, is the total current required for the sensing task, and is the total time-duration of sense.…”

Section: Energy Modelmentioning

confidence: 99%

A Two Vector Data-Prediction Model for Energy-Efficient Data Aggregation in Wireless Sensor Network

Jain

Singh

2021

Preprint

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Most ecological management applications use Wireless Sensor Networks (WSNs) to collect data regularly, with great temporal redundancy. As a result, a significant amount of energy is used transmitting redundant data, making it tremendously problematic to attain a satisfactory network lifetime, which is a bottleneck in enduring such environmental monitoring applications. A two-vector prediction model based on Normalized Quantile Regression (NQR) for Data Aggregation is proposed to proficiently accomplish energy reduction in synchronized data collecting cycles. The introduced NQR algorithm provides high-accuracy prediction. With accurate estimates, energy usage is reduced.Furthermore, it extends the network's lifetime. In intracluster transmissions, NQR uses a two-vector data-prediction algorithm to coordinate the anticipated sensor's reading and, as a result, minimize cumulative inefficiencies from unin-terrupted predictions. NQR algorithm can be integrated with both homogeneous and heterogeneous WSNs. When compared to existing methods, the suggested NQR methodology is shown to have high energy efficiency.The results show greater prediction accuracy, more positive predictions with high data quality, which help the network last longer.

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“…Ding and Fang [9] proposed a fault estimation algorithm based on a particle filter, through the study of the fault prediction of nonlinear stochastic systems with initial faults. Yue et al [10] proposed a fault prediction method based on kernel function, which is used to evaluate the network performance of Ribbon WSN. Zhang et al [11] established a back propagation (BP) neural network prediction model of industrial equipment based on a dynamic cuckoo search optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Fault Prediction Method for Wireless Sensor Network Based on Evidential Reasoning and Belief-Rule-Base

Chen

Zhou

et al. 2019

IEEE Access

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Wireless sensor network (WSN) is a distributed intelligent network, which can independently achieve the information collection task of monitoring targets. However, the WSN is susceptible to faults due to various factors, such as sensor resources, network bandwidth, and work environment. The WSN fault prediction technology can estimate the fault trend of the WSN, which can provide the basis for the formulation and implementation of emergency strategies. In this paper, a new WSN fault prediction method is proposed based on evidential reasoning (ER) and belief rule base (BRB). First, the process of the WSN fault prediction is described, which mainly includes the fault assessment of the current WSN and the fault prediction of future WSN. Second, the WSN fault prediction model is constructed, including the ER-based fault assessment model and BRB-based fault prediction model. The projection covariance matrix adaptation evolutionary strategies (P-CMA-ESs) are used to optimize model parameters. Finally, a case study is constructed to verify the validity of the WSN fault prediction model. The experimental results show that the model can adequately estimate the fault state of the current WSN and then predict the fault status of future WSN. INDEX TERMS Fault prediction, belief rule base (BRB), fault assessment, evidential reasoning (ER) wireless sensor network (WSN).

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Fault Prediction Based on the Kernel Function for Ribbon Wireless Sensor Networks

Cited by 10 publications

References 14 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

A Two Vector Data-Prediction Model for Energy-Efficient Data Aggregation in Wireless Sensor Network

Fault Prediction Method for Wireless Sensor Network Based on Evidential Reasoning and Belief-Rule-Base

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