[1]WU Jiawen,LI Guanghui.GABP-KF-based blind calibration algorithm of data drift in wireless sensor networks[J].CAAI Transactions on Intelligent Systems,2019,14(2):254-262.[doi:10.11992/tis.201712003]
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GABP-KF-based blind calibration algorithm of data drift in wireless sensor networks

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 2 Page number: 254-262 Column: 学术论文—机器学习 Public date: 2019-03-05
Title:
GABP-KF-based blind calibration algorithm of data drift in wireless sensor networks
Author(s):
WU Jiawen1; 2;  LI Guanghui1; 2; 3
1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
2. Research Center of IoT Technology Application Engineering (MOE), Wuxi 214122, China;
3. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Wuxi 214122, China
Keywords:
wireless sensor network; data drift; blind correction; BP neural network; genetic algorithm; Kalman filter; denoising; model building
CLC:
TP274.2
DOI:
10.11992/tis.201712003
Abstract:
Data drifts easily occur in wireless sensor network. To solve this problem, we propose a novel algorithm for tracking and calibrating the drifts of sensor data stream. First, backpropagation (BP) neural network optimized by genetic algorithm is applied to model the spatio-temporal correlations between the target node and its neighbor nodes to predict the value of the node, and then, the data drift of the node is tracked and calibrated by a Kalman filter. The simulation results using different datasets demonstrate that this method has superior prediction accuracy and calibration performance, compared with other methods. The experimental results show that this method can accurately calibrate the sensor drift and improve the reliability of node data.
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