Increasing sensor reliability through confidence attribution

[1]  Antônio Augusto Fröhlich,et al.  WSN Data Confidence Attribution Using Predictors , 2018, 2018 Eighth Latin-American Symposium on Dependable Computing (LADC).

[2]  François Chollet,et al.  Keras: The Python Deep Learning library , 2018 .

[3]  Pabitra Mohan Khilar,et al.  Composite Fault Diagnosis in Wireless Sensor Networks Using Neural Networks , 2017, Wirel. Pers. Commun..

[4]  Makhlouf Aliouat,et al.  Distributed Fault-Tolerant Algorithm for Wireless Sensor Networks , 2017, Int. J. Commun. Networks Inf. Secur..

[5]  Yukikazu Nakamoto,et al.  Faulty Sensor Data Detection in Wireless Sensor Networks Using Logistical Regression , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW).

[6]  Shaoyong Guo,et al.  Distributed Fault Detection Based on Credibility and Cooperation for WSNs in Smart Grids , 2017, Sensors.

[7]  Makhlouf Aliouat,et al.  Distributed Fault-Tolerant Algorithm for Wireless Sensor Network , 2017 .

[8]  John Salvatier,et al.  Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.

[9]  Makhlouf Aliouat,et al.  FDS: Fault Detection Scheme for Wireless Sensor Networks , 2015, Wireless Personal Communications.

[10]  Geoffrey I. Webb,et al.  Characterizing concept drift , 2015, Data Mining and Knowledge Discovery.

[11]  Gregory Ditzler,et al.  Learning in Nonstationary Environments: A Survey , 2015, IEEE Computational Intelligence Magazine.

[12]  Jie Ma,et al.  Spatiotemporal Correlation Based Fault-Tolerant Event Detection in Wireless Sensor Networks , 2015, Int. J. Distributed Sens. Networks.

[13]  Davide Dardari,et al.  Low-complexity distributed fault detection for wireless sensor networks , 2015, 2015 IEEE International Conference on Communications (ICC).

[14]  Ahmed Zouinkhi,et al.  Distributed fault detection based on HMM for Wireless Sensor Networks , 2015, 2015 4th International Conference on Systems and Control (ICSC).

[15]  Pabitra Mohan Khilar,et al.  Distributed self fault diagnosis algorithm for large scale wireless sensor networks using modified three sigma edit test , 2015, Ad Hoc Networks.

[16]  Hao Yuan,et al.  A Distributed Bayesian Algorithm for data fault detection in wireless sensor networks , 2015, 2015 International Conference on Information Networking (ICOIN).

[17]  Marie-Christine Suhner,et al.  A New Multilayer Perceptron Pruning Algorithm for Classification and Regression Applications , 2014, Neural Processing Letters.

[18]  V. Radha,et al.  A literature review of feature selection techniques and applications: Review of feature selection in data mining , 2014 .

[19]  Emilio Corchado,et al.  A survey of multiple classifier systems as hybrid systems , 2014, Inf. Fusion.

[20]  A. Bifet,et al.  A survey on concept drift adaptation , 2014, ACM Comput. Surv..

[21]  Muhammad Ali Imran,et al.  Anomaly Detection in Wireless Sensor Networks in a Non-Stationary Environment , 2014, IEEE Communications Surveys & Tutorials.

[22]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[23]  Doina Bucur,et al.  Applying time series analysis and neighbourhood voting in a decentralised approach for fault detection and classification in WSNs , 2013, SoICT.

[24]  Muhammad Zahid Khan,et al.  FAULT MANAGEMENT IN WIRELESS SENSOR NETWORKS , 2013 .

[25]  Dimitris K. Tasoulis,et al.  Exponentially weighted moving average charts for detecting concept drift , 2012, Pattern Recognit. Lett..

[26]  P. M. Khilar,et al.  Distributed soft fault detection algorithm in wireless sensor networks using statistical test , 2012, 2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing.

[27]  Daniel Curiac,et al.  Ensemble based sensing anomaly detection in wireless sensor networks , 2012, Expert Syst. Appl..

[28]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[29]  Paulo Martins Engel,et al.  IGMN : An Incremental Gaussian Mixture Network that Learns Instantaneously from Data Flows , 2011 .

[30]  Ramesh Govindan,et al.  Sensor faults: Detection methods and prevalence in real-world datasets , 2010, TOSN.

[31]  Gregory J. Pottie,et al.  Sensor network data fault types , 2007, TOSN.

[32]  Stephan Trenn,et al.  Multilayer Perceptrons: Approximation Order and Necessary Number of Hidden Units , 2008, IEEE Transactions on Neural Networks.

[33]  François Ingelrest,et al.  SensorScope: Out-of-the-Box Environmental Monitoring , 2008, 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008).

[34]  R.R. Selmic,et al.  Wireless Sensor Network Modeling Using Modified Recurrent Neural Networks: Application to Fault Detection , 2008, 2007 IEEE International Conference on Networking, Sensing and Control.

[35]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[36]  Ian Witten,et al.  Data Mining , 2000 .

[37]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .