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ANFIS and Deep Learning based missing sensor data prediction in IoT
Author(s) -
Guzel Metehan,
Kok Ibrahim,
Akay Diyar,
Ozdemir Suat
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5400
Subject(s) - adaptive neuro fuzzy inference system , computer science , missing data , data mining , inference , construct (python library) , artificial intelligence , wireless sensor network , machine learning , deep learning , big data , volume (thermodynamics) , data modeling , internet of things , fuzzy logic , fuzzy control system , embedded system , database , computer network , physics , quantum mechanics , programming language
Summary Internet of Things (IoT) consists of billions of devices that generate big data which is characterized by the large volume, velocity, and heterogeneity. In the heterogeneous IoT ecosystem, it is not so surprising that these sensor‐generated data are considered to be noisy, uncertain, erroneous, and missing due to the lack of battery power, communication errors, and malfunctioning devices. This paper presents Deep Learning (DL) and Adaptive‐Network based Fuzzy Inference System (ANFIS) based prediction models for missing sensor data problem in IoT ecosystem. First, we build ANFIS based models and optimize their parameters. Then, we construct DL based models by using Long Short Term Memory (LSTM) network structure and optimize its parameters by applying the grid search method. Finally, we evaluate all the proposed models with Intel Berkeley Lab dataset. Experimental results demonstrate that the proposed models can significantly improve the prediction accuracy and may be promising for missing sensor data prediction.