Intelligent Web of Things Based on Fuzzy Neural Networks

Abstract

Web of thing is modern technology and a subset of the internet of things (IoT) which brings many application and new possibilities to improve usability and the interoperability of IoT. WoT has many challenges spatially data analytics and storage due to the increasing number of sensing devices capable of acquiring huge amounts of data. This paper presents two techniques of analytics sensors data which distributed in smart home to measure temperature and humidity of rooms: firstly, clustering analysis to sensors measurements of room’s temperature and humidity in the smart home as one solution to the challenge of data analytics and storage and to determine the temperature and humidity patterns which help in making better decisions at right time, secondly, prediction model of room’s temperature and humidity as a safety system in the smart home when compared the current value with predictive value depends on historical data to detect the deviation of the measured data from the sensors, then the output of two techniques are shown in designed web pages to provide better services for the citizens living in the home. Those techniques are implemented by using intelligence neural networks and fuzzy logic, fuzzy adaptive resonance theory neural network (Fuzzy ART—NN) for clustering model and long-short term memory recurrent neural network (LSTM—RNN) for the prediction model. The performance of the clustering model evaluated by training time and the accuracy when the results are shown the clustering approach has short training time and accuracy reached 99%. While the performance of prediction approach evaluated using root mean square error (RMSE) and training time, and the results are shown RMSE reached 0.02 and training time approximation of 4.76 s for 4550 samples.