Research Article
A Predictive System for IoTs Reconfiguration Based on TensorFlow Framework
@INPROCEEDINGS{10.1007/978-3-030-63083-6_16, author={Tuan Nguyen-Anh and Quan Le-Trung}, title={A Predictive System for IoTs Reconfiguration Based on TensorFlow Framework}, proceedings={Industrial Networks and Intelligent Systems. 6th EAI International Conference, INISCOM 2020, Hanoi, Vietnam, August 27--28, 2020, Proceedings}, proceedings_a={INISCOM}, year={2020}, month={11}, keywords={IoTs Reconfiguration Intelligent context management IoTs prediction system}, doi={10.1007/978-3-030-63083-6_16} }- Tuan Nguyen-Anh
Quan Le-Trung
Year: 2020
A Predictive System for IoTs Reconfiguration Based on TensorFlow Framework
INISCOM
Springer
DOI: 10.1007/978-3-030-63083-6_16
Abstract
IoTs are rapidly growing with the addition of new sensors and devices to existing IoTs. The demand of IoT nodes keeps increasing to adapt to changing environment conditions and application requirements, the need for reconfiguring these already existing IoTs is rapidly increasing. It is also important to manage the intelligent context to execute when it will trigger the appropriate behavior. Yet, many algorithms based on different models for time-series sensor data prediction can be used for this purpose. However, each algorithm has its own advantages and disadvantages, resulting in different reconfiguration behavior predictions for each specific IoTs application. Developing an IoTs reconfiguration application has difficulty implementing many different data prediction algorithms for different sensor measurements to find the most suitable algorithm. In this paper, we propose IoTs Reconfiguration Prediction System (IRPS), a tool that helps IoT developers to choose the most suitable time-series sensor data prediction algorithms for trigger IoTs reconfiguration actions.
