Title: Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in real-time system
Authors: Riyadh Arridha; Sritrusta Sukaridhoto; Dadet Pramadihanto; Nobuo Funabiki
Addresses: Department of Information and Computer Engineering, Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia ' Department of Multimedia Creative Technology, Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia ' Department of Information and Computer Engineering, Oliteknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia ' Department of Electrical and Communication Engineering, Okayama University, Okayama, 700-8530, Japan
Abstract: Monitoring water conditions in real-time is a critical mission to preserve the water ecosystem in maritime and archipelagic countries, such as Indonesia that is relying on the wealth of water resources. To integrate the water monitoring system into the big data technology for real-time analysis, we have engaged in the ongoing project named smart environment monitoring and analytic in real-time system (SEMAR), which provides the IoT-big data platform for water monitoring. However, SEMAR does not have an analytical system yet. This paper proposes the analytical system for water quality classification using Pollution Index method, which is an extension of SEMAR. Besides, the communication protocol is updated from REST to MQTT. Furthermore, the real-time user interface is implemented for visualisation. The evaluations confirmed that the data analytic function adopting the linear SVM and decision tree algorithms achieves more than 90% for the estimation accuracy with 0.019075 for the MSE.
Keywords: smart environment monitoring and analytic in real-time system; SEMAR; water condition monitoring; real-time analysis; IoT; big data; classification; machine learning.
International Journal of Space-Based and Situated Computing, 2017 Vol.7 No.2, pp.82 - 93
Received: 04 May 2017
Accepted: 20 May 2017
Published online: 28 Sep 2017 *