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A real-time data analysis platform for the Internet of Things (IOT): prediction and anomaly detection for smart spaces

Abstract

There is an increasing demand for data analysis mechanisms for Internet of Things (IoT) systems. Data analytics can provide high-value insights to business, such as better decision making, failure prevention, longer equipment life, lower power consumption, among others. The objective of this project is to implement advanced methods of statistical learning and machine learning to analyze, in real time, data from sensors in smart spaces. As a result of the research, we aim to contribute to the selection of the best set of methods to perform predictions and anomaly detection in real time within this domain. This project counts on the commercial partnership of an IoT company that operates in the European market and will share with us real sensor data and analytics use cases. The partnership will allow us to incursion in this field, targeting future customers with similar demands. The commercial application of this project will be a platform for real-time sensor data analysis for Internet of Things (IoT) systems. The platform will have two purposes: the first is to be the basis of a consulting service for prediction and detection of data anomalies with a focus on companies that develop IoT solutions for smart spaces. The second purpose is to sell the platform as a cloud service to be integrated with the customer's business logic in order to gain insights from sensor data. The development of this first phase will allow us to acquire a deep technical mastery of the area of environment sensing, which will allow us to reach customers with a similar demand in national and international markets. Also, the infrastructure developed will serve as the basis for further incursion into other Internet of Things (IoT) domains in order to expand our market. (AU)