Data Driven Dynamic Sensor Selection in Internet of Things

The advent of Internet Of Things has led to the problem of an explosive outburst of data. Because of this, there is a need of an advanced data acquisition and data reduction system. We present two approaches for data reduction by sensor selection, leveraging the power of Machine Learning. The first approach uses Principal Component Analysis and the second approach uses Reinforcement Learning to identify k significant sensors from the n total sensors that closely resemble the original data statistically. This approach is applied on the data available from sensors deployed in South Brazil, as well as on the data collected from a sensor field we set up in the Computer Engineering Department. The results from both the datasets show significant data reduction while maintaining the characteristics of the original data.