Generalized framework for Prescriptive Machine Learning using IoT datastreams.

Internet of things (IoT) includes of multitude of sensors from a wide variety of applications. These sensors produce high volume and high velocity data. Recently there has been much interest in application of such technologies to improve energy management and agricultural practices. The sensors that are installed in the field transmit real time data regarding numerous environmental variables of interest. This data is then used to forecast a future state and to make a well informed business/operation decision according to an expected future state.

Visualization of IoT signals and optimization of sampling rate using machine learning

Internet of things (IoT) includes of multitude of sensors from a wide variety of applications. These sensors produce high volume and high velocity data. In addition, the transmission errors and malfunction of devices also necessitates checking the reliability/veracity of incoming data. The volume, velocity, and veracity are three Vs that are commonly describe in the big data context. Creating a meaningful real-time visual summary of the sensor data streams for the operators is an important research problem.