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. This project will design a framework based on machine learning and advances in human computer interaction (HCI) to help human operators monitor Internet enabled devices and appliances. The objective of the project is to determine to minimize the transmission cost and capture semantics from the data stream so the human operators can make meaningful decision for optimum operation of the devices.

Faculty Supervisor:

Pawan Lingras


Meghana Chillal


Hanatech Inc


Computer science


Information and communications technologies




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