Information Abstraction from IoT Streaming Greenhouse Data
V. Srividhya, S A Niketh, N Kavarvizhv · 2019
Internet of Things (IoT) is a platform which gives the billions of computing devices, sensory devices that constantly produce and exchange the huge amounts of data over the network. Several interesting applications, like Greenhouse, involving extracting the higher level information from the raw data and representing it in human readable format, exists. An effective mechanism is required to process streaming data and inferring it, to get insight about the data and get some actionable information from processing and measurements. The aim is to represent the data from device point of view to user-centric point of view using Data analytics and Machine learning mechanism. Raw sensor data is created, based on the sensor threshold values. Parameters considered are temperature, humidity, co2 concentration, luminosity, radiation, soil moisture and soil temperature. Numerical values are converted to the strings to create higher level abstraction by applying set of rules. Abstracted data is cleaned by some cleaning processes. Then Latent Dirichlet Allocation (LDA),a topic extraction model, is applied to extract the hidden correlation among the data