Context Aware Data Fusion on Massive IOT Data in Dynamic IOT Analytics

Educational Data management is a critical task for the researchers due to mammoth data generated by sensors and IoT (Internet of Things) devices. Managing this huge volume of data, cleaning this data from impurities is an inherent need. DF (Data Fusion) processes combine data from multiple sources based on their similarity for an easy management. DF processes focus on many factors like nature of data and application that uses that data. Many DFAs (Data Fusion approaches) have been proposed without detailing on the context for integrating data in fusion tasks. This work attempts to cover this gap of context’s relevance by proposing a technique CDFT (Context aware Data Fusion technique). In this research work, initially data from IoT devices will be gathered and pre-processed to make it clear for the fusion processing. In this work, boundary based noise reduction algorithm is introduced for data pre-processing which attempts to label the unlabelled attributes in the data’s that are gathered, so that data fusion can be done accurately. After pre-processing Context aware data fusion is performed which will combine the data’s from multiple IoT devices together with the concern of context. Finally this combined data will be learnt using the convolution neural network for data fusion performance checking. The proposed CDFT is simulated on Matlab whose results prove that the proposed technique obtains optimal outcomes.