Edinburgh Napier University

  As the streaming data generated by Internet of Things (IoT) ubiquitous sensors grow in massive scale, extracting interesting information (anomalies) in real-time becomes more challenging. Traditional systems which retrospectively perform all the processing in the cloud do not capture real-time changes in the data. Similarly, real-time solutions which rely on human monitors have the tendency to miss the anomalies due to their rare nature. In recent times, several machine learning techniques have been proposed for stream processing. Approaches based on supervised or semi-supervised learning fail to adapt to changing patterns of the streaming data and the data labelling costs are huge. To address these limitations, we propose a cloud-assisted framework where an intermediary node (edge) is introduced between the end devices and the cloud to assist in stream processing. A model deployed on the edge is designed to learn in an iterative manner to discriminate between similar and dissimilar data representations, making it easier to distinguish the anomalies. In this work, we have proposed an iterative method that combines the capabilities of deep clustering and l 2 -normalisation to achieve better discriminative representations. Experimental results demonstrate the proposed method achieves robust performance over state-of-the-art discriminative representation algorithms and sets new benchmark accuracy on transformation invariant image dataset.