Lagrangian-based Pattern Extraction for Edge Computing in the Internet of Things
(From the article abstract)
Edge computing can improve the scalability and efﬁciency of IoT systems by performing some of the analysis and operations on the nodes or on intermediary edge devices. This will reduce the energy consumption, data transmission load and latency by shifting some of the processes to the edge devices. In this paper, we introduce a pattern extraction method which uses both the Lagrangian Multiplier and the Principal Component Analysis (PCA) to create patterns from raw sensory data. We have evaluated our method by applying a clustering method on constructed patterns. The results show that by using our proposed Lagrangian-based pattern extraction method, the existing clustering algorithms perform more accurately – by up to 20% higher compared with the state-of-the-art methods, especially in dealing with dynamic real-world data. We have conducted our evaluations based on synthetic and real-world data sets and have compared the results to the existing state-of-the-art approaches. We also discuss how the proposed methods can be embedded into the edge computing devices in IoT systems and applications.
We introduced a new method for extracting patterns from multivariate IoT data streams. The method uses Lagrangian Multiplier to scale the data and then it uses Principal Component Analysis (PCA) to reduce the length of the data.