A Stable Dimensionality-Reduction Method for Internet-of-Things (IoT) Streaming Data

With the growing popularity of Internet-of-Things (IoT), large amount of IoT devices are widely used in many spheres of life and tremendous volume of data (which are real-time, streaming and high-dimensional) have been collected, exchanged and processed. In order to reduce the computational complexity and communication cost, it is necessary to reduce the high dimension of these collected data. Because of the data complexity and limited capacity of IoT devices, most of dimensionality reduction algorithms are no longer convenient. To overcome this problem, we propose a lightweight Instance-based Neighbor Embedding (INE) method to accomplish the dimensionality reduction for the IoT streaming data on the resource-constrained devices, which not only keeps the results stable but also maintains the similarity structures of data. Besides, we also propose some techniques and detailed proofs for efficient computation. Extensive experimental results validate the effectiveness of our method.