A Stable DimensionalityReduction Method for InternetofThings (IoT) Streaming Data With the growing popularity of InternetofThings (IoT), large amount of IoT devices are widely used in many spheres of life and tremendous volume of data (which are realtime, streaming and highdimensional) 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 Instancebased Neighbor Embedding (INE) method to accomplish the dimensionality reduction for the IoT streaming data on the resourceconstrained 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.

Robust and Efficient Privacy Preservation in Industrial IoT via correlation completion and tracking The Industrial IoT (IIoT) is a key element of Industry 4.0, bringing together modern sensor technology, fog  cloud computing platforms, and artificial intelligence (AI) to create smart, selfoptimizing industrial equipment and facilities. Though, the scale and sensitivity degree of information continuously increases, giving rise to serious privacy concerns. In this work we address the problem of efficiently and effectively tracking the structure of multivariate streams recorded in a network of IIoT devices. The time varying correlation data values are used to add noise which maximally preserves privacy, in the sense that it is very hard to be removed. To improve communication efficiency between connected IoT devices, we exploit low rank properties of the correlation matrices, and track the essential correlations from a small subset of correlation values estimated by a subset of network nodes. Extensive simulation studies, validate the correctness, efficiency, and effectiveness of our approach in terms of computational complexity, transmission energy efficiency and privacy preservation.