An efficient privacy-preserving blockchain storage method for internet of things environment.

Blockchain is a key technology to realize decentralized trust management. In recent studies, sharding-based blockchain models are proposed and applied to the resource-constrained Internet of Things (IoT) scenario, and machine learning-based models are presented to improve the query efficiency of the sharding-based blockchains by classifying hot data and storing them locally. However, in some scenarios, these presented blockchain models cannot be deployed because the block features used as input in the learning method are privacy. In this paper, we propose an efficient privacy-preserving blockchain storage method for the IoT environment. The new method classifies hot blocks based on the federated extreme learning machine method and saves the hot blocks through one of the sharded blockchain models called ElasticChain. The features of hot blocks will not be read by other nodes in this method, and user privacy is effectively protected. Meanwhile, hot blocks are saved locally, and data query speed is improved. Furthermore, in order to comprehensively evaluate a hot block, five features of hot blocks are defined, including objective feature, historical popularity, potential popularity, storage requirements and training value. Finally, the experimental results on synthetic data demonstrate the accuracy and efficiency of the proposed blockchain storage model.

Determination of the Three-Dimensional Rate of Cancer Cell Rotation in an Optically-Induced Electrokinetics Chip Using an Optical Flow Algorithm.

Our group has reported that Melan-A cells and lymphocytes undergo self-rotation in a homogeneous AC electric field, and found that the rotation velocity of these cells is a key indicator to characterize their physical properties. However, the determination of the rotation properties of a cell by human eyes is both gruesome and time consuming, and not always accurate. In this paper, a method is presented to more accurately determine the 3D cell rotation velocity and axis from a 2D image sequence captured by a single camera. Using the optical flow method, we obtained the 2D motion field data from the image sequence and back-project it onto a 3D sphere model, and then the rotation axis and velocity of the cell were calculated. After testing the algorithm on animated image sequences, experiments were also performed on image sequences of real rotating cells. All of these results indicate that this method is accurate, practical, and useful. Furthermore, the method presented there can also be used to determine the 3D rotation velocity of other types of spherical objects that are commonly used in microfluidic applications, such as beads and microparticles.