Enabling secure mutual authentication and storage checking in cloud-assisted IoT.

Internet of things (IoT) is a technology that can collect the data sensed by the devices for the further real-time services. Using the technique of cloud computing to assist IoT devices in data storing can eliminate the disadvantage of the constrained local storage and computing capability. However, the complex network environment makes cloud servers vulnerable to attacks, and adversaries pretend to be legal IoT clients trying to access the cloud server.

PDC-Net: Robust point cloud registration using deep cyclic neural network combined with PCA.

It is important to improve the registration precision and speed in the process of registration. In order to solve this problem, we proposed a robust point cloud registration method based on deep learning, called PDC-Net, using a principal component analysis based adjustment network that quickly adjusts the initial position between two slices of the point cloud, then using an iterative neural network based on the inverse compositional algorithm to complete the final registration transformation. We compare it on the ModelNet40 dataset with iterative closest point, which is the traditional point cloud registration method, and the learning-based methods including PointNet-LK and deep closest point.

Two-Factor-Based Public Data Protection Scheme in Smart Ocean Management.

Nowadays, two-factor data security protection has become a research hotspot in smart ocean management. With the increasing popularity of smart ocean management, how to achieve the two-factor protection of public data resources in smart ocean management is a serious problem to be tackled. Furthermore, how to achieve both security and revocation is also a challenge for two-factor protection.