Point cloud completion aims to infer the complete point clouds from incomplete ones. In real-world scenarios, where the paired data is absent, self-supervised methods have emerged as a promising solution. Although existing self-supervised methods perform well at relatively low resolutions, they suffer significant performance degradation at higher resolution primarily because they focus on point cloud reconstruction at patch-level or point-level. In this paper, we propose a self-supervised method based on Geometric Continuity and Consistency Learning (GCCL) at multi-scale level to improve the accuracy of predicting local details and global shapes of point clouds. Specifically, to capture local details, we employ a patch-topoint strategy and a coarse-fine manner for geometric continuity learning. To constrain the global shapes, we construct multiple branches for mutual supervision and utilize class priors to build a memory queue for contrasting current features, enhancing the network focus on geometric consistency learning. We evaluate GCCL on multiple datasets, and the results show that our method outperforms existing self-supervised methods by a 4.4 improvement in CD-$\ell_2$on the synthetic PCN dataset and can generate more uniformly distributed completion results on realworld datasets

Ubiquitous personal content transfer in a heterogeneous environment includes both global infrastructure based communications and local infrastructureless transfer, which leads to a hybrid networking environment. The MIP/NEMO standard can support ubiquitous content transfer, but is inefficient for local content transfer. Infrastructureless communication using ad hoc mode is often utilized by individual users to transfer local content, but it can not support device mobility. In this paper, a scheme based on a personal distributed environment (PDE) is proposed to implement ubiquitous content transfer in a hybrid networking environment. It can improve performance by combining the virtues of MIP/NEMO and the advantages of ad hoc mode.

This paper proposes a dynamic and adaptive algorithm (NACPA) to control the control phase of TDMA-based MAC that can be used in cluster-based wireless sensor networks. This algorithm takes advantage of the wireless sensor hardware feature and presents a more accurate while simpler means to calculate the number of contention nodes in one round. Based on the analysis of the features of contention probability against the number of contention nodes, this algorithm can significantly reduce its computation complexity rendering it practically feasible for resource-constrained sensor networks.