As one of the crucial scenarios of the Internet of Things, wireless sensor networks (WSNs) perform tasks invariably on the basis of the effective cover effect of targets. The cover effect is improved commonly by redeployment of the sensor nodes since they tend to deviate from the optimal deployment location frequently. Given the complex and harsh environment of 3-D WSNs, which impede the recharging and restoration of the batteries of the sensor nodes, the intention of our research is to deal with the multiobjective optimization problem of minimizing total energy consumption and balancing the residual energy based on coverage enhancement during redeployment. By using truncated octahedrons to stack the 3-D environment seamlessly, the coverage enhancement and energy optimization problems are transformed into a task-assignment problem of moving nodes to truncated octahedrons, and an energy-efficient coverage enhancement strategy based on the vampire bat optimizer (VBO) is proposed to solve the above problem, which is inspired by the egoistic and altruistic behavior of vampire bats. the simulation results demonstrate that the proposed strategy can enhance the uniformity of residual energy of nodes by 30.53%, 43.44%, and 32.03% compared to the virtual force-directed particle swarm optimization (VFPSO), the 3-D virtual force algorithm (3DVFA), and the Hungarian algorithm (HA), respectively. Additionally, the proposed strategy can reduce the total energy consumption of nodes and also perform well in terms of energy consumption of the maximum energy consumption nodes, final coverage rate, and time consumption.

Self-learning super-resolution (SLSR) algorithms have the advantage of being independent of an external training database. This paper proposes an SLSR algorithm that uses convolutional principal component analysis (CPCA) and random matching. The technologies of CPCA and random matching greatly improve the efficiency of self-learning. There are two main steps in this algorithm: forming the training and testing the data sets and patch matching. In the data set forming step, we propose the CPCA to extract the low-dimensional features of the data set. The CPCA uses a convolutional method to quickly extract the principal component analysis (PCA) features of each image patch in every training and testing image. In the patch matching step, we propose a two-step random oscillation accompanied with propagation to accelerate the matching process. This patch matching method avoids exhaustive searching by utilizing the local similarity prior of natural images. The two-step random oscillation first performs a coarse patch matching using the variance feature and then performs a detailed matching using the PCA feature, which is useful to find reliable matching patches. The propagation strategy enables patches to propagate the good matching patches to their neighbors. The experimental results demonstrate that the proposed algorithm has a substantially lower time cost than that of many existing self-learning algorithms, leading to better reconstruction quality.