In this article, a multiunmanned aerial vehicle-mobile-edge computing (UAV-MEC) network is proposed for mobile devices (MDs) located far from a terrestrial base station (BS)-MEC. In the UAV-MEC network, the MDs offload tasks to the UAV-MECs, followed by task offloading from the UAV-MECs to the BS-MEC. Each UAV-MEC aims to jointly optimize its energy consumption, queue stability, and the energy consumption of the MDs by controlling its trajectory and offloading ratio. Specifically, the trajectories of the UAV-MECs are controlled to locate themselves at the optimal positions where the transmission energy of the MDs is minimized. Additionally, the UAV-MECs consider the constraints of task processing time and queue stability in determining the amount of task to be offloaded. Thus, an independent proximal policy optimization (IPPO)-based offloading and trajectory control learning model (IM) is proposed to solve these problems, and a convex optimization model (CM) is also presented to show the optimality of the proposed IM. Specifically, in IM, a near-optimal trajectory control for the randomly located MDs is enabled by exploiting channel gain information only without resorting to accurate location information of the MDs. Furthermore, a dynamic offloading ratio control of each UAV-MEC is achieved by considering its queue dynamics. The simulation results show that the proposed IM jointly optimizes energy consumption and queue stability. Consequently, it outperforms other deep reinforcement learning (DRL)-based algorithms by achieving low energy consumption and long service duration. Moreover, it achieves similar performance to CM while requiring remarkably less time on action decision.

Skeleton-based human action recognition (HAR) is being utilized in various fields like action classification and abnormal behavior detection. The accurate coordinates of the human joints are a crucial factor for the high performance in skeleton-based HAR. However, the missing joints caused by occlusion and invisibility result in performance degradation. Hence, in this paper, a missing joint reconstruction model is proposed to improve the performance of skeleton-based HAR. The proposed model, based on a denoising graph autoencoder (DGAE), regards missing joints as noise corrupted information and aims to reconstruct them to be close to their original coordinates. When the encoder of the proposed model compresses the noised input into a latent vector, a masking Laplacian matrix is introduced to reduce the effect of the missing joints’ features. The masking Laplacian matrix adjusts the effect of features between a missing joint and its adjacent joints by altering the weights of an adjacent matrix. In the decoder, a Laplacian matrix, which represents the connections among the joints, is utilized to reconstruct an output from the latent vector. The experiment result shows that the proposed model reconstructs the coordinates of missing joints with a marginal error. In addition, the performance of skeleton-based HAR is enhanced by reconstructing the missing joints.

In this paper, a novel distributed scheduling scheme for an ad-hoc network is proposed. Specifically, the throughput and the delay of packets with different importance are flexibly adjusted by quantifying the importance as weight factors. In this scheme, each node is equipped with two queues, one for packets with high importance and the other for packets with low importance. The proposed scheduling scheme consists of two procedures: intra-node slot reallocation and inter-node reallocation. In the intra-node slot reallocation, self-fairness is adopted as a key metric, which is a composite of the quantified weight factors and traffic loads. This intra-node slot reallocation improves the throughput and the delay performance. Subsequently, through an inter-node reallocation algorithm adopted from LocalVoting (slot exchange among queues having the same importance), the fairness of traffics with the same importance is enhanced. Thorough simulations were conducted under various traffic load and weight factor settings. The simulation results show that the proposed algorithm can adjust packet delivery performance according to a predefined weight factor. Moreover, compared with conventional algorithms, the proposed algorithm achieves better performance in throughput and delay. The low average delay while attaining the high throughput ensures the excellent performance of the proposed algorithm.