The ability to ensure covert unmanned aerial vehicle (UAV) communications is imperative in critical missions such as military surveillance and emergency response. In this paper, a multi-UAV covert communication system with informed jammers is investigated. To increase ground wardens’ detection uncertainty, we propose a joint dynamic scheduling and sensing jamming (DSSJ) scheme. Unlike existing approaches with fixed UAV roles and non-informed jamming, DSSJ dynamically schedules UAVs across adjacent time slots (TSs), while the jamming UAV performs sensing-based informed jamming per TS. Closed-form expressions are derived for the covert rate and minimum detection error probability (MDEP) under the worst-case scenario with optimal warden detection. An optimization problem is formulated to maximize the normalized weighted sum of covert rate and MDEP, subject to multiple constraints, including scheduling, sensing ratio, and other key factors. To solve this mixed-integer non-convex problem, we design a double deep Q-network (DDQN)-DSSJ algorithm, integrating DSSJ within a deep reinforcement learning framework, accelerated by experience replay and dynamic exploration, achieving real-time covert decision-making with polynomial complexity. Simulations demonstrate that DDQN-DSSJ achieves 25% faster convergence, enhanced stability, and superior covertness compared to proximal policy optimization and deep Q-network. Additionally, DDQN-DSSJ improves the covert rate by over 4× and MDEP by up to 28.3%, outperforming state-of-the-art schemes.

This paper proposes a digital twin network (DTN)-driven framework for multi-unmanned aerial vehicle (UAV) covert communication with informed jammers. To maximize warden detection uncertainty, we design a dynamic scheduling and sensing jamming (DSSJ) scheme that dynamically schedules UAV roles (communication/jamming/backup) across adjacent time slots (TSs), while the jamming UAV employs sensing-based informed jamming per TS. Leveraging DTN’s real-time digital-physical synchronization, closed-form expressions for the user’s covert rate and warden’s minimum detection error probability (MDEP) are derived under worst-case detection. A DTN-driven double deep Q-network (DDQN)-DSSJ algorithm solves the normalized weighted-sum maximization problem under covertness, sensing ratio, power, and speed constraints. Simulations demonstrate that DTN-driven DDQN-DSSJ achieves 25% faster convergence, enhanced stability, and superior covertness compared to proximal policy optimization (PPO) and deep Q-network (DQN).

By fully exploring the edge computing “supply-demand” relationship between the mobile edge computing (MEC) servers and the differentiated application requests, the computing pricing (i.e.“, supply”) and allocating (i.e.“, demand”) can be coordinated well for the practical network consisting of heterogeneous users and MEC operator. In this paper, the fair-aware computing pricing, beneficial offloading (i.e., obtaining positive utility) and local computing adjustment are jointly discussed under a pricing-enabled MEC. By considering heterogeneous application requests, fair service demand and limited computing provisioning, a multi-objective composite utility optimization is developed to maximize the user utility and the MEC operator profit simultaneously. Therein, the fair service condition is proposed, under which each user can experience a similar chance to obtain beneficial offloading. In order to solve the goal problem with undetermined objective function and conditions, a fair service enabled pricing and allocating algorithm (FS_PAA) with extremely low complexity is proposed by exploiting classification discussion method and convex optimization. Our FS_PAA reveals the explicit relationship between the optimal offloading decision and computing pricing, and the explicit relationship between the optimal computing pricing and the maximum computing provisioning, which helps to provide an effective reference for practical edge computing deployment. Simulation results show that our FS_PAA can 1) ensure fair offloading services for practical differentiated requests; 2) provide green offloading service for more users; 3) greatly improve the utilization of edge computing resource.

3D object classification is an important task in computer vision. In order to explore the high-order and multi-modal correlations among 3D data, we propose an adaptive multi-hypergraph convolutional networks (AMHCN) framework to enhance 3D object classification performance. The proposed network improves the current hypergraph neural networks in two aspects. Firstly, existing networks rely on hyperedge constrained neighborhoods for feature aggregation, which may introduce noise or ignore positive information outside the hyperedges. To this end, we develop the partially absorbing random walks (PARW) to hypergraph for capturing optimal vertex neighborhoods from hypergraph globally. Then, based on the PARW on hypergraph, we design a new hypergraph convolution operator to learn deep embeddings from the optimized high-order correlation, which enables effective information propagation among the most relevant vertices. Secondly, concerning the multi-modal representations in practice, the current multi-modal hypergraph learning models either treat all modalities equally or introduce abundant parameters to learn weights of different modalities. To overcome these shortcomings, we propose a simple but effective dynamic weighting strategy for combining multi-modal representations, in which the importance of each modality can be adjusted adaptively by the loss function. We apply the proposed model to 3D object classification, and the experimental results on two 3D benchmark datasets demonstrate that our method outperforms the state-of-the-art methods, testifying to the effectiveness of both our convolution method and multi-modality fusion strategy.

By fully exploiting the cooperative communication capacities among mobile terminals (MTs), the MTs can adapt the offloading designs well to the practical network with dynamic features. In this paper, joint multi-user cooperative partial offloading, transmission scheduling and computation allocating is discussed for device-to-device (D2D) underlay mobile edge computing (MEC). By considering stochastic application requests, unpredictable MTs states, time-varying channel states and computation resources, a customized application offloading model, which aims to minimize the network-wide response latency and energy consumption simultaneously, is formulated. In order to solve this non-convex and non-smooth optimization problem, an online resource coordinating and allocating scheme (ORCAS) is proposed by exploiting Lyapunov optimization theory, variable substitution technique and resource provisioning priority mechanism. Both theoretical analyses and simulation results demonstrate that the proposed ORCAS can 1) drive the application response cost converge to the minimum; 2) achieve superior performance (e.g., the average network-wide response cost under ORCAS is approximately 19.14% lower than that under partial offloading directly); 3) adapt to dynamic situations in terms of stochastic user demands and channel states.

Offloading application to cloud can augment mobile devices' computation capabilities for emerging resource-hungry mobile applications, however it can consume both much time and energy for mobile devices offloading application remotely to cloud. In this paper, we develop a newly adaptive application offloading decision-transmission scheduling scheme which can solve the above problem efficiently. Specifically, we first propose an adaptive application offloading model which allows multiple target clouds coexisting. Second, based on Lyapunov optimization theory, a low complexity adaptive offloading decision-transmission scheduling scheme has been proposed. Finally, the simulation results show that, compared with that all applications are executed locally, mobile device can save 68.557% average execution time and 67.095% average energy consumption under situations.