In remote industrial Internet of Things (IIoT) monitoring systems, the uncrewed aerial vehicle (UAV) serves as supplementary infrastructure to aggregate data from a large number of distributed sensors, and achieve industrial operation intelligence. In the wireless data aggregation process, using conventional orthogonal multiple access techniques face challenges such as scarce bandwidth, high communication latency and energy consumption. To tackle these issues, the over-the-air computation (AirComp) technique has emerged. It allows concurrent data transmissions from sensors, as well as integrates communication and computation processes, ultimately enabling fast data aggregation. However, the energy consumption issue remains unresolved. In this paper, we exploit spatial correlations among sensor measurements, and design an energy-efficient AirComp in UAV-assisted IIoT networks, where only a subset of sensors transmit data instead of all sensors. Then, we derive a closed-form expression for the mean square error (MSE) of each combination under a specific number of sensor transmissions. By jointly optimizing the UAV deployment and pre-coding coefficients of sensors, we formulate the problem of minimizing MSE for each combination of transmitted sensors. Furthermore, the MSE optimization algorithm is developed to output the average MSE of all combinations. Finally, we evaluate the average MSE and network lifetime performance of proposed scheme.

To alleviate the pressure on the ground base station (BS) from intensive video requests, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has become a promising and flexible solution. The UAV carries a MEC server to provide caching and transcoding services for adaptive bitrate video streaming, which can reduce duplicate transmissions of the BS and the content acquisition latency of users, while improving the flexibility of video delivery. However, considering the uncertainty of user requests and content popularity distribution, improving the robustness of video caching is a challenge to promote practical applications. Thus, by integrating caching and transcoding on the UAV, as well as backhaul retrieving, we study the bitrate-aware video caching and processing with uncertain popularity distribution. Then, the problem of joint cache placement and video delivery scheduling under the worst-case distribution is formulated to minimize the total expected system latency with energy consumption constrained. Specifically, we use$\zeta$-structure probability metrics to characterize the uncertainty and construct confidence sets of arrival distribution. Furthermore, a distributionally robust latency optimization algorithm based on convex optimization theory is designed to obtain a robust solution. Finally, we conduct extensive simulations using real-world datasets to evaluate the effectiveness and robustness of the proposed scheme.

Federated Learning (FL) is an emerging distributed learning paradigm with the privacy-preserving advantage of collaboratively training a shared model across multiple participants. Considering the prevailing device heterogeneity circumstance in practice, asynchronous interaction is introduced into FL to break the straggler barrier of synchronization, at the cost of significant accuracy degradation derived from model staleness. Although quite a few works attempt to partially mitigate the detrimental impact after occurring staleness issue, they neglect to control the overall staleness degree of clients-side local models from the whole training perspective, resulting in highly-stale models for aggregation and slow convergence speed. To this end, we propose a Staleness-Controlled Asynchronous Federated Learning (SC-AFL) method, which enables to restrict staleness degree of local models within a certain bound via dynamically tuning the aggregated strategy of each round, aiming to strike a good balance between accuracy guarantee and convergence acceleration. Specifically, we leverage the Lyapunov optimization framework to decouple the troublesome round-coupling problem into the single-round sequential solving problem, and further develop a deterministic algorithm that selects the aggregated number of clients to minimize training time under the constraint of maintaining staleness queue stability. Besides, we derive the theoretical convergence analysis of SC-AFL and also present the upper bound of the performance gap with the optimum. Extensive experiments on three datasets demonstrate the superiority of SC-AFL in terms of time-to-accuracy speedup on both IID and Non-IID data distributions, achieving a good balance between model accuracy and convergence efficiency in AFL system.

Neighbor discovery (ND) is a critical network initialization stage, particularly challenging for highly-dynamic unmanned aerial vehicle (UAVs) with directional antennas. Considering that directional antennas focus signal energy in one direction, successful ND requires a pair of UAVs to point antennas towards each other simultaneously. However, due to the inherent constraints of autonomous UAVs (e.g., high mobility and decentralized coordination), spatial alignment of directional beams is difficult. Existing works resort to ideal assumptions (e.g., clock synchronization, assistance of omni-directional antennas and prior information) for simplification. Moreover, previous ND algorithms assume unlimited switching capability for directional antennas, often unrealistic for traditional mechanically steered antennas. In this paper, we proposeSkyOrbs, a fast directional ND algorithm for UAV networks without these ideal assumptions. To reduce ND latency,SkyOrbspresents a skip scanning strategy, dynamically adjusting antenna rotation speed to enhance discovery probability. Furthermore, to mitigate the uncertain rotation overhead induced by time-variant angular speed,SkyOrbsdesigns a novel antenna scanning path that accommodates limited mechanical rotation capacity. We analyze the theoretical delay performance ofSkyOrbs, and expand its applicability to broader scenarios. Evaluation results show thatSkyOrbscan reduce discovery latency by 40.8% and rotation overhead by 55.0% compared to the baseline method.

For mobile robotic networks in industrial scenarios, reliable and energy-efficient communications are crucial yet challenging. Fortunately, collaborative beamforming (CB) emerges as a promising solution, which can increase the transmission gain and reduce the transmit power of robots by constructing a mobile robot-enabled virtual antenna array (MRVAA). The performance of CB is tightly related to robot positions, necessitating proper robot selection. However, robot selection may expose the network to the risk of unbalanced energy distribution, reducing network lifetime. Additionally, the mobility and variable numbers of robots require flexible and scalable robot selection algorithms. To tackle these challenges, we first formulate a multi-objective optimization problem to reduce the maximum sidelobe level (MSLL) of MRVAA while minimizing the standard deviation of the network energy distribution (SDNED) by selecting robots for CB. Then, based on distributed multi-agent learning (MARL), we propose an effective and scalable robot selection algorithm with energy considered (RoSE) to solve the problem, where difference-rewards function (DRF) and policy sharing are designed for enhancing convergence rate and policy stability. Simulation results show that the RoSE has the scalability to positions and numbers of robots. Furthermore, RoSE surpasses existing selection algorithms in network lifetime and time efficiency, while still maintaining comparable MSLL.