The integration of satellite and autonomous aerial vehicle (AAV) communications has become essential for the scenarios requiring both wide coverage and rapid deployment, particularly in remote or disaster-stricken areas where the terrestrial infrastructure is unavailable. Furthermore, emerging applications increasingly demand simultaneous mobile edge computing (MEC) and data collection (DC) capabilities within the same aerial network. However, jointly optimizing these operations in heterogeneous satellite-AAV systems presents significant challenges due to limited on-board resources and competing demands under dynamic channel conditions. In this work, we investigate a satellite-AAV-enabled joint MEC-DC system where these platforms collaborate to serve ground devices (GDs). Specifically, we formulate a joint optimization problem to minimize the average MEC end-to-end delay and AAV energy consumption while maximizing the collected data. Since the formulated optimization problem is a non-convex mixed-integer nonlinear programming (MINLP) problem, we propose a Q-weighted variational policy optimization-based joint AAV movement control, GD association, offloading decision, and bandwidth allocation (QAGOB) approach. Specifically, we reformulate the optimization problem as an action space-transformed Markov decision process to adapt the variable action dimensions and hybrid action space. Subsequently, QAGOB leverages the multi-modal generation capacities of diffusion models to optimize policies and can achieve better sample efficiency while controlling the diffusion costs during training. Simulation results show that QAGOB outperforms five other benchmarks, including traditional DRL and diffusion-based DRL algorithms. Furthermore, the MEC-DC joint optimization achieves significant advantages when compared to the separate optimization of MEC and DC.

The integration of simultaneous wireless information and power transfer (SWIPT) technology in 6G Internet of Things (IoT) networks faces significant challenges in remote areas and disaster scenarios where ground infrastructure is unavailable. This paper proposes a novel unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system enhanced by directional antennas to provide both computational resources and energy support for ground IoT terminals. However, such systems require multiple trade-off policies to balance UAV energy consumption, terminal battery levels, and computational resource allocation under various constraints, including limited UAV battery capacity, non-linear energy harvesting characteristics, and dynamic task arrivals. To address these challenges comprehensively, we formulate a bi-objective optimization problem that simultaneously considers system energy efficiency and terminal battery sustainability. We then reformulate this non-convex problem with a hybrid solution space as a Markov decision process (MDP) and propose an improved soft actor-critic (SAC) algorithm with an action simplification mechanism to enhance its convergence and generalization capabilities. Simulation results have demonstrated that our proposed approach outperforms various baselines in different scenarios, achieving efficient energy management while maintaining high computational performance. Furthermore, our method shows strong generalization ability across different scenarios, particularly in complex environments, validating the effectiveness of our designed boundary penalty and charging reward mechanisms.

Autonomous aerial vehicle (AAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this article investigates a multi-AAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization problem to minimize the MEC latency and maximize the collected data volume. This problem can be classified as a nonconvex mixed integer programming problem that exhibits long-term optimization and dynamics. Thus, we propose a deep reinforcement learning-based approach that jointly optimizes the AAV movement, user transmit power, and user association in real time to solve the problem efficiently. Specifically, we reformulate the optimization problem into an action space-reduced Markov decision process (MDP) and optimize the user association by using a two-phase matching-based association (TMA) strategy. Subsequently, we propose a soft actor-critic (SAC)-based approach that integrates the proposed TMA strategy (SAC-TMA) to solve the formulated joint optimization problem collaboratively. Simulation results demonstrate that the proposed SAC-TMA is able to coordinate the two subsystems and can effectively reduce the system latency and improve the DC volume compared with other benchmark algorithms.

The rapid development of the Internet-of-Things (IoT) also brings security and other problems. Device identification is a crucial tool for IoT security issues, which can detect and prevent cyber-attacks. Feature selection is an effective data preprocessing technique in IoT device identification, which can improve the performance of classification and reduce computational complexity. In this paper, we propose a novel wrapper feature selection approach based on the improved binary honey badger algorithm (IBHBA) to select features in IoT traffic. Four improved factors are employed in IBHBA to expand the search scope, balance the exploration and exploitation phases, and enhance the search capability. Moreover, a binary mechanism is adopted to make the algorithm more suitable for feature selection in IoT device identification. The experimental results on several real IoT traffic datasets denote that IBHBA outperforms some classical and latest comparison algorithms in the feature selection of IoT device identification.

For horizontal directional drilling (HDD), traditional measurement-while-drilling magnetic surveying system suffers from interferences on geomagnetic field and accumulated errors. To solve these problems, a new method based on a rotating magnetic field for measuring the azimuth and the position of drilling bit is proposed. A utility algorithm is deduced, and a fitting model is established. Simulations were conducted using MATLAB software to prove the feasibility of the method. The results show that the method has an adequate accuracy for HDD and is easy to implement for underground locations.

In image detection, it is often necessary to detect the sizes of foreign bodies in binary images. A new method to detect particle sizes of objects is proposed in this paper based on binary image analysis. A difference sum template is first used to extract edge of an object, a half-8-connected domain algorithm is then employed to obtain the coordinates set that corresponds to each object, and finally evaluates the particle size of every object through rectangular segmentation. In comparison with the method of geometrical principal axis and the MER method, the algorithm can obtain particle sizes of objects in binary image more rapidly and more accurately.