The use of unmanned aerial vehicles (UAVs) for long-range energy replenishment and data collection in wireless sensor networks (WSNs) across various scenarios seeks to minimize energy consumption by optimizing the scheduling and visiting sequence of the UAV’s hovering points. The problem can be modeled as a joint scheduling and trajectory optimization problem (JSTOP). To address the challenges posed by the limitations of the traditional JSTOP algorithm, we propose a multi-objective optimization algorithm-based joint scheduling and trajectory optimization scheme (MOJSTOP), which consists of two sub-algorithms, the improved multi-objective simulated annealing algorithm (Improved MSA) and the improved non-dominated sorting genetic algorithm II (Improved NSGA-II). The Improved MSA algorithm is well-suited for small-scale scenarios. In contrast, the Improved NSGA-II algorithm is designed for large-scale scenarios where the UAV has limited energy capacity. In addition, it introduces a new optimization objective that focuses on the energy utilization efficiency of the UAV. The simulation results demonstrate that the proposed MOJSTOP scheme exhibits superior computational efficiency, convergence, and stability in solving the JSTOP problem compared to other algorithms and is applicable to various terrains.

Edge-end collaborative learning greatly reduces latency by eliminating the need for processing on the cloud side, showing promising results in machine learning applications due to collaborating on training tasks through the computational resources of edge servers and end devices. However, current edge-end collaborative learning methods suffer from unbearable latency which result from heavy transmission burden and long synchronization time. We propose a new Prototype-based Semi-Asynchronous Edge-end Collaborative Learning approach (ProSACL) that carefully integrates prototype-based end-side training and edge clustering, allowing any end device to synchronize knowledge, significantly reducing the time spent on training latency. Our approach includes (1) prototype training and asynchronous prototype transfer on end devices: Unlike traditional training methods, we use the average of the same class of feature vectors, i.e., the prototype, as the knowledge transfer means, which significantly reduces the transfer burden and eliminates the need for end devices to wait for other devices to finish. (2) prototype-based clustering and asynchronous aggregation on the edge server: The end devices are segmented by prototype-based clustering to obtain unbiased prototypes for performance enhancement, and prototypes from different rounds are aggregated for fine-grained knowledge transfer. We evaluate the proposed approach by training on three datasets, which show substantial performance improvement compared to previous work.

Unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) is emerging as a promising paradigm to provide aerial-terrestrial computing services close to mobile devices (MDs). However, meeting the demands of computation-intensive and delay-sensitive tasks for MDs poses several challenges, including the demand-supply contradiction between MDs and MEC servers, the demand-supply heterogeneity between MDs and MEC servers, the trajectory control requirements on energy efficiency and timeliness, and the different timescale dynamics of the network. To address these issues, we first present a hierarchical architecture by incorporating terrestrial-aerial computing capabilities and leveraging UAV flexibility. Furthermore, we formulate a joint computing resource allocation, computation offloading, and trajectory control problem to maximize the system utility. Since the problem is a non-convex mixed integer nonlinear programming (MINLP), we propose a two timescale joint computing resource allocation, computation offloading, and trajectory control (TJCCT) approach. In the short time scale, we propose a price-incentive method for on-demand computing resource allocation and a matching mechanism-based method for computation offloading. In the long time scale, we propose a convex optimization-based method for UAV trajectory control. Besides, we prove the stability, optimality, and polynomial complexity of TJCCT. Simulation results demonstrate that TJCCT outperforms the comparative algorithms in terms of the utility of the system, the QoE of MDs, and the revenue of MEC servers.

With the widespread use of unmanned aerial vehicles (UAVs), the issue of data confidentiality is becoming more and more prominent. For this reason, this paper intends to build a UAV-enabled virtual antenna array (UVAA) and communicate with the BS on a vessel in a maritime environment using cooperative beamforming (CB) techniques. To improve safety, the UAV elements can carry optimal excitation current weights and fly to appropriate locations for CB transmission. However, this will result in more energy consumption. To address several critical issues in UAV communication, a secure communication multi-objective optimization problem (SCMOP) is proposed to simultaneously improve the total secrecy rate, the total maximum sidelobe levels (SLLs), and the total motion energy consumption of the UAVs by jointly optimizing the position and the excitation current weights. Because the SCMOP is non-convexity and NP-hard, we adopt a non-dominated sorting whale optimization algorithm(INSWOA) with chaotic solution initialization, optimal position update based on the sine cosine algorithm (SCA), and adaptive weights to solve the problem. Experiments show that this method can better solve the SCMOP and is superior to some existing standard methods.

With the rapid development of wireless communication, unmanned aerial vehicle (UAV) networks have received extensive attention and been applied in many fields, but some challenges exist in their applications, such as the issue of security when implementing communication. In this paper, a virtual antenna array (VAA) is formed in multiple UAV units using collaborative beamforming (CB) technology. Under the interference of multiple eavesdroppers, secure communication with the ground base station (BS) is achieved. To achieve better performance, we formulate a multi-objective optimization problem for UAV network security communication (SCMOP), and jointly optimize the positions and excitation current weights of UAVs to set the null values in the direction of known eavesdroppers, reduce the sidelobe levels (SLLs) and enhance the directivity of the main lobe (ML). Since the formulated SCMOP is an NP-hard problem, we propose an improved the third non-dominated sorting genetic algorithm (IMNSGA-III) with chaos operator and crossover and mutation operators to solve the problem in this paper. The simulation results show that the IMNSGA-III can solve the SCMOP well and obtain the best results compared with other benchmark algorithms.

Unmanned aerial vehicles (UAVs) have found enor-mous applications and are expected to bring tremendous op-portunities in the forthcoming 5G/6G wireless communications. However, there exist some challenges that need to be tackled for achieving reliable communications in UAV networks due to the open channels. In this work, we propose to perform a UAV-enabled virtual antenna array (UVAA) and adopt collaborative beamforming (CB) to transmit data towards different base stations (BSs), while an aerial user (AU) that locates near the UVAA is carrying out another task. In the considered scenario, we formulate a reliable communication multi-objective optimization problem (RCMOP) to simultaneously maximize the total receiving signal-to-noise ratio (SNR) of the BSs, minimize the total receiving SNR of the AU and minimize the flying energy consumptions of UAVs, which is achieved by cooperatively optimizing the positions and excitation current weights of UAVs and the order of transmitting data towards different BSs. The formulated RCMOP is sophisticated so that we propose an improved multi-objective salp swarm algorithm (IMSSA) to solve the problem. Simulation results verify that the proposed IMSSA can effectively solve the formulated RCMOP and it outperforms some other traditional strategies,

Copy-move forgery is the most predominant forgery technique in the field of digital image forgery. Block-based and interest-based are currently the two mainstream categories for copy-move forgery detection methods. However, block-based algorithm lacks the ability to resist affine transformation attacks, and interest point-based algorithm is limited to accurately locate the tampered region. To tackle these challenges, a coarse-to-fine model (CFM) is proposed. By extracting features, affine transformation matrix and detecting forgery regions, the localization of tampered areas from sparse to precise is realized. Specifically, in order to further exactly extract the forged regions and improve performance of the model, a two-level local search algorithm is designed in the refinement stage. In the first level, the image blocks are used as search units for feature matching, and the second level is to refine the edge of the region at pixel level. The method maintains a good balance between the complexity and effectiveness of forgery detection, and the experimental results show that it has a better detection effect than the traditional interest-based copy and move forgery detection method. In addition, CFM method has high robustness on postprocessing operations, such as scaling, rotation, noise, and JPEG compression.