Cross-scene hyperspectral image classification is currently receiving widespread attention. However, domain adaptation-based methods usually perform domain alignment by accessing specific target scenes during training and require retraining for new scenes. In contrast, domain generalization only trains using the source domain and then gradually generalizes to unseen domains. However, existing methods based on domain generalization ignore the impact of domain invariant semantics on the invariant representation of the domain. To solve the above problem, an invariant semantic domain generalization shuffle network for cross-scene hyperspectral image classification is proposed, which follows a framework on the generative adversarial network. Feature style covariance in style and content randomization generator with invariant semantic features is designed to safely extend the style and content of features without changing the domain invariant semantics. We proposed a spatial shuffling discriminator , which can reduce the impact of special spatial relationships within the domain on class semantics. In addition, we proposed a dual sampling direct adversarial contrastive learning strategy. It uses a dual sampling in two-stage training design to prevent the model from lazily entering the local nash equilibrium point. And based on dual sampling, directly adversarial contrastive learning using clearer contrastive samples is used to reduce the difficulty of network training. We conduct extensive experiments on four datasets and demonstrate that the proposed method outperforms other current domain generalization methods. The code will be open source at https://github.com/jixiangyu0501/ISDGS .

The unmanned aerial vehicle (UAV), serving as an aerial platform, is capable of assisting IoT devices in providing global emergency coverage. To incentivize the macro base station (MBS) operator to lease idle spectrum resources to the UAV operator, an appropriate cross-layer spectrum trading mechanism needs to be established. To address the time-variation and asymmetry of information in spectrum trading, this article adopts multiterm contract theory to analyze this issue. We formulate the problem of designing multiterm optimal trading for these three scenarios: 1) 1-D static transactions; 2) multidimensional static transactions; and 3) dynamic transactions. We employed deviations instead of derivatives to decompose the original problem into several subproblems. Based on the numerical results, we conclude that multiterm transactions cannot be simply decomposed into repeated single-term transactions. The optimal contract design should fully consider the composition of private information in multiterm transactions, as well as obtaining discounts from future revenue commitments.

The characteristics of UAV-assisted edge computing-low cost, high mobility, and fast response speed-render it more suitable for the marine environment. Deploying edge servers in UAVs is a feasible approach to enhance the performance of the marine communication network; however, resource and energy constraints exist when applying UAVs to the marine network. This paper is based on the UAV-assisted edge computing architecture with NOMA. We establish multi-dimensional variable coupling constraints for task completion timeframe, task offloading decision, power, computational resources, and UAV trajectory. We design a weighted minimization model for system energy consumption. We propose a vertically layered alternating iteration optimization scheme. To escape local optimal solutions effectively, a simulated annealing algorithm is employed for the integer planning problem in the outer layer. The inner layer problem is decomposed into subproblems: user device transmit power, computational resource allocation, and corresponding UAV trajectory planning, given a task offloading decision scheme. However, these decomposed subproblems remain non-convex. To address this, the constraint structure is converted, and the problem is approximated and solved as a convex optimization problem using successive convex approximations. The system utility optimization is approximated using a two-layer algorithm and iterative optimization of subproblems. Simulation experiments show that the proposed joint optimization scheme not only reduces the total energy consumption of the system but also outperforms the scheme under OMA in optimizing task offloading costs.

In situations where ground communication networks are unavailable due to disasters or other reasons, drones can leverage their flexibility as airborne base stations to provide emergency coverage for ground users. However, the Line-of-Sight-dominant (LoS-dominant) channel between the Drone Small Cell (DSC) and ground users often results in strong downlink interference to non-cooperative users. This interference can hinder the full utilization of spectrum resources. To address this issue, this paper proposes an interference cancellation scheme based on cooperative transmission. The paper first discusses the power allocation strategy for the DSC. It then proposes an ideal phase condition, which is shown to be difficult to achieve. Finally, by taking into account the power allocation optimization and phase setting strategy for all base station-user pairs, the paper formulates a complete information static game problem. The Nash Equilibrium is obtained by combining the strategy equations of all DSCs. Simulation results demonstrate that the proposed scheme effectively eliminates interference in the multi-drone auxiliary communication system, and achieves a considerable achievable rate.

Internet of Things (IoT) devices operate in an untrusted environment, and blockchain technology can provide a secure distributed collaboration method. However, the widespread distribution of heterogeneous devices in the IoT creates complex information asymmetry issues, leading to inefficient resource allocation. This is especially true when information asymmetry occurs in long-term cooperative relationships, where contract design often faces more complex incentive problems. This article focuses on studying long-term cooperation between IoT devices based on blockchain technology and proposes a solution to information asymmetry in long-term cooperation based on contract theory and reputation mechanism. 1) We design more flexible pricing strategies based on contract theory to attract more devices to become validators and provide different long-term job contracts to overcome information asymmetry issues in the cooperative process. 2) We propose a subjective reliability-linked reputation evaluation mechanism to construct the career of validators, which can constrain unethical behavior and incentivize validators to comply with rules and protect data security in long-term cooperation. Experimental results show that our scheme can improve device willingness to cooperate, overcome information asymmetry in long-term cooperation, improve the efficiency of computing resource allocation, reduce network security risks, achieve long-term trusted cooperation between devices, and provide effective security for the development of the IoT.

Unlike normal images, remote sensing images (RSI) often contain complex backgrounds and multi-scale targets with arbitrary directions. This makes existing detection methods ineffective. In contrast to the usual rotating frame RSI target detection methods, the Reppoints-based method can learn autonomously to capture target features of arbitrary pose based on the target’s characteristics. Therefore, a new Reppoints-based detector is proposed in this letter to improve the accuracy from multiple perspectives. In order to better expand the receptive fields and obtain finer features, the multi-scale task enhancement Network (MSTEN) preserves the multi-scale receptive fields and multi-scale information through the deformable convolution and the skip connection, while improving the feature extraction capability of the network for targets of arbitrary orientation. At the same time, the network adapts the features to the characteristics of different tasks in order to better suit the needs of the respective tasks. Finally, the dynamic reppoints learning (DRL) is proposed in order to select samples that perform well in both the regression and classification tasks. The experimental results on two challenging datasets, DOTA and HRSC2016, show the effectiveness of our proposed method.

Oriented object detection in remote sensing images (RSI) has received more and more attention due to its broader applicability in natural scenes relative to horizontal bounding boxes. The complex scenes and multi-scale targets in remote sensing images make it often difficult for existing studies to extract key features of the targets effectively. At the same time, due to the problem of feature inconsistency in different layers, the direct fusion of these features is likely to cause feature conflicts, resulting in degradation of detection accuracy. To solve these problems, the dual-path multi-head feature enhancement detector (DP-MHFE Det), which contains two novel architectures, is proposed in this letter. The dual-path rotation feature aggregation module (DP-RFAM) improves the feature extraction capability of the network for rotating objects through dual-path structure and deformable convolution (DCN). To use these features effectively, the multi-head multi-level feature fusion enhancement network (MMFFENet) is proposed to guide the feature layers to learn and retain the key features they need autonomously, and then enhance their features according to the characteristics of different subtasks. Experiments conducted on two remote sensing datasets, DOTA and HRSC2016, show that DP-MHFE Det is faster than almost all detection methods compared to the state-of-the-art methods while showing strong competitiveness in accuracy.

In the emerging Internet-of-Things (IoT) paradigm, the lifetime of energy-constrained devices (ECDs) cannot be ensured due to the limited battery capacity. In this article, unmanned aerial vehicles (UAVs) are served as carriers of wireless power chargers (WPCs) to charge the ECDs. Aiming at maximizing the total amount of charging energy under the constraints of the UAVs and WPCs, a multiple-period charging process problem is formulated. To address this problem, bipartite matching with one-sided preferences is introduced to model the charging relationship between the ECDs and UAVs. Nevertheless, the traditional one-shot static matching is not suitable for this dynamic scenario, and thus the problem is further solved by the novel multiple-stage dynamic matching. Besides, the wireless charging process is history dependent since the current matching result will influence the future initial charging status, and consequently, the Markov decision process (MDP) and Bellman equation are leveraged. Then, by combining the MDP and random serial dictatorship (RSD) matching algorithm together, a four-step algorithm is proposed. In our proposed algorithm, the local MDPs for the ECDs are set up first. Next, using the RSD algorithm, all possible actions can be presented according to the current state. Then, the joint MDP is built based on the local MDPs and all the possible matching results. Finally, the Bellman equation is utilized to select the optimal branch. Finally, simulation results demonstrate the effectiveness of our proposed algorithm.

Mobile edge computing (MEC) distributes computation and storage resources to network edges, which enables computation-intensive and latency-critical applications for resource-constrained devices. In a MEC network, computation offloading decision plays a critical role, which allocates specific computation nodes (CNs) for different tasks. However, few existing works consider the practical scenario in which different tasks have different arrival time and relative computation deadlines. In this paper, the computation offloading problem with dynamic service requests is considered, and at the same time we assume different computation tasks have different response deadlines and computation deadlines. Taking the quality of service (QoS) requirements of all users and the utility constraints of tasks and CNs into consideration, the service-oriented and CN- oriented optimization problem are formulated, respectively. Next the online matching game is introduced to model the dynamic association between the tasks and CNs, and then the rank based final acceptation (RBFA) matching algorithm is proposed to solve the problems. Finally, we validate the effectiveness of the proposed algorithm through the simulation results.