The rapid development of drone technology has spurred significant interest in multi-UAV collaborative systems, particularly for complex tasks like cooperative target jamming. However, realizing their full potential is hindered by significant challenges, primarily stemming from uncertain three-dimensional (3-D) target positions and operational time constraints. These factors complicate crucial aspects like path planning and efficient task allocation, ultimately jeopardizing jamming mission success. Furthermore, the specific complexities introduced by uncertain 3-D target positions are often overlooked in existing cooperative jamming strategies. To address these issues, we propose cooperative multi-agent jamming techniques using reinforcement learning (RL) to maximize interference effectiveness against designated targets under target position uncertainty. Our methodology is based on a task framework that unifies the models of target position uncertainty, 3-D probabilistic perception for high-fidelity UAV sensing, and directional antenna interference to achieve optimal jamming. Within this framework, we formalize the task as a Markov Decision Process (MDP) and employ reinforcement learning to optimize collaborative jamming policies under target positions uncertainty. The proposed RL algorithm, by utilizing both individual and collaborator rewards, adaptively balances exploration and exploitation across different mission stages. This balance is achieved by adjusting the amplitude of noise used for action selection. We conducted simulation experiments with various UAV, target and no-fly zone configurations to validate the effectiveness of our proposed method, demonstrating its scalability and strong joint task performance in achieving jamming objectives.

Mobile Edge Computing (MEC) systems integrated with Reconfigurable Intelligent Surfaces (RIS) can significantly enhance task offloading efficiency and overall system performance in complex urban environments by enabling high-quality, energy-efficient communication. This paper focuses on urban scenarios with obstacles such as trees and high-rise buildings, and investigates the joint optimization of task offloading decisions, resource allocation, and RIS control in multi-user MEC systems. In the proposed framework, RIS panels are deployed on building exteriors to assist communication, while terminal devices (TDs) can either process tasks locally or offload them to MEC servers. The primary objective is to minimize the total system delay and energy consumption while ensuring task success rates and meeting both energy and latency constraints. To accurately model the environment, differentiated channel models are adopted: Rayleigh fading for occluded user-MEC links and Rician fading for unobstructed RIS-user and RIS-MEC links. A comprehensive joint optimization problem is formulated, encompassing task offloading decisions, user-server association, bandwidth allocation, computing resource scheduling, and RIS phase shift configuration. To solve this multidimensional optimization problem, we propose an MCEG-PSO algorithm-Particle Swarm Optimization enhanced with crossover, mutation, and evolution-ary game theory mechanisms. Simulation results demonstrate that the proposed algorithm effectively improves wireless link quality, reduces delay and energy consumption, and enhances task offloading efficiency in complex urban environments.

In multi-UAV wireless communication networks, limited spectrum resources and environmental interference jointly pose significant challenges. To address the limitations of spectrum resources and the uncertainty of interference distribution in complex interference environments, this paper proposes an intelligent cooperative scheduling approach based on local sensing assistance. Each UAV autonomously senses the local spectrum state-including channel availability and interference intensity-and collaboratively makes resource selection and allocation decisions through multi-agent coordination. Considering the uncertainty of spectrum dynamics and the coupling of inter-agent interference, we construct a sensing-driven spectrum scheduling model and introduce a Multi-Agent Dueling Double Deep Q-Network (MAD3QN) to enable decentralized spectrum sharing and conflict avoidance without relying on centralized control. The proposed method emphasizes robust cooperative scheduling mechanisms under adversarial interference conditions, improving communication efficiency and task resilience in dynamic environments. Simulation results demonstrate that our method outperforms existing schemes in terms of spectrum utilization, system throughput, and anti-interference capability, validating its effectiveness for efficient cooperative communication in dynamic spectrum environments.

Unmanned aerial vehicles (UAVs) are increasingly deployed in next-generation communication networks due to their flexibility, low cost, and ability to provide rapid connectivity in dynamic environments. In such scenarios, efficient path planning is essential to ensure reliable communication links, minimize energy consumption, and improve overall mission performance. Traditional reinforcement learning approaches, such as the multi-agent deep deterministic policy gradient (MADDPG), have demonstrated strong performance in multi-UAV coordination and decision making. However, their high computational complexity and large-scale neural architectures restrict their deployment on resource-constrained UAV platforms with limited onboard processing and energy budgets. To address this challenge, we propose a lightweight path planning framework that incorporates knowledge distillation into the actor-critic structure of MAD-D PG. In the proposed method, a large teacher network is first trained to learn optimal strategies in complex communication environments. The knowledge learned by the teacher is then transferred to a compact student network, which significantly reduces the number of parameters and inference latency. This design enables the UAVs to achieve near real-time decision making while maintaining high planning accuracy. Extensive simulation experiments validate the effectiveness of the proposed approach. Results show that the distilled model achieves comparable or even improved performance compared to the original MADDPG framework, while reducing computational overhead and convergence time. These advantages make the proposed method highly suitable for real-time UAV communication scenarios, especially in dynamic and resource-limited environments. The study provides a promising direction for integrating lightweight deep reinforcement learning with UAV communication and path planning systems.

In this work, we introduce an MLP-Mixer-based unrolling UAMPSBL approach for near/far-field structured channel estimation in terahertz extremely large-scale antenna arrays (ELAA) systems. The MLP-Mixer-based unrolling UAMPSBL approach can effectively alleviate the diverge problem of the original UAMP-SBL algorithm. Moreover, the MLP-Mixer-based unrolling UAMPSBL approach has a more simplified structure and suite for block-sparse structures compared to the CNN model. Simulation results show that the proposed MLP-Mixer-based unrolling UAMPSBL approach significantly outperforms the benchmark algorithms for near/far-field channel estimation problem.

In complex electromagnetic environments, targets that need to be interfered with often possess high levels of concealment and anti-interference capabilities. Additionally, due to the dynamic characteristics of these targets, interference tasks must be conducted within strict time constraints to ensure interference effect. In this article, we adopt a reconnaissance-first approach for concealed targets. After detecting the accurate location of the target, we deploy autonomous aerial vehicles (AAVs) to interfere with the targets. First, we established a AAV swarm task scheduling optimization model after considering constraints, such as target threat range, priority of reconnaissance and interference tasks, interference task time, and AAV energy consumption. Meanwhile, we model the anti-interference capability of the target as a threat range. Second, we propose a nondominated sorting genetic algorithm based on distance in the solution space and a dynamic parent selection strategy (DPSNSGA-II) to solve AAV resource scheduling optimization problem. The diversity of the population is increased and the situation of falling into local optima is reduced by improving the parent individual selection strategy, mutation strategy, and elite solution retention mechanism. Finally, we construct two data sets of varying sizes to evaluate the quality of solution sets and the convergence performance of the proposed algorithm. The simulation results indicate that the proposed DPSNSGA-II algorithm has better result for population diversity and convergence compared to state-of-the-art algorithms.

The autonomous underwater vehicle (AUV) are experiencing widely deployment for the advantages of low cost and flexibility. However, the open light-of-sight communication links between clusters to clusters (C2C) in flying ad-hoc networks (FANETs) are vulnerable to malicious jamming in communication denied environments (CDEs), leading to the C2C communication with low-signal-to-interference-plus-noise ratio (SINR). Aiming at the collaborative search inefficiency resulting from unreliable C2C communication caused by low SINR in existing researches, we propose a distributed collaborative search planning method based on anti-jamming strategy by virtual multiple input-multiple output (VMIMO) in FANETs. First, we formulate the anti-jamming C2C collaborative communication model based on VMIMO to combat the malicious jamming utilizing the advantage of large-scale AUVs under limited resource. Then, we model the overall search objective function, considering search, connectivity and collision avoidance between AUV clusters. And we establish a distributed collaborative search optimization problem model based on distributed model predictive control (DMPC) framework, so that all AUV clusters can optimize the overall search objective by interacting with neighbor clusters. Finally, we propose a three-level collaborative optimization problem model with joint considering search, anti-jamming communication and network connectivity. To calculate the optimal solution, an alternating optimization method based on distributed stochastic algorithm is proposed to solve the three-level collaborative optimization problem model. Moreover, we conduct extensive simulations and the results show that the proposed method is efficient in terms of the anti-jamming ability and search efficiency compared with other state-of-the-art algorithms without cooperative strategy.

Sparsity-based joint active user detection and channel estimation (JADCE) algorithms are crucial in grant-free massive machine-type communication (mMTC) systems. The conventional compressed sensing algorithms are tailored for noncoherent communication systems, where the correlation between any two measurements is as minimal as possible. However, existing sparsity-based JADCE approaches may not achieve optimal performance in strongly coherent systems, especially with a small number of pilot subcarriers. To tackle this challenge, we formulate JADCE as a joint sparse signal recovery problem, leveraging the block-type row-sparse structure of millimeter-wave (mmWave) channels in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Then, we propose an efficient difference-of-convex function algorithm (DCA) based JADCE algorithm with multiple measurement vector (MMV) frameworks, promoting the row-sparsity of the channel matrix. To mitigate the computational complexity further, we introduce a fast DCA-based JADCE algorithm via a proximal operator, which allows a low-complexity alternating direction multiplier method (ADMM) to resolve the optimization problem directly. Finally, simulation results demonstrate that the two proposed difference-of-convex (DC) algorithms achieve effective active user detection and accurate channel estimation compared with state-of-the-art compressed sensing based JADCE techniques.

Unmanned aerial vehicle (UAV) assisted communication has emerged as a powerful technology for reliable and flexible emergency communications (e.g., earthquakes, hurricanes and floods), especially when the mobile infrastructure is seriously damaged. UAV assisted mobile edge computing (UAV-MEC) system can be deployed in the natural disaster area as communication relay or air mobile base stations to resume communication and provide computing resources for the users in disaster areas. However, the optimized resource allocation performance of UAV-MEC system can be further guaranteed with the human-fusion decision making. In this paper, we construct an emergency human-machine fusion UAV-MEC system consisting of multiple UAVs equipped with computing resources, and the human-machine decision makings are fused for UAV deployment. In order to solve the resource allocation problem of human-machine fusion UAV-MEC system, we establish an human-machine deep integration model for UAV-MEC system, and the UAVs are dispatched reasonably through human-machine fusion decision makings to maintain efficient communication in emergency communication areas. To minimize task latency and improve the computation efficiency in emergency human-machine fusion UAV-MEC system, we consider the number of dispatched UAVs, deployment plans, flight plans, and simultaneously optimize the task allocation scheme, priority order, and task offloading ratio. We propose a reinforcement learning framework combined with evolutionary algorithms, which is named as multi-agent Q-net enhanced cooperative genetic algorithm (MQCGA), for resource allocation of UAV. Based on neural network forecasts, the greedy rate during training processing can be dynamically controlled, and the learning ability of different agents can be strengthened. Simulation experiments are conducted to evaluate the proposed framework, and the results show that our proposed MQCGA algorithm is significantly superior to other algorithms in terms of latency and energy consumption. Note to Practitioners—With the development of MEC and the popularity of UAVs, the potential of UAV-assisted MEC draw much attention from industrial field. Considering the lack of communication capabilities in a certain area in an unexpected situation, UAVs can be quickly deployed to corresponding locations and provide computing services. In this paper, an UAV-MEC system that integrates human-machine decision-making for emergency communication situations, named human-machine fusion UAV-MEC system, is considered. The system divides the scene into regions, models users and UAVs, and provides detailed deployment schemes, maximizing the practicality and applicability of the scene. In order to improve the communication efficiency in the case of emergency communication, this paper proposes a new resource scheduling algorithm and adds human-machine decision-making to enable UAVs to continuously provide efficient services for a certain area. The experimental results provide practitioners with a theoretical basis, such as the task completion time, UAV energy consumption and computing resource scheduling. Applying the system to actual scenarios also requires two preconditions of the system, one is the information collected by the large UAV, and the other is the communication among the UAVs. These two preconditions facilitate the human-machine fusion UAV-MEC system deployment in practical applications.

Unmanned aerial vehicle (UAV) swarm shows great potential as an effective tool for target tracking through completing complex tasks by collaboration of heterogeneous UAVs. However, UAV swarm scheduling faces challenges with poor quality communication and obstacles, especially in communication denial environment with multiple obstacles. To overcome these challenges, first, this paper proposes a scheduling slot model which divides the scheduling process into multiple time slots, allowing UAVs to communicate in communication slots while predicting instead of communication in communication denial slots. In communication denial slots, this model utilizes route fitting and two-stage Kalman filtering for UAV location prediction and optimizes UAV scheduling to align with predicted positions. In enabled slots, this model corrects position deviations to obtain precise UAV locations manually. Then, we propose an obstacle avoidance strategy to facilitate swarm scheduling for target searching under communication constraints. The obstacle avoidance strategy simplifies obstacles as regular hexagons and facilitates the determination of UAV avoidance routes by introducing intermediary points. Finally, to optimize UAV scheduling strategy, we propose a region co-evolution algorithm (RCEA), which emphasizes the collaboration among diverse individuals or populations. RCEA adopts area evaluation and Pareto strategy to enhance scheduling efficiency with following three steps. RCEA divides the overall scheduling region into multiple sub-regions, generates the foundational solution pool through the implementation of the area evaluation or Pareto strategy, and then proceeds to execute the region cooperation process base on the foundational solution pool. Simulation experiments are conducted to validate the performance of human-UAV interaction scheduling model with proposed scheduling methods and obstacle avoidance strategy. The simulation results demonstrate that RCEA outperforms other scheduling algorithms for UAV swarm in communication denial environment with multiple obstacles. Note to Practitioners—This paper addresses challenges inherent in real-world application scenarios, and the proposed algorithm has the potential to bring many benefits to practitioners. Firstly, the scheduling slot model can be applied not only to UAV swarm for target searching but can also be extended to other swarm devices for complex tasks with collaboration relying on communication support while facing poor quality communication or obstacles. Secondly, the proposed RCEA focuses on collaboration and region partitioning, the algorithm demonstrates remarkable scalability, effectively tackling challenges across diverse scales and complexities. Thirdly, the experimental scenarios can serve as a validation dataset for other peer researchers, and although the simulation experiment is based on a 2D movement model, this study still offers theoretical support applicable to a 3D movement model.

Specific Emitter Identification (SEI) is a critical component of the Industrial Internet of Things (IIoT), enabling effective identification and validation of unauthorized communication devices, thereby preventing malicious interference and signal spoofing. However, SEI methods based on deep learning involve significant computational overhead, and SEI methods based on feature engineering require specialized expertise for feature design, limiting their ability to capture complex patterns. In this paper, we propose a data-knowledge dual-driven adaptive feature fusion approach for specific emitter recognition. Specifically, we present an adaptive feature fusion strategy that integrates domain experts’ prior knowledge with the complex feature recognition capability of deep learning models to achieve efficient SEI classifiers. The approach is evaluated using Automatic Dependent Surveillance-Broadcast (ADS-B) data. The experimental results demonstrate that the proposed method achieves a higher level of identification accuracy and lower time complexity.

In existing graph data, the connection relationships often exhibit uniform weights, leading to the model aggregating neighboring nodes with equal weights across various connection types. However, this uniform aggregation of diverse information diminishes the discriminability of node representations, contributing significantly to the over-smoothing issue in models. In this paper, we propose the Flexible Graph Neural Diffusion (FGND) model, incorporating latent class representation to address the misalignment between graph topology and node features. In particular, we combine latent class representation learning with the inherent graph topology to reconstruct the diffusion matrix during the graph diffusion process. We introduce the sim metric to quantify the degree of mismatch between graph topology and node features. By flexibly adjusting the dependency level on node features through the hyperparameter, we accommodate diverse adjacency relationships. The effective filtering of noise in the topology also allows the model to capture higher order information, significantly alleviating the over-smoothing problem. Meanwhile, we model the graphical diffusion process as a set of differential equations and employ advanced partial differential equation tools to obtain more accurate solutions. Empirical evaluations on five benchmarks reveal that our FGND model outperforms existing popular GNN methods in terms of both overall performance and stability under data perturbations. Meanwhile, our model exhibits superior performance in comparison to models tailored for heterogeneous graphs and those designed to address oversmoothing issues.

Unmanned aerial vehicles (UAVs) can be deployed in the areas where traditional network infrastructure is insufficient or absent because of flexibility and collaboration. The deployment of edge intelligence on UAVs in UAV-assisted mobile-edge networks significantly enhance data processing efficiency, which is a critical factor for applications requiring delay-sensitive data processing in the areas mentioned above. However, the limited energy capacity poses a challenge when running complex deployment algorithms. Therefore, lightweight network model is essential for deployment algorithms, as it significantly reduces energy and time consumption. In this article, we propose a cooperative framework-based knowledge distillation to compressed deep neural network (DNN). The subnetworks collaborate to train interactive node parameters, resulting in the optimal evolution of the student network. Then, we introduce a novel result-driven model training approach for simulation data sets. To further enhance efficiency and significantly reduce overall latency, we meticulously refine the internal architecture of the knowledge distillation algorithm. We incorporate a collaborative evolution mechanism into the core of the algorithm, utilizing multinetwork and subnetwork learning to facilitate knowledge transfer, and incorporate some optimization mechanisms into the framework. Finally, we perform a series of experiments to acquiredata sets and conduct algorithm simulation analysis to evaluate the proposed method. The results demonstrate that our work achieves good research results.

Accidents are inevitable in the transportation systems; however, harnessing the big data generated from traffic accidents can significantly enhance the intelligence of the transportation system. Multiple unmanned aerial vehicles (multi-UAVs), owing to its flexibility and collaboration, can rapidly track the accidents and gather the acquire relevant data with reasonable deployment. Therefore, we propose an architecture utilizing multiple UAVs to track traffic accidents (targets) and collect relevant data. However, dynamic factors such as no-fly zones and reappearing targets in real-world traffic environments pose challenges to the rapid deployment of UAVs with current algorithms. In this paper, we design a model for deploying multiple UAVs, considering no-fly zones and dynamically reappearing targets. This model aims to optimize UAV deployment by minimizing both the flight distance of each UAV and the associated risk. Risk is defined as the urgency of processing accident scenes, which escalates with the elapsed time from the initial appearance of the targets to their processing. To address the joint UAV deployment problem, we first introduce an algorithm termed preprocessing and group-crossover nondominated sort genetic algorithm II (PGC-NSGAII). In PGC-NSGAII, we employ a group-based selection crossover (GBSC) method to enhance the algorithm’s search capability. This method segregates the initial population into two groups, selecting two individuals for crossover within each group. Secondly, building upon the framework of NSGAII, we incorporate a novel preprocessing component to enrich solution diversity. Thirdly, we develop a prediction method for dynamic environment parameters and a no-fly zone avoidance strategy for multi-UAV deployment. Finally, our experimental results demonstrate that PGC-NSGAII surpasses other existing methods in scenarios with varying numbers of UAVs or targets. Compared with state-of-the-art optimization methods, PGC-NSGAII proves to be more efficient in UAV deployment in dynamic environment.

Network embedding aims to represent nodes with low dimensional vectors while preserving structural information. It has been recently shown that many popular network embedding methods can be transformed into matrix factorization problems. In this paper, we propose the unifying framework “Z-NetMF,” which generalizes random walk samplers to Z-Laplacian graph filters, leading to embedding algorithms with interpretable parameters. In particular, by controlling biases in the time domain, we propose the Z-NetMF-t algorithm, making it possible to scale contributions of random walks of different length. Inspired by node2vec, we design the Z-NetMF-g algorithm, capturing the random walk biases in the graph domain. Moreover, we evaluate the effect of the bias parameters based on node classification and link prediction tasks. The results show that our algorithms, especially the combined model Z-NetMF-gt with biases in both domains, outperform the state-of-art methods while providing interpretable insights at the same time. Finally, we discuss future directions of the Z-NetMF framework.

This paper considers a joint active user detection and channel estimation (JADCE) problem in the grant-free massive machine-type communications (mMTC) circumstances. Specifically, we exploit the millimeter-wave (mmWave) channel in the uplink with the continuous angular domains based on massive multi-input multi-output systems. The sporadic communication nature of the mMTC scenario and the inherent angular domain sparsity of the mmWave channel make the space-angle domain sparsity of the mmWave channel even more serious. Hence, the JADCE problem is formulated as a convex optimization problem under the gridless reweighted atomic norm minimization (RAM) framework in a multiple measurement vector settings (MMV), which can enhance the sparsity in the continuous anular domains. Moreover, RAM has nature of Semidefinite programming (SDP) which can be computed by CVX solver. Meanwhile, to reduce the computational complexity of CVX, we design a fast alternating direction method of multipliers approach to settle the SDP formulation. The simulation results demonstrate that our proposed method has achieved excellent estimation performance and a substantial reduction in computational complexity compared with conventional JADCE methods.

Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. One critical objective of GCL is to retain original graph information. For this purpose, we design an encoder based on the idea of unsupervised dimensionality reduction of locally linear embedding (LLE). Specifically, we map one iteration of the LLE to one layer of the network. To guide the encoder to better retain the original graph information, we propose an unbalanced contrastive model consisting of two views, which are the learning view and the teaching view, respectively. Furthermore, we consider the nodes that are identical in muti-views as positive node pairs, and design the node similarity scorer so that the model can select positive samples of a target node. Extensive experiments have been conducted over multiple datasets to evaluate the performance of GraphTL in comparison with baseline models. Results demonstrate that GraphTL can reduce distances between similar nodes while preserving network topological and feature information, yielding better performance in node classification.

Graph convolutional neural network (GCN) is a powerful deep learning framework for network data. However, variants of graph neural architectures can lead to drastically different performance on different tasks. Model comparison calls for a unifying framework with interpretability and principled experimental procedures. Based on the theories from graph signal processing (GSP), we show that GCN’s capability is fundamentally limited by the uncertainty principle, and wavelets provide a controllable trade-off between local and global information. We adapt wavelet denoising filters to the graph domain, unifying popular variants of GCN under a common interpretable mathematical framework. Furthermore, we propose WaveThresh and WaveShrink which are novel GCN models based on proven denoising filters from the signal processing literature. Empirically, we evaluate our models and other popular GCNs under a more principled procedure and analyze how trade-offs between local and global graph signals can lead to better performance in different datasets.

The sudden outbreak of COVID-19 brings many unpredictable situations to human travel, such as temporarily closed highways, parking lots, etc. The scenarios mentioned above will lead to a large backlog of vehicles, and the requirements of Internet of vehicle (IoV) applications increase sharply in a period of short time correspondingly. Mobile edge computing (MEC) is a key enabling technology that can guarantee the diverse requirements of IoV applications through the optimization of resource scheduling. However, the sharp increasing in requirements of IoV applications caused by the congestion of highways or parking lots still bring great challenges to the deployment of traditional MEC. Therefore, in this paper, we construct an unmanned aerial vehicle (UAV) enabled MEC system, in which the data generated from IoV applications is processed by offloading to UAVs with MEC servers to ensure the efficiency of data processing and the response time of IoV applications. In order to approximate real-world UAV enabled MEC system, we consider the stochastic offloading and downloading processing time. Moreover, the priority constraints of sensors from the same vehicle are taken into consideration since they have different importance degrees. Then, we propose an Markov network-based cooperative evolutionary algorithm (MNCEA) to search out the optimal UAV scheduling solution to guarantee the shortest response time, in which the solution space is divided into multiple sub-solution spaces with the help of MN structure and parameters. Finally, we construct multiple simulation experiments with different probability distributions to simulate uncertainty factors. The simulation results verify the validity of MNCEA compared with the state-of-the-art methods, which is reflected by the shortest response time of requirements of IoV applications.

The essence of connection in vehicle network is the social relationship between people, and thus Vehicular Social Networks (VSNs), characterized by social aspects and features, can be formed. The information collected by VSNs can be used for context prediction of autonomous vehicles. Multivariate relations are common in square connected relations caused by geographic characteristics in VSNs. They can effectively reflect the high-order structural features of the network dataset. It is necessary to exploit the multivariate relations of VSNs to improve the performance of context prediction. However, The representation of entity-relationes in the network often adopts a binary form, and the existing graph learning methods rely on the neighborhood information of nodes to achieve the aggregation or diffusion of information. Using this to represent multivariate relations will result in partial omissions or even complete loss of valuable information, which ultimately affects the learning effect of learning methods. In order to better understand the social behavior of the VSNs, this paper uses the network motif to implement the representation of the multivariate relations in the network, and proposes the graPh learnIng with moTif mAtrix (PITA) method. This method can be used as a preprocessing step for the measurement strategy of the relations in VSNs and the graph learning, which can mine the information in VSNs and improve the accuracy of the original graph learning method by the multivariate relation information. We performed experiments on 6 network datasets. The experimental results show that in the node classification task, the baseline method modified by the PITA method has a higher classification accuracy than the original method.

Multiple unmanned aerial vehicle (Multi-UAV) cooperative area search is an important and effective means of intelligence acquisition and disaster rescue. Search path planning is a critical factor to improve multi-UAV search performance. Aiming at the search inefficiency resulting from insufficient cooperation between UAVs in existing researches, we present a novel distributed real-time search path planning method based on distributed model predictive control (DMPC) framework. Firstly, we formulate the overall search objective function in finite time domain, considering not only repeated searches, but also maintenance of connectivity and collision avoidance between UAVs. Secondly, we decompose the overall search objective function to establish a distributed constrained optimization problem (DCOP) model, so that all UAVs optimize the overall search objective by interacting with neighbors. Thirdly, aiming at the problem of falling into the local optima in existing algorithms, distributed stochastic algorithm based on enhanced genetic algorithm (DSA-EGA) is proposed to solve the established DCOP model. We design a point crossover operator and introduce anytime local search (ALS) framework that stores the global optimal solution explored. Finally, the simulation results of different benchmark problems demonstrate that the proposed DSA-EGA outperforms other state-of-the-art algorithms in terms of the quality of solution. The simulation results of cooperative area search problems illustrate that the established DCOP model improves the search efficiency by 7.7%, and DSA-EGA improves the search efficiency by 4.3% at least. In addition, we also verify that our method has high scalability.

Centrality is an effective method to identify important nodes in complex networks, but it is still a challenge to find influential nodes by making full use of multiple relationships and global network topological features in complex networks. To address these problems, this article proposes an importance identification method for multilayer heterogeneous network node by incorporating multirelational information (MLC). This method studies the relational characteristics of heterogeneous nodes in detail and divides the heterogeneous nodes into different layers according to the node types, which can be further divided into core and auxiliary layers. The importance of the auxiliary layer is quantified by designing the interlayer influence and determining the interlayer influence weights of different connectivity influences; the centrality score of heterogeneous nodes under multiconnectivity relationships is fused using the transmission characteristics of internode relationships in the auxiliary layer, which in turn measures the importance of nodes in the core layer. To evaluate the proposed algorithm, we conduct experiments on five real multilayer heterogeneous networks of different sizes. The results show that MLC can make full use of different types of internode association relationship information, effectively fuse network structure information such as the neighbor weights of core and auxiliary layer nodes, and outperform the existing techniques in identifying important nodes.

The industrial Internet of Things (IIoTs) are well deployed to monitor pollutant emissions or device statuses in the industrial factory, especially in the chemical plants. To give a quick response of monitoring results, the industrial big data generated by IIoTs containing various tasks need to be processed as soon as possible. However, the priority constraints among the tasks generated by sensors are not considered in the existing architectures, which may result in the delayed response. Thus, in this article, we propose a mobile edge computing (MEC)-enabled architecture considering the priority constraints among tasks with the objective to minimize the response time. The tasks can be executed in MEC servers or cloud servers based on task complexity. Traditional methods search the optimal task allocation strategy through a set of initial strategies and a optimizer without the consideration of relationship among tasks. Thus, we propose a Bayesian network based evolutionary algorithm (BNEA) for optimizing a task allocation strategy. To fully consider the priority among tasks, the BNEA studies a Bayesian network based decomposition strategy in which the tasks are decomposed based on the relationship reflected by the learned Bayesian network structure. The BNEA searches the optimal task allocation strategy with the help of decomposed tasks cooperatively. Moreover, we propose a probability-based update strategy for particles to avoid draping into local optima. The experimental results verify that the BNEA can achieve the best response time through the corresponding task allocation strategy, which means that the data generated from the IIoTs can be transmitted to the destination in the shortest time.

Forest fire monitoring plays an important role in forest resource protection. Although satellite remote sensing is an effective way for forest fire monitoring, satellite-based methods can only monitor large-scale forest areas, and they are weak in predicting the specific areas of forest fires. In this article, we first propose an unmanned aerial vehicle (UAV)-enabled system architecture consisting of multiple industrial Internet of Things (IIoTs), in which the data collected by sensors in IIoTs can be delivered to UAVs for processing directly. As the sensors of IIoTs are deployed to monitor different indexes of forest fires, fully considering the priority constraints among sensors can guarantee a quick response of forest fire monitoring. Thus, the priority constraints among the sensors are taken into consideration in this system architecture, and the objective is to minimize the maximum response time of forest fire monitoring. To search for the optimal UAV resource allocation strategy, a learning-based cooperative particle swarm optimization (LCPSO) algorithm with a Markov random field (MRF)-based decomposition strategy is proposed. The solution space of UAV resource allocation is decomposed into subsolution spaces according to the decomposed decision variables by the MRF network structure, and the optimal resource allocation strategy is searched by LCPSO in multiple subsolution spaces cooperatively. Three simulation experiments on two datasets are designed, and the simulation results compared with the state-of-the-art methods verify the validity of LCPSO, which are reflected by the quickest response time of forest fire monitoring.

The traffic congestion and accidents can be relieved by deploying the software defined internet of vehicles (SDN-IoV). However, the traffic of pedestrians and vehicles is particularly heavy near commercial streets and campuses. In particular scenarios, the SDN-IoV may not ensure the quality of service (QoS) for pedestrians and vehicles. In this paper, we construct a novel system architecture consisting of multiple non-cooperative unmanned aerial vehicles (UAVs) and a SDN-IoV. The non-cooperative UAV is equipped with an antenna array to receive the signals from the vehicles and pedestrians of SDN-IoV. In order to locate the positions of vehicles and pedestrians, two source enumeration methods are proposed in a complex SDN-IoV environment with color noise. The projection matrix of the low dimensional signal subspace is constructed by the proposed criterion based on signal subspace projection (SSP). The sequence of the projected difference values of the local covariance matrix is applied to estimate the number of vehicles and pedestrians. The eigenvalues can be grouped to construct different subspaces by the proposed eigen-subspace projection (ESP). By projecting a new covariance matrix into the eigen-subspaces, the variance of values represents the projection difference can be exploited to estimate the number of vehicles and pedestrians. Simulation results and real system test verify the validity of the two proposed methods by comparing them with the state-of-the-art methods. Both of the methods have excellent estimation performance especially in color noise.

In this paper, a novel system architecture including a massive multi-input multi-output (MIMO) or a reconfigurable intelligent surface (RIS) and multiple autonomous vehicles is considered in vehicle location systems. The location parameters of autonomous vehicles can be estimated based on the deep unfolding technique, which is a recent advance of deep learning. Traditional vehicle location methods such as the global position system (GPS) can only locate the target vehicles with relatively low accuracy. The super resolution cannot be achieved when two vehicles are too close, which means that the safety incidents exist when autonomous vehicles are deployed in future intelligent transportation systems (ITS). Different from the existing massive MIMO or RIS equipped with a regular array such as uniform rectangular array (URA) and uniform circular array (UCA), we exploit a massive MIMO or a RIS equipped with a conformal array extended from traditional regular array. First, the rotation from the global coordinate system to the local coordinate system is achieved based on geometric algebra. Second, 2D-DOA estimation of autonomous vehicles is modeled as a novel block sparse recovery problem. Third, the deep network architecture SBLNet is implemented to learn the nonlinear characteristic from the DOAs of autonomous vehicles and the data received by massive MIMOs or RISs. The 2D-DOA and polarization parameters can be estimated based on SBLNet with relatively low computational complexity. Simulation results demonstrate that SBLNet performs better than the state-of-the-art methods in terms of estimation accuracy and successful probability. The SBLNet is also suitable for the practical scenario considering fast moving autonomous vehicles, while, the traditional block sparse recovery methods fail in this complex scenario.

Intelligent Transportation System (ITS) can boost the development of smart cities, and artificial intelligence and edge computing are key technologies that support the implementation of ITS. Vehicle localization is critical for ITS since the safety driving and location-aware serves highly depend on the accurate location information. In this article, we construct a vehicle localization system architecture composed of multiple Internet of Things (IoT) with arbitrary array configuration and a large amount of vehicles in smart cities. In order to deal with the coexisting of circular and non-circular signals transmitted by vehicles, we proposed several vehicle number estimation methods for non-circular signals. Based on the machine learning technique, we extend the vehicle number estimation method into mixed signals in more complex scenario of smart cities. Then the DOA estimation method for non-circular signals based on IoT is proposed, and then the performance of this method is analyzed as well. Simulation outcomes verify the excellent performance of the proposed vehicle number estimation methods and the DOA estimation method in smart cities, and the vehicle positions can be achieved with high estimation accuracy.

The recently developed technique of wireless power transfer (WPT) provides a promising way to charge the wireless sensor networks (WSNs) of cognitive industrial Internet of Things (IoT) deployed in areas that are difficult for humans to access. Previous work has focused on the power allocation strategy at the wireless node level. However, the priority among different modes in an identical wireless node has not been taken into consideration, and different modes equipped with different types of batteries accomplish different tasks in an identical wireless node. One challenging scenario is rechargeable WSNs with a large number of wireless nodes. In this article, we aim to optimize the power allocation strategy in priority constraint WPT systems with a large number of wireless nodes. Traditional WPT systems consist of a rechargeable WSN and a mobile charger, which are deployed for charging wireless nodes in a wireless manner. However, the constructed WPT system consists of a rechargeable WSN and multiple mobile chargers with adequate power, which can charge wireless nodes simultaneously. Each solution of the power allocation strategy can be represented as one disjunctive graph, and the critical path (CP) in the disjunctive graph is the core factor in determining the final maximum cost. Thus, we propose a decomposition strategy that can identify the interacting variables based on the CP by exploiting the perturbation technique. Then, the decomposed subcomponents are cooperatively evolved by adopting a cooperative evolutionary algorithm (CEA). The proposed CP-based grouping strategy combined with CEA is named CPCEA. Three state-of-the-art methods are tested and compared with CPCEA, and three scales of datasets are considered. The experimental results demonstrate the validity of CPCEA.

In this paper, a sparse recovery algorithm based on a double-pulse FDA-MIMO radar is proposed to jointly extract the angle and range estimates of targets. Firstly, the angle estimates of targets are calculated by transmitting a pulse with a zero frequency increment and employing the improved l 1 -SVD method. Subsequently, the range estimates of targets are achieved by utilizing a pulse with a nonzero frequency increment. Specifically, after obtaining the angle estimates of targets, we perform dimensionality reduction processing on the overcomplete dictionary to achieve the automatically paired range and angle in range estimation. Grid partition will bring a heavy computational burden. Therefore, we adopt an iterative grid refinement method to alleviate the above limitation on parameter estimation and propose a new iteration criterion to improve the error between real parameters and their estimates to get a trade-off between the high-precision grid and the atomic correlation. Finally, the proposed algorithm is evaluated by providing the results of the Cramér-Rao lower bound (CRLB) and numerical root mean square error (RMSE).

Flexible scheduling is one of the most significant core techniques for intelligent manufacturing systems. Realization of an optimized schedule through flexible resources assignment is critical to the application and popularization of flexible scheduling, especially in uncertain manufacturing environments. In this paper, we consider flexible job shop scheduling with uncertain processing time represented by fuzzy numbers, which is named fuzzy flexible job shop scheduling. We propose an effective hybrid cooperative coevolution algorithm (hCEA) for the minimization of fuzzy makespan. The hCEA combines particle swarm optimization with the genetic algorithm to improve the convergence ability. A parameter self-adaptive strategy is applied to the problems with different scale effectively as well. Five benchmarks and three large-scale problems with fuzzy processing time are adopted to test the hCEA. Computational results show that the hCEA performs better than the existing methods from the literature.