Introducing friendly jammer to unmanned aerial vehicle (UAV) covert communication could further enhance the covert performance. However, due to the additional communication overhead required for information synchronization (IS) among cooperating parties, the security risk is further elevated. It is noteworthy that cognitive jamming (CJ) could sense the cooperating node signals without IS links. Therefore, in this paper, we propose the cooperative UAVs covert communication scheme assisted by the CJ that maximize the minimum average covert rate (MACR). Specifically, the UAV (Alice) tries to transmit its private message to legitimate users under the surveillance of the Wardens. Since the Warden is in passive surveillance, the practical location uncertainty of the Warden is considered, and we further analyze the worst-case situation that derives the minimal detection error probability of the Wardens. Moreover, we maximize the MACR by alternatively optimizing the sense time ratio and trajectory of CJ, the transmit power and trajectory of the Alice. To expedite algorithmic convergence, we further introduce a principled initialization scheme. The simulation results reveal that the covert performance of the proposed algorithm outperforms the other schemes, especially, in the multi-warden scenario. Furthermore, compared with the full-time jammer and probabilistic jammer, the CJ can not only achieve a higher covert rate with less average jamming power, but also require no information synchronization link with Alice based on spectrum sensing, which promises considerable application prospects in real-world systems.

In this paper, by integrating the advantages of synchronous federated learning (SFL) and asynchronous federated learning (AFL), an efficient federated learning (FL) framework with hybrid aggregation is proposed for the connected and autonomous vehicles (CAVs) over multiple base stations (BSs). Specifically, to cope with the stragglers caused by traffic accidents, extreme weather or other uncontrollable factors, the AFL with periodic aggregation is proposed to perform edge model aggregation within a single BS. Furtherly, taking the freshness of local model updates and the training data distribution into account, a novel weighting strategy is designed correspondingly. Moreover, to reconcile the contradiction between the scarce wireless communication resources and the enormous communication overhead caused by frequent exchanges of model parameters, an effective model compression mechanism is constructed based on the inherent statistical property of FL. In addition, considering a relatively small number of autonomous vehicles (AVs) within the limited coverage of a single BS and the instant guidance required for the CAVs, the SFL based on FedAvg is introduced to aggregate edge models trained from multiple BSs at network edge instead of remote cloud. The superior performance of the proposed method is verified by various simulations on real dataset.

Autonomous aerial vehicle (AAV) covert communication could further enhance the quality and coverage of covert channels. However, in complex low-altitude environments, the air-to-ground (A2G) links are susceptible to the fading effects, which may degrade the performance of covert communication. In this paper, we investigate the fluid antenna (FA) enabled AAV covert communication, where a AAV equipped with FA serves multiple ground users in the presence of an active Warden. We aim to maximize the minimum average covert rate by jointly optimizing the beamforming vectors, AAV trajectory and fluid antenna positions. First, we derive closed-form expressions for the minimum detection error probability (MDEP) and the optimal detection threshold, accounting for uncertainties in both the noise variance and the self-interference channel coefficient. Secondly, we propose an alternating optimization algorithm subject to the covertness constraint, power constraint, and the Warden’s position uncertainty. Specifically, the original nonconvex problem is decomposed into tractable subproblems via the block coordinate descent, which could be solved successively by successive convex approximation, semidefinite relaxation, and Dinkelbach transformation. What’s more, a low-complexity algorithm is developed for the single-user scenario to improve the practical applicability of the proposed framework. Finally, simulation results validate the effectiveness of the proposed FA-AAV covert communication scheme. Moreover, compared to the fixed position antenna scheme, the FA-AAV could improve the covert performance, especially in strong channel fading environments, which is beneficial for practical application.

To realize the benefits expected and ensure user privacy and data security simultaneously, a cost-efficient edge federated learning (FL) architecture over multiple base stations (BSs) is proposed for the intelligent transportation system (ITS) based on connected and autonomous vehicles (CAVs). Firstly, in the proposed FL architecture, the road side units (RSUs) are designed to train the machine learning (ML) model with the BSs equipped with edge servers collaboratively. In this way, since the autonomous vehicles do not participate in model training, the negative impact of unreliable communication caused by vehicle mobility can be eliminated. Then, considering that the limited amount of data involved within the coverage of a single base station (BS), the FL architecture over multiple BSs at network edge is proposed for better learning performance. Along this line, the joint edge aggregation and association problem is studied, and a set function optimization problem is formulated with the objective of minimizing the costs considering latency and energy consumption comprehensively. Finally, after analyzing the complexity, monotonicity, and modularity of the problem formulated, the NP-hardness optimization problem is further decomposed and transformed, and an innovative solution is proposed. The abundant simulation results demonstrate the superior performance of the cost-efficient FL architecture proposed.

The Connected and Autonomous Vehicles (CAVs) is considered to be a promising technology to improve the traffic congestion. However, to realize its expected benefits, the real-time, accurate and forward-looking guidance message are required. Based on this, considering various practical constraints and the characteristics of the CAVs fully, multiple distributed edge computing servers are deployed at the network edge to provide the real-time storage and computation support in our paper. Furtherly, based on the fruitful deployment for edge computing servers, an efficient traffic flow online prediction model is constructed, which can adaptively modify according to the dynamic changes of the actual road traffic state, thus providing more accurate and forward-looking results. The simulation results based on MATLAB platform show the proposed deployment scheme for edge computing servers only needs more cost than the enumeration method. Moreover, compared with other baseline methods, the online prediction model proposed can improve 37.5%–76.9% in terms of prediction accuracy.

This article investigates an unmanned-aerial-vehicle (UAV)-assisted Internet of Things system, where multiple UAVs take off from a data center (DC), collect data from multiple ground sensor nodes (SNs), distribute data for multiple users, and finally return to DC. A centralized information-sharing mechanism is designed among UAVs before data distribution, such that UAVs’ repeatedly visiting the same user can be avoided to save energy and time. A nonconvex Age-of-Information (AoI) minimization problem is formulated, where the task assignment, selection of interaction point (IPT), and UAVs’ trajectories are optimized jointly. It is decomposed into two subproblems: one is a joint task assignment and UAV trajectory optimization problem, which is solved by a multipopulation-based genetic algorithm (MPGA), as well as a hybrid algorithm combining dynamic programming (DP) with an improved$K$-means clustering; and the other is an IPT selection problem, which is modeled as a Fermat–Weber location problem and is solved by the convex optimization technique. Simulation results show that the MPGA performs better than the hybrid algorithm as well as several benchmark algorithms.

Data prediction research is very important for applications based on the Internet of things (IoT). However, the large-scale, high-dimension, and the inevitable missing values seriously hinder accurate prediction result. To meet this challenge, an integrated online prediction model is proposed, which can solve future values prediction and missing values imputation simultaneously in the low-dimensional space. Specifically, we embed the classic prediction models into the objective function of matrix factorization (MF) to obtain low-dimensional embedding parameters, which are then used to construct the prediction model. Furthermore, a rolling prediction mechanism is proposed to update the prediction model when new sampled data comes, based on the online MF with time-varying parameters. Simulation results on four UCI public data sets show the superior performance of the proposed method.

Unmanned aerial vehicles (UAVs) can be deployed in the air to provide high probabilities of line of sight (LoS) transmission, thus UAVs bring much gain for wireless communication systems. In this paper, we study a UAVs-aided self-organized device-to-device (D2D) network. Relay deployment, channel allocation and relay assignment are jointly optimized, aiming to maximize the capacity of the relay network. On account of the coupled relationship between the three optimization variables, an alternating optimization approach is proposed to solve this problem. The original problem is divided into two sub-problems. The first one is that of optimizing the channel allocation and relay assignment with fixed relay deployment. Considering without central controller, a reinforcement learning algorithm is proposed to solve this sub-problem. The second sub-problem is that of optimizing the relay deployment with fixed channel allocation and relay assignment. Assuming no knowledge of channel model and exact positions of the communication nodes, an online learning algorithm based on real-time capacity is proposed to solve this sub-problem. By solving the two sub-problems alternately and iteratively, the original problem is finally solved. Simulation results show that the UAVs-aided D2D network can achieve a high capacity via the joint optimization of relay deployment, channel allocation, and relay assignment.

The benefits of compressive sensing (CS) on counting and localization in wireless sensor networks have already been demonstrated in previous works. However, most existing solutions usually become infeasible in the presence of off-grid targets, which is inevitable for the traditional CS-based localization methods. To mitigate the error caused by off-grid targets, in this paper, a more accurate sparse approximation model is applied where the true and unknown sparsifying dictionary is approximated with its first-order Taylor expansion around a known dictionary. Based on the model, the counting and localization problem is formulated as a sparse reconstruction problem that recovers two sparse signals with related supports. To solve the problem, we develop an iterative algorithm under a variational Bayesian inference framework. Moreover, in practice, some faulty prior information (e.g., coarse positions) is usually available. To incorporate the information, a three-level hierarchical prior model is introduced and imposed on the sparse signals to be recovered, where the supports of sparse signals are learned in the third level. Additionally, a grid pruning mechanism and matrix inversion lemma are leveraged to accelerate the inference process. Extensive simulation results demonstrate that, compared with state-of-the-art algorithms, the proposed algorithm can achieve counting and localization with higher accuracy as well as lower cost.

Localization technologies play an increasingly important role in pervasive applications of wireless sensor networks. Since the number of targets is usually limited, localization benefits from compressed sensing (CS): measurements number can be greatly reduced. Despite many CS-based localization schemes, existing solutions implicitly assume that all targets fall on a fixed grid exactly. When the assumption is violated, the mismatch between the assumed and actual sparsifying dictionaries can deteriorate the localization performance significantly. To address such a problem, in this paper, we propose a novel and iterative multiple target counting and localization framework. The key idea behind the framework is to dynamically adjust the grid to alleviate or even eliminate dictionary mismatch. The contribution of this paper is twofold. First, we consider the off-grid target issue in CS-based localization and formulate multiple target counting and localization as a joint sparse signal recovery and parameter estimation problem. Second, we solve the joint optimization problem using a variational Bayesian expectation-maximization algorithm where the sparse signal and parameter are iteratively updated in the variational Bayesian expectation-step and variational Bayesian maximization-step, respectively. Extensive simulation results highlight the superior performance of the proposed framework in terms of probability of correct counting and average localization error.

Owing to the ability of improving spectrum utilisation and quality‐of‐service (QoS) in wireless communications, multiple‐input–multiple‐output (MIMO) cognitive radio (CR) network has been identified as an excellent choice for the next generation communication. To protect primary users (PUs) from excessive interference while ensuring a meaningful QoS of secondary users (SUs), one key challenge of such system is the joint design of transceiver beamforming for SUs. In this study, the authors investigate the beamforming in MIMO CR network, and two optimisation problems in terms of the minimisation of the total transmission power and the maximisation of the smallest received signal‐to‐interference‐plus‐noise ratio (SINR) are considered. The problems can be formulated as indefinite quadratic optimisation programs which are known to be non‐convex NP‐hard. Hence, a distributed algorithm based on semi‐definite programming and minimum‐mean‐squared‐error is proposed to achieve the optimal beamforming weight vector. The findings of this study demonstrated that the proposed algorithm outperforms the existing ones with respect to the total transmitted power and SUs SINR.