With the unprecedented capability of configuring antenna positions, fluid antenna systems (FASs) have been recognized as a key enabler for secure communications. However, it is challenging to optimize the secrecy rate by reconfiguring positions of fluid antennas based on the fast-changing instantaneous channel state information (CSI). Considering the effectiveness of regularized zero-forcing (RZF) and zero-forcing (ZF) precoding in mitigating information leakage in physical layer security, we propose a two-timescale design to maximize ergodic secrecy sum rate (ESSR), where only the statistical CSI is utilized for the port selection of FASs. For that purpose, we first derive the analytical expression for the ESSR of FASs with RZF/ZF precoding by utilizing random matrix theory (RMT). Then, based on the evaluation results, we propose a two-timescale algorithm to maximize the ESSR by optimizing both port selection of FASs and regularization factor of RZF. Numerical simulations validate the accuracy of the proposed ESSR evaluation and show that the proposed two-timescale design could improve the ESSR performance significantly when compared with the uniform port selection.

As claimed in International Telecommunications Union (ITU) Recommendation ITU-R M.2160, 6G communication systems require extremely high-security and low-latency communication, necessitating the study of covert communication operating at finite blocklengths. In the point-to-point (P2P) setting, covert communication enables a transmitter to send messages reliably over a noisy channel to a legitimate receiver without being detected by any third party. Under the covertness metric of Kullback–Leibler divergence (KLD), we derive exact second-order asymptotics for P2P covert communication over a multiple-input multiple-output (MIMO) additive white Gaussian noise (AWGN) channel. In particular, our theoretical benchmarks refine the first-order asymptotics of Wang and Bloch (TIFS 2021), which is known as the square root law by showing that the non-asymptotic maximal number of transmitted messages has a back off that scales in the order \(\Theta(n^\frac{1}{4})\) beyond the first-order term scaling in the order \(\Theta(n^\frac{1}{2})\) when the blocklength is \(n\). Thus, our second-order asymptotic bound provides a better approximation to the finite blocklength performance of optimal codes. Furthermore, compared with the single antenna result of Yu et al. (arXiv:2305.17924v3), we demonstrate the impact of the number of antennas \(m\) and reveal spatial diversity gains of MIMO, advocating the use of MIMO for covert communication to achieve a high transmission rate. To prove our results, we extend the quasi-\(\eta\)-neighborhood framework from single-antenna real value channels to multi-antenna complex value MIMO channels. To ensure covertness, the transmission power vanishes as the blocklength \(n\) increases. Thus, we judiciously analyze the finite blocklength performance of MIMO communication by modifying critical steps concerning the Berry–Esseen Theorem to deal with vanishing second and third absolute moments of information densities that rely on blocklength, which is in stark contrast with the non-covert case.

Low earth orbit Internet of things (LEO-IoT) networks offer global coverage and low-latency communication, making them attractive for large-scale IoT deployments. However, rapidly varying satellite connectivity and uneven, burst ground traffic lead to unstable routing performance, resulting in fluctuating delays and increased packet loss. To address these challenges, we propose a delay-aware routing optimization (DARO) algorithm that integrates traffic prediction and distributed control. A CNN-BiLSTM-Attention model is developed to capture spatial-temporal traffic patterns, enhancing the accuracy of dynamic traffic prediction. A closed-form end-to-end delay model is derived to characterize the effect of routing decisions on network latency. The routing problem is then formulated as a delay and packet loss minimization task and solved using multi-agent proximal policy optimization (MAPPO), enabling each satellite to adapt its routing strategy based on local observations and a shared critic. Simulation results show that DARO improves prediction accuracy by 19.05% to 76.39%, reduces packet loss by 27.35% to 90.76%, and lowers end-to-end delay by 2.43% to 56.04%, demonstrating its effectiveness in dynamic LEO-IoT environments.

The integration of blockchain technology with the sixth generation (6G) networks offers a promising approach to enhance the reliability and trustworthiness of industrial Internet of Things (IIoT) systems. Since IIoT devices typically lack the capability to directly participate in blockchain consensus, drone networks offer a viable alternative by providing dynamic coverage and reducing dependence on fixed infrastructure such as centralized servers. Sharding is an effective method to improve the scalability of blockchain systems, yet existing sharding schemes overlook the complexity and dynamic nature of drone network topologies. These networks frequently experience changes due to drone mobility, task variations, and energy constraints, all of which can disrupt consensus communications. To address these challenges, we propose a time-varying graph-based blockchain sharding scheme (BSTVG) tailored for large-scale drone blockchain networks. The time-varying graph-based model captures the temporal dynamics of drone communications. We adopt an improved K-Means++ clustering algorithm that incorporates communication conditions to adapt network sharding. Additionally, we develop mechanisms for intra-shard consensus and cross-shard transaction processing. To accommodate node joins, exits, and significant topological changes, we introduce a slot–epoch coupling mechanism that dynamically adjusts the epoch length. We analyze the security of the proposed scheme and validate its performance through simulations. Experimental results demonstrate that our scheme not only enhances the throughput but also reduces energy consumption of the drone blockchain network.

The reconfigurability of fluid antenna systems (FASs) and reconfigurable intelligent surfaces (RISs) provides significant flexibility in optimizing channel conditions by jointly adjusting the positions of fluid antennas and the phase shifts of RISs. However, it is challenging to acquire the instantaneous channel state information (CSI) for both fluid antennas and RISs, while frequent adjustment of antenna positions and phase shifts will significantly increase the system complexity. To tackle this issue, this paper investigates the two-timescale design for FAS-RIS multi-user systems with linear precoding, where only the linear precoder design requires instantaneous CSI of the end-to-end channel, while the FAS and RIS optimization relies on statistical CSI. The main challenge comes from the complex structure of channel and inverse operations in linear precoding, such as regularized zero-forcing (RZF) and zero-forcing (ZF). Leveraging on random matrix theory (RMT), we first investigate the fundamental limits of FAS-RIS systems with RZF/ZF precoding by deriving the ergodic sum rate (ESR). This result is utilized to determine the minimum number of selected antennas to achieve a given ESR. Based on the evaluation result, we propose an algorithm to jointly optimize the antenna selection, regularization factor of RZF, and phase shifts at the RIS. Numerical results validate the accuracy of performance evaluation and demonstrate that the performance gain brought by joint FAS and RIS design is more pronounced with a larger number of users.

With its capability to obtain a good tradeoff between complexity and performance, regularized zero-forcing (RZF) has been widely investigated to enhance the physical layer security. However, the associated reliability performance, i.e., secrecy outage probability (SOP), is not yet available in the literature. In this paper, we characterize the secrecy performance of RZF in the multi-user, downlink multiple-input single-output system. For this purpose, we first set up a central limit theorem for the joint distribution of users’ signal-to-interference-plus-noise ratio and eavesdropper’s signal-to-noise ratio by leveraging random matrix theory. The result is then utilized to obtain a closed-form approximation for the ergodic secrecy rate and SOP of three typical scenarios: the case with only external Eves, the case with only internal Eves, and that with both. The derived results are then used to evaluate the percentage of users in secrecy outage and the required number of transmit antennas to achieve a positive secrecy rate. It is shown that, with equally-capable Eves, the secrecy loss caused by external Eves is higher than that caused by internal Eves. Numerical simulations validate the accuracy of the theoretical results and demonstrate the advantage of RZF over other linear transmitters such as ZF.

Motivated by the practical application of beam tracking of multiple devices in Multiple Input Multiple Output (MIMO) communication, we study the problem of non-adaptive twenty questions estimation for locating and tracking multiple moving targets under a query-dependent noisy channel. Specifically, we derive a non-asymptotic bound and a second-order asymptotic bound on resolution for optimal query procedures and provide numerical examples to illustrate our results. In particular, we demonstrate that the bound is achieved by a state estimator that thresholds the mutual information density over possible target locations. This single threshold decoding rule has reduced the computational complexity compared to the multiple threshold scheme proposed for locating multiple stationary targets (Zhou, Bai and Hero, TIT 2022). We discuss two special cases of our setting: the case with unknown initial location and known velocity, and the case with known initial location and unknown velocity. Both cases share the same theoretical benchmark that applies to stationary multiple target search in Zhou, Bai and Hero (TIT 2022) while the known initial location case is close to the theoretical benchmark for stationary target search when the maximal speed is inversely proportional to the number of queries. We also generalize our results to account for a piecewise constant velocity model introduced in Zhou and Hero (TIT 2023), where targets change velocity periodically. Finally, we illustrate our proposed algorithm for the application of beam tracking of multiple mobile transmitters in a 5G wireless network.

With the exponential growth of smart devices connected to wireless networks, data production is increasing rapidly, requiring machine learning (ML) techniques to unlock its value. However, the centralized ML paradigm raises concerns over communication overhead and privacy. Federated learning (FL) offers an alternative at the network edge, but practical deployment in wireless networks remains challenging. This paper proposes a lightweight FL (LTFL) framework integrating wireless transmission power control, model pruning, and gradient quantization. We derive a closed-form expression of the FL convergence gap, considering transmission error, model pruning error, and gradient quantization error. Based on these insights, we formulate an optimization problem to minimize the convergence gap while meeting delay and energy constraints. To solve the non-convex problem efficiently, we derive closed-form solutions for the optimal model pruning ratio and gradient quantization level, and employ Bayesian optimization for transmission power control. Extensive experiments on real-world datasets show that LTFL outperforms state-of-the-art schemes.

Unmanned aerial vehicles (UAVs) have been extensively employed in the low-altitude intelligent network (LAIN) on data collection and transmission, enabling predictive maintenance, enhanced safety, and improved operational efficiency. However, the openness of wireless communication networks makes UAVs vulnerable to numerous security threats. To secure the critical transmitted data, many authenticated key agreement (AKA) schemes have been developed. Nevertheless, most existing AKA schemes fail to efficiently and securely authenticate communications between a single user and multiple UAVs in IIoT environments. To this end, we propose an extensible multi-party AKA scheme for LAINs. Specifically, we employ the physical unclonable functions and the Chinese remainder theorem to facilitate efficient authentication and data aggregation. Furthermore, by leveraging the additive homomorphic cryptography and blockchain, our scheme ensures privacy even in the presence of semi-trusted mobile operators. Formal security analyses and performance evaluations indicate that the proposed scheme meets the security requirements for LAINs while maintaining lightweight and extensible energy consumption.

Autonomous aerial vehicle (AAV) target tracking technology is an essential component for enabling diverse low-altitude activities. Due to the constraints on energy and computing resources of AAVs, current approaches face challenges in balancing prolonged flight duration with precise tracking while avoiding high computational complexity. Therefore, this paper proposes an energy-aware formation control algorithm for multiple AAVs to cooperatively track a target while retaining a desired formation pattern. Firstly, to achieve a balanced outcome in terms of tracking performance and control effort, an actor-critic based learning predictive rule is explored to develop a near-optimal control protocol that stabilizes error dynamics and minimizes value functions for discrete-time AAV systems. By decomposing the infinite-horizon target tracking problem into a sequence of finite-horizon sub-problems, the reinforcement learning (RL)-based predictive control algorithm can achieve fast convergence in approximating the solution of Hamilton-Jacobi-Bellman (HJB) equation. Furthermore, by employing a delicately designed asynchronous policy iteration mechanism with adjustable learning intervals in RL, the cumbersome learning process can be effectively mitigated, thereby attaining both high learning efficiency and a reduced computational burden simultaneously. The involved errors are proven to be convergent and simulation results validate the optimality of our method.

Flying ad hoc networks (FANETs) offer flexible, real-time wireless communication solutions for multi-drone systems by utilizing drones as network routers. However, FANETs’ unique characteristics, including high mobility, unstable network topology, and intermittent connectivity, pose significant challenges in designing efficient and reliable routing protocols. Traditional routing protocols for mobile ad hoc networks often fall short in highly dynamic airborne environments due to excessive control overhead, increased latency, and inefficient route maintenance. To address these issues, this paper proposes an enhanced on-demand predictive (EDP) routing protocol that integrates a neighbor coverage-based predictive flooding mechanism and an adaptive link quality-based route maintenance strategy. The flooding mechanism mitigates directional deafness by using a Kalman filter-based probabilistic forwarding model, while the route maintenance method optimizes path selection based on distance, traffic load, and link lifetime. Simulation results show that EDP significantly improves packet delivery rate, reduces network delay, and lowers overhead compared to benchmarks, making it well-suited for applications in FANETs.

Current mainstream multiagent reinforcement learning (MARL) algorithms primarily focus on acquiring the global maximum reward throughout the entire training process, from the initial to the final stage. Whereas directly pursuing the global maximum return tends to be inefficient, particularly in environments with sparse rewards or the large-scale multiagent system. To address these challenges, previous algorithms have been developed to maintain individual policies to guide global training. Nevertheless, these approaches generally neglect either efficiency or the potential for local collaboration at the early stage of training. In this article, we propose the role-policy enhanced global policy (RPEGP) algorithm, which integrates the concept of distinct roles within the actor–critic-based MARL framework. RPEGP simultaneously considers both collaborative behaviors among agents and efficient global policy training. Specifically, RPEGP exploits the similarities among agents to assign distinct roles, training role-policies and the global policy concurrently. Through the initialization and enhancement of the role-policies, the global policy is trained more efficiently and effectively. Empirical experiments are conducted in well-known cooperative multiagent environments, including StarCraft II micromanagement (SMAC) and multiagent particle environment (MPE). Experimental results demonstrate that RPEGP outperforms baseline algorithms across various evaluation metrics and training efficiency, confirming its ability to address complex cooperative tasks generically and efficiently.

To satisfy diverse security requirements of ground users in unmanned aerial vehicle (UAV) networks, we propose a channel inversion power control (CIPC) aided multi-user collaborative secret and covert uplink transmission strategy for UAV secure communication. Specifically, using the non-orthogonal multiple access (NOMA) technology, multiple ground covert users named Carlo, hide their weak covert signals in the strong secret signal from a secret user named Bob, and transmit to the UAV named Alice. An adversary Willie attempts to eavesdrop Bob’s confidential message and detect whether Carlo is transmitting or not. To evaluate the link reliability and security of secret and covert transmissions, we first derive closed-form expressions of the secret connection probability (SCP), secrecy outage probability (SOP), covert connection probability (CCP), and detection error probability (DEP) under perfect channel state information while accounting for the uncertainty of the adversary’s noise power. We then further incorporate the legitimate-link channel uncertainty into the analysis and characterize its impact on the key performance metrics, particularly the average values of SCP, SOP, and CCP. To characterize the theoretical benchmark of the proposed transmission strategy, we investigate the performance in both rotary-wing and fixed-wing UAV scenarios. Particularly, in the rotary-wing UAV scenario, we formulate an optimization problem to maximize the average effective sum covert rate subject to constraints of SCP, SOP, DEP, CIPC parameter, ground user’s transmission power, and the UAV’s altitude. Subsequently, we provide an optimal and a sub-optimal solution to the optimization problem. In the fixed-wing UAV scenario, we formulate an optimization problem to maximize the average covert rate subject to the constraints of SCP, SOP, DEP, CIPC parameter, user scheduling, and the UAV’s flight parameters. Furthermore, using the successive convex approximation (SCA) method, we propose an alternating optimization (AO) algorithm to obtain a high-quality feasible solution. Finally, our results reveal the influence of key parameters on the system performance, analytically and numerically.

This paper considers a passive intelligent reflecting surface (IRS)-assisted integrated radar and communication system for multi-target detection and multi-user communications. To balance the communication and sensing performance, we propose an alternating optimization algorithm to optimize the worst-case weighted sum of the radar waveform minimum mean square error (MSE) and Multiuser interference (MUI) in Communication, under the spectrum compatibility and power constraints. The proposed algorithm utilizes a novel Tchebycheff optimization framework that decomposes the multi-objective optimization problem into three subproblems by optimizing the radar transmitted sequences, communication transmitted sequences, and IRS phase configuration. We propose an alternating optimization algorithm which incorporates alternating direction penalty method (ADPM) and element-wise block coordinate descent (E-BCD) frameworks to efficiently solve the optimization problem. Extensive numerical simulations validate the effectiveness of the proposed method, demonstrating significant performance improvements in both minimizing radar MSE and communication MUI and better convergence speed.

Mobile Mdge Computing (MEC) has emerged as a crucial technology for supporting computation-intensive and latency-sensitive Internet of things (IoT) applications. Meanwhile, UAVs can serve as MEC servers, providing cost-effective computation offloading services to IoT terminals with their flexible deployment capabilities, especially in areas lacking ground infrastructure. Nevertheless, the computation offloading process suffers from potential security risks, while the randomness and uncertainty of terminals’ data sensing may exacerbate the queue backlogs. To tackle these challenges, a UAV-enabled secure aerial computing network that integrates MEC and blockchain is proposed, with the aim of jointly designing data sensing, offloading, and computing, together with UAV trajectory planning to maximize the long-term average data sensing rate under queuing delay and block creation delay constraints. To address the coupling between long-term constraints and short-term decisions, we apply Lyapunov optimization to decompose the original problem into three deterministic subproblems for each time slot. We then develop a multi-agent learning-based approach to collaboratively train terminal transmission power and UAV flight trajectories. Moreover, sensing rate and edge resource allocation are adaptively optimized in response to real-time data arrivals and queue backlogs. Simulation results demonstrate the superior performance of our solution, achieving over a 13.16% improvement in data sensing rate and more than a 29.47% reduction in queue delay compared to benchmark methods.

Device-edge collaborative inference, which deploys well-trained artificial intelligence (AI) models at the network edge via the cooperation of edge devices and edge servers, emerges as a promising technique to provide ubiquitous intelligent services. In this paper, a multiple-input multiple-output (MIMO) over-the-air computation (AirComp) scheme is proposed for the efficient implementation of device-edge collaborative inference. In the considered system, the technique of MIMO AirComp is utilized to aggregate local feature vectors, extracted from noise-corrupted sensory data on devices, at the server to efficiently derive a denoised global one for completing the downstream inference task. Device-edge collaborative inference features a task-oriented property, that concerns the effectiveness and efficiency of the task execution. In this case, the traditional AirComp criterion, i.e., minimum mean square error (MMSE), is not effective, since the same distortion level on different feature elements may have different influences on the inference performance. To this end, this paper directly adopts inference accuracy as the design objective. As the instantaneous inference accuracy is unknown during the design stage, an approximated but tractable metric, called discriminant gain, which measures the discernibility of different classes, is adopted. To maximize the inference accuracy measured by discriminant gain, a MIMO AirComp technique is proposed to jointly optimize all feature elements. The problem is nonconvex because of the complicated form of the objective function and the constraints. The solution based on semidefinite relaxation (SDR) and successive convex approximation (SCA) is employed to design a joint transmit precoding and receive beamforming scheme. Besides, to enhance the robustness of practical AI models in the inference stage, a post-processing design of feature magnitude normalization is proposed. Extensive experiments are conducted based on a practical human motion recognition task, which verifies our theoretical analysis and the superiority of our proposed scheme.

To support the development of the Low Altitude Economy (LAE), it is essential to achieve precise localization of unmanned aerial vehicles (UAVs) in urban areas where global positioning system (GPS) signals are unavailable. Vision-based methods offer a viable alternative but face severe bandwidth, memory and processing constraints on lightweight UAVs. Inspired by mammalian spatial cognition, we propose a task-oriented communication framework, where UAVs equipped with multi-camera systems extract compact multi-view features and offload localization tasks to edge servers. We introduce the Orthogonally-constrained Variational Information Bottleneck encoder (O-VIB), which incorporates automatic relevance determination (ARD) to prune non-informative features while enforcing orthogonality to minimize redundancy. This enables efficient and accurate localization with minimal transmission cost. Extensive evaluation on a dedicated LAE UAV dataset shows that O-VIB achieves high-precision localization under stringent bandwidth budgets. Code and dataset will be made publicly available: github.com/fangzr/TOC-Edge-Aerial.

Satellite-Air-Ground Integrated Networks (SAGINs) provide ubiquitous connectivity, global coverage and flexible deployment convenience for terrestrial users, which are beneficial to optimizing network resources and achieving task scheduling functions. However, the corresponding SAGIN nodes are dynamic and complex, leading to intractable multi-modal features and high network latency when graph model is used for collaborative task completion. Therefore, we establish a directed SAGIN federated graph model to minimize the total transmission latency via computation offloading and quantization methods. Specifically, we utilize the federated graph learning to process the time-varying graph nodes and sizes, and then perform deep reinforcement learning (DRL) to optimize the computation and quantization resources. Moreover, federated learning is convoked to accelerate the convergence speed. Finally, our simulation results show that the proposed method outperforms some advanced benchmarks in terms of convergence performance and transmission latency for multiple data modals.

The reconfigurability of fluid antenna systems (FASs) and reconfigurable intelligent surfaces (RISs) can be jointly utilized to achieve unprecedented degrees of freedom for wireless communication systems. However, adjusting fluid antennas and RISs based on instantaneous channel state information (CSI) is highly challenging. To tackle this challenge, we propose a two-timescale approach for FAS-RIS-aided multi-user systems with regularized zero-forcing (RZF)/zero-forcing (ZF) precoding, where only statistical CSI is required for FAS and RIS optimization. To achieve this goal, we first obtain the closed-form evaluation for the ergodic sum rate (ESR) of FAS-RIS aided multi-user systems with RZF/ZF precoding by exploiting random matrix theory (RMT). Then, we propose an ESR maximization algorithm by jointly optimizing the port selection for FASs, phase shifts at the RIS, and regularization factor of RZF. Numerical results validate the approximation accuracy of the derived ESR evaluation and demonstrate that the performance enhancement benefiting from the joint design of FASs and RISs becomes more prominent when the number of users becomes larger.

The rapid advancement of technologies such as the Internet of Things (IoT), autonomous driving, and smart manufacturing has led to a massive increase in data generation at the edge of networks. This necessitates effective machine learning (ML) methods that address challenges like communication overhead and privacy concerns. Federated learning (FL) has emerged as a promising solution for distributed model training, but the increasing complexity of ML models limits its communication efficiency. To address these challenges, we propose an ultra energy-efficient FL framework (FedUEE). FedUEE utilizes model pruning-based compressive sensing, outage compensation, and joint optimization of learning and resource configurations to comprehensively reduce energy consumption. We develop analytical models that quantify the energy impact of each proposed mechanism, ultimately providing an optimized solution for communication efficiency in edge FL environments.

Collaborative underwater target hunting, facilitated by multiple autonomous underwater vehicles (AUVs), plays a significant role in various domains, especially military missions. Existing research predominantly focuses on designing efficient and high-success-rate hunting policy, particularly addressing the target's evasion capabilities. However, in real-world scenarios, the target can not only adjust its evasion policy based on its observations and predictions but also possess eavesdropping capabilities. If communication among hunter AUVs, such as hunting policy exchanges, is intercepted by the target, it can adapt its escape policy accordingly, significantly reducing the success rate of the hunting mission. To address this challenge, we propose a covert communication-guaranteed collaborative target hunting framework, which ensures efficient hunting in complex underwater environments while defending against the target's eavesdropping. To the best of our knowledge, this is the first study to incorporate the confidentiality of inter-agent communication into the design of target hunting policy. Furthermore, given the complexity of coordinating multiple AUVs in dynamic and unpredictable environments, we propose an adaptive multi-agent diffusion policy (AMADP), which incorporates the strong generative ability of diffusion models into the multi-agent reinforcement learning (MARL) algorithm. Experimental results demonstrate that AMADP achieves faster convergence and higher hunting success rates while maintaining covertness constraints.

Unmanned aerial vehicles (UAVs), which can leverage their integrated sensing, computation, and communication (ISCC) capabilities to enable distributed intelligence at the network edge, play a critical role in next-generation wireless networks. However, UAVs face significant challenges in decentralized model training, including limited computational resources, energy constraints, and inefficient communication. This paper introduces SFL-ISCC, a novel framework that combines model splitting with federated learning (FL)-supported UAV ISCC systems, designed to train a global machine learning (ML) model with minimal energy consumption across multiple UAVs. To our knowledge, this is the first attempt to integrate model splitting into FL-supported UAV ISCC systems. Within the framework, we first theoretically explore the effects of UAV deployment strategies, split layer selection, and client-side aggregation frequency on model convergence performance. Then, we formulate a joint optimization problem to minimize UAV energy consumption while guaranteeing target model convergence accuracy, and propose a low-complexity solution. Moreover, we incorporate semisupervised learning into the SFL-ISCC framework to handle the sparsity of labeled data in UAV networks. Experimental results show that our proposed scheme significantly outperforms baseline schemes in both energy efficiency and model convergence.

Low-earth orbit (LEO) satellite-assisted marine communication networks have become a research focus with the growth of marine activities. However, establishing communication links between underwater devices and maritime satellites is a challenge. Additionally, dynamic environments and multidomain media pose significant challenges in allocating resources effectively within this network. To address these issues, this paper constructs a Space-Surface-Submarine Unmanned Network (3SUN) incorporating LEO satellites, unmanned surface vehicles (USVs), and unmanned underwater vehicles (UUVs). We formulate the resource allocation problem in the 3SUN as a satellite revenue maximization problem. We propose a satelliteprioritized restricted three-sided matching algorithm to solve the match within a single time slot. Additionally, we incorporate deep reinforcement learning (DPL), using the previous stable matching results as training initialization to tackle dynamic connections across multiple slots. Simulation results show that our algorithm achieves satellite revenue closer to the optimal solution compared to other methods while maintaining lower time complexity.

Motivated by the practical application of beam tracking of multiple devices in Multiple Input Multiple Output (MIMO) communication, we study the problem of non-adaptive twenty questions estimation for locating and tracking multiple moving targets under a query-dependent noisy channel. Specifically, we derive a second-order asymptotic bound on resolution for optimal query procedures and provide numerical examples to illustrate our results. In particular, we demonstrate that a single threshold decoding rule achieves the asymptotic bound. The single threshold decoding rule has reduced the computational complexity compared to the multiple threshold method proposed for locating multiple stationary targets (Zhou, Bai and Hero, TIT 2022). Finally, we illustrate our proposed algorithm for the application of beam tracking of multiple mobile transmitters in a 5G wireless network.

Flying ad hoc networks (FANETs) provide high flexibility and real-time wireless communication solutions for multiunmanned aerial vehicle (UAV) systems by utilizing UAVs as routers. However, conventional routing protocols are inadequate for FANETs due to high mobility and dynamic topology of UAV networks. To address these challenges, this paper proposes an enhanced on-demand predictive (EDP) routing protocol for UAV networks. The EDP protocol incorporates a neighbor-coverage-based predictive flooding mechanism and an adaptive link-quality-based route maintenance method. The flooding mechanism utilizes Kalman filter theory to predict mobility and the route maintenance method selects optimal route by evaluating multiple factors. Simulation results demonstrate that the EDP protocol significantly improves the packet delivery rate while reducing network delay and overhead under varying environments, outperforming benchmark routing protocols for FANETs.

Semantic communication (SC), by compressing raw data at the semantic level, significantly improves the information entropy of transmitted data and is considered as one of the key enabling technologies for the next-generation communication. However, most current research underestimates the impact of channel interference on SC systems. As an innovative generative artificial intelligence technique, the diffusion model (DM) has demonstrated remarkable performance in image denoising and enhancement. In this paper, we focus on the effects of wireless channels on SC image transmission and propose an unmanned aerial vehicle (UAV)-enhanced SC framework, termed diffusion joint source-channel coding (D-JSCC). Initially, we deploy a ground-to-air SC system on UAVs, utilizing the aerial advantage to provide favorable channels. Subsequently, we employ DM for intelligent signal processing, adaptively denoising channel interferences and optimizing received images with respect to numerical errors and perceptual loss. The results show that DJSCC consistently exhibits superior performance across various metrics over different channel conditions.

Semantic communication (SC) is considered a critical technology for breaking through the Shannon limit and achieving low-latency, high-capacity 6 G transmission. However, previous SC systems have typically employed a symmetrical architecture to enhance data recovery capabilities, resulting in a strong coupling between the encoder and decoder. In this paper, we introduce a novel asymmetric SC system, termed masked joint source-channel coding (M-JSCC), which significantly enhances the encoder's versatility by allowing it to adapt to different decoder models tailored to specific task requirements. Moreover, we abandon traditional convolutional neural networks and adopt the innovative transformer to increase model capacity further. Additionally, we empower the model with data generation capabilities to combat interference and distortion during wireless transmission, achieving robust semantic transmission. As a result, extensive experiments verify that our M-JSCC achieves better semantic understanding and performance across various tasks and different channel conditions.

Machine learning (ML) enhanced unmanned aerial vehicle (UAV) networks are envisioned to facilitate extensive applications in next-generation wireless networks. Due to the privacy concern and communication overhead in cloud-centric ML, federated reinforcement learning (FRL) enables UAVs to collaboratively train a policy model without disclosing raw observation data. However, the model aggregator in centralized FRL architecture poses various potential threats such as a single point of failure and is inappropriate to distributed networks with unreliable links and nodes. In this paper, we propose AirFRL, a topology-aware decentralized federated reinforcement learning framework for UAV-enabled networks. In AirFRL, we consider the topology dynamics influenced by nodes' mobility and communication quality and its impact on AirFRL. To accelerate training process and guarantee the model performance, we also incorporate the model compression to lighten the local model and introduce the consensus distance and data correlation to reflect the discrepancy between local models and local data. Furthermore, we propose an efficient algorithm to decide the optimal neighbour node selection and model compression ratio. A case study and numerical results demonstrate that AirFRL can achieve linear training speedup and guarantee the learning performance for UAV-enabled networks.

The integration of blockchain technology with unmanned aerial vehicles (UAVs) offers considerable potential, enhancing cybersecurity and driving innovation within the UAV industry. However, due to the dynamic nature of UAVs and limited resources, existing blockchain consensus technologies cannot be directly applied to UAVs. To this end, we propose a chainbased optimized blockchain consensus protocol designed for UAV ad hoc networks, which employs the particle swarm optimization (PSO) algorithm to optimize chain consensus. We design several sub-protocols to cope with malicious nodes in the UAV network, node changes during UAV missions, topology changes. Numerical results show that our protocol increases throughput, reduces communication overhead, and enhances operation efficiency in UAV networks.

Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive, and fast to deploy, which have been used in an increasing number of mission scenarios. However, unstable link quality and frequently changing network topology pose significant challenges for adopting existing routing protocols in mobile ad hoc networks (MANETs). In this paper, we propose a time-slotted on-demand predictive (TSDP) routing protocol designed specifically for UAV networks. The TSDP protocol introduces a novel approach to route selection by incorporating multiple criteria, including delivery ratio, adjacent degree, and mobility prediction factor, to ensure reliable and efficient data transmission. By addressing high latency in route discovery and excessive broadcast overhead, TSDP employs a time-slotted communication mechanism that reduces packet drop rates and enhances route stability. Simulation results demonstrate that TSDP consistently outperforms ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR) protocols in terms of throughput, packet delivery ratio, end-to-end delay, and overhead, particularly in highly dynamic network environments.

With the advancement of large model technologies, AI -generated content is gradually emerging as a mainstream method for content creation. The metaverse, as a key application scenario for the next-generation communication technologies, heavily depends on advanced content generation technologies. Nevertheless, the diverse types of metaverse applications and their stringent real-time requirements constrain the full potential of AIGC technologies within this environment. In order to tackle with this problem, we construct a priority-aware multi-UAV aided metaverse system and formulate it as a Markov decision process (MDP). We propose a diffusion-based reinforcement learning algorithm to solve the resource allocation problem and demonstrate its superiority through enough comparison and ablation experiments.

Autonomous aerial vehicles (AAVs), as the key Internet of Things (IoT) devices, play a dominant position in low-altitude environments. Semantic communication (SC), as the next-generation communication technology, serves as a bridge for surpassing the Shannon limit toward the 6G wireless network. Establishing air-ground SC to provide intelligent IoT services is a crucial initiative for building future smart cities. In this article, we propose an AAV-based SC framework, named diffusion joint source-channel coding (D-JSCC). Abandoning traditional convolutional neural networks, we use transformers as the backbone and innovatively incorporate the diffusion model (DM) for image enhancement, achieving an optimal balance between image distortion and human perception. To accurately capture the numerical and perceptual loss induced by wireless channels and seamlessly amalgamate the DM with SC, we integrate channel states as strong prior information to refine the sampling process. Furthermore, we employ the gradient guidance strategy, which counteracts the randomness of sampling ensuring high robustness in harsh communication conditions. Additionally, we strike a balance between performance and sampling steps, ensuring both efficient computation and high-quality image enhancement. Comprehensive experiments demonstrate the advantages of D-JSCC across different communication environments.

Drone networks are becoming increasingly significant in industrial Internet of Things (IIoT) applications. The limited resources of drones pose challenges in implementing robust security mechanisms that require substantial computation and power resources. Specifically, the inherent complexity of drone networks makes traditional intrusion detection systems (IDS) ineffective due to data imbalance and data scarcity. To address these challenges, this paper proposes a novel IDS framework that integrates conditional generative adversarial networks (CGANs) and utilizes the benefits from the systematic integration of foundation models within a federated learning (FL) paradigm. It leverages the CGANs to address the data issues ensures reliable performance and stable convergence against the foundation model. Moreover, our approach enhances data privacy relying on the differential privacy in FL and protects global model integrity through secure aggregation and updating. Simulation results show that the proposed framework achieve the accuracy rates of 91% and 99% on cyber and physical datasets, respectively. This framework achieves improvement ranging from 0.47% to 3.24% for cyber datasets and from 0.93% to 4.84% for physical datasets, which yields its superior performance in drone networks intrusion detection.

Effective conflict resolution is crucial to ensure the safety of unmanned aerial vehicles (UAVs) in increasingly congested low-altitude airspace. However, the inefficiency in representing large-scale UAV information hinders the performance of existing learning-based methods. To overcome this challenge, we propose an enhanced graph-based reinforcement learning (GRL) approach that models multi-agent interactions relationships. Specifically, a novel multi-scale graph reinforcement learning (MS-GRL) approach is utilized to learn UAV avoidance strategies in a dense environment. MS-GRL utilizes the time-evolving intensity of local conflicts to evaluate the global attention weights for UAVs in conflict, while aggregating UAV observation data through graph embedding and requisite feature refinement. In addition, to adaptively limit the safety region of the action space while minimize deviations from the original trajectory, a safety-constrained maneuver strategy is proposed. Experimental results demonstrate that MS-GRL outperforms state-of-the-art GRL methods when there are up to 100 UAVs and multiple obstacles.

Owing to the advantage of flexible deployment of uncrewed aerial vehicles (UAVs), the problem of long-distance transmission of sensor devices in Internet of Things (IoT) networks can be effectively addressed. However, the line-of-sight (LoS) channels of UAVs also make the transmitted data vulnerable to interception by eavesdroppers. In this article, a security transmission scheme is proposed for UAV-aided IoT networks, in which a UAV with variable transmission power is deployed between the sensor device and the monitor. In addition, the concept of Age of Information (AoI) is introduced to measure the freshness of information, while the difference between the AoI of the eavesdropper and the monitor is considered as the metric for the security transmission performance. To enhance system security, we derive a closed-form solution for the AoI difference and propose a UAV deployment algorithm to maximize the gap. The simulation results demonstrate a strong agreement with the theoretical analysis, and the proposed method can significantly improve the security communication performance of the system compared with the traditional method.

Due to the outstanding maneuverability, unmanned aerial vehicles (UAVs) garner increasing applications in the Internet of Things (IoT), such as data collection, environmental monitoring, emergency communication, search and rescue, and autonomous exploration is the foundation of these missions which can obtain a prebuilt map automatically. However, current methods suffer from low efficiency. To address this, we propose a hierarchical exploration framework for UAVs with limited field-of-view (FOV) sensor, encompassing frontier and viewpoint generation, global coverage path planning, and active perception trajectory generation. First, we employ the random seeds frontier generation and anisotropic Gaussian sampling for environment information update, which can efficiently utilize sensor’s sensing range. Then, we design an appropriate heuristic function to represent the connection cost between different viewpoints and solve the global coverage path as a traveling salesman problem (TSP) to balance the long-term and short-term information gain. Moreover, active perception trajectory planning is proposed to enhance flight safety, smoothness, and exploration efficiency. Simulation and real-world scenario results indicate that the proposed method achieves higher efficiency in frontier generation and viewpoint sampling, and the difficulty of solving global coverage path does not significantly increase with the environment scale. Our proposed method improves exploration efficiency by 17%–27% compared to the state-of-the-art (SOTA) method.

To address the demand of digital twin (DT) model construction, we introduce the autonomous exploration framework for Internet of Drones (IoD) network, to autonomously construct models in the preset areas. However, current multi-UAV autonomous exploration frameworks suffer from low efficiency. To address this, we propose an innovative multi-UAV autonomous exploration framework. Using the LiDAR point cloud, our proposed framework firstly generates mesh frontiers based on the unit sphere point cloud flip, without building an occupancy grid map. Secondly, we design a multi-UAV exploration information interaction method, which can efficiently integrates the information obtained from various UAVs. Based on the information, a sparse topological graph is constructed to store the previous local feasible regions. A heuristic function is used to allocate the exploration targets for UAVs. The simulation results indicate that the proposed method achieves higher efficiency in frontier generation and improves exploration efficiency by 13.3-64.6% compared to the state-of-the-art methods.

With the growing demand for low-altitude transportation, the application of the Internet of drones (IoD) in urban logistics has become increasingly significant. However, the complex obstacles present in urban environments, such as tall buildings and no-fly zones, pose numerous challenges for the IoD, including low data-driven efficiency and difficulties in representing implicit knowledge. To address these challenges, this paper proposes an IoD swarm collaborative scheduling method based on distributed knowledge-enhanced multi-agent reinforcement learning (DKEMARL). The method leverages prior environmental knowledge to design a knowledge embedding and expansion module, which provides comprehensive and detailed environmental observation data during training and introduces a reward mechanism that balances task timeliness with flight safety. Within a centralized training and decentralized execution framework, the multi-agent deep deterministic policy gradient (MADDPG) approach is employed to facilitate efficient cooperation among the IoD. Specifically, we develop a scenario model for low-altitude transportation tasks involving an IoD swarm, considering factors such as task timeliness, flight distance cost, and safety constraints, and propose an optimization objective to balance timely task completion with flight safety. Simulation results demonstrate that the proposed DKEMARL algorithm can significantly enhance task completion efficiency compared to baseline methods that do not incorporate knowledge enhancement.

The extensive deployment of Internet of Things (IoT) devices across diverse industries has introduced substantial challenges in information collection, which exactly hinders its further advancement. By virtue of its flexibility and mobility, mobile crowdsensing (MCS), particularly drone-assisted mobile crowdsensing, is regarded as a new paradigm for addressing information collection problems in IoT environments. Nonetheless, owing to the inherent size and energy constraints of drones, planning their trajectories to efficiently perform the crowdsensing tasks from a large number of heterogeneous and spatiotemporally distributed IoT devices presents a significant problem. In this paper, we develop a multi-drone assisted crowdsensing social IoT (SIoT) system that integrates the social attributes of IoT devices and enables performing social community-oriented crowdsensing. Given the significance of information freshness in crowdsensing tasks, we jointly optimize the age of information (AoI) and drone energy consumption within the problem of multi-drone trajectory planning. We formulate the aforementioned problem as a decentralized partially observable Markov decision process (Dec-POMDP) and propose a deep-reinforcement-learning-based algorithm to address this problem. A series of experiments is conducted and the simulation results demonstrate the superiority and robustness of the proposed algorithm in effectively balancing the AoI of the whole SIoT system and energy consumption of drones during the crowdsensing tasks.

Collaborative perception enhances sensing in multi-robot and vehicular networks by fusing information from multiple agents, improving perception accuracy and sensing range. However, mobility and non-rigid sensor mounts introduce extrinsic calibration errors, necessitating online calibration, further complicated by limited overlap in sensing regions. Moreover, maintaining fresh information is crucial for timely and accurate sensing. To address calibration errors and ensure timely and accurate perception, we propose a robust task-oriented communication strategy to optimize online self-calibration and efficient feature sharing for Real-time Adaptive Collaborative Perception (R-ACP). Specifically, we first formulate an Age of Perceived Targets (AoPT) minimization problem to capture data timeliness of multi-view streaming. Then, in the calibration phase, we introduce a channel-aware self-calibration technique based on reidentification (Re-ID), which adaptively compresses key features according to channel capacities, effectively addressing calibration issues via spatial and temporal cross-camera correlations. In the streaming phase, we tackle the trade-off between bandwidth and inference accuracy by leveraging an Information Bottleneck (IB)-based encoding method to adjust video compression rates based on task relevance, thereby reducing communication overhead and latency. Finally, we design a priority-aware network to filter corrupted features to mitigate performance degradation from packet corruption. Extensive studies demonstrate that our framework outperforms five baselines, improving multiple object detection accuracy (MODA) by 25.49% and reducing communication costs by 51.36% under severely poor channel conditions. Code will be made publicly available: github.com/fangzr/R-ACP.

Emerging real-time computer vision (CV) applications on wireless edge devices demand energy-efficient and privacy-preserving learning. Federated learning (FL) enables on-device training without raw data sharing, yet remains challenging in resource-constrained environments due to energy-intensive computation and communication, as well as limited and non-i.i.d. local data. We propose FedDPQ, an ultra energy-efficient FL framework for real-time CV over unreliable wireless networks. FedDPQ integrates diffusion-based data augmentation, model pruning, communication quantization, and transmission power control to enhance training efficiency. It expands local datasets using synthetic data, reduces computation through pruning, compresses updates via quantization, and mitigates transmission outages with adaptive power control. We further derive a closed-form energy–convergence model capturing the coupled impact of these components, and develop a Bayesian optimization (BO)-based algorithm to jointly tune data augmentation strategy, pruning ratio, quantization level, and power control. To the best of our knowledge, this is the first work to jointly optimize FL performance from the perspectives of data, computation, and communication under unreliable wireless conditions. Experiments on representative CV tasks show that FedDPQ achieves superior convergence speed and energy efficiency.

Integrated sensing and communication (ISAC) technology is a promising contender for the future Internet of Underwater Things (IoUT). However, the complexity of underwater acoustic (UWA) channels and the randomness of ISAC signals may pose challenges to underwater communication and sensing. To address this issue, this paper investigates a novel communication-assisted bi-static sensing scheme capable of facilitating underwater multi-node collaboration using orthogonal frequency division multiplexing (OFDM), which refers to as UWA-OFDM-ISAC. Moreover, two efficient receivers are designed based on compressed sensing to enhance communication and sensing performance. In this paper, we first portray the UWA-OFDM-ISAC system model and emphasize that the Doppler and symbols estimated at the communication side are beneficial in enhancing the bi-static sensing performance. To estimate doubly spread UWA channels, an orthogonal matching pursuit-based interference cancellation channel estimation method is developed, which decouples Doppler and delay in OFDM signals and significantly reduces the parameter search dimension. Furthermore, we propose an enhanced detection algorithm based on matching pursuit, which can exploit sparse multipath information of echoes to improve target detection performance under doubly spread channels. The detection probability is improved by more than 30% at the 10−2bit error rate level compared with the energy detector. Finally, simulation results illustrate the effectiveness of the proposed UWA-OFDM-ISAC and demonstrate that the designed receivers have significant advantages relative to various existing algorithms.

In this paper, we study uplink covert communication in a space-air system, where an unmanned aerial vehicle (UAV) transmits sensitive data to a Geosynchronous Earth Orbit (GEO) satellite while preventing the transmission action from being discovered by a warden. We derive the optimal decision threshold of the warden. We investigate the 3-dimensional (3D) beamformer and 3D trajectory design for the transmitter UAV against this optimum warden to maximize the covert transmission rate in the presence of imperfect channel state information and uncertain noise. Due to the non-convex structure and dependence between beamforming vectors and locations of the transmitter UAV, we develop a decoupling method that specifies a feasible flight region of the transmitter UAV at each time slot, enabling the decomposition of the original optimization problem into two sub-problems that optimize the trajectory and beamforming vectors individually. We design an iterative algorithm with a new initialization method to solve the sub-problems alternately with the semi-definite relaxation (SDR) and the successive convex approximation (SCA) technique. Numerical results show that the average covert rate of our design approaches the ideal case without the warden and increases by about 102.3% and 19.1% compared with benchmark schemes that do not employ beamforming or design 2D trajectory, respectively.

Autonomous exploration is a fundamental challenge for numerous applications of mobile robots. Traditional methods often lead to impractical and discontinuous trajectories, which may substantially deteriorate the exploration time. In this work, we propose a rapid autonomous exploration framework with a field-of-view (FOV) expansion mechanism. We present a 1-degree-of-freedom (DOF) FOV expansion mechanism, coupled with a frontier-gravitation FOV direction planning method to decouple the direction of the sensor's FOV from the robot velocity direction. Our approach includes a rapid frontier viewpoint generation method utilizing principal component analysis (PCA). Moreover, we introduce a sliding window travelling salesman problem (TSP) for global coverage path planning, incorporating an attenuation coefficient to increase the exploration priority of independent small frontiers and reduce revisit probability. Finally, compared to state-of-the-art (SOTA) approaches, our proposed mechanism and framework beneficially reduce exploration time by 30%-44% and enhance the continuity of the robot movement in both simulation and real-world scenarios.

Reconfigurable intelligent surfaces (RISs) have been shown effective in strengthening the physical layer security of wireless systems, and the two-timescale design was proposed to tackle the challenges in channel estimation and phase-shift control. However, existing maximum ratio transmission (MRT) based precoding design is not efficient in mitigating information leakage. To this end, this paper considers the performance analysis and two-timescale design for RIS-aided multiple-input single-output (MISO) secure communications with regularized zero-forcing (RZF) and zero-forcing (ZF) precoding, which is not available in the literature. The major challenges come from the two-hop channel and the inverse structure in the precoding matrix. By utilizing random matrix theory, we first evaluate the fundamental limits of the considered system by deriving a closed-form expression for the ergodic secrecy sum rate (ESSR). Then, we determine the optimal regularization factor of the RZF precoder and evaluate the ESSR over independent and identically distributed (i.i.d.) channels in the high SNR regime. The results indicate that when the number of reconfigurable elements at the RIS is overwhelmingly larger than that of transmit antennas and users, the ESSR of the two-hop channel approaches that of the single-hop channel. Based on the performance analysis, we propose a two-timescale algorithm to maximize the ESSR by optimizing the regularization factor of RZF and the phase shifts of the RIS alternatively. Simulation results validate the accuracy of the theoretical analysis and the effectiveness of the proposed algorithm.

Underwater target hunting (UTH) is a critical and complex mission involving the search, monitoring, and hunting of targets in an underwater environment. However, the unpredictable trajectories and flexibility of these targets, along with complex underwater environments, significantly impede the efficiency and success of traditional schemes that depend solely on unmanned underwater vehicles (UUVs). Consequently, this paper presents the “3U network”, a novel heterogeneous framework integrating unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs), and UUVs for UTH. Within this framework, a UAV searches and monitors the target, a USV acts as a communication relay, and a swarm of UUVs hunts the target. Moreover, to improve the timeliness of target search, we propose the age of information (AoI)-based UAV search strategy. Additionally, we construct a constrained multi-objective optimization problem aiming to minimize energy consumption and mission duration by optimizing vehicles' trajectories, considering mobility limitations, safety, and connectivity constraints. To tackle this problem, we design an AoI- and energy-aware deep reinforcement learning (DRL) algorithm to optimize control policies for heterogeneous vehicles. The experimental results demonstrate that the proposed scheme outperforms the baseline schemes in terms of energy consumption, and mission duration and success rates.

In the Internet of Things (IoT) networks, monitoring information collection is critical for intelligent decision-making, which is a significant challenge for the sensors deployed at remote locations. Relay can effectively improve the transmission quality and transmission range of sensors by means of multi-hop transmission. It is an effective method for remote data collection in IoT networks. However, the lifetime of the relay may be dramatically reduced due to the heavy resource overhead for frequent short packet delivery. In this paper, we present an efficient relay transmission scheme for IoT networks, in which the frame aggregation technology is employed at the relay to reduce the resource overhead by sharing a common frame header and tail. Meanwhile, for the delay caused by frame aggregation, we analyze the freshness of the sensing data in terms of age of information (AoI) and take it as a constraint for the frame aggregation system. Besides, the optimal frame aggregation period is determined based on the closed-form expressions derived for the average AoI and transmission efficiency. Simulation results show that the theoretical analysis closely matches the simulations, and the proposed method significantly improves transmission efficiency compared to the traditional decode-and-forward method.

The popular multiagent reinforcement learning (MARL) methods primarily focus on exploring the capability of value functions to facilitate multiagent coordination. These MARL methods, following the centralized training with decentralized execution (CTDE) paradigm, tend to design ingenious network architectures while overlooking the impact of coordination through expanding local observation information. To tackle this deficiency, we model the multiagent systems (MASs) as a graph and use a graph neural network (GNN) to extract rich information between one agent and the others efficiently. Moreover, we propose a multigraph-neural-network information representation (MGIR) method that uses the power of GNN in local observation information extraction, enabling the acquisition of higher quality information. Specifically, multiple GNNs are used during centralized training to characterize the MAS from different perspectives and extract representations of latent variables. During distributed execution, these latent variables are leveraged to expand local observation information. Extensive comparative experiments substantiate that our proposed MGIR demonstrates superior coordination performance when compared with baseline methods. In addition, it can be flexibly integrated into various value function decomposition methods of MARL.

To meet requirements for real-time trajectory scheduling and distributed coordination, underwater target hunting task is challenging in terms of turbulent ocean environments and dynamic adversarial environment. Despite the existing research in game-based target hunting area, few approaches have considered dynamic environmental factors, such as sea currents, winds, and communication delay. In this article, we focus on a target hunting system consisted of multiple unmanned underwater vehicles (UUVs) and a target with high maneuverability. Besides, differential game theory is leveraged to analyze adversarial behaviors between hunters and the escapee. However, it is intractable that UUVs have to deploy an adaptive scheme to guarantee the consistency and avoid the escape of the target without collision. Therefore, we conceive the Hamiltonian function with Leibniz's formula to obtain feedback control policies. In addition, it proves that the target hunting system is asymptotically stable in the mean, and the system can satisfy Nash equilibrium relying on the proposed control policies. Furthermore, we design a modified multiagent reinforcement learning (MARL) to facilitate the underwater target hunting task under the constraints of energetic flows and acoustic propagation delay. Simulation results show that the proposed scheme is superior to the typical MARL algorithm in terms of reward and success rate.

Collaborative perception systems leverage multiple edge devices, such as surveillance cameras or autonomous cars, to enhance sensing quality and eliminate blind spots. Despite their advantages, challenges such as limited channel capacity and data redundancy impede their effectiveness. To address these issues, we introduce the Prioritized Information Bottleneck (PIB) framework for edge video analytics. This framework prioritizes the shared data based on the signal-to-noise ratio (SNR) and camera coverage of the region of interest (RoI), reducing spatial-temporal data redundancy to transmit only essential information. This strategy avoids the need for video reconstruction at edge servers and maintains low latency. It leverages a deterministic information bottleneck method to extract compact, relevant features, balancing informativeness and communication costs. For high-dimensional data, we apply variational approximations for practical optimization. To reduce communication costs in fluctuating connections, we propose a gate mechanism based on distributed online learning (DOL) to filter out less informative messages and efficiently select edge servers. Moreover, we establish the asymptotic optimality of DOL by proving the sublinearity of its regrets. To validate the effectiveness of the PIB framework, we conduct real-world experiments on three types of edge devices with varied computing capabilities. Compared to five coding methods for image and video compression, PIB improves mean object detection accuracy (MODA) by 17.8% while reducing communication costs by 82.65% under poor channel conditions.

In Internet of Things(IoT), the combination of cloud computing and edge computing becomes a new computing paradigm to provide users with low-latency data services. However, for the limited resource, high dynamic, and wide distributed characteristics of IoT devices, it becomes a great challenge to realize the universal application of edge servers and the cloud-edge computing allocation. Meanwhile, most of the schemes ignore the leakage of data access pattern privacy when accessing data. Therefore, in this paper, we propose a privacy-preserving access control scheme for IoT data. Based on the cloud-edge-end framework, we design a pervasive edge computing protocol, which allows well-resourced devices to become edge servers at suitable geographic locations and users to outsource and access IoT data through the nearest edge server. It increases the flexibility of the cloud-edge collaborative system as well as the efficiency of data processing. Users do not need to interact beyond the network edge to enjoy the data services. Furthermore, a novel attribute-based encryption scheme is designed based on a modified Lightweight Secret Sharing Scheme to optimize computing task allocation and reduce the computation burden on end devices, without attribute information leakage. In addition, we also design a Transform algorithm and a Cloud-edge Interaction protocol to hide access pattern privacy efficiently. We analyze the feasibility of the scheme and demonstrate that the scheme is semantically secure and conceals access pattern privacy. Simulation experiments based on real IoT data show that the scheme is efficient and suitable for IoT scenarios.

Integrated sensing and communication (ISAC) technology has been envisioned to revolutionize the future intelligent vehicle networks (IVNs). Recently, due to the mobility and flexible deployment, unmanned aerial vehicle (UAV) has been regarded as a promising aerial ISAC platform in future IVNs. In this paper, comprehensively considering all the performance on the throughput, sensing accuracy, and sensing rate, we propose a Multi-Agent joint Trajectory control and RadCom task schedule (MA-TRC) scheme for the ISAC-UAV assisted IVN. To make UAVs achieve the optimal decisions autonomously and adaptively, we propose a Game-Embedding Multi-Agent Deep Reinforcement Learning (GE-MADRL) approach. Specifically, considering the complex action space with both discrete and continuous decision variables of UAVs, we develop a multi-agent Parametrized deep Q-network (MAPDQN) based solution, which can help UAVs learn the dynamic and uncertain environment to adaptively obtain the MA-TRC scheme. Furthermore, since UAVs work in a distributed decision making manner, the potential conflicting decisions will impact the network performance. To avoid the decision conflicts among UAVs during the network parameter training, a distributed two-stage Game method is designed as an action adjuster embedded in MAPDQN, by which the learning convergence performance will be further improved and the strategy conflicts can be avoided.

Crowdsourcing is an effective method to alleviate the burden of conducting a site-survey procedure for localization tasks. However, crowdsourced data is typically inaccurately and scarcely annotated, rendering accurate localization a rather challenging problem. To alleviate this problem, we propose VRLoc, a deep reinforcement learning (DRL)-based unsupervised wireless localization framework using crowdsourced trajectory data. The proposed VRLoc primarily encompasses three components, i.e., a robust K-means (RKM) clustering method for generating a series of virtual reference points (VRPs), DRL for determining the physical layout for VRPs, and online localization based on VRPs. Specifically, the proposed RKM method employs a density-based approach for the initialization of cluster centers, rather than the commonly used random solution, yielding repeatable and reliable VRP generation results. To accurately determine the physical locations for VRPs, we develop a modified soft actor-critic (SAC)- based VRP layout method with multiple objectives, i.e., the connection topology among VRPs, the floor-plan information, and the near-field condition. Then, we effectively predict locations of target users by utilizing classification models to match the online collected samples with the VRPs annotated by physical locations. The proposed framework is advantageous in achieving high-accuracy unsupervised localization, with the VRPs bridging the unlabeled crowdsourced data and physical location space. Both experimental and simulation results demonstrate the effectiveness and superiority of the proposed VRLoc framework as an accurate and practical solution for unsupervised localization.

Autonomous underwater vehicles (AUVs) are increasingly utilized across various domains, employing diverse machine learning (ML) algorithms to enhance functionality. However, the dynamic underwater environment, characterized by high temporal and spatial variability, makes offline-trained models on static datasets inadequate for practical AUV operations. This necessitates the integration of online learning capabilities that can adapt to changing conditions in real time. The quality of training data plays a crucial role in the performance of these models, and the ability to utilize distributed data from multiple AUVs can be beneficial. Nonetheless, the challenge lies in the limited communication resources available underwater. The typical acoustic communication rates, which are just in the tens of kilobits per second, pose a significant barrier to implementing centralized ML strategies that require extensive data sharing among AUVs. To overcome these hurdles, we propose an underwater federated learning (UFL) framework that incorporates model pruning and gradient quantization. This approach aims to establish a communication-efficient distributed learning paradigm. Furthermore, we have derived a closed-form expression to quantify the upper bound of the convergence error, which highlights the impact of pruning and quantization on the federated learning (FL) convergence. Additionally, we utilize a heuristic algorithm to optimize the pruning and quantization strategies, aiming to minimize the convergence error while adhering to delay constraints. The effectiveness of our proposed framework is demonstrated through its application in a cooperative navigation task involving multiple AUVs, showing significant resource conservation and enhanced operational efficiency.

Under the advancement of emerging communication technologies, the utilization of Internet of Things (IoT) is progressively expanding across diverse domains. The age of information (AoI) stands as a crucial measurement in evaluating the efficiency of IoT networks. For efficient and reliable data collection, Unmanned aerial vehicle (UAV) have been extensively applied in IoT networks. However, the escalating number of sensor nodes and random data sampling mode within IoT networks have made it challenging for UAV trajectory planning with the constraint of energy consumption. In response to this challenge, our solution entails an attention-based actor-critic algorithm for multi-UAV path planning in a pre-clustered IoT network, which takes into account both the average AoI of clusters and the energy consumption of each UAV. The simulation outcomes validate that our algorithm achieves a trade-off between the information freshness and energy consumption in the multi-UAV data gathering scenario.

We revisit the problem of statistical sequence matching between two databases of sequences initiated by Unnikrishnan (TIT 2015) and derive achievable theoretical performance guar-antees for a generalized likelihood ratio test (G LRT) in the large deviations regime, when the number of matched pairs of sequences between two databases is unknown. In this case, the task is to accurately estimate the number of matched pairs and identify the matched pairs of sequences among all possible matches between the sequences in the two databases. We generalize the GLRT by Unnikrishnan and explicitly characterize the tradeoff among the exponential decay rates for probabilities of mismatch, false reject and false alarm. When one of the two databases contains a single sequence, the problem of statistical sequence matching specializes to the problem of multiple classification introduced by Gutman (TIT 1989). For this special case, our result strengthens previous result of Gutman (TIT 1989) and Zhou, Tan and Motani (Information and Inference 2020) by allowing the testing sequence to be generated from a distribution that is different from generating distributions of all training sequences.

While symbol-level (SL) precoders have been shown to improve transmission performance by treating multi-user interference (MUI) as a useful resource, the SL precoders only employ uniform modulation for all downlink users, and the incurred complexity increases linearly with the block length. In this letter, we investigate the possibility of mixed-modulations interference exploitation (IE) for satellite communications, at a sub-block level. By exploiting the specific detection regions of constellation points of different modulations, a novel sub-block level mixed-modulation (BL-MIE) design is proposed, guaranteeing that MUI is always constructive in each sub-block duration, regardless of the users’ heterogeneous modulation schemes. Compared to the classic SL design, it is proved that the BL-MIE provides complexity reduction on the order of square root of the sub-block length, i.e., ${\mathcal{O}}(\sqrt{n})$, with n denoting the number of symbols per sub-block. Hence, it well strikes the balance between the performance and complexity. Simulation demonstrates that the proposed designs significantly outperform the benchmarks in terms of power consumption and throughput performance.

The development of wireless communication technology is reshaping the landscape of intelligent transportation systems, particularly in the realm of internet of vehicles (IoV). Among these, integrated sensing and communications (ISAC) has garnered widespread attention by leveraging shared hardware resources or even spectrum between sensing and communication to achieve integrated benefits. For IoV, parameters such as target position and speed estimated by ISAC can be used as prior information for resource allocation and beamforming to improve communication performance. In this paper, we focus on ISAC-enabled IoV, where radar sensing signals and communication signals are transmitted within different slots of a subframe to avoid interference. We propose a resource allocation scheme to maximize system throughput while meeting the differentiated needs of all users, where spatial division multiple access is dynamically employed based on network load. Simulation outcomes verify the efficiency of the suggested algorithm.

In this study, an autonomous underwater vehicle (AUV) is considered to collect data in Internet of Underwater Things (loUT) networks. The AUV is tasked with timely visits to sensor nodes (SNs) to collect data using a navigation-hover-communication protocol. Considering the AUV's limited energy, the dynamic data upload demand of SNs and the diminishing Value of Information (Vol) during data transmission, efficient path planning for AUV is required. Therefore, we formulate a multi-objective optimization problem and employ the deep deterministic policy gradient (DDPG) algorithm to address it. Our objectives encompass the maximization of the sum data rate, the maximization of the sum Vol, and the minimization of the AUV's energy consumption within specified task time constraints. To mitigate the challenges posed by sparse rewards, we enhance the DDPG algorithm with hindsight experience replay (HER). The simulation results show that the data collection policy trained by our proposed algorithm can converge quickly and has excellent generalization. When the communication range changes, it can still effectively reduce the AUV's energy consumption while ensuring the quality and timeliness of the data collection task.

With the rapid development of wireless communication networks, UAVs serving as base stations are increasingly being applied in various scenarios which not only include edge computation and task offloading, but also involve emergency communication, vehicular network enhancement, etc. In order to enhance the utility of UAV base stations’ allocation and deployment, a series of algorithms have been proposed, utilizing heuristic methods, learning-based algorithms or optimization approaches. However, it is intractable for current algorithms to handle the exponential computation increment with UAV base stations increasing, and complicated application scenarios with high dynamic demands. To solve the above issues, we formulate a decision problem with a long sequence to optimize the deployment of multi-UAV base stations for maximizing vehicular networks’ communication coverage ratio, which needs to be subject to co-constraints consisting of moving velocity, energy consumption and communication coverage radius. To solve this optimization problem, we creatively propose an algorithm named dense multi-agent reinforcement learning (DMARL), which is under the dual-layer nested decision-making framework, centralized training with decentralized deployment, and accelerates training by only collecting critical states into the dense sampling buffer. To prove our proposed algorithm’s effectiveness and generalization ability, we conduct experimental simulations in scenarios with different scales. Corresponding results have been provided to verify our algorithm’s superiority in training efficiency and performance metrics, including coverage ratio and energy consumption, compared with other algorithms.

Efficient data processing is crucial for industrial Internet of Things (IIoT) applications, but the limited energy and computing resources in IIoT devices (IIoT-Ds) pose constraints. This article utilizes a unmanned aerial vehicle (UAV) as a computing server for enhanced IIoT mission execution. Specifically, the energy consumption of IIoT-Ds and the UAV, as well as the weighted cost of the communication and computing scheduling strategy in the UAV-aided IIoT, are jointly taken into account. An optimization problem based on the system energy consumption is built under the constraints of UAV motion, computing offloading, and transmitting power allocation. A problem decoupling-based alternating optimization method is proposed to solve the minimization problem by decomposing it into three subproblems: 1) UAV motion optimization; 2) computing offloading configuration; and 3) transmitting power allocation. Through comparing the proposed communication and computing scheduling strategy with existing methods, simulation results illustrate its attainment of quasi-optimal performance, thereby validating the effectiveness of the alternating optimization method.

Federated learning (FL), utilizing data from the edge devices (EDs) while protecting user privacy has gained much attention. Its efficacy is substantially influenced by both the quantity of connected devices and the quality of wireless communications. Network congestion, resulting from multiple access and signal attenuation caused by physical obstacles may severely impact the convergence of the FL model. To address these issues, this article employs nonorthogonal multiple access (NOMA) for uplink transmission and designs a two-tier FL framework consisting of ground devices and unmanned aerial vehicles (UAVs) to ensure the construction of Line of Sight (LoS) channels from EDs to the base station. Moreover, we construct a multiobjective joint optimization problem to minimize the FL convergence time considering constraints, such as the NOMA uplink latency, ED selection strategy, local training latency, and energy consumption. We also deduce the theoretical upper bound of the convergence time and transform the proposed multiobjective problem into a solvable form by eliminating the discrete variables determined by the ED selection. In turn, we utilize the proximal policy optimization (PPO) algorithm to solve this optimization problem. Finally, the extensive experimental results demonstrate the advantages of our proposed algorithm in terms of latency and energy consumption, while yielding a high robustness and scalability.

Unmanned aerial vehicles (UAVs) empowered Internet of things (IoT) networks have emerged as a burgeoning paradigm in the era of 6G. However, due to substantial data volume and privacy concerns, the conventional UAV backhaul to cloud center framework is not applicable to various latency and privacy-sensitive applications. Therefore, we propose a blockchain-based hierarchical federated learning (FL) framework for UAV-enabled IoT networks. Specifically, we utilize the total data distance-aware device association to mitigate model impairment arising from imbalanced data distribution. Besides, we introduce a lightweight blockchain into FL to tackle the trust deficit caused in decentralized global model aggregation. Furthermore, we design an optimization framework that jointly orchestrating device association, wireless resource allocation, and UAV deployment, aiming at a balance between the learning latency and model accuracy. To address the formulated optimization problem, we proposed a two-stage algorithm that integrates both greedy strategy and soft actor-critic algorithm. Extensive experiments show that our proposed scheme outperforms contemporary relative to state-of-the-art alternatives.

In recent years, the Internet of Underwater Things (IoUT) has garnered significant attention owing to its potential in ocean exploration and monitoring. However, environmental erosion and limited energy can cause node failures, leading to routing voids, communication congestion, and even IoUT breakdowns. Addressing these challenges, this work considers a node repair scheme for multiple autonomous underwater vehicles (AUVs) to search and repair faulty nodes to ensure the stable operation of the IoUT networks. Moreover, AUVs should adapt automatically to the unknown environment, working in cooperative or separative modes to balance repair efficiency and coverage. We propose a multiagent reinforcement learning-based AUV-assisted node repair (RANR) scheme, which considers limited underwater communication and scheduling between AUVs. To further enhance work efficiency, we introduce area information entropy to reduce redundant coverage among AUVs. Simulation results demonstrate that the RANR scheme is highly applicable to different working conditions.

The success of the Internet of Things has recently spread to the industrial sector, commonly referred to as Industrial IoT (IIoT). IIoT, which has a far-reaching impact on the operation of industries around the world, is recognized as a key enabler for the fourth industrial revolution. It has the potential to prompt economic growth and global competitiveness, in terms of improving productivity, efficiency, and so on.

In this article, we investigate the pursuit-evasion game of multiple autonomous underwater vehicles (AUVs) in a complex ocean environment. The pursuer AUVs need to optimize their trajectories to avoid obstacles and dangerous vortex regions in the environment in order to pursue the escaper AUV. Both the pursuer and escaper can sense each other with limited detection capabilities for further pursuit or escape. As the underwater pursuit-evasion (UPE) game is a high-dimensional NP-hard problem, we innovatively transform it into a finite-horizon Markov game process and propose a decentralized training and decentralized execution efficient training framework based on the offline reinforcement learning. During the training process, we propose multiagent independent soft actor–critic to facilitate policy improvement and generate the offline data set, and propose multiagent independent decision transformer for model training in the UPE game. Extensive simulations demonstrate the scalability and generalization ability of our proposed training framework, which can achieve excellent performance in the UPE games under different conditions and environments with only a few AUVs participating in policy improvement to generate the high-quality offline data set.

As a typical nonterrestrial network (NTN)-enabled Internet of Things (IoT), the multi-Unmanned aerial vehicle (UAV) collaborative surveillance network boasts efficient capabilities in information collection and transmission. However, manufacturing techniques and environmental conditions can lead to UAV failures, thereby impacting network performance. To recover the performance of the multi-UAV collaborative surveillance network, the effective movement of multiple UAVs is under investigation in order to improve target coverage and data backhaul efficiency. In this article, we present a novel multiagent deep reinforcement learning-based algorithm to accomplish network recovery. The proposed algorithm employs a multihead attention network to facilitate coupled multiobjective learning and overcome the limitations imposed by local information. Additionally, a stable learning method is introduced to address the difficult convergence problem caused by dynamic topology changes due to UAV motion. Experimental results show that the proposed algorithm can generate feasible multi-UAV motion strategies, effectively facilitating network recovery and improving the performance of the multi-UAV collaborative surveillance network in different scenarios.

Considering the wide-area distribution and limited transmission power of sensing devices in the Internet of Underwater Things (IoUT), employing autonomous underwater vehicles (AUVs) to collect data is considered a promising solution. While most existing AUV-assisted data collection schemes primarily focus on enhancing data collection throughput and identifying the shortest path, they often overlook the influence of the underwater environment on AUV and the timeliness of data collection. In this article, we design a multi-AUV-assisted data collection system, in which AUVs select their own target devices to collect data according to the data upload urgencies of IoUT devices. Considering the disturbance of turbulent ocean environment and the limited energy of AUV, we propose an environment- and energy-aware AUV-assisted data collection scheme. This scheme aims to conduct path planning for multiple AUVs based on perceived environmental information, including turbulent fields and device statuses. The primary goals are to maximize the sum data collection rate and total data throughput, minimize AUV energy consumption, reduce the average data overflow times. To solve this high-dimensional NP-hard problem, we first model the problem as a Markov decision process, and propose a multiagent independent soft actor–critic to solve it. Extensive simulations validate the effectiveness and adaptability of our approach.

Metaverse is envisioned to merge the actual world with a virtual world to bring users unprecedented immersive feelings. To ensure user experience, federated learning (FL) has been expected as a critical enabler to provide metaverse users with high-quality sensing, communicating, and rendering. However, considering the limitation of wireless communication resources and the stringent requirements of users, collaborating with massive metaverse users to realize FL still has tremendous challenges. Most pioneer works on improving the performance of FL assume that the system states are static, which is unsuitable in the metaverse. Because the FL in the metaverse is always a complicated long-term iteration process, where the fluctuations of channel status and available computing resources of users are inevitable, a changeless strategy may lead to poor results. Therefore, this paper proposes an efficient FL scheme relying on dynamic user selection, gradient quantization, and resource allocation. Specifically, we derive the convergence error bound to reveal the impact of user selection, wireless transmission error, and gradient quantization error of each iteration on FL’s convergence. Based on the theoretical analysis, we jointly and dynamically optimize the user selection, gradient quantization, and resource allocation to minimize the error bound with time and energy consumption budgets. Furthermore, to make the formulated sequential decision-making problem tractable, we transform it into a Markov decision process and design a soft actor-critic-based solution. Extensive experiments validate that our proposed scheme has superior performance compared to conventional schemes in dynamic-changing network environments.

Wireless ad-hoc networks play a significant role in environments without fixed infrastructure, especially in military and emergency situations. Due to the openness and wide coverage of wireless channel, it is necessary to establish a secure transmission mechanism against potential eavesdroppers. Traditional secure transmission schemes almost rely on single path transmission and physical layer security techniques which may not provide enough secrecy when eavesdroppers are widely distributed. To address this, in this paper, we propose a multipath secure transmission mechanism for the sake of both guaranteeing the transmission security and maximizing the end-to-end throughput. Furthermore, we give the approximated closed-form expressions of multipath secrecy connection probability (SCP) relying on secret sharing, and a novel bilevel optimization problem is formulated with the constraints of the end-to-end delay and the derived SCP. Finally, simulation results show that our proposed mechanism is beneficial concerning secrecy performance in comparison to traditional mechanisms. Also, a near-optimal throughput is obtained.

Underwater covert communication (UCC) technology can prevent legitimate transmission from being intercepted upon by potential eavesdroppers while ensuring a certain rate at the receiver under the condition of underwater acoustic channels. Previous studies have focused on UCC designs that rely on fixed transmitters and receivers, with limited attention given to dynamic moving senders, such as the widely-used autonomous underwater vehicle (AUV). Therefore, the establishment of a secure link between the mobile AUV and the receiver remains unexplored. In this paper, we construct an AUV-aided UCC architecture. Specifically, leveraging the unique characteristics of the underwater environment i.e., time-variant channel, severe attenuation, and ambient noise, the AUV plans its trajectory from the settled start point to the destination, adjusting its transmission power for covert communications. Accounting for both green energy consumption and communication security, we develop a novel multi-objective deep deterministic policy gradient (MODDPG) framework for jointly optimizing AUV’s diving energy consumption as well as effective throughput under the covertness constraint. Moreover, we propose an active-trust mechanism at the receiving side to pose an extra safe guard. To handle this, an evolutionary game model between the receiver and eavesdropper is built. Simulations and numerical results demonstrate that our proposed method can achieve a Pareto-optimal solution for covert communications with rapid convergence speed. The evolutionary stable strategy (ESS) enables the receiver to attain superior benefits and security compared to other strategies.

In this paper, we consider a wireless network enabled by multiple unmanned aerial vehicles (UAVs), which observe physical processes and transmit status updates to a monitor node over an error-prone communication channel. The communication scenarios are classified into two modes based on monitor types: UAV-to-UAV (U2U) and UAV-to-network (U2N) scenarios. Specifically, the U2N scenario is capable of covering a larger area compared to U2U scenario with a high signal-to-noise ratio (SNR). However, the U2U scenario constructs communication links more quickly, resulting in lower latency and higher rates. To evaluate the timeliness of the UAV-aided network, we utilize the age of information (AoI) as a fundamental indicator. AoI measures the time delay between the most recent data generation and the current moment. To compensate for the error-prone channel, this study employs a combination of hybrid automatic repeat request (HARQ) protocols, which include fixed-redundancy HARQ (FR-HARQ) and infinite incremental redundancy HARQ (IIR-HARQ) protocols. Furthermore, the average and peak Age of Information (AAoI and PAoI) of UAV-aided networks are derived for both U2U and U2N scenarios, and the theoretical expressions agree with simulation results. Additionally, FR-HARQ performs a better time performance than IIR-HARQ, and such observation van be verified through simulations.

With the rapid development of cloud computing, massive amounts of data are uploaded to cloud servers for storage. For privacy protection, sensitive data should be encrypted before outsourcing, and ciphertext retrieval technologies based on similarity come into being. In cloud computing with massive data, the efficiency and accuracy of retrieval are crucial. However, most of the current similarity retrieval schemes do not perform well in these two aspects. Therefore, we propose SESR scheme, a secure and efficient similarity retrieval scheme based on homomorphic encryption. Firstly, we use Hamming distance to calculate the similarity between the feature vector of the data and query vector from the data user. Secondly, the homomorphic encryption algorithm is used to encrypt data to protect data privacy. Furthermore, we creatively design a BK-KD tree structure that hierarchically implements similarity search and fine-grained access control, thereby speeding up the retrieval efficiency. In addition, we design a two-cloud-server cooperative retrieval model and a message authentication scheme, which ensure access pattern privacy security and the integrity of the transmitted data simultaneously. We also propose an improved SESR scheme. In this scheme, we use Simhash algorithm to generate feature vectors and query vectors, which reduces storage overhead. Finally, the security of SESR is formally proved and the simulation results show the efficiency and accuracy of the retrieval scheme.

We revisit the problem of statistical sequence matching between two databases of sequences initiated by Unnikrishnan, (2015) and derive theoretical performance guarantees for the generalized likelihood ratio test (GLRT). We first consider the case where the number of matched pairs of sequences between the databases is known. In this case, the task is to accurately find the matched pairs of sequences among all possible matches between the sequences in the two databases. We analyze the performance of the GLRT by Unnikrishnan and explicitly characterize the tradeoff between the mismatch and false reject probabilities under each hypothesis in both large and small deviations regimes. Furthermore, we demonstrate the optimality of Unnikrishnan’s GLRT test under the generalized Neyman-Person criterion for both regimes and illustrate our theoretical results via numerical examples. Subsequently, we generalize our achievability analyses to the case where the number of matched pairs is unknown, and an additional error probability needs to be considered. When one of the two databases contains a single sequence, the problem of statistical sequence matching specializes to the problem of multiple classification introduced by Gutman, (1989). For this special case, our result for the small deviations regime strengthens previous result of Zhou et al., (2020) by removing unnecessary conditions on the generating distributions.

Surrounding perceptions are quintessential for safe driving for connected and autonomous vehicles (CAVs), where the Bird's Eye View has been employed to accurately capture spatial relationships among vehicles. However, severe inherent limitations of BEV, like blind spots, have been identified. Collaborative perception has emerged as an effective solution to overcoming these limitations through data fusion from multiple views of surrounding vehicles. While most existing collaborative perception strategies adopt a fully connected graph predicated on fairness in transmissions, they often neglect the varying importance of individual vehicles due to channel variations and perception redundancy. To address these challenges, we propose a novelPriority-AwareCollaborativePerception (PACP) framework to employ a BEV-match mechanism to determine the priority levels based on the correlation between nearby CAVs and the ego vehicle for perception. By leveraging submodular optimization, we find near-optimal transmission rates, link connectivity, and compression metrics. Moreover, we deploy a deep learning-based adaptive autoencoder to modulate the image reconstruction quality under dynamic channel conditions. Finally, we conduct extensive studies and demonstrate that our scheme significantly outperforms the state-of-the-art schemes by 8.27% and 13.60%, respectively, in terms of utility and precision of the Intersection over Union.

Unmanned aerial vehicles (UAVs) associated with federated learning (FL) have been deemed as a prospective framework by utilizing private data generated in the edge devices. However, despite various privacy-preserving and cryptography technologies adopted at the data level, FL still faces a range of security threats to raw data considering the broadcast nature of wireless channel. In this paper, to facilitate the communication-efficiency and privacy-preservation capability, we propose a UAV-enhanced covert federated learning architecture over mmWave massive multiple input multiple output (MIMO) channel, where we harness the covert communication technique in FL in order to avoid eavesdropping of illegal wardens. To achieve a trade-off between the security performance and training cost, we formulate a joint optimization problem involving the UAV’s trajectory, transmitting power, analog beamforming, and the required accuracy of FL. Furthermore, we propose the multi-agent deep deterministic policy gradient (MADDPG) algorithm to solve the above-mentioned problem. Numerous simulations have been performed to demonstrate both the effectiveness and convergence of the proposed algorithm.

Large Language Models (LLMs), representing a significant achievement in artificial intelligence (AI) research, have demonstrated their ability in a multitude of tasks. This project aims to explore the capabilities of GPT-3.5, a leading example of LLMs, in processing the sentiment analysis of Internet memes. Memes, which include both verbal and visual aspects, act as a powerful yet complex tool for expressing ideas and sentiments, demanding an understanding of societal norms and cultural contexts. Notably, the detection and moderation of hateful memes pose a significant challenge due to their implicit offensive nature. This project investigates GPT's proficiency in such subjective tasks, revealing its strengths and potential limitations. The tasks include the classification of meme sentiment, determination of humor type, and detection of implicit hate in memes. The performance evaluation, using datasets from SemEval-2020 Task 8 and Facebook hateful memes, offers a comparative understanding of GPT responses against human annotations. Despite GPT's remarkable progress, our findings underscore the challenges faced by these models in handling subjective tasks, which are rooted in their inherent limitations including contextual understanding, interpretation of implicit meanings, and data biases. This research contributes to the broader discourse on the applicability of AI in handling complex, context-dependent tasks, and offers valuable insights for future advancements.

The proliferation of users and data traffic poses substantial pressure on resource management in the Internet of Things (IoT). In addition to beneficially allocating scarce network resources, it also needs to meet differentiated users’ Quality-of-Service (QoS) requirements, such as low delay, high security, etc. The distributed management architecture of blockchain and its inherent security features bring inspiration to resource management in the IoT. In this article, we propose a blockchain-enabled resource orchestration scheme for IoT by deep reinforcement learning (DRL), where the IoT edge server and the end user can reach a consensus on the allocation of network resources based on blockchain theory. Moreover, relying on the policy network, the intelligent agent can be trained by these resource attributes to fully perceive the change of the network’s state and hence make dynamic resource allocation decisions. Finally, simulation results show that the proposed resource orchestration scheme has good performance in comparison to other security resource allocation algorithms. The average revenue, the user request acceptance rate, and the profitability are increased by an average of 8.5%, 1.8%, and 11.9%, respectively, compared with other algorithms.

The Internet of underwater things (IoUT) is envisioned to be an essential part of maritime activities. Given the IoUT devices’ wide-area distribution and constrained transmit power, autonomous underwater vehicles (AUVs) have been widely adopted for collecting and forwarding the data sensed by IoUT devices to the surface-stations. In order to accommodate the diverse requirements of IoUT applications, it is imperative to conceive a multi-tier underwater computing (MTUC) framework by carefully harnessing both the computing and the communications as well as the storage resources of both the surface-station and of the AUVs as well as of the IoUT devices. Furthermore, to meet the stringent energy constraints of the IoUT devices and to reduce the operating cost of the MTUC framework, a joint environment-aware AUV trajectory design and resource management problem is formulated, which is a high-dimensional NP-hard problem. To tackle this challenge, we first transform the problem into a Markov decision process (MDP) and solve it with the aid of the asynchronous advantage actor-critic (A3C) algorithm. Our simulation results demonstrate the superiority of our scheme.

Non-orthogonal multiple access (NOMA) is regarded as a promising technology in achieving high capacity and massive connectivity. In this paper, the reliable transmission scheme of downlink NOMA systems is investigated. In particular, we divide the disc covered by the base station into several annular areas, where the receivers are randomly located following a uniform distribution. In this way, NOMA pairing is performed by randomly selecting receivers from two different areas. Firstly, we derive the closed-form expressions of bit error rate (BER) with quadrature phase-shift keying (QPSK) modulation, where the channel is modeled as small-scale Rayleigh fading and large-scale path loss. To achieve reliable communications, then, the BER performance of the receiver with the worst channel gain in each area is studied. Finally, an optimal power allocation algorithm is proposed, which obtains the minimum transmission power and optimal power allocation factor with a given BER constraint of all receivers. Extensive simulations demonstrate the accuracy of obtained BER expressions and the effectiveness of the proposed algorithm. These results provide valuable insight into realizing on reliable transmission of NOMA with randomly deployed receivers.

Integrating unmanned aerial vehicles (UAVs) with federated learning (FL) has been seen as a promising paradigm for dealing with the massive amounts of data generated by intelligent devices. Nevertheless, although FL has natural advantages in data security protection, eavesdroppers can also deduce the raw data according to the shared parameters. Existing works mainly focused on encrypting the content of uploaded parameters, but we believe that it can improve security further by hiding the presence of parameter updating. Therefore, in this paper, we conceive a UAV-enabled covert federated learning architecture, where the UAV is not only responsible for orchestrating the operation of FL but also for emitting artificial noise (AN) to interfere with the eavesdropping of unintended users. To strike a balance between the security level and the training cost (including time overhead and energy consumption), we propose a distributed proximal policy optimization-based strategy for the sake of jointly optimizing the trajectory and AN transmitting power of the UAV, the CPU frequency, the transmitting power and the bandwidth allocation of the participated devices, as well as the needed accuracy of the local model. Furthermore, a series of experiments have been conducted to validate the effectiveness of our proposed scheme.

Given the increasing attention paid to the security of underwater communications, covert communication system has been envisaged as a key enabler for empowering the marine information networks to address the challenge of ever-increasing demand of anti-eavesdropping. However, dynamic underwater hydrology environment and ambient noise make it substantially difficult to reduce the decoding error probability as much as possible on the premise of meeting the concealment requirements. In this paper, we first build up underwater covert communication (UCC) system and analyze its secrecy performance at the physical (PHY) layer. Moreover, we propose a dynamic power-threshold based bargaining game model to preserve the receiver’s concealment, while ensuring high receiver-side signal-to-interface-noise ratio (SINR). The simulation results show the detection probability at the equilibrium of our model is optimal, and thus it is capable of both overcoming the dynamic changes and of increasing the system’s stability significantly by analyzing the time discount factor.

This work considers designing an unmanned target hunting system for a swarm of unmanned underwater vehicles (UUVs) to hunt a target with high maneuverability. Differential game theory is used to analyze combat policies of UUVs and the target within finite time. The challenge lies in UUVs must conduct their control policies in consideration of not only the consistency of the hunting team but also escaping behaviors of the target. To obtain stable feedback control policies satisfying Nash equilibrium, we construct the Hamiltonian function with Leibniz’s formula. For further taken underwater disturbances and communication delay into consideration, modified deep reinforcement learning (DRL) is provided to investigate the underwater target hunting task in an unknown dynamic environment. Simulations show that underwater disturbances have a large impact on the system considering communication delay. Moreover, consistency tests show that UUVs perform better consistency with a relatively small range of disturbances.

In the face of deeply exploring and exploiting marine resources, the Internet of Underwater Things (IoUT) networks have drawn great attention considering its widely distributed low-cost and easy-deployment smart sensing nodes. However, given the hostile underwater environment, it is critical to conceive energy-efficient information collection because of limited underwater energy supply and inefficient artificial recharge methods. Characterized by high flexibility and maneuverability, autonomous underwater vehicles (AUVs) are regarded as a promising solution for information collection in the IoUT relying upon delicate AUVs’ trajectory and information collection strategy design with the spirit of balancing their energy consumption and information processing capability. In this article, we propose a heterogeneous AUV-aided information collection system with the aim of maximizing the energy efficiency of IoUT nodes taking into account AUV trajectory, resource allocation, and the Age of Information (AoI). Moreover, based on the particle swarm optimization (PSO), we obtain the trajectory of AUVs with low time complexity. Additionally, a two-stage joint optimization algorithm based on the Lyapunov optimization is constructed to strike a tradeoff between energy efficiency and system queue backlog iteratively. Finally, simulation results validate the effectiveness and superiority of our proposed strategy.

In this article, a distributed optical fiber sensing system for large infrastructure temperature monitoring is proposed. To meet the requirements of monitoring networks in terms of measurement accuracy, spatial resolution, and real-time or quasireal-time performance, a quaternion wavelet transform (QWT) image denoising algorithm is proposed to address the original edge node data for the structural monitoring networks of large infrastructures. A distributed Brillouin optical time-domain analysis (BOTDA) sensing system with a 40-km sensing fiber is established. The raw Brillouin gain spectrum (BGS) image is decomposed into one magnitude image and three phase images by QWT. The phase images of the OWT are distributed randomly and disorderly with respect to the noise, while the magnitude image of the quaternion wavelet is greatly affected by the noise. The useful message energy of the magnitude image is concentrated on a small number of coefficients with large amplitude, while the noise mainly corresponds to the coefficients with smaller amplitude. Then, the Bayes shrink threshold method is introduced to filter out noise in the magnitude image. The results indicate that the signal-to-noise ratio (SNR) and the frequency uncertainty have been improved significantly. The accuracy of the retrieved Brillouin frequency shift from denoised BGS images reaches 0.2 MHz, which corresponds to a temperature error of ±0.1 °C. Less than 4 s are required to process a BGS image with 50$\times $40 000 pixels by the QWT denoising technique. The uploaded data obtained from 40 M bytes of raw data are reduced to 0.08 M bytes for each measurement. We hope that with technological progress and algorithm optimization, the distributed optical fiber sensing system based on the QWT image denoising algorithm will have an important role in the real-time application of large-scale infrastructure structural health monitoring for the Internet of Things.

Given the proliferation of the massive machine type communication devices (MTCDs) in beyond 5G (B5G) wireless networks, energy harvesting (EH) aided next generation multiple access (NGMA) systems have drawn substantial attention in the context of energy-efficient data sensing and transmission. However, without adaptive time slot (TS) and power allocation schemes, NGMA systems relying on stochastic sampling instants might lead to tardy actions associated both with high age of information (AoI) as well as high power consumption. For mitigating the energy consumption, we exploit a pair of sleep-scheduling policies, namely the multiple vacation (MV) policy and start-up threshold (ST) policy, which are characterized in the context of three typical multiple access protocols, including time-division multiple access (TDMA), frequency-division multiple access (FDMA) and non-orthogonal multiple access (NOMA). Furthermore, we derive closed-form expressions for the MTCD system’s peak AoI, which are formulated as the optimization objective under the constraints of EH power, status update rate and stability conditions. An exact linear search based algorithm is proposed for finding the optimal solution by fixing the status update rate. As a design alternative, a low complexity concave-convex procedure (CCP) is also formulated for finding a near-optimal solution relying on the original problem’s transformation into a form represented by the difference of two convex problems. Our simulation results show that the proposed algorithms are beneficial in terms of yielding a lower peak AoI at a low power consumption in the context of the multiple access protocols considered.

In the era of e-Health, privacy protection has become imperative in applications that carry personal and sensitive data. Departing from the data-perturbation based privacy-preserving techniques that reduce the fidelity of the disclosed data, in this paper we investigate anonymous communications, which mask the identity of the data sender while providing high data reliability. Focusing on the physical (PHY) layer, we first explore the break of privacy through a statistical attribute based sender detection (SD) from the receiver. Compared to the existing literature, this enables a much enhanced SD performance, especially when the users are equipped with different numbers of antennas. To counteract the advanced SD approach above, we formulate explicit anonymity constraints for the design of the anonymous precoder, which mask the sender’s PHY attributes that can be exploited by SD, while at the same time preserving the reliability of the data. Then, anonymity entropy-oriented precoders are proposed for different antenna configurations at the users, which adaptively construct a maximum number of aliases while obeying users’ signal-to-noise-ratio requirements for data accuracy. Simulation results demonstrate that the proposed anonymous precoders provide the highest level of anonymity entropy over the benchmarks, while achieving reasonable symbol error rate for the communication signal.

Benefiting from dense connections in data center networks (DCNs), load balancing algorithms are capable of steering traffic into multiple paths for the sake of preventing traffic congestion. However, given each path’s time-varying and asymmetrical traffic state, this may also lead to worse congestion when some paths are overutilised. Especially in the two-tier hybrid optical/electrical DCNs (Hoe-DCNs), the port contentions and large-grained optical packets of the fast optical switch (FOS) require the top-of-rack (TOR) switch to have microsecond-level load balancing capability for microburst traffic. This paper establishes a leaf-spine Hoe-DCN model to illustrate the principal characteristic of dynamic load balancing in TOR switches for the first time. Moreover, we propose the dynamic distributed multi-path (DDMP) load balancing algorithm that relies on dynamic hashing computing for network flow distribution in DCNs, which dynamically adjusts traffic flow distribution at microsecond level according to the inverse ratio of the buffer occupancy. The simulation results show that our proposed algorithm reduces the TOR-to-TOR latency by 15.88% and decreases the packet loss by 22.06% compared to conventional algorithms under regular load conditions, which effectively improves the overall performance of the Hoe-DCNs. Moreover, our proposed algorithm prevents more than 90% packet loss under low load conditions.

Space-air-ground integration to support seamless coverage of ground, satellite, airborne, and marine communications, is likely to be a key trend in the 6G era. One of several key challenges in such space-air-ground integration networks (SAGINs) is to design efficient scheduling approaches for multi-dimension network resources. Due to the inherent heterogeneity characteristics, we demonstrate how can transform the network resource allocation problem in SAGINs into a multi-domain virtual network resource allocation problem, as well as proposing a reinforcement learning assisted bandwidth aware virtual network resource allocation algorithm (RL-BA-VNA). Specifically, RL-BA-VNA leverages reinforcement learning and uses a policy network as an agent to perform the node embedding. In order to support users’ exacting bandwidth requirements, we prefer to select virtual network requests with large bandwidth for embedding. Experiment findings show that the proposed algorithm RL-BA-VNA outperforms respectively the other three conventional virtual network resource allocation algorithms RL, DRL and BASELINE by an average of 2.06%, 4.93%, 11.07% in terms of long-term average reward, acceptance rate, and long term reward/cost.

Adaptive optics (AO) can efficiently compensate for turbulence-induced distortion in orbital angular momentum (OAM)-based optical wireless communication (OWC) systems. In this paper, we design a modified phase diversity algorithm (MPDA)-based wavefront sensor to enhance the reconstruction accuracy of distorted OAM wavefront information. Aiming to further strike a compelling trade-off between AO system complexity and compensation accuracy, we first construct a novel AO system that applies a quickly and electronically controlled focus-tunable lens (FTL). It decontaminates distorted OAM signaling beams while having a low systemic complexity and superior convergence performance. Furthermore, we propose the 3-modified phase diversity algorithm (3-MPDA) AO scheme relying upon a Fourier intensity and two defocused intensities as the prior information, which beneficially balances the compensation effect and the number of defocused intensities and exhibits good noise robustness against charge-coupled device (CCD) detectors. In summary, this paper provides new insight for designing AO schemes with high compensation performance in communication links.

In this paper, we consider the physical-layer security for single-input single-output (SISO) indoor visible light communication (VLC) wiretap systems in the presence of multipath reflections. We derive both the lower and upper bounds on the secrecy capacity in the context of both the symbol- and block-based transmission policies. To enhance the secrecy performance relying on block transmission policy, we propose a low-complexity amplitude scaling (AS) scheme by scaling the amplitudes of different symbols in each block to maximize the achievable secrecy rate. We further provide the upper bound on the optimal secrecy rate achieved by precoding for the sake of validating the effectiveness of the proposed AS scheme. Numerical results suggest that the secrecy performance is severely degraded by inter-symbol interference (ISI) imposed by multipath reflections, which yet can be alleviated by long-block based transmission. Moreover, the proposed AS scheme can enhance the secrecy performance significantly and achieve a secrecy rate very close to the optimal solution.

Autonomous underwater vehicles (AUVs) have been envisaged as a key enabler for empowering the Internet of Underwater Things (IoUT) networks to address the challenge of ever-increasing demand of ocean exploration. However, the energy constraint of AUVs' movement makes it challengeable to obtain full-space movement and extravagant information exchange considering complex underwater environment and hostile acoustic channel characteristics. For the sake of enhancing the sustainability of the power supply, it is significant to design a green underwater information collection scheme for beneficially utilizing the maneuverability of AUVs. In this paper, we propose a heterogeneous multi-AUV aided underwater information collection scheme for optimizing the unit energy consumption under the constraint of the age of information (AoI). Moreover, the limited service M/G/1 vacation queueing system is used to model the process of information exchange, where the steady-state distribution and the waiting time of queue are derived. Finally, simulation results show the effectiveness of our proposed scheme and low-complexity solution, which outperform single-AUV scheme in terms of both energy efficiency and AoI.

In recent years, unmanned aerial vehicles (UAV) have been widely adopted to support complex target tracking tasks for military and civilian applications, especially in open and unknown environments. In practical cases, the moving trajectory of the target cannot be known to the UAVs in advance, which brings great challenges to UAVs to realize real-time and effective tracking. In addition, the limited tracking ability of a single UAV can hardly meet the requirements of a high tracking success rate. To deal with these problems above, this paper establishes a multi-UAV cooperative target tracking system. Besides, a deep reinforcement learning (DRL) based algorithm is designed to enable UAVs to make flight action decisions intelligently to track the moving air target, according to the past and current position information of the target only. To further increase the detection coverage of the UAV network when tracking, spatial information entropy is introduced to the reward designing in this algorithm. Simulation results validate that the proposed algorithm yields impressive target tracking performances, and significantly outperforms several common DRL baselines in terms of the tracking success rate. The convergence of the algorithm is also verified by the simulations.

The sixth generation wireless communication networks (6G) are anticipated to bring a disruptive innovation on multiple scenarios, where deep edge networks (DENs) turn into a vital network structure on vertical industrial paradigms, including the combination of communication, computing and caching (3C). In this paper, we present the DENs scene to facilitate the deep convergence of computing and communication resources. More specifically, we formulate the optimization problem in terms of energy consumption and latency in order to minimize the total agents overhead. At the same time, for the sake of executing tasks and alleviating interference among different edge networks and high-dynamic network environments, we propose a CPU cycle frequency aided multi-agent deep deterministic policy gradient (C-MADDPG) algorithm framework to optimize the task scheduling, transmission power, CPU cycle frequency and mutual interference from multiple channels to obtain the optimal overhead. Finally, extensive simulation and experimental results demonstrate that our proposed C-MADDPG algorithm has better performance gain in term of execution overhead for different network parameters.

At present, the development and exploration of the ocean are blossoming, but the maritime communication coverage still remains limited. By deploying unmanned aerial vehicle (UAV) mounted relay nodes between shore base stations and vessel users, the off-shore communication coverage and transmission efficiency can be substantially enhanced. Considering the specific transmission characteristics of air-sea and of air-shore channels and time-varying traffic of maritime information services, we formulate a minimum-maximization optimization problem of link capacity, where both the deployment of UAV-mounted relay node and the configuration of communication resources are optimized. To address this non-convex problem, we propose a particle swarm based algorithm, which is capable of three-dimensional position, antenna direction and time slot allocation scheme joint optimization. The simulation results demonstrate the high efficiency and reliability of our proposed algorithm in diverse offshore relay scenarios with different coastal environments, vessel distributions and network traffic.

Queuing delay is of essential importance in the Internet-of-Things scenarios where the buffer sizes of devices are limited. The existing cross-layer research contributions aiming at minimizing the queuing delay usually rely on either transmit power control or dynamic spectrum allocation. Bearing in mind that the transmission throughput is dependent on the distance between the transmitter and the receiver, in this context we exploit the agility of the unmanned-aerial-vehicle (UAV)-mounted base stations (BSs) for proactively adjusting the aerial BS (ABS)’s placement in accordance with wireless teletraffic dynamics. Specifically, we formulate a minimum-delay ABS placement problem for UAV-enabled networks, subject to realistic constraints on the ABS’s battery life and velocity. Its solutions are technically realized under three different assumptions in regard to the wireless teletraffic dynamics. The backward induction technique is invoked for both the scenario where the full knowledge of the wireless teletraffic dynamics is available, and for the case where only their statistical knowledge is available. In contrast, a reinforcement learning aided approach is invoked for the case when neither the exact number of arriving packets nor that of their statistical knowledge is available. The numerical results demonstrate that our proposed algorithms are capable of improving the system’s performance compared to the benchmark schemes in terms of both the average delay and of the buffer overflow probability.

In order to better explore the ocean, autonomous underwater vehicles (AUVs) have been widely applied to facilitate the information collection. However, considering the extremely large-scale deployment of sensor nodes in the Internet of Underwater Things (IoUT), a homogeneous AUV-enabled information collection system cannot support timely and reliable information collection considering the time-varying underwater environment as well as AUV’s energy and mobility constraints. In this article, we propose a multi-AUV-assisted heterogeneous underwater information collection scheme for the sake of optimizing the peak Age of Information (AoI). Moreover, the limited service M/G/1 vacation queueing model is utilized to model the process of information exchange, where the optimal upper limit of the number of AUVs served in the queueing system as well the steady-state distribution of the queue length are derived. A low-complexity adaptive algorithm for adjusting the upper limit of the queuing length is also proposed. Finally, simulation results validate the effectiveness of our proposed scheme and algorithm, which outperform traditional methods in terms of the peak AoI.

The Link-a-Pix puzzle, which is also known as Piczle, PathPix, Pictlink, Number Net, or Paint by Pairs, is a popular picture logic puzzle game where the player paints a grid by linking the hint points with number-color labels to obtain the solution as a pixel art picture. In this article, we propose a joint depth first searching and linear programming aided algorithm for the sake of automatically solving the Link-a-Pix puzzle. The experiments implemented on 40 puzzles with various types verify the effectiveness and feasibility of our proposed solver, which is conducive to both designing and solving the Link-a-Pix puzzles and related applications.

In this paper, we propose a hierarchical information acquisition system composed of a marine stationary sensor layer and an autonomous underwater vehicle (AUV) motion layer. Specifically, in the sensor layer, we design an energy-efficient clustering protocol based on the improved K-Means algorithm (ECBIK), which can implement uniform classification and select the cluster head dynamically according to energy awareness. Compared with the traditional K-Means and LEACH algorithm, our method achieves lower energy consumption and higher node survival rate, which can balance the energy load effectively to extend the life of the network. Additionally, in the AUV motion layer, we define the rotation-angle of AUV and analyze its influence quantitatively for the AUV information collection. Meanwhile, a novel Ant Colony (ACO) algorithm based on Markov Reward Process (MRP) is proposed for AUV path planning. As the simulation experiments indicate, our algorithm can achieve shorter distance, smaller angle, and faster convergence speed in path optimization.

Deploying unmanned aerial vehicles (UAVs) as flying base stations (BSs) is a promising solution to alleviate the burden of communication infrastructure during the peak-traffic hours. However, when a UAV is deployed as a flying BS to serve the vehicle users in the hotspot, and the vehicle-to-vehicle (V2V) communication is introduced to further improve the network capacity, the performance analysis and optimization problems have not gained well investigated. In this paper, aforementioned problems are carefully studied from a statistical point of view, where the system performance is captured by the users' successful service probability. Specifically, we first derive the successful service probability for the UAV-to-vehicle (U2V) transmission. Meanwhile, taking those important factors, i.e., vehicle mobility and social proximity, into account, we estimate the successful service probability for the V2V transmission. The average successful service probability for the considered scenario is then derived. Based on the mathematical analysis results, we further improve the system performance by adjusting the UAV's altitude position, where the UAV deployment problem is formulated as a service probability maximization problem. To find the optimal solution, a particle swarm optimization algorithm is proposed. Finally, numerical simulations are conducted to verify the theoretical analysis and the efficiency of our proposed scheme.

We examine the problem of Value of Information (VoI) based underwater information collection, which is the essence of many underwater applications such as depth surrounding oil platforms, monitoring of algal blooms and so on. Even if the information collection work has been carried out much in the terrestrial scenario, due to complicated physical, technological and economic differences between the terrestrial and underwater cases, it is not feasible to simply apply the existing terrestrial tricks. The existing cooperative Autonomous Underwater Vehicle (AUV) working paradigms are limited to omniscience of communication channel information among the AUVs, which is yet not practical in such a harsh communication environment. Therefore, we propose a contract based model which has little restriction on the communication channel to overcome information asymmetry and jointly optimizes energy consumption and VoI. Besides, we provide a concrete theoretical proof of the contract items and carry out a performance simulation which shows the mechanism we design is operative. At last, we summarize our work and give an insight of the future research directions.

Recently, the technology of underwater wireless sensors networks (UWSNs) has received more attention on the exploitation of marine resources. However, underwater acoustic communication is still the only reliable means of ocean communication, which is entirely different from the terrestrial scene. In this paper, we propose Q-learning aided ant colony routing protocol (QLACO) to address the issues of energy-efficiency and link instability in UWSNs, which uses both the reward mechanism and artificial ants to determine a global optimal routing selection. QLACO uses the reward function to adapt to the dynamic underwater environment and enhance the packet delivery ratio (PDR). Moreover, we propose an anti-void mechanism to solve the void region dilemma. Simulation results show that QLACO outperforms Q-learning-based energy-efficient and lifetime-aware routing protocol (QELAR) and the depth-based protocol (DBR) in terms of PDR, energy consumption and latency.

Multi-access edge computing (MEC) is viewed as a promising technology to improve the real time video service in vehicular networks. However, in the traditional vehicular networks, the road side units (RSUs) are usually only equipped with communication modules, and the unmanned aerial vehicles(UAVs) are seldom used. In this paper, a new UAV-aided time-varying vehicular network is introduced for vehicle users (VUEs) to obtain better experience, where the RSUs and the UAV are equipped with MEC servers for the real time video transcoding. Considering that the video service always lasts for a period of time, we investigate the user association policy from a long-term perspective. Specifically, to characterize the time-varying features of communication links and the heterogeneity of available resources, we theoretically derive the achievable video chunks and link reliability based on the vehicle mobility model and content caching model. Then, the user association problem is formulated as the utility optimization problem, where both the VUE’s quality of experience (QoE) and handover cost are taken into consideration. Furthermore, we propose an improved Dijkstra algorithm to solve the original NP-hard problem after it is transformed to a shortest path selection problem. Finally, by numerical results, we verify that the proposed scheme outperforms existing schemes in terms of the VUE’s QoE and the handover numbers.

Internet of Vehicles (IoV) has drawn great interest recent years. Various IoV applications have emerged for improving the safety, efficiency, and comfort on the road. Cloud computing constitutes a popular technique for supporting delay-tolerant entertainment applications. However, for advanced latency-sensitive applications (e.g., auto/assisted driving and emergency failure management), cloud computing may result in excessive delay. Edge computing, which extends computing and storage capabilities to the edge of the network, emerges as an attractive technology. Therefore, to support these computationally intensive and latency-sensitive applications in IoVs, in this article, we integrate mobile-edge computing nodes (i.e., mobile vehicles) and fixed edge computing nodes (i.e., fixed road infrastructures) to provide low-latency computing services cooperatively. For better exploiting these heterogeneous edge computing resources, the concept of software-defined networking (SDN) and edge-computing-aided IoV (EC-SDIoV) is conceived. Moreover, in a complex and dynamic IoV environment, the outage of both processing nodes and communication links becomes inevitable, which may have life-threatening consequences. In order to ensure the completion with high reliability of latency-sensitive IoV services, we introduce both partial computation offloading and reliable task allocation with the reprocessing mechanism to EC-SDIoV. Since the optimization problem is nonconvex and NP-hard, a heuristic algorithm, fault-tolerant particle swarm optimization algorithm is designed for maximizing the reliability (FPSO-MR) with latency constraints. Performance evaluation results validate that the proposed scheme is indeed capable of reducing the latency as well as improving the reliability of the EC-SDIoV.

Unmanned aerial vehicles (UAVs) have the potential in delivering Internet-of-Things (IoT) services from a great height, creating an airborne domain of the IoT. In this article, we address the problem of autonomous UAV navigation in large-scale complex environments by formulating it as a Markov decision process with sparse rewards and propose an algorithm named deep reinforcement learning (RL) with nonexpert helpers (LwH). In contrast to prior RL-based methods that put huge efforts into reward shaping, we adopt the sparse reward scheme, i.e., a UAV will be rewarded if and only if it completes navigation tasks. Using the sparse reward scheme ensures that the solution is not biased toward potentially suboptimal directions. However, having no intermediate rewards hinders the agent from efficient learning since informative states are rarely encountered. To handle the challenge, we assume that a prior policy (nonexpert helper) that might be of poor performance is available to the learning agent. The prior policy plays the role of guiding the agent in exploring the state space by reshaping the behavior policy used for environmental interaction. It also assists the agent in achieving goals by setting dynamic learning objectives with increasing difficulty. To evaluate our proposed method, we construct a simulator for UAV navigation in large-scale complex environments and compare our algorithm with several baselines. Experimental results demonstrate that LwH significantly outperforms the state-of-the-art algorithms handling sparse rewards and yields impressive navigation policies comparable to those learned in the environment with dense rewards.

Unmanned aerial vehicles (UAVs) have been widely used to provide enhanced information coverage as well as relay services for ground Internet-of-Things (IoT) networks. Considering the substantially limited processing capability, the IoT devices may not be able to tackle with heavy computing tasks. In this article, a multi-UAV-aided mobile-edge computing (MEC) system is constructed, where multiple UAVs act as MEC nodes in order to provide computing offloading services for ground IoT nodes which have limited local computing capabilities. For the sake of balancing the load for UAVs, the differential evolution (DE)-based multi-UAV deployment mechanism is proposed, where we model the access problem as a generalized assignment problem (GAP), which is then solved by a near-optimal solution algorithm. Based on this, we are capable of achieving the load balance of these drones while guaranteeing the coverage constraint and satisfying the quality of service (QoS) of IoT nodes. Furthermore, a deep reinforcement learning (DRL) algorithm is conceived for the task scheduling in a certain UAV, which improves the efficiency of the task execution in each UAV. Finally, sufficient simulation results show the feasibility and superiority of our proposed load-balance-oriented UAV deployment scheme as well as the task scheduling algorithm.

To achieve the goal of “Industrial 4.0,” cellular network with wide coverage has gradually become an intensely important carrier for industrial Internet of Things (IIoT). The fifth generation cellular network is expected to be a unifying network that may connect billions of IIoT devices for the sake of supporting advanced IIoT business. In order to realize wide and seamless information coverage, heterogeneous network architecture becomes a beneficial method, which can also improve the near-ceiling network capacity. In order to guarantee the quality of service (QoS) as well as the fairness of different IIoT devices with limited network resources, the network association in IIoT should be performed in a more intelligent manner. In this article, we propose a distributed Q-learning aided power allocation algorithm for two-layer heterogeneous IIoT networks. Moreover, we discuss the spirit of designing reward functions, followed by four delicately defined reward functions considering both the QoS of femtocell IoT user equipments and macrocell IoT user equipments and their fairness. Also, both fixed and dynamic learning rates and different kinds of multiagent cooperation modes are investigated. Finally, simulation results show the effectiveness and superiority of our proposed Q-learning based power allocation algorithm.

Network Virtualization (NV) techniques allow multiple virtual network requests to beneficially share resources on the same substrate network, such as node computational resources and link bandwidth. As the most famous family member of NV techniques, virtual network embedding is capable of efficiently allocating the limited network resources to the users on the same substrate network. However, traditional heuristic virtual network embedding algorithms generally follow a static operating mechanism, which cannot adapt well to the dynamic network structures and environments, resulting in inferior nodes ranking and embedding strategies. Some reinforcement learning aided embedding algorithms have been conceived to dynamically update the decision-making strategies, while the node embedding of the same request is discretized and its continuity is ignored. To address this problem, a Continuous-Decision virtual network embedding scheme relying on Reinforcement Learning (CDRL) is proposed in our paper, which regards the node embedding of the same request as a time-series problem formulated by the classic seq2seq model. Moreover, two traditional heuristic embedding algorithms as well as the classic reinforcement learning aided embedding algorithm are used for benchmarking our prpposed CDRL algorithm. Finally, simulation results show that our proposed algorithm is superior to the other three algorithms in terms of long-term average revenue, revenue to cost and acceptance ratio.

Standard blind interference alignment (sBIA) suffers from noise accumulation which severely deteriorates received signal-to-noise ratio (SNR) and significantly reduces transmission rate. A noise accumulation factor is proposed to describe the loss between the user's received SNR, and the final post processing SNR which determines the performance of the decoding of the encoded data streams (EDSs). A heterogeneous semi-BIA (H-SBIA) framework where users with different noise accumulation factors can be flexibly allocated effective EDSs (E-EDSs) is constructed. Relying on the H-SBIA framework, a heuristic H-SBIA algorithm is designed for maximizing the overall E-EDSs considering both fairness and coherence time constraints. Extensive simulations demonstrate that H-SBIA produces great fairness performance improvement at a limited cost in the achievable sum rate. The Jain's fairness index is about 2.2 times greater than that for SNR-SBIA proposed in previous work, at the cost of sacrificing 10% of the achievable sum rate.

Due to the easy deployment, low cost and high maneuverability, unmanned aerial vehicles (UAVs) serving as aerial base stations can be efficiently deployed according to realtime situations for providing high-quality coverage, which can improve the communication efficiency and meet the requirements of green communications. However, due to the finite flight energy, a single UAV has limited capability of providing seamless long-term service to ground users. Therefore, the cooperation of multiple drones relying on sophisticated recharging and reshuffling schemes is necessary. In this paper, we investigate an energy- efficient cooperation strategy of multi-UAVs for providing seamless long-term coverage, where the positioning and the flight strategy are jointly considered. We first introduce a novel UAV power model, based on which we derive the cyclic UAV recharging and reshuffling constraint in order to satisfy the seamless long-term coverage requirement. For maximizing the energy-efficiency, we introduce a two-stage joint optimization algorithm for solving both the optimal UAV deployment as well as the cyclic UAV recharging and reshuffling strategy (CRRS). Finally, the efficiency of our proposed algorithm is shown by the simulation results.

Unmanned aerial vehicles (UAVs) have been widely used in a range of compelling applications. However, some of them are incompetent in tackling with computation-intensive tasks due to limited processing capability and battery life. In this paper, we combine the mobile edge computing and traditional cloud computing techniques for offloading the tasks from multi-UAV systems. Specifically, we jointly optimize the task scheduling and resource allocation in the heterogeneous cloud architecture, where we strike a power-delay trade-off of the system relying on the queue theory and Lyapunov optimization, followed by its optimal strategy analysis in each time slot. Moreover, we conceive an iterative algorithm with a closed-form solution at each iteration round in order to reduce the computational complexity. Finally, numerical results demonstrate both the feasibility and effectiveness of our proposed scheme. This paper validates that the heterogeneous cloud structure can be the beneficial for improving quality-of-service performance of multi-UAV systems.

Satellite image is an important resource for weather forecast. It can indicate the evolution of weather systems and is beneficial in terms of guiding people to make accurate weather forecasting. However, the use of satellite images is encountered with the dilemma of such as small data volume and of poor real-time performance. Hence it is important to make accurate prediction for satellite images. The goal of satellite image prediction is to predict the next few images of the image sequence. Essentially, it is a a spatiotemporal sequence prediction problem, where the prediction of satellite images is difficult due to its large-scale observation area. In this paper, we propose a generative adversarial networks-long short-term memory (GAN-LSTM) model for the satellite image prediction by combining the generating ability of the GAN with the forecasting ability of the LSTM network. For evaluation, we conduct our experiments on the FY-2E satellite cloud maps. In addition, we use a score correct rate (CR) to measure the degree of similarity between predictions and ground truth. Experiment results show that the proposed GAN-LSTM network is capable of efficiently capturing the evolution rules of weather systems, which outperforms the traditional autoencoder-LSTM.

In this paper, we propose a distributed detection system for hierarchical information acquisition based on autonomous underwater vehicle (AUV) and underwater fixed sensor networks. Different from the previous information collection systems, where the AUV traverses each node to obtain information, we propose a layered network architecture in this work, which is composed of an underwater fixed sensor networks layer and an AUV information acquisition layer. Such information acquisition system does not need to modify the original underlying fixed sensor networks, resulting from its flexible deployability. Additionally, because of the power sensitivity of sensor nodes in underwater fixed sensor networks, an improved algorithm based on classical low energy adaptive clustering hierarchy (Leach) algorithm is proposed in this work. Simulation results validate that the proposed algorithm can effectively improve the life cycle of sensor networks. At the same time, for the AUV information acquisition layer, we propose an angle optimization path planning algorithm based on the ant colony algorithm, which effectively takes the angle and path length as joint optimization objects. Experiments show that introducing the angle optimization jointly not only helps to optimize the AUV rotation angle, but also contributes to improving the convergence of the algorithm.

With the development of Next-generation Wireless Networks (NWNs), delay-sensitive traffic triggered by mobile applications (such as video stream and online games) will become an important part of the NWNs. With the increasing demand for massive video content transmission and good quality of users' experience, NWNs have to face up to some serious challenges. As a remedy, efficient routing schemes are capable of achieving load balance. In this article, we propose a load balance routing based on machine learning. First, a dimension-reduced vector matrix can be obtained from the original adjacency matrix of the network topology by Principal Component Analysis (PCA). Then, a neural network is used for the prediction of the network queue status, which can be used as a metric for making intelligent routing decisions. Finally, a load balance routing algorithm considering Queue Utilization (QU) is designed accordingly. Simulation results show the performance of our proposed machine learning-based routing scheme compared to the shortest path algorithm (Bellman-Ford (BF)) and its variant (QUBF) in terms of the packet loss ratio, the throughput and the delay.

Unmanned aerial vehicle (UAV) communication is a promising technology for Internet of Things (IoT) systems. In this paper, we combine UAV communication and nonorthogonal multiple access (NOMA) for constructing high capacity IoT uplink transmission systems, where UAVs are used as aerial base stations for collecting data from IoT nodes while NOMA is invoked for uplink transmission. We aim to maximize the system capacity by jointly optimize the subchannel assignment, the uplink transmit power of IoT nodes, and the flying heights of UAVs. We commence by proposing an efficient subchannel assignment algorithm relying on the classic K-means clustering method and matching theory. Then, we determine both the distributed uplink transmit power of IoT nodes and flying heights of UAVs based on successive optimization approach. An alternative optimization algorithm is also proposed for finding the near-optimal solutions. Finally, the numerical results demonstrate the superiority of our proposed scheme.

Due to their high flexibility, high maneuverability, and line-of-sight (LOS) predominant channel, unmanned aerial vehicles (UAVs) serving as flying base stations have received a lot of interest in emerging Internet of Things (IoT) networks. This article studies the energy-efficient cooperative strategy of rechargeable multi-UAVs for providing seamless coverage and long-term information services for IoT nodes. Considering the limited cruising duration of the UAV, multiple rechargeable UAVs are capable of constructing a closed chain for the sake of alternately supporting IoT nodes. Moreover, a joint IoT node assignment and UAV configuration optimization problem is proposed in order to maximize the energy efficiency of the system. Since the proposed problem is a mixed-integer nonconvex problem, we divide it into three subproblems, namely, node assignment scheduling, UAV trajectory planning, and transmit power control. By exploiting sequential convex optimization techniques, we reformulate the nonconvex subproblems into three convex optimization problems which can be solved within the polynomial time. A block coordinate descent-based iterative algorithm is proposed for solving these energy-efficiency oriented subproblems. Finally, the simulation results corroborate the effectiveness of our proposed method.

Unmanned aerial vehicles (UAVs) have been widely used in both military and civilian applications. Equipped with diverse communication payloads, UAVs cooperating with satellites and base stations constitute a space-air-ground three-tier heterogeneous network, which are beneficial in terms of both providing the seamless coverage as well as of improving the capacity for increasingly prosperous Internet of Things networks. However, cross-tier interference may be inevitable among these tightly embraced heterogeneous networks when sharing the same spectrum. The power association problem in satellite, UAV and macrocell three-tier networks becomes a critical issue. In this paper, we propose a two-stage joint hovering altitude and power control solution for the resource allocation problem in UAV networks considering the inevitable cross-tier interference from space-air-ground heterogeneous networks. Furthermore, Lagrange dual decomposition and concave-convex procedure method are used to solve this problem, followed by a low-complexity greedy search algorithm. Finally, simulation results show the effectiveness of our proposed two-stage joint optimization algorithm in terms of UAV network's total throughput.

With rapid development of compelling application scenarios of the Internet of Things (IoT), such as smart cities, it becomes substantially important to strengthen the management of data traffic in IoT networks. Traffic classification is beneficial in terms of both ensuring network security and improving quality of service. Traditional IoT traffic classification methods separate the classification algorithm and the design of feature engineering, which includes feature extraction and feature selection. Then, traffic identification or classification is performed by combining both. This paper proposes an end-to-end IoT traffic classification method relying on a deep learning aided capsule network for the sake of forming an efficient classification mechanism that integrates feature extraction, feature selection, and classification model. Our proposed traffic classification method beneficially eliminates the process of manually selecting traffic features, and is particularly applicable to smart city scenarios. To the best of our knowledge, this is the first time that capsule networks have been used in the context of traffic classification. Experimental results show the feasibility and effectiveness of our proposed traffic classification mechanism, which yields high classification accuracy.

In order to beneficially exploit the scarce wireless spectral resources, spectrum sharing between communication and radar systems has become a promising research topic. However, traditional network association strategies may not result in efficient hybrid communication and radar systems. We circumvent this problem by formulating a partially observable Markov decision processes (POMDP) aided network association scheme, where the radar user acts as the primary user (PU), while the cognitive communication user is the secondary user (SU). For maximizing the network throughput, whilst minimizing the interference imposed on the radar user, the communication user is configured for adaptively selecting its underlay or overlay access mode. Moreover, a low-complexity near-optimal reinforcement learning algorithm is proposed for the co-design by considering both its complexity and feasibility. Finally, we quantify the performance of our proposed POMDP based network association scheme.

The birth of wireless communication systems nearly a century ago has transformed and redefined the way humans communicate and interact. This transformation has evolved over many years and has brought along not only seamless connectivity for human interactions but also communication between machines and devices. While these communication systems are manmade artifacts, the research community has more recently turned its attention to other communication strategies that have spontaneously evolved in nature.

The engineering vision of relying on the “smart sky” for supporting air traffic and the “internet-above-the-clouds” for in-flight entertainment has become imperative for the future aircraft industry. Aeronautical ad hoc networking (AANET) constitutes a compelling concept for providing broadband communications above clouds by extending the coverage of air-to-ground (A2G) networks to oceanic and remote airspace via autonomous and self-configured wireless networking among commercial passenger airplanes. The AANET concept may be viewed as a new member of the family of mobile ad hoc networks (MANETs) in action above the clouds. However, AANETs have more dynamic topologies, larger and more variable geographical network size, stricter security requirements, and more hostile transmission conditions. These specific characteristics lead to more grave challenges in aircraft mobility modeling, aeronautical channel modeling, and interference mitigation as well as in network scheduling and routing. This paper provides an overview of AANET solutions by characterizing the associated scenarios, requirements, and challenges. Explicitly, the research addressing the key techniques of AANETs, such as their mobility models, network scheduling and routing, security, and interference, is reviewed. Furthermore, we also identify the remaining challenges associated with developing AANETs and present their prospective solutions as well as open issues. The design framework of AANETs and the key technical issues are investigated along with some recent research results. Furthermore, a range of performance metrics optimized in designing AANETs and a number of representative multiobjective optimization algorithms are outlined.

Unmanned Aerial Vehicle (UAV) technology has been widely applied in both military and civilian applications. Recent researches on UAV systems feature in the dramatic augment of the variety and number of equipped sensors, which results in such an issue that multiple UAVs cannot afford to handle the big data generated by a range of sensors in the air. Considering this practical problem, in this paper, we propose a cloud-based UAV system which incorporates the computing capability of the terrestrial cloud into the UAV systems. Relying on proposed cloud-based UAV system, one critical theoretic issue is how to acquire the big data generated by the sensors while guaranteeing a stable operation state of the system. First, we analyze the cloud-based system's on-demand service ability as well as its impact on UAVs' control procedure. Second, the UAV cloud control system is modeled as a network control system. Moreover, the stable condition of the UAV cloud control system is derived, which reveals the relationship between the acquisition rate of sensor data and the stability of the cloud-based UAV system. Finally, simulations are conducted to verify the effectiveness of our theoretical analysis.

Unmanned aerial vehicles (UAVs) have been widely used in a range of compelling applications. In this paper, we integrate both the networking techniques and the cloud computing tasks of multi-UAV systems. We commence by proposing an energy efficient scheme for selecting the gateway of UAVs invoked for relaying data to the heterogenous cloud. Then, relying on queuing theory and Lyapunov optimization, we strike a power-delay trade-off by jointly optimizing the computational task scheduling and resource allocation in the heterogeneous cloud architecture, which is comprised of an edge cloud and a powerful remote cloud. We analyze the optimal resource-allocation strategy for each time slot and an iterative algorithm is conceived for reducing the computational complexity. Finally, our numerical results demonstrate the superiority of the proposed scheme.

Past few years have witnessed the compelling applications of the blockchain technique in our daily life ranging from the financial market to health care. Considering the integration of the blockchain technique and the industrial Internet of Things (IoT), blockchain may act as a distributed ledger for beneficially establishing a decentralized autonomous trading platform for industrial IoT (IIoT) networks. However, the power and computation constraints prevent IoT devices from directly participating in this proof-of-work process. As a remedy, in this treatise, the cloud computing service is introduced into the blockchain platform for the sake of assisting to offload computational task from the IIoT network itself. In addition, we study the resource management and pricing problem between the cloud provider and miners. More explicitly, we model the interaction between the cloud provider and miners as a Stackelberg game, where the leader, i.e., cloud provider, makes the price first, and then miners act as the followers. Moreover, in order to find the Nash equilibrium of the proposed Stackelberg game, a multiagent reinforcement learning algorithm is conceived for searching the near-optimal policy. Finally, extensive simulations are conducted to evaluate our proposed algorithm in comparison to some state-of-the-art schemes.

With growing human maritime activities, supporting low-cost and high-speed information services for users at sea has become an imperative focus. In this paper, we consider a maritime relay multicast system including a shore-based base station and several offshore relay nodes, and propose an energy efficient relay design scheme. Specifically, we formulate the relay design problem as a power minimization problem under users' quality-of-service (QoS) constraints, and the problem is approximated and solved using the feasible point pursuit successive convex approach. Furthermore, an iterative algorithm is proposed with exponential complexity. In order to reduce the computational complexity, a low-complexity distributed algorithm is conceived and its closed-form solution is derived. Finally, simulation results show that our proposed scheme is beneficial in terms of achieving a higher communication rate as well as of yielding a better energy efficiency.

Large scale network systems such as Internet, smart grids and social networks become an indispensable part of our daily life. However, due to their inherent vulnerability as well as the limited management and operational capability, these network systems are constantly under the threat of malicious attackers. In such attack-defense scenarios, it is particularly significant to make the best use of defenders' limited resources and capability. In this paper, we propose a networked Colonel Blotto game, where the attackers and defenders allocate the limited resources on network nodes, and their utility depends on certain network performance metrics, which are defined for evaluating the performance of the whole network system. Furthermore, considering the complexity of the equilibrium analysis in large scale network systems, a co-evolution based algorithm is proposed for obtaining the practical action sets as well as achieving the mixed-strategy Nash equilibrium. Finally, relying on three real- world network systems, i.e., computer networks, Internet of vehicles and online social networks, simulation results show the effectiveness and feasibility of our proposed model, which is conducive to the design, management and maintenance of real-world network systems.

Unmanned aerial vehicles (UAVs) cooperating with satellites and base stations (BSs) constitute a space-air-ground three-tier heterogeneous network, which is beneficial in terms of both providing the seamless coverage as well as of improving the capacity for the users. However, cross-tier interference may be inevitable among these tightly embraced heterogeneous networks. In our paper, we propose a two-stage joint hovering altitude and power control solution for the resource allocation problem. Furthermore, Lagrange dual decomposition and concave-convex procedure (CCP) method are used to solve this problem. Finally, simulation results show the effectiveness of our proposed two-stage joint optimization algorithm in terms of UAV network's total throughput.

Relying on mini wearable or implantable biosensors, the wireless body area network (WBAN) is capable of efficiently collecting as well as of analyzing human physiological information. It has shown great potential in terms of beneficially improving healthcare quality. However, due to stringent resource constraints of biosensors, traditional security schemes, i.e. the encryption and the authentication, may not do well in countering security threats. Moreover, they are not competent in protecting the network from inside attacks and deny of service (DoS) attacks. In this paper, we propose a sink node assisted lightweight intrusion detection mechanism for WBAN, where the sink node can periodically monitor the packet transmission and record the abnormality for further analysis. Our lightweight mechanism results in a very high true positive rate and an ultra-low false positive rate. Extensive analysis and simulations based on Castalia are conducted and verify the validity and efficiency of our proposed mechanism.

In order to beneficially exploit wireless spectral resources, spectrum sharing between communication systems and radar systems has become a popular research topic. However, traditional network association strategies may not result in an efficient co-system. We circumvent this problem by formulating a partially observable Markov decision process (POMDP) aided network association scheme. For maximizing the network throughput, whilst minimizing the interference imposed on the radar user, communication users are capable of adaptively selecting underlay or overlay access mode. Moreover, a near-optimal reinforcement learning algorithm is proposed considering both the computational complexity and feasibility. Finally, simulations are conducted in order to evaluate the effectiveness of our proposed POMDP based network association scheme.

In view of the compelling applications in both military and civilian fields, wireless sensor networks (WSNs) have attracted an unprecedented focus on their easy configuration and low cost. Due to the openness of wireless media and constrained resources of WSNs, it is of paramount importance to timely discern the malicious intrusion and unauthorized manipulation. In this paper, we engage in providing an intrusion detection mechanism relying on a novel multi-criteria game. In our model, the interaction between potential attackers and defenders is formulated as a two-player non-zero-sum multi-criteria game, where multiple objectives, i.e. the information security, reputation and energy consumption, are considered when searching for the Pareto equilibrium. Moreover, a light weighting strategy is proposed in order to construct the payoff vector. Finally, simulation results and theoretical analysis show the effectiveness and feasibility of our proposed mechanism.

The wireless body area network (WBAN) is a new-type wireless sensor network which has a steep demand for improving energy efficiency and reducing packet delay. However, in a multi-priority environment, the current IEEE Std. 802.15.6 MAC protocol for WBAN may result in excess transmission delay and power consumption due to the selfishness of high-priority sensor nodes. To overcome the deficiency, in this paper, a contention-oriented node sleeping MAC protocol is proposed. The MAC protocol utilizes a contention orientation mechanism between different contention levels to achieve a fair resource allocation. Furthermore, the sleeping scheme of redundant nodes yields energy efficiency. Finally, simulation results show that our proposed protocol outperforms 802.15.6 MAC, AD-MAC as well as DTD-MAC protocols in terms of both packet delay and energy efficiency.

Advanced informatics technologies facilitate the construction of green smart cities, especially the Wi-Fi implementation and management, for rapidly increasing personal Wi-Fi devices in autonomous environments residing in nonoverlapped channels often result in low energy efficiency and severe cochannel interference. In this paper, a green Wi-Fi management framework is constructed in order to reduce the overall energy consumption through turning off a portion of access points (APs) and aggregating their users to the other active APs. A Tabu-search-assisted active AP selection algorithm is proposed to minimize the power consumption with a seamless wireless converge. For the active APs, based on our defined metric airtime cost that is integrated by the in-range interference and the hidden terminal interference, a reinforcement-learning-aided AP self-management algorithm is proposed to dynamically adjust APs' channels in the partially overlapped channel space. Extensive simulations and field experiments demonstrate that the power consumption can be reduced by about 65%, and the airtime cost of APs can be reduced by 50% compared with the typical least congestion channel search algorithm.

As a promising solution for the next generation mobile networks (5G) deployment, long term evolution-unlicensed (LTE-U) is expected to improve spectrum utilization and channel capacity, which is beneficial for the industrial Internet of Things integrating many heterogeneous networks. However, such technology migration can potentially induce severe interference to the devices and networks originally operating on unlicensed bands, especially to the Wi-Fi network. In this paper, first of all, we emulate the coexistence of LTE-U networks and Wi-Fi networks, and experimentally evaluate their mutual interference relying on deploying time division duplex based OpenWrt wireless routers. Moreover, an LTE-U and Wi-Fi coexistence mechanism is proposed, where users with diverse traffic demands are capable of accessing either the public LTE-U network or its nearby Wi-Fi network. Finally, an access point selection algorithm with the aid of coalition formation game is developed for improving the system's throughput. Sufficient simulations based on Network Simulator 3 demonstrate that the overall throughput of the LTE-U and Wi-Fi hybrid cosystem outperforms that of the single LTE-U network as well as of the single Wi-Fi network.

The proliferation of information technologies results in a complex network evolution of online social networks. Traditional model driven aided description cannot be appropriate for the dynamic evolution of social networks. However, in this paper, relying on the big data collected from a range of real-world online social networks, we try to explore the underlying evolution for online social networks. Firstly, we define a pair of big data driven similarity based utility models (U- models), i.e. the undirected U-model as well as the directed U-model, which can effectively reflect the statistical characteristics of online social networks. Secondly, we analyze the small-world property, scale-free property and high clustering coefficient property of our proposed U-models which consider nodes' similarity, popularity and asymmetry in a network. Finally, relying on three real-world big datasets, i.e. Sina Weibo, Tencent Weibo and Twitter, sufficient experiments show that the U-models outperform the traditional models in portraying the evolution statistical characteristic of online social networks.

Unmanned Aerial Vehicle (UAV) technology has been widely applied in both military and civilian applications. With the increasing complexity of application scenarios, the coordination of multiple UAVs has become a hot topic. However, the limited capability of UAVs make it hard to achieve stable and reliable control. Considering this practical problem, we propose a cloud-based UAV system. It extricates the computing and data storage from UAVs and utilizes the cloud to process the sensor data and to maintain the stable operation of multi-UAV systems. Firstly, we analyze the cloud-based system's on-demand service ability and its impact on UAVs' control procedure. Secondly, we propose a UAV cloud control system (CCS) which serves as a network control system. Moreover, the stable condition of the UAV cloud control system is derived. It reveals the relationship between the acquisition rate of sensor data and the stability of the cloud-based UAV system. Finally, simulations are conducted to verify the effectiveness of previous theoretical analysis.

Motivated by the high data rate requirement, full- duplex (FD) multiple-input multiple-output (MIMO) has attracted much attention in both academia and industry. However, FD and MIMO techniques require high power consumption. To strike the balance between the data rate and power consumption, we propose a novel hybrid duplexing strategy, in which antennas are distributed across the service areas and are capable of working in hybrid modes of FD, half- duplex (HD) and sleeping mode, leading to higher energy efficiency (EE) than FD mode only due to enhanced degree of freedom. Based on the hybrid duplexing strategy, we maximize system EE by jointly designing transmitting/receiving chains' activation/deactivation at DAs, downlink beamformer, and uplink transmission power. Novel optimization algorithms are developed. Simulation results confirm that the hybrid duplexing distributed antenna (DA) system provides significant EE improvement over the conventional centralized FD MIMO system, showing its green evolution and applicability to future network deployment.

How to improve the information diffusion coverage rate in socially-aware mobile networks has drawn great attention. To address this issue, the concept of the tie strength, the partial strength and the value strength were proposed in order to achieve a superior criterion for information diffusion. However, the previous works did not consider the existence of various patterns among the nodes in socially-aware mobile networks. In this paper, we propose the asymmetric normalization forms of the partial strength as well as the value strength. Moreover, we explore the essence of asymmetry and its influence on information diffusion relying on analyzing the characteristics of graph structures as well as information local traps. Simulation results on the real-world social network and on the mobile network verify that our proposed asymmetric normalization forms are beneficial to promoting information diffusion.

Thanks to recent advance in massive social data and increasingly mature big data mining technologies, information diffusion and its control strategies have attracted much attention, which play pivotal roles in public opinion control, virus marketing as well as other social applications. In this paper, relying on social big data, we focus on the analysis and control of information diffusion. Specifically, we commence with analyzing the topological role of the social strengths, i.e., tie strength, partial strength, value strength, and their corresponding symmetric as well as asymmetric forms. Then, we define two critical points for the cascade information diffusion model, i.e., the information coverage critical point (CCP) and the information heat critical point (HCP). Furthermore, based on the two real-world datasets, the proposed two critical points are verified and analyzed. Our work may be beneficial in terms of analyzing and designing the information diffusion algorithms and relevant control strategies.

How to enhance security and stability of the cyber physical systems (CPSs) becomes a critical issue. In this paper, we propose a solution from the perspective of users' information diffusion process in CPCs. Relying on the virus propagation model, we conceive an idle-carrier-idle (ICI) model to characterize the information diffusion. Moreover, `effective diffusion rate' is defined in order to benchmark the efficiency of users' information diffusion. Based on the complex network theory, the threshold of the information diffusion both in homogenous networks and in free-scale networks is derived. Furthermore, a pair of game models are proposed for efficiently controlling the information diffusion and then for enhancing the security of the system. Furthermore, the equilibriums of two proposed games are demonstrated. Finally, essential numerical analysis and simulations show the effectiveness and feasibility of our proposed models.

The key factor for training a good neural network lies in both model capacity and large-scale training data. As more datasets are available nowadays, one may wonder whether the success of deep learning descends from data augmentation only. In this paper, we propose a new dataset, namely, Extended ImageNet Classification (EIC) dataset based on the original ILSVRC CLS 2012 set to investigate if more training data is a crucial step. We address the problem of object localization where given an image, some boxes (also called anchors) are generated to localize multiple instances. Different from previous work to place all anchors at the last layer, we split boxes of different sizes at various resolutions in the network, since small anchors are more prone to be identified at larger spatial location in the shallow layers. Inspired by the hourglass work, we apply a conv-deconv network architecture to generate object proposals. The motivation is to fully leverage high-level summarized semantics and to utilize their up-sampling version to help guide local details in the low-level maps. Experimental results demonstrate the effectiveness of such a design. Based on the newly proposed dataset, we find more data could enhance the average recall, but a more balanced data distribution among categories could obtain better results at the cost of fewer training samples.

Relying on clustering and the cluster head selection algorithms, vehicle-to-vehicle (V2V) and vehicle-to- infrastructure (V2I) based vehicular ad hoc networks (VANETs) play a critical role in intelligent transport system (ITS). However, the existing clustering and cluster head selection algorithms did not consider the influence of the vehicles' communication contents and their correlations. Specifically, the power-law characteristics of vehicle content demands are beneficial in terms both of achieving efficient clustering algorithm and selecting optimal cluster heads. In order to simulate the real vehicular communication scenarios, we commence with the mobility model design in this paper. Moreover, a novel clustering algorithm relying on content demands is proposed, which attracts vehicles to adopt V2V network through price advantage. Furthermore, based on the Fermi rule, i.e., one of the stochastic evolutionary strategies in complex networks, and evolution game, our cluster head selection algorithm is capable of representing more realistic vehicles' features, including selfishness, fairness and bounded rationality. Finally, the effectiveness and feasibility of our proposed algorithms are verified.

How to enhance the communication efficiency and quality on vehicular networks is one critical important issue. While with the larger and larger scale of vehicular networks in dense cities, the real-world datasets show that the vehicular networks essentially belong to the complex network model. Meanwhile, the extensive research on complex networks has shown that the complex network theory can both provide an accurate network illustration model and further make great contributions to the network design, optimization and management. In this paper, we start with analyzing characteristics of a taxi GPS dataset and then establishing the vehicular-to-infrastructure, vehicle-to-vehicle and the hybrid communication model, respectively. Moreover, we propose a clustering algorithm for station selection, a traffic allocation optimization model and an information source selection model based on the communication performances and complex network theory.

The recently announced Super Wi-Fi Network proposal in United States is aiming to enable Internet access in a nation-wide area. As traditional cable-connected power supply system becomes impractical or costly for a wide range wireless network, new infrastructure deployment for Super Wi-Fi is required. The fast developing Energy Harvesting (EH) techniques receive global attentions for their potential of solving the above power supply problem. It is a critical issue, from the user's perspective, how to make efficient network selection and access strategies. Unlike traditional wireless networks, the battery charge state and tendency in EH based networks have to be taken into account when making network selection and access, which has not been well investigated. In this paper, we propose a practical and efficient framework for multiple base stations access strategy in an EH powered Super Wi-Fi network. We consider the access strategy from the user's perspective, who exploits downlink transmission opportunities from one base station. To formulate the problem, we used Partially Observable Markov Decision Process (POMDP) to model users' observations on the base stations' battery situation and decisions on the base station selection and access. Simulation results show that our methods are efficacious and significantly outperform the traditional widely used CSMA method.

The super-WiFi network concept has been proposed for nationwide Internet access in the United States. However, the traditional mains power supply is not necessarily ubiquitous in this large-scale wireless network. Furthermore, the non-uniform geographic distribution of both the based-stations and the tele-traffic requires carefully considered user association. Relying on the rapidly developing energy harvesting techniques, we focus our attention on the sophisticated access point (AP) selection strategies conceived for the energy harvesting aided super-WiFi network. Explicitly, we propose a solar radiation model relying on the historical solar activity observation data provided by the University of Queensland, followed by a beneficial radiation parameter estimation method. Furthermore, we formulate both a Markov decision process (MDP) as well as a partially observable MDP (POMDP) for supporting the users' decisions on beneficially selecting APs. Moreover, we conceive iterative algorithms for implementing our MDP and POMDP-based AP-selection, respectively. Finally, our performance results are benchmarked against a range of traditional decision-making algorithms.