Current sign language generation relies on technical metrics that often overlook capturing actual Deaf user comprehension. This is particularly challenging for Chinese Natural Sign Language (CNSL) with its scene-dependent expressions and spatial grammar. We assembled a scene-aware CNSL generation prototype using established components to serve as a controlled evaluation stimulus. In partnership with four Deaf co-researchers, we developed a seven-dimensional evaluation framework combining objective comprehension tests with validated subjective measures. An evaluation with 24 Deaf participants revealed that while technical metrics indicated success, objective comprehension tests showed lower comprehension and higher cognitive load, particularly for complex spatial grammar. These results demonstrate that current evaluation methods do not align with user needs. Our framework shifts from computer benchmarking toward human-centered assessment, prioritizing comprehension, cognitive load, and cultural authenticity. The findings underscore the importance of creating accessibility technology with, rather than for, Deaf communities.

Despite the rapid proliferation of Internet of Things applications driving widespread wireless sensor network (WSN) deployment, traditional WSNs remain fundamentally constrained by persistent energy limitations that severely restrict network lifetime and operational sustainability. Wireless rechargeable sensor networks (WRSNs) integrated with wireless power transfer (WPT) technology emerge as a transformative paradigm, theoretically enabling unlimited operational lifetime. In this paper, we investigate a heterogeneous mobile charging architecture that strategically combines an automated aerial vehicle (AAV) and a ground smart vehicle (SV) in heterogeneous deployment scenarios to collaboratively exploit the superior mobility of the AAV and extended endurance of the SV for energy distribution. We formulate a multi-objective optimization problem that simultaneously addresses the dynamic balance of heterogeneous charger advantages, charging efficiency versus mobility energy consumption trade-offs, and real-time adaptive coordination under time-varying network conditions. This problem presents significant computational challenges due to its high-dimensional continuous action space, non-convex optimization landscape, and dynamic environmental constraints. To address these challenges, we propose the improved heterogeneous agent trust region policy optimization (IHATRPO) algorithm that integrates a self-attention mechanism for enhanced complex environmental state processing and employs a Beta sampling strategy to achieve unbiased gradient computation in continuous action spaces. Simulation results demonstrate that IHATRPO achieves a 51% performance improvement over the original HATRPO, significantly outperforming state-of-the-art baseline algorithms while substantially decreasing sensor node mortality rate and improving charging system efficiency.

Despite significant advancements in terrestrial networks, inherent limitations persist in providing reliable coverage to remote areas and maintaining resilience during natural disasters. Multi-tier networks with low Earth orbit (LEO) satellites and high-altitude platforms (HAPs) offer promising solutions, but face challenges from high mobility and dynamic channel conditions that cause unstable connections and frequent handovers. In this paper, we design a three-tier network architecture that integrates LEO satellites, HAPs, and ground terminals with hybrid free-space optical (FSO) and radio frequency (RF) links to maximize coverage while maintaining connectivity reliability. This hybrid approach leverages the high bandwidth of FSO for satellite-to-HAP links and the weather resilience of RF for HAP-to-ground links. We formulate a joint optimization problem to simultaneously balance downlink transmission rate and handover frequency by optimizing network configuration and satellite handover decisions. The problem is highly dynamic and non-convex with time-coupled constraints. To address these challenges, we propose a novel large language model (LLM)-guided truncated quantile critics algorithm with dynamic action masking (LTQC-DAM) that utilizes dynamic action masking to eliminate unnecessary exploration and employs LLMs to adaptively tune hyperparameters. Simulation results demonstrate that the proposed LTQC-DAM algorithm outperforms baseline algorithms in terms of convergence, downlink transmission rate, and handover frequency. We also reveal that compared to other state-of-the-art LLMs, DeepSeek delivers the best performance through gradual, contextually-aware parameter adjustments.

While low-altitude wireless networks (LAWNs) based on uncrewed aerial vehicles (UAVs) offer high mobility, flexibility, and coverage for urban communications, they face severe signal attenuation in dense environments due to obstructions. To address this critical issue, we consider introducing collaborative beamforming (CB) of UAVs and omnidirectional reconfigurable beamforming (ORB) of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) to enhance the signal quality and directionality. On this basis, we formulate a joint rate and energy optimization problem (JREOP) to maximize the transmission rate of the overall system, while minimizing the energy consumption of the UAV swarm. Due to the non-convex and NP-hard nature of JREOP, we propose a heterogeneous multi-agent collaborative dynamic (HMCD) optimization framework, which has two core components. The first component is a simulated annealing (SA)-based STAR-RIS control method, which dynamically optimizes reflection and transmission coefficients to enhance signal propagation. The second component is an improved multi-agent deep reinforcement learning (MADRL) control method, which incorporates a self-attention evaluation mechanism to capture interactions between UAVs and an adaptive velocity transition mechanism to enhance training stability. Simulation results demonstrate that HMCD outperforms various baselines in terms of convergence speed, average transmission rate, and energy consumption. Further analysis reveals that the average transmission rate of the overall system scales positively with both UAV count and STAR-RIS element numbers.

Low Earth orbit (LEO) satellites can be used to assist maritime wireless communications for wide-area data transmission. However, the extensive coverage of LEO satellites, combined with the openness of channels, can cause the communication process to suffer from security risks. This paper presents a LEO satellite-maritime communication system assisted by low-altitude unmanned aerial vehicle (UAV) friendly-jamming to ensure data security at the physical layer. Since such a system requires balancing the conflicting performance metrics of secrecy rate and energy consumption of the UAV to meet evolving scenario demands, we formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP). In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process. We then propose a transformer-enhanced soft actor-critic (TransSAC) algorithm, which is a generative artificial intelligence-enabled deep reinforcement learning approach to solve the reformulated problem, thus capturing strong temporal correlations and diversely exploring weights. Simulation results demonstrate that the TransSAC algorithm outperforms comparative approaches and algorithms, maximizing the secrecy rate while effectively minimizing the energy consumption of the UAV. Moreover, the results identify more suitable constraints for the system.

Low-altitude wireless networks (LAWNs) have emerged as a viable solution for maritime communications. In these maritime LAWNs, uncrewed aerial vehicles (UAVs) serve as practical low-altitude platforms for wireless communications due to their flexibility and ease of deployment. However, the open and clear UAV communication channels make maritime LAWNs vulnerable to eavesdropping attacks. Existing security approaches often assume eavesdroppers follow predefined trajectories, which fail to capture the dynamic mobility patterns of eavesdroppers in realistic maritime environments. To address this challenge, we consider a low-altitude maritime communication system that employs intelligent jamming to counter dynamic eavesdroppers with uncertain positions to enhance the physical layer security. Since such a system requires balancing the conflicting performance metrics of the secrecy rate and energy consumption of UAVs, we formulate a secure and energy-efficient maritime communication multi-objective optimization problem (SEMCMOP). To solve this dynamic and long-term optimization problem, we first reformulate it as a partially observable Markov decision process (POMDP). We then propose a novel soft actor-critic with conditional variational autoencoder (SAC-CVAE) algorithm, which is a deep reinforcement learning algorithm improved by generative artificial intelligence. Specifically, the SAC-CVAE algorithm employs advantage-conditioned latent representations to disentangle and optimize policies, while enhancing computational efficiency by reducing the state space dimension. Simulation results demonstrate that our proposed intelligent jamming approach achieves secure and energy-efficient maritime communications. Furthermore, comparison results show that the proposed SAC-CVAE algorithm outperforms baseline methods across various eavesdropper movement patterns, simultaneously maximizing the secrecy rate and minimizing the energy consumption of UAVs.

Despite the widespread deployment of terrestrial networks, providing reliable communication services to remote areas and maintaining connectivity during emergencies remains challenging. Low Earth orbit (LEO) satellite constellations offer promising solutions with their global coverage capabilities and reduced latency, yet struggle with intermittent coverage and limited communication windows due to orbital dynamics. This paper introduces an age of information (AoI)-aware space-air-ground integrated network (SAGIN) architecture that leverages a high-altitude platform (HAP) as intelligent relay between the LEO satellites and ground terminals. Our three-layer design employs hybrid free-space optical (FSO) links for high-capacity satellite-to-HAP communication and reliable radio frequency (RF) links for HAP-to-ground transmission, and thus addressing the temporal discontinuity in LEO satellite coverage while serving diverse user priorities. Specifically, we formulate a joint optimization problem to simultaneously minimize the AoI and satellite handover frequency through optimal transmit power distribution and satellite selection decisions. This highly dynamic, non-convex problem with time-coupled constraints presents significant computational challenges for traditional approaches. To address these difficulties, we propose a novel diffusion model (DM)-enhanced dueling double deep Q-network withaction decomposition andstate transformer encoder (DD3QN-AS) algorithm that incorporates transformer-based temporal feature extraction and employs a DM-based latent prompt generative module to refine state-action representations through conditional denoising. Simulation results highlight the superior performance of the proposed approach compared with policy-based methods and some other deep reinforcement learning (DRL) benchmarks. Moreover, performance analysis under various system settings verifies the robustness of the proposed approach.

The rapid development of the low-altitude economy (LAE) has significantly increased the utilization of autonomous aerial vehicles (AAVs) in various applications, necessitating efficient and secure communication methods among AAV swarms. In this work, we aim to introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two AAV swarms and construct these swarms as two AAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of AAVs when constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives by formulating a multi-objective optimization problem, which is NP-hard and with a large number of decision variables. Accordingly, we design a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead, which contains a conditional variational autoencoder (CVAE)-based generative method and a proposed powerful swarm intelligence algorithm. In this framework, CVAE can collect expert solutions obtained by the swarm intelligence algorithm in other environment states to explore characteristics and patterns, thereby directly generating high-quality initial solutions in new environment factors for the swarm intelligence algorithm to search solution space efficiently. Simulation results show that the proposed swarm intelligence algorithm outperforms other state-of-the-art baseline algorithms, and the GenSI can achieve similar optimization results by using far fewer iterations than the ordinary swarm intelligence algorithm. Experimental tests demonstrate that introducing the CVAE mechanism achieves a 58.7% reduction in execution time, which enables the deployment of GenSI even on AAV platforms with limited computing power.

The integration of satellite and autonomous aerial vehicle (AAV) communications has become essential for the scenarios requiring both wide coverage and rapid deployment, particularly in remote or disaster-stricken areas where the terrestrial infrastructure is unavailable. Furthermore, emerging applications increasingly demand simultaneous mobile edge computing (MEC) and data collection (DC) capabilities within the same aerial network. However, jointly optimizing these operations in heterogeneous satellite-AAV systems presents significant challenges due to limited on-board resources and competing demands under dynamic channel conditions. In this work, we investigate a satellite-AAV-enabled joint MEC-DC system where these platforms collaborate to serve ground devices (GDs). Specifically, we formulate a joint optimization problem to minimize the average MEC end-to-end delay and AAV energy consumption while maximizing the collected data. Since the formulated optimization problem is a non-convex mixed-integer nonlinear programming (MINLP) problem, we propose a Q-weighted variational policy optimization-based joint AAV movement control, GD association, offloading decision, and bandwidth allocation (QAGOB) approach. Specifically, we reformulate the optimization problem as an action space-transformed Markov decision process to adapt the variable action dimensions and hybrid action space. Subsequently, QAGOB leverages the multi-modal generation capacities of diffusion models to optimize policies and can achieve better sample efficiency while controlling the diffusion costs during training. Simulation results show that QAGOB outperforms five other benchmarks, including traditional DRL and diffusion-based DRL algorithms. Furthermore, the MEC-DC joint optimization achieves significant advantages when compared to the separate optimization of MEC and DC.

Emerging technologies in sixth generation (6G) of wireless communications, such as terahertz communication and ultra-massive multiple-input multiple-output, present promising prospects. Despite the high data rate potential of millimeter wave communications, millimeter wave (mmWave) communications in urban low altitude economy (LAE) environments are constrained by challenges such as signal attenuation and multipath interference. Specially, in urban environments, mmWave communication experiences significant attenuation due to buildings, owing to its short wavelength, which necessitates developing innovative approaches to improve the robustness of such communications in LAE networking. In this paper, we explore the use of an unmanned aerial vehicle (UAV)-carried intelligent reflecting surface (IRS) to support low altitude mmWave communication. Specifically, we consider a typical urban low altitude communication scenario where a UAV-carried IRS establishes a line-of-sight (LoS) channel between the mobile users and a source user (SU) despite the presence of obstacles. Subsequently, we formulate an optimization problem aimed at maximizing the transmission rates and minimizing the energy consumption of the UAV by jointly optimizing phase shifts of the IRS and UAV trajectory. Given the non-convex nature of the problem and its high dynamics, we propose a deep reinforcement learning-based approach incorporating neural episodic control, long short-term memory, and an IRS phase shift control method to enhance the stability and accelerate the convergence. Simulation results show that the proposed algorithm effectively resolves the problem and surpasses other benchmark algorithms in various performances.

This paper proposes a UAV-assisted forwarding system based on distributed beamforming to enhance age of information (AoI) in Internet of Things (IoT). Specifically, UAVs collect and relay data between sensor nodes (SNs) and the remote base station (BS). However, flight delays increase the AoI and degrade the network performance. To mitigate this, we adopt distributed beamforming to extend the communication range, reduce the flight frequency and ensure the continuous data relay and efficient energy utilization. Then, we formulate an optimization problem to minimize AoI and UAV energy consumption, by jointly optimizing the UAV trajectories and communication schedules. The problem is non-convex and with high dynamic, and thus we propose a deep reinforcement learning (DRL)-based algorithm to solve the problem, thereby enhancing the stability and accelerate convergence speed. Simulation results show that the proposed algorithm effectively addresses the problem and outperforms other benchmark algorithms.

In this paper, we explore an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) data collection system, where multiple UAVs are deployed and each UAV can simultaneously collect data from multiple IoT devices. Specifically, we formulate a UAV-enabled data collection multi-objective optimization problem (UDCMOP) to simultaneously maximize the collected data of UAVs and minimize the total energy consumption of UAVs that contains the moving and hovering energy consumption by optimizing the flight trajectories of UAVs. Given the dynamic nature of the system and the need for coordination among multiple UAVs, we propose an enhanced multi-agent deep reinforcement learning (MADRL) algorithm, namely, multi-agent proximal policy optimization with curiositydriven exploration (MAPPOC). This algorithm incorporates a curiosity-driven exploration mechanism to improve exploration capabilities. Simulation results demonstrate the effectiveness of the proposed MAPPOC and prove that the learned strategy of MAPPOC is better compared with other baseline methods.

The integration of simultaneous wireless information and power transfer (SWIPT) technology in 6G Internet of Things (IoT) networks faces significant challenges in remote areas and disaster scenarios where ground infrastructure is unavailable. This paper proposes a novel unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system enhanced by directional antennas to provide both computational resources and energy support for ground IoT terminals. However, such systems require multiple trade-off policies to balance UAV energy consumption, terminal battery levels, and computational resource allocation under various constraints, including limited UAV battery capacity, non-linear energy harvesting characteristics, and dynamic task arrivals. To address these challenges comprehensively, we formulate a bi-objective optimization problem that simultaneously considers system energy efficiency and terminal battery sustainability. We then reformulate this non-convex problem with a hybrid solution space as a Markov decision process (MDP) and propose an improved soft actor-critic (SAC) algorithm with an action simplification mechanism to enhance its convergence and generalization capabilities. Simulation results have demonstrated that our proposed approach outperforms various baselines in different scenarios, achieving efficient energy management while maintaining high computational performance. Furthermore, our method shows strong generalization ability across different scenarios, particularly in complex environments, validating the effectiveness of our designed boundary penalty and charging reward mechanisms.

Autonomous aerial vehicles (AAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely affect the quality of AAV transmitted signals. Collaborative beamforming (CB) can enhance the signal strength and range to assist the AAV relay for remote maritime communications. However, due to the open nature of AAV channels, security issue requires special consideration. This article proposes a dual AAV cluster-assisted system via CB to achieve physical-layer security in maritime wireless communications. Specifically, one AAV cluster forms a maritime AAV-enabled virtual antenna array (MUVAA) relay to forward data signals to the remote legitimate vessel, and the other AAV cluster forms an MUVAA jammer to send jamming signals to the remote eavesdropper. In this system, we formulate a secure and energy-efficient maritime communication multiobjective optimization problem (SEMCMOP) to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the total flight energy consumption of AAVs. Since the SEMCMOP is an NP-hard and large-scale optimization problem, we propose an improved swarm intelligence optimization algorithm with chaotic solution initialization and hybrid solution update strategies to solve the problem. Simulation results indicate that the proposed algorithm outperforms other comparison algorithms, and it can achieve more efficient signal transmission by using the CB-based method.

An expansion of Internet of Things (IoT) has led to significant challenges in wireless data harvesting, dissemination, and energy management due to the massive volumes of data generated by IoT devices. These challenges are exacerbated by data redundancy arising from spatial and temporal correlations. To address these issues, this article proposes a novel data-driven collaborative beamforming (CB)-based communication framework for IoT networks. Specifically, the framework integrates CB with an overlap-based multihop routing protocol (OMRP) to enhance data transmission efficiency while mitigating energy consumption and addressing hot spot issues in remotely deployed IoT networks. Based on the data aggregation to a specific node by OMRP, we formulate a node selection problem for the CB stage, with the objective of optimizing uplink transmission energy consumption. Given the complexity of the problem, we introduce a softmax-based proximal policy optimization with long-short-term memory (SoftPPO-LSTM) algorithm to intelligently select CB nodes for improving transmission efficiency. Simulation results show that the proposed OMRP improves network lifetime by 17% compared to benchmark routing protocols, while the SoftPPO-LSTM method for CB node selection achieves an 8.3% increase in throughput over benchmark algorithms. The results also reveal that the combined OMRP with the SoftPPO-LSTM method effectively mitigates hot spot problems and offers superior performance compared to traditional strategies.

Autonomous aerial vehicles (AAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the Line of Sight (LoS) dominance in air-to-ground (A2G) communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This article explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-AAV network systems. Specifically, the AAV swarm forms an AAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a tradeoff tradeoff between the effectiveness of CB-based interference mitigation and the energy conservation of AAVs. Thus, by optimizing the excitation current weights and hover position of AAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multiobjective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic nondominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by AAVs to nonreceiving BSs.

The Internet of Things (IoT) serves a vital function in supporting real-time decision-making across various applications by facilitating seamless data exchange between devices. However, as the IoT networks typically exchange data over wireless channels, the data transmission process is highly susceptible to malicious interference from jammers in the environment. Moreover, ensuring the freshness of the collected data of the decision center and managing the limited energy resources of IoT devices present significant challenges in the IoT networks. In this article, we consider a unmanned aerial vehicle (UAV)-assisted IoT network in the presence of a jammer, where the UAV is deployed to charge IoT devices through radio frequency (RF) energy transfer, and the IoT devices subsequently use the harvested energy to upload sensing data to the UAV using time division multiple access (TDMA). We aim to minimize both the secure Age of Information (AoI) of IoT devices and the energy consumption of the UAV by optimizing the UAV trajectory, IoT device scheduling, and proportion of data transmission duration. Given the nonconvex and dynamic nature of this optimization problem, we propose a diffusion model-enhanced twin delayed deep deterministic policy gradient (DM-TD3) algorithm to solve the problem. Specifically, considering the analytical and reasoning capabilities of the diffusion model, we integrate it into the actor network of TD3 to generate rational actions based on the observed state. Simulation results demonstrate the effectiveness of the proposed DM-TD3 algorithm compared to five benchmark approaches.

Autonomous aerial vehicle (AAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this article investigates a multi-AAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization problem to minimize the MEC latency and maximize the collected data volume. This problem can be classified as a nonconvex mixed integer programming problem that exhibits long-term optimization and dynamics. Thus, we propose a deep reinforcement learning-based approach that jointly optimizes the AAV movement, user transmit power, and user association in real time to solve the problem efficiently. Specifically, we reformulate the optimization problem into an action space-reduced Markov decision process (MDP) and optimize the user association by using a two-phase matching-based association (TMA) strategy. Subsequently, we propose a soft actor-critic (SAC)-based approach that integrates the proposed TMA strategy (SAC-TMA) to solve the formulated joint optimization problem collaboratively. Simulation results demonstrate that the proposed SAC-TMA is able to coordinate the two subsystems and can effectively reduce the system latency and improve the DC volume compared with other benchmark algorithms.

Autonomous aerial vehicles (AAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a AAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a AAV-enabled virtual antenna array to improve transmission performance from the AAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the AAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances.

Due to flexibility and low-cost, unmanned aerial vehicles (UAVs) are increasingly crucial for enhancing coverage and functionality of wireless networks. However, incorporating UAVs into next-generation wireless communication systems poses significant challenges, particularly in sustaining high-rate and long-range secure communications against eavesdropping attacks. In this work, we consider a UAV swarm-enabled secure surveillance network system, where a UAV swarm forms a virtual antenna array to transmit sensitive surveillance data to a remote base station (RBS) via collaborative beamforming (CB) so as to resist mobile eavesdroppers. Specifically, we formulate an aerial secure communication and energy efficiency multi-objective optimization problem (ASCEE-MOP) to maximize the secrecy rate of the system and to minimize the flight energy consumption of the UAV swarm. To address the non-convex, NP-hard and dynamic ASCEE-MOP, we propose a generative diffusion model-enabled twin delayed deep deterministic policy gradient (GDMTD3) method. Specifically, GDMTD3 leverages an innovative application of diffusion models to determine optimal excitation current weights and position decisions of UAVs. The diffusion models can better capture the complex dynamics and the trade-off of the ASCEE-MOP, thereby yielding promising solutions. Simulation results highlight the superior performance of the proposed approach compared with traditional deployment strategies and some other deep reinforcement learning (DRL) benchmarks. Moreover, performance analysis under various parameter settings of GDMTD3 and different numbers of UAVs verifies the robustness of the proposed approach.

The low-altitude economy (LAE), as a new economic paradigm, plays an indispensable role in cargo transportation, healthcare, infrastructure inspection, and especially post-disaster communications. Specifically, unmanned aerial vehicles (UAVs), as one of the core technologies of the LAE, can be deployed to provide communication coverage, facilitate data collection, and relay data for trapped users, thereby significantly enhancing the efficiency of post-disaster response efforts. However, conventional UAV self-organizing networks exhibit low reliability in long-range cases due to their limited onboard energy and transmit ability. Therefore, in this paper, we design an efficient and robust UAV-swarm enabled collaborative self-organizing network to facilitate post-disaster communications. Specifically, a ground device transmits data to UAV swarms, which then use collaborative beamforming (CB) technique to form virtual antenna arrays and relay the data to a remote access point (AP) efficiently. Then, we formulate a rescue-oriented post-disaster transmission rate maximization optimization problem (RPTRMOP), aimed at maximizing the transmission rate of the whole network. Given the challenges of solving the formulated RPTRMOP by using traditional algorithms, we propose a two-stage optimization approach to address it.In the first stage, the optimal multi-path traffic routing and the theoretical upper bound on the transmission rate of the network are derived.In the second stage, we transform the formulated RPTRMOP into a variant named V-RPTRMOP based on the obtained optimal multi-path traffic routing, aimed at rendering the actual transmission rate closely approaches its theoretical upper bound by optimizing the excitation current weight and the placement of each participating UAV via a diffusion model-enabled particle swarm optimization (DM-PSO) algorithm. Simulation results show the effectiveness of the proposed two-stage optimization approach in improving the transmission rate of the constructed network, which demonstrates the great potential for post-disaster communications. Moreover, the robustness of the constructed network is also validated via evaluating the impact of three unexpected situations on the system transmission rate.

With the advancement of smart cities, smart manufacturing, and smart transportation, the Internet of Things (IoT) big data platform operating on wireless networks has emerged as a pivotal sector. In such systems, unmanned aerial vehicles (UAVs) play an indispensable support due to their flexibility and adaptability, but the energy sensitivity and limited communication capabilities pose further challenges. In this paper, we study a UAV-assisted secure communication system, where a UAV-enabled virtual antenna array (UVAA) consisting of multiple UAVs communicates with a remote mobile user (MU) by executing collaborative beamforming (CB), and then an eavesdropper exists for eavesdropping the transmission data from UVAA to MU. Then, a UAV-enabled secure communication optimization problem is formulated to maximize the total secrecy rate between the UVAA and the MU by optimizing the roles, locations and excitation current weights of UAVs. Since the considered scenario is dynamic and the UAVs need to cooperate with each other, we propose a hybrid-action multi-agent deep reinforcement learning (MADRL) algorithm (HMAPPO) to efficiently solve the optimization problem. Simulation results verify the effectiveness of the HMAPPO and illustrate that it learns the best strategy compared with other baseline methods.

An intelligent reflecting surface (IRS)-enabled wireless power transfer (WPT) and data collection scheme for unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) network is investigated in this paper. Specifically, IoT devices (IoTDs) first harvest energy from the UAV and then upload the sensed data by applying time-division multiple access (TDMA), where an IRS is deployed to improve the transmission quality. We aim to minimize the age of information (AoI) and energy consumption of the UAV. For achieving this, we formulate an optimization problem by jointly optimizing the UAV trajectory, IRS phase shits, charging time allocation, and binary IoTD scheduling, which is a mixed-integer non-convex optimization problem. To address the issue, we first formulate our problem into a Markov decision process (MDP) and then propose an alternating optimization-double parameterized deep Q-network (AO-DPDQN) approach to solve the optimization problem. Specifically, an AO-based method is adopted to optimize the phase shifts of IRS to simplify the action space of MDP, and then double parameterized deep Q-network (DPDQN) is employed to optimize UAV trajectory, charging time allocation, and IoTD scheduling. Simulation results demonstrate the effectiveness and superiority of the proposed approach compared to various baselines.

Emerging 6G technologies, such as terahertz communication and ultra-massive multiple-input multiple-output, offer exciting prospects but face challenges like limited range and multipath interference. In this paper, we seek to use an unmanned aerial vehicle (UAV)-carried intelligent reflecting surface (IRS) to assist the terrestrial mmWave networks. Specifically, we consider a typical urban scenario where a UAV-carried IRS rebuilds the line of sight (LoS) channel between a mobile user and a base station under the existence of obstacles. Then, we formulate an optimization problem to maximize the transmission rates and minimize the UAV energy consumption, by jointly optimizing the UAV trajectory and the phase shifts of IRS. The problem is non-convex and with high dynamic, and thus we propose a deep reinforcement learning (DRL)-based algorithm with neural episodic control, long short-term memory (LSTM), and a phase control method to solve the problem, thereby enhancing the stability and accelerate convergence speed. Simulation results demonstrate that the proposed algorithm effectively addresses the problem and outperforms other benchmark algorithms.

Maritime wireless communications as the promising applications gradually spur people's interest. However, they are suffering from critical security issues due to the feature of open channels. This paper propose to utilize a group of unmanned aerial vehicles (UAVs) as a jammer to achieve physical layer security during the maritime wireless communications. Specifically, these UAVs form a maritime UAV-enabled virtual antenna array (UVAA), which can be allowed to transmit jamming signals to the eavesdropper. In the designed system, we formulate a maritime wireless communication multi-objective optimization problem (MWCMOP) to simultaneously achieve the maximization of the signal-to-interference-plus-noise ratio (SINR) of the legal vessel, minimization of the SINR of the eavesdropping vessel, and minimization of the total flying energy cost of UAVs. It is challenging to solve the formulated MWCMOP since it is complex and NP-hard. Thus, we present a novel improved evolutionary algorithm to deal with the problem. Simulation results show that the presented algorithm is superior to other peer algorithms and it is capable of searching a transmission-maximizing flight scheme.

Unmanned aerial vehicles (UAVs)-enabled aerial communication provides a flexible, reliable, and cost-effective solution for a range of wireless applications. However, due to the high line-of-sight (LoS) probability, aerial communications between UAVs are vulnerable to eavesdropping attacks, particularly when multiple eavesdroppers collude. In this work, we aim to introduce distributed collaborative beamforming (DCB) into UAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two UAV swarms and construct these swarms as two UAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and the maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of UAVs for constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives as a multi-objective optimization problem. Following this, we propose an enhanced multi-objective swarm intelligence algorithm via the characterized properties of the problem. Simulation results show that our proposed algorithm can obtain a set of informative solutions and outperform other state-of-the-art baseline algorithms. Experimental tests demonstrate that our method can be deployed in limited computing power platforms of UAVs and is beneficial for saving computational resources.

Given flexible mobility, rapid deployment, and low cost, unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) shows great potential to compensate for the lack of terrestrial edge computing coverage. However, limited battery capacity, computing and spectrum resources also pose serious challenges for UAV-enabled MEC, which shorten the service time of UAVs and degrade the quality of experience (QoE) of user devices (UDs) without effective control approach. In this work, we consider a UAV-enabled MEC scenario where a UAV serves as an aerial edge server to provide computing services for multiple ground UDs. Then, a joint task offloading, resource allocation, and UAV trajectory planning optimization problem (JTRTOP) is formulated to maximize the QoE of UDs under the UAV energy consumption constraint. To solve the JTRTOP that is proved to be a future-dependent and NP-hard problem, an online joint optimization approach (OJOA) is proposed. Specifically, the JTRTOP is first transformed into a per-slot real-time optimization problem (PROP) by using the Lyapunov optimization framework. Then, a two-stage optimization method based on game theory and convex optimization is proposed to solve the PROP. Simulation results validate that the proposed approach can achieve superior system performance compared to the other benchmark schemes.

In the sixth generation (6G) era, unmanned aerial vehicle (UAV) and satellite communications offer promising prospects in terms of the increasing demand for network coverage by the explosive growth of Internet of Things (IoT) devices. However, the lack of spectrum resources has become the bottleneck affecting network service performance. In this paper, we seek to use cognitive radio (CR) technology to assist the satellite-UAV networks. Specifically, we consider an integrated satellite-aerial network (SAN) and UAV-enabled MEC system in which CR is applied for the allocation and management of spectrum resources. Then, we formulate an optimization problem to maximize task execution and data transmission in the SAN system and minimize the energy consumption of UAVs by jointly optimizing the UAV trajectory and the strategy of task offloading. The problem is non-convex with high dynamic and hybrid action space, and thus we propose a matching-aided multi-agent deep reinforcement learning (MADRL)-based algorithm to solve the problem. Simulation results show that the proposed algorithm can improve the efficiency of task collection and reduce energy consumption while ensuring the anti-jamming ability.

Unmanned aerial vehicle (UAV) networks play a crucial role in 5G/6G wireless communications. However, the presence of known and potential unknown eavesdroppers poses security risks to UAV communications. To overcome this issue, we construct part UAVs within a UAV swarm as a virtual antenna array (VAA) and then introduce intelligent reflecting surface (IRS) to avoid eavesdropping from these eavesdroppers. By adopting joint collaborative and passive beamforming of VAA and IRS, our objective is to jointly optimize the secrecy rate, maximum sidelobe level, and total energy consumption of the system, by determining appropriate excitation current weights and trajectories of the UAVs, and phase shifts of IRS elements. Considering the dynamic and heterogeneity of the system, we transform the problem into a heterogeneity Markov decision process (MDP). Then, a heterogeneous multi-agent control approach (HMCA) consisting of an IRS control policy and a multi-agent soft actor-critic UAV control policy is proposed. Simulation results show that the proposed HMCA effectively solves the optimization problem and it has better performance than other baseline approaches.

Unmanned aerial vehicles (UAVs) are playing an increasingly important role in wireless network communications applied to disaster rescue with wireless communication technology. However, due to shortcomings such as low on-board energy and limited transmit power, they are unable to achieve high performance transmission. In this work, to improve the transmission performance, UAVs can form a virtual antenna array (VAA) and transmit information to other UAV arrays or base stations (BSs) by using collaborative beamforming techniques (CB). We formulate the transmission rate maximization and energy minimization communication multi-objective optimization problem (REMCMOP) for wireless networks to jointly maximize the total transmission rates, maximize the minimum transmission rate in all UAV arrays and minimize the total motion energy consumptions of UAVs. Therefore, an improved multi-objective Gray Wolf algorithm (MOGWO-CCH) is put forward to solve the problem. Finally, by comparing the simulation results, it is verified that MOGWO-CCH performs better than other comparative methods in dealing with the formulated REMCMOP.

The goal of feature selection in machine learning is to simultaneously maintain more classification accuracy, while reducing lager amount of attributes. In this paper, we firstly design a fitness function that achieves both objectives jointly. Then we come up with a chaos-based binary dragonfly algorithm (CBDA) that incorporates several improvements over the conventional dragonfly algorithm (DA) for developing a wrapper-based feature selection method to solve the fitness function. Specifically, the CBDA innovatively introduces three improved factors, namely the chaotic map, evolutionary population dynamics (EPD) mechanism, and binarization strategy on the basis of conventional DA to balance the exploitation and exploration capabilities of the algorithm and make it more suitable to handle the formulated problem. We conduct experiments on 24 well-known data sets from the UCI repository with three ablated versions of CBDA targeting different components of the algorithm in order to explain their contributions in CBDA and also with five established comparative algorithms in terms of fitness value, classification accuracy, CPU running time, and number of selected features. The results show that the proposed CBDA has remarkable advantages in most of the tested data sets.

Feature selection is a complicated multi-objective optimization problem with aims at reaching to the best subset of features while remaining a high accuracy in the field of machine learning, which is considered to be a difficult task. In this paper, we design a fitness function to jointly optimize the classification accuracy and the selected features in the linear weighting manner. Then, we propose two hybrid meta-heuristic methods which are the hybrid basic bald eagle search-particle swarm optimization (HBBP) and hybrid chaos-based bald eagle search-particle swarm optimization (HCBP) that alleviate the drawbacks of bald eagle search (BES) by utilizing the advantages of particle swarm optimization (PSO) to efficiently optimize the designed fitness function. Specifically, HBBP is proposed to overcome the disadvantages of the originals (i.e., BES and PSO) and HCBP is proposed to further improve the performance of HBBP. Moreover, a binary optimization is utilized to effectively transfer the solution space from continuous to binary. To evaluate the effectiveness, 17 well-known data sets from the UCI repository are employed as well as a set of well-established algorithms from the literature are adopted to jointly confirm the effectiveness of the proposed methods in terms of fitness value, classification accuracy, computational time and selected features. The results support the superiority of the proposed hybrid methods against the basic optimizers and the comparative algorithms on the most tested data sets.

Communications and networks of unmanned aerial vehicles (UAVs) are of paramount importance, owing to their flexible mobility and fast deployment. However, how to enhance the communication efficiency under the restricted on-board energy and transmit power is still one of the most critical problems. In this article, we consider a UAV-assisted communication scenario, in which a virtual antenna array (VAA) performed by a swarm of UAVs utilize collaborative beamforming (CB) to communicate with several faraway base stations (BSs). For achieving a superior transmission performance, we formulate a hovering and motion energy consumption multiobjective optimization problem (HMECMOP) of UAV-assisted CB to simultaneously minimize the total hovering and motion energy consumptions of UAVs by jointly optimizing the positions, excitation current weights of UAVs, and the order of communicating with different BSs. Moreover, the formulated HMECMOP is analyzed and proven as an NP-hard and classical hybrid multiobjective optimization problem (MOP) with a complex solution vector that contains continuous and discrete variables. Thus, we propose an improved multiobjective multiverse optimizer (IMOMVO), which uses the vertical and horizontal renewal strategy and nearest neighbor procedure (NNP) to solve the complex HMECMOP. Extensive simulations are carried out to demonstrate that the proposed algorithm can effectively reduce the energy consumption of UAVs communicating with multiple remote BSs so that improving the communication performance.

Low Earth Orbit (LEO) satellites have emerged as crucial enablers of direct connections with remote terrestrial terminals. However, energy limitations and insufficient antenna capabilities at the terminals often hamper these connections, resulting in inefficient communications and frequent ping-pong handovers. This paper proposes a Distributed Collaborative Beamforming (DCB)-based uplink communication paradigm for enabling ground-space direct communications. Specifically, DCB treats the terminals that are unable to establish efficient direct connections with the LEO satellites as distributed antennas, forming a virtual antenna array to enhance the terminal-to-satellite uplink achievable rates and durations. However, such systems need multiple trade-off policies that jointly balance the terminal-satellite uplink achievable rate, energy consumption of terminals, and satellite switching frequency to satisfy the scenario requirement changes. Thus, we formulate a long-term multi-objective optimization problem to optimize these goals simultaneously. To address availability in different terminal cluster scales, we reformulate this problem into an action space-reduced and universal Multi-Objective Markov Decision Process (MOMDP). Then, we propose an Evolutionary Multi-Objective Deep Reinforcement Learning (EMODRL) algorithm to obtain multiple policies, in which the low-value actions are masked to speed up the training process. Simulation results show that DCB enables terminals that cannot reach the uplink achievable rate threshold to achieve efficient direct uplink transmission. Moreover, the proposed algorithm outmatches various baselines and saves 30% handover frequency with a similar uplink achievable rate compared with the rate greedy method, which thus reveals that the proposed method is an effective solution for enabling direct ground-space communications.

Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT), where a UAV can use its limited service coverage to harvest and disseminate data from IoT devices with low transmission abilities. The existing UAV-assisted data harvesting and dissemination schemes largely require UAVs to frequently fly between the IoTs and access points, resulting in extra energy and time costs. To reduce both energy and time costs, a key way is to enhance the transmission performance of IoT and UAVs. In this work, we introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination from multiple IoT clusters to remote base stations (BSs). Except for reducing these costs, another non-ignorable threat lies in the existence of the potential eavesdroppers, whereas the handling of eavesdroppers often increases the energy and time costs, resulting in a conflict with the minimization of the costs. Moreover, the importance of these goals may vary relatively in different applications. Thus, we formulate a multi-objective optimization problem (MOP) to simultaneously minimize the mission completion time, signal strength towards the eavesdropper, and total energy cost of the UAVs. We prove that the formulated MOP is an NP-hard, mixed-variable optimization, and large-scale optimization problem. Thus, we propose a swarm intelligence-based algorithm to find a set of candidate solutions with different trade-offs which can meet various requirements in a low computational complexity. We also show that swarm intelligence methods need to enhance solution initialization, solution update, and algorithm parameter update phases when dealing with mixed-variable optimization and large-scale problems. Simulation results demonstrate the proposed algorithm outperforms state-of-the-art swarm intelligence algorithms and also show that the proposed method can reduce time and energy costs significantly compared with the benchmark strategies based on multi-hop and long-range flight.

In this paper, we investigate an unmanned aerial vehicle (UAV)-assistant air-to-ground communication system, where multiple UAVs form a UAV-enabled virtual antenna array (UVAA) to communicate with remote base stations by utilizing collaborative beamforming. To improve the work efficiency of the UVAA, we formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to simultaneously maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs by optimizing the positions and excitation current weights of all UAVs. This problem is challenging because these two optimization objectives conflict with each other, and they are non-concave to the optimization variables. Moreover, the system is dynamic, and the cooperation among UAVs is complex, making traditional methods take much time to compute the optimization solution for a single task. In addition, as the task changes, the previously obtained solution will become obsolete and invalid. To handle these issues, we leverage the multi-agent deep reinforcement learning (MADRL) to address the UCBMOP. Specifically, we use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB, where three techniques are introduced to enhance the performance. Simulation results demonstrate that the proposed algorithm can learn a better strategy compared with other methods. Moreover, extensive experiments also demonstrate the effectiveness of the proposed techniques.

Unmanned aerial vehicles (UAVs) are playing an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost. However, UAVs face the challenges of limited battery capacity and computing resources, which could shorten the expected flight endurance of UAVs and increase the rescue response delay during performing mission-critical tasks. To address these challenges, we first present a three-layer post-disaster rescue computing architecture by leveraging the aerial-terrestrial edge capabilities of mobile edge computing (MEC) and vehicle fog computing (VFC), which consists of a vehicle fog layer, a UAV client layer, and a UAV edge layer. Moreover, we formulate a joint task offloading and resource allocation optimization problem (JTRAOP) with the aim of maximizing the time-average system utility. Since the formulated JTRAOP is proved to be NP-hard, we propose an MEC-VFC-aided task offloading and resource allocation (MVTORA) approach, which consists of a game theoretic algorithm for task offloading decision, a convex optimization-based algorithm for MEC resource allocation, and an evolutionary computation-based hybrid algorithm for VFC resource allocation. Simulation results validate that the proposed approach can achieve superior system performance compared to alternative approaches, especially under heavy system workloads.

Unmanned aerial vehicles (UAVs) are playing a pivotal role in wireless networks due to their high mobility and on-demand deployment advantages. However, the UAV-enabled communications are susceptible to be wiretapped by eavesdroppers due to the strong line-of-sight (LoS) dominated air-ground channel. In this paper, we consider a UAV-enabled secure communication scenario, in which a group of UAVs form a UAV-enabled virtual antenna array (UVAA) to transmit information towards the remote base stations (BSs) via collaborative beamforming (CB), while multiple known and unknown eavesdroppers aiming to wiretap the information. Specifically, a secure communication multi-objective optimization problem (SCMOP) is formulated to achieve the maximization of the worst-case secrecy rate, the minimization of the maximum sidelobe level (SLL) as well as the minimization of the flight energy consumption of UAVs by obtaining optimal locations and excitation current weights concerning the UAVs as well as determining an optimal receiver BS that can achieve superior communication performance. To solve the formulated SCMOP which is demonstrated to be non-convex and NP-hard, an improved multi-objective salp swarm algorithm (IMSSA) with several specific operating factors is proposed. Simulations results demonstrate that the proposed IMSSA can deal with the formulated SCMOP effectively and outperforms other benchmark strategies. Moreover, the multi-hop relay is introduced to verify the reasonability of the UVAA system, and two benchmark schemes of the formulated SCMOP are introduced to demonstrate the necessity of the formulated SCMOP. In addition, the performance of the UVAA system under certain unexpected circumstances is estimated. Finally, experimental implementation is conducted by using a Raspberry Pi and the results demonstrate the practicality of the proposed CB-based secure communication approach in real-world scenarios.

Unmanned aerial vehicles (UAVs) as aerial relays are practically appealing for assisting the Internet of Things (IoT) network. In this work, we aim to utilize a UAV swarm to assist the secure communication between the micro base station (MBS) equipped with the planar antenna array (PAA) and the IoT terminal devices by collaborative beamforming (CB), so as to counteract the effects of the eavesdropper colluding in the time domain. Specifically, we formulate a UAV swarm-enabled secure relay multi-objective optimization problem (US*****RMOP) for simultaneously maximizing the achievable sum rate of the associated IoT terminal devices, minimizing the achievable sum rate of the eavesdropper and minimizing the energy consumption of UAV swarm, by jointly optimizing the excitation current weights of both MBS and UAV swarm, the selection of the UAV receiver, the position of UAVs and user association order of IoT terminal devices. Furthermore, the formulated US*****RMOP is proved to be a non-convex, NP-hard and large-scale optimization problem. Therefore, we propose an improved multi-objective grasshopper algorithm (IMOGOA) with some specific designs to address the problem. Simulation results exhibit the effectiveness of the proposed UAV swarm-enabled collaborative secure relay strategy and demonstrate the superiority of IMOGOA.

Unmanned aerial vehicles (UAVs) have been demonstrated to be a prominent component for wireless communications. In this work, we consider an emergency communication scenario wherein a UAV-based relay system collects data from ground users, and then uses different UAV-enabled virtual antenna arrays (UVAAs) to transmit the collected data to several remote base stations (BSs) via collaborative beamforming (CB). However, several adjacent aerial users (AUs) are carrying out other missions at the same time, which may be interfered by the signal transmitted by the UVAAs. Thus, we formulate a reliable and energy-efficient communication multi-objective optimization problem (RECMOP) to jointly maximize the minimum receiving signal-to-noise ratio (SNR) of the BSs, minimize the maximum average receiving SNR of the AUs, and minimize the propulsion power consumption of the UAVs, so that diminishing the energy cost while enhancing the system performance. The formulated RECMOP is intricate since it is proven to be NP-hard and non-convex. Therefore, an improved multi-objective gravitational search algorithm (IMOGSA) with several specific designs is proposed to handle the formulated problem. Simulation results manifest that the proposed IMOGSA can effectively solve the formulated RECMOP, and it outperforms other benchmarks in both smaller and larger scale UAV networks. Moreover, extended simulation demonstrates the robustness of the proposed CB-based approach under several unexpected circumstances.

Unmanned aerial vehicles (UAVs) are widely used as relay platforms to assist in maritime wireless communications. However, due to the open channel of UAV communications, security issue needs to be paid extra attention. This paper proposes a dual-UAVs jamming-aided system to implement physical layer encryption in maritime wireless communication. Specifically, one UAV acts as a relay to transmit data to the legitimate vessel and a set of UAVs can form a maritime UAV-enabled virtual antenna array (MUVAA) as a jammer to send jamming signals to the eavesdropper. In this system, an encrypted and energy-efficient maritime communication multi-objective optimization problem (EEMCMOP) is formulated to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the overall flight energy consumption of UAVs. Since the EEMCMOP is proven to be NP-hard, we propose an enhanced evolutionary computation algorithm with some upgraded characteristics to solve the problem. Simulation results indicate that the performance of the proposed algorithm is superior to other benchmark algorithms.

Unmanned aerial vehicles (UAVs) as the aerial relay become a highly desired scheme to assist terrestrial network. In this work, we intend to utilize the UAV swarm to assist the communication between the base station (BS) equipped with the planar array antenna (PAA) and the IoT devices by collaborative beamforming (CB). Specifically, we formulate an average achievable rate and energy bi-objective optimization problem (AREBOP) to improve the average achievable rate of IoT terminal devices and energy consumption of UAV swarm by jointly optimize the excitation current weights of BS and UAVs, the position of UAVs and user association order of IoT terminal devices. Moreover, the formulated AREBOP is proved to be NP-hard. Thus, we proposed an multi-objective grasshopper algorithm with specific initialization (MOGOASI) to solve this problem. Simulation results show the effectiveness of MOGOASI and illustrate that the performance of MOGOASI is superior compared to some benchmarks.

As the superior improvement on wireless network coverage, spectrum efficiency and energy efficiency, Intelligent reflecting surface (IRS) has received more and more attention. In this work, we consider a large-scale IRS-assisted heterogeneous cellular network (HCN) consisting of$K\ (K\geq 2)$tiers of base stations (BSs) and one tier of passive IRSs. With tools from stochastic geometry, we analyze the coverage probability and network spatial throughput of the downlink IRS-assisted$K$-tier HCN. Compared with the conventional HCN, we observe the significant gain achieved by IRSs in coverage probability and network spatial throughput. The proposed analytical framework can be used to understand the limit of gain achieved by IRSs in HCN.

The rapid development of the Internet-of-Things (IoT) also brings security and other problems. Device identification is a crucial tool for IoT security issues, which can detect and prevent cyber-attacks. Feature selection is an effective data preprocessing technique in IoT device identification, which can improve the performance of classification and reduce computational complexity. In this paper, we propose a novel wrapper feature selection approach based on the improved binary honey badger algorithm (IBHBA) to select features in IoT traffic. Four improved factors are employed in IBHBA to expand the search scope, balance the exploration and exploitation phases, and enhance the search capability. Moreover, a binary mechanism is adopted to make the algorithm more suitable for feature selection in IoT device identification. The experimental results on several real IoT traffic datasets denote that IBHBA outperforms some classical and latest comparison algorithms in the feature selection of IoT device identification.

Unmanned aerial vehicle (UAV) communications and networks are promising technologies in the forthcoming 5G/6G wireless communications. However, they have challenges for realizing secure communications. In this paper, we consider to construct a virtual antenna array consists UAV elements and use collaborative beamforming (CB) to achieve the UAV secure communications with different base stations (BSs), subject to the known and unknown eavesdroppers on the ground. To achieve a better secure performance, the UAV elements can fly to optimal positions with optimal excitation current weights for performing CB transmissions. However, this leads to extra motion energy consumption. We formulate a physical layer secure communication multi-objective optimization problem (MOP) of UAV networks to simultaneously improve the total secrecy rates, total maximum sidelobe level (SLL) and total motion energy consumption of UAVs by jointly optimizing the positions and excitation current weights of UAVs, and the order of communicating with different BSs. Due to the complexity and NP-hardness of the formulated MOP, we propose an improved multi-objective dragonfly algorithm with chaotic solution initialization and hybrid solution update operators (IMODACH) and a parallel-IMODACH (P-IMODACH) to solve the problem. Simulation results verify that the proposed approaches can effectively solve the formulated MOP and it has better performance than some other benchmark algorithms and approaches. Moreover, some unexpected circumstances are considered and discussed.

Unmanned aerial vehicles (UAVs) have the advantages of low-cost and flexible deployment. It can be used as the aerial transmitter in forests scenarios. However, forest terrain has severe multi-path reflections and may have several eavesdroppers, which degrade the performance of the communications. In this paper, we consider a collaborative UAV secure communication model under a forest channel, where multiple UAVs form a virtual antenna array (VAA) to communicate with a ground base station (BS) and circumvent the influence of the eavesdropper. Moreover, we formulate a multi-objective optimization problem (MOP) to simultaneously optimize the secure performance and energy efficiency of the considered system. Then, we propose an enhanced multi-objective particle swarm optimization algorithm (EMOPSO) which is based on chaos theory, linear weight reduction method and optimal set-based mutation approach to solve the formulated MOP. Simulation results show that the proposed method is effective and the proposed EMOPSO outperforms other benchmark algorithms.

Unmanned aerial vehicles (UAVs) Communications and networks are of paramount importance in the 5G/6G networks. However, how to deploy the limited on-board energy and restricted transmit power of the UAV so that enhancing the communication efficiency is still a key issue. In this work, we consider a UAV-enabled communication scenario that a set of UAVs perform a virtual antenna array (VAA) to communicate with different remote base stations (BSs) by using collaborative beamforming (CB). For achieving a better energy efficiency, we formulate a hovering and motion energy consumption multiobjective optimization problem (HMECMOP) of UAV-enable CB to simultaneously minimize the total hovering and motion energy consumptions of UAVs by jointly optimizing the positions, excitation current weights of UAVs and the order of communicating with different BSs. Then, we propose an improved multiobjective multi-verse optimizer (IMOMVO) to solve the formulated HMECMOP. IMOMVO uses the vertical and horizontal renewal strategy and nearest neighbor procedure (NNP) to deal with the complex solution space which contains continuous and discrete solutions, so that making the algorithm more suitable for solving the formulated optimization problem. Simulation results demonstrate that the proposed algorithm is effective for solving the HMECMOP and it has better performance than some other comparison algorithms.

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

Unmanned aerial vehicle (UAV) communications have a promising prospect in the next-generation network. Among various technologies, collaborative beamforming (CB) can enhance communication abilities and ranges. However, the CB method may be subject to interference issues in the physical en-vironment such as the wind and incorrect positioning, especially in outdoor conditions. In this paper, we aim to form a virtual antenna array (VAA) consists of UAV elements and simulate the interference of the physical environment on the positions of the UAV elements by considering positions errors. Then, we propose a model based on deep learning to predict the impact of errors on UAV-enabled VAA. Finally, aiming to repair the performance loss caused by the position errors of the UAV elements, we propose a performance recovery method by re-optimizing the excitation current weights of the UAV elements via the firefly algorithm (FA). Simulation results show that the position errors decrease the directivity and increase the maximum sidelobe level (SLL). Moreover, the proposed deep learning model can reflect the performance loss under different conditions. In addition, the proposed performance recovery method is effective.

Due to more flexible mobility, better line-of-sight (LOS) and faster on-demand deployment, unmanned aerial vehicles (UAVs) play a unique role for assisting post-disaster rescues, which often require UAVs to perform computationintensive rescue missions. However, UAVs generally have inherent limited computational capacity and battery storage, which makes it challenging to complete the heavy computing tasks within short period of time during the complicated postdisaster recovery. To overcome this issue, we introduce the vehicular fog computing (VFC) system in which a UAV splits and assigns the heavy tasks to the ground vehicles. First, to evaluate the performance of the VFC-assisted UAV network task offloading, the task processing latency and energy consumption are incorporated into a system utility construction. Moreover, we propose a joint UAV and vehicular task assignment scheme (JUVTAS) with the aim of optimizing the performance of the network. Specifically, we propose a genetic algorithminvasive weed optimization (GA-IWO) algorithm to achieve the approximately optimal task assignment strategy. The GA-IWO algorithm combines the global search ability of genetic algorithm and the local search ability of invasive weed optimization to achieve a better optimization performance. Simulation results show that the proposed JUVTAS is able to effectively reduce the latency and energy consumption for task processing. Moreover, JUVTAS achieves superior performance compared to several conventional methods.

In recent years, the dramatic increase in vehicles and the limited resources of VEC servers make it challenging for vehicles to execute intensive and sensitive tasks on the local own CPU. The mobile edge computing (MEC) is viewed as a promising paradigm by deploying the cloud resources on roadside road side units (RSU). However, compared to cloud server, MEC servers have limited resources. Moreover, the vehicular tasks with different priorities have different requirements on the edge resources. In this work, we propose a priority -aware collaborative task offloading and resource allocation approach for vehicular edge computing networks (VECN). Specifically, we propose a variant grey wolf optimizer (VGWO) algorithm for resource optimization and a dynamic task offloading strategy (DOS) algorithm for task offloading. Simulation results show that the proposed VGWO algorithm outperforms the basic swarm intelligence optimization algorithm, and the collaborative offloading method is able to effectively reduce the task processing latency and energy consumption.

Automated guided vehicle (AGV) communications and networks have attracted extensive attention and have broad prospects in the field of wireless transmission. Unmanned aerial vehicle (UAV) can be used as flight base station (BS) to receive information from the ground AGVs. In this paper, we study an AGV-UAV communication scenario, in which a group of AGVs form an AGV-based virtual antenna array (AVAA) and communicate with different UAVs based on distributed collaborative beamforming (DCB). We formulate an AGV-UAV communication multi-objective optimization problem (AUCMOP) to simultaneously maximize the total transmission rate, minimize the total repositioning time of AGVs and minimize the total motion energy consumptions of AGVs by optimizing the positions, excitation current weights and moving speeds of AGVs, as well as the sequence for communicating with different UAVs. The formulated AUCMOP is complicated, and thus we propose an improved multi-objective ant lion optimization algorithm with Chebyshev chaos-opposition based learning solution initialization and hybrid solution update method (IMOALOCH). Simulation results show that the proposed IMOALOCH can solve the formulated AUCMOP effectively and it is superior to other comparison algorithms.

Feature selection aims at reducing the number of attributes while achieving a high classification accuracy in machine learning. In this paper, we design a fitness function to jointly reduce the number of the selected features and enhance the accuracy. Then, we propose a chaotic binary dragonfly algorithm (CBDA) with several improved factors on the conventional dragonfly algorithm (DA) for developing a wrapper-based feature selection method to solve the fitness function. Specifically, the CBDA introduces three improved factors that are the chaotic map, evolutionary population dynamics mechanism and binarization strategy to make the algorithm more suitable for the problem. Experiments are conducted to evaluate the performance of the proposed CBDA on 24 well-known data sets from the UCI repository, and the results demonstrate that the proposed CBDA outperforms other comparative algorithms on the majority of the tested data sets.

Automated guided vehicle (AGV) has the advantages of high endurance, autonomy and security, which render them appealing for applications such as monitoring and sensing networks. However, AGVs may be limited in energy and transmission range. Unmanned Aerial Vehicle (UAV) is a promising platform that can assist terrestrial networks. In this work, we aim to adopt a UAV swarm to assist the data forwarding of AGVs and propose a novel data transmission framework based on collaborative beamforming (CB) where the AGVs and UAVs jointly construct a virtual antenna array (VAA) to transmit data to the remote air base stations (ABSs). Specifically, we formulate a terrestrial and air collaboratively data transmission multiobjective optimization problem (TATFMOP) to optimize the excitation current weights and locations of the AGVs and UAVs, and the communication order of different remote ABSs. Since TATFMOP is an NP-hard problem, we present an extended multiobjective ant-lion optimization (EMOALOIB) with opposition-based population initialization and black hole update operators to solve the problem. Simulations results demonstrate that the proposed EMOALOIB outperforms other existing benchmark algorithms and can obtain more valuable solutions.

Due to the flexibility and high line-of-sight (LoS) probability, unmanned aerial vehicles (UAVs) can play an important role in 5G/6G networks. In this work, we study a novel air-to-air (A2A) communication scheme, in which two UAV swarms perform two UAV-enabled virtual antenna arrays (UVAAs) for exchanging data by using collaborative beamforming (CB). In order to improve the transmission efficiency and save the energy consumptions of the UAVs, we formulate an A2A communication multi-objective optimization problem (A2ACMOP) to simultaneously enhance the duplex transmission rates and reduce the total energy consumptions of the UAV swarms by deploying the UAVs and adjusting their excitation current weights. Due to the complexity and NP-hardness of the formulated A2ACMOP, we propose an improved non-dominated sorting genetic algorithm-III (INSGA-III) with opposition-based learning solution initialization and hybrid solution update operators to solve the problem. Simulation results verify that the proposed INSGA-III can effectively solve the formulated A2ACMOP and it has better performance than some other benchmark strategies.

Wireless rechargeable sensor networks with a charging unmanned aerial vehicle (CUAV) have broad application prospects in the power supply of the rechargeable sensor nodes (SNs). However, how to schedule a CUAV and design the trajectory to improve the charging efficiency of the entire system is still a vital problem. In this article, we formulate a joint-CUAV scheduling and trajectory optimization problem (JSTOP) to simultaneously minimize the hovering points of CUAV, the number of the repeatedly covered SNs, and the flying distance of CUAV for charging all SNs. Due to the complexity of JSTOP, it is decomposed into two optimization subproblems that are CUAV scheduling optimization problem (CSOP) and CUAV trajectory optimization problem (CTOP). CSOP is a hybrid optimization problem that consists of the continuous and discrete solution space, and the solution dimension in CSOP is not fixed since it should be changed with the number of hovering points of CUAV. Moreover, CTOP is a completely discrete optimization problem. Thus, we propose a particle swarm optimization (PSO) with a flexible dimension mechanism, a$K$-means operator, and a punishment-compensation mechanism (PSOFKP) and a PSO with a discretization factor, a 2-opt operator, and a path crossover reduction mechanism (PSOD2P) to solve the converted CSOP and CTOP, respectively. Simulation results evaluate the benefits of PSOFKP and PSOD2P under different scales and settings of the network, and the stability of the proposed algorithms is verified.

Unmanned-aerial-vehicle (UAV)-aided wireless communication in Internet of Things (IoT) applications is becoming the focus of attention of researchers. This article investigates a UAV-assisted communication system for serving IoT, in which multiple rotary-wing UAVs are employed to communicate with multiple terrestrial IoT devices. Specifically, we formulate a UAV deployment multiobjective optimization problem (UAVDMOP) to simultaneously maximize the minimum throughput of the UAV–device pairs, maximize the total throughput of the whole system, and minimize the total energy consumptions of UAVs via a joint optimization of the locations of UAVs, transmission power of UAVs, and association relationship between the UAVs and IoT devices. UAVDMOP consists of both discrete and continuous solution spaces, which is difficult to be solved. Thus, we propose an improved discrete and continuous multiobjective evolutionary algorithm based on decomposition (IDCMOEA/D) with a hybrid solution initialization operation and a hybrid solution reproduction operation for increasing the performance of the algorithm so that making it more suitable for dealing with the UAVDMOP. Simulation results demonstrate that the proposed method is effective to enhance the throughput and energy efficiency of the system and it has superior performance compared to other methods.

Unmanned aerial vehicle (UAV) is a promising communication platform to assist terrestrial networks. In this work, we aim to provide relay communication to the blocked or low-quality terrestrial networks via an aerial relay. Nevertheless, major issues of the considered system are the worrying security and limited service time. Thus, we study a novel aerial relay system via collaborative beamforming (CB) by exploiting a UAV-enabled virtual antenna array (UVAA) to achieve a secure and energy-efficient communication for remote ground users (GUs). Specifically, we formulate a secure and energy-efficient communication multi-objective optimization problem (SECMOP) to circumvent the effects of the known and unknown eavesdroppers and minimize the propulsion energy consumption of UAVs, by optimizing the hovering positions and excitation current weights of UAVs and the scheduling for communicating with the remote GUs. The formulated SECMOP is challenging and proven to be NP-hard. Thus, we propose an improved evolutionary computation method with several enhanced designs to solve this problem. Simulation results demonstrate the benefits of the proposed IMODAOM against various benchmark algorithms. Moreover, we find that the UVAA-based relay can achieve substantial energy consumption reduction as compared to the multi-hop relay scheme.

Unmanned aerial vehicles (UAVs) have attracted growing attention in enhancing the performance of mobile wireless sensor networks (MWSNs) since they can act as the aerial base stations (ABSs) and have the autonomous nature to collect data. In this paper, we consider to construct a virtual antenna array (VAA) consists of mobile sensor nodes (MSNs) and adopt the collaborative beamforming (CB) to achieve the long-distance and efficient uplink data transmissions with the ABSs. First, we formulate a high data transmission rate multi-objective optimization problem (HDTRMOP) of the CB-based UAV-assisted MWSN to simultaneously improve the total transmission rates, suppress the total maximum sidelobe levels (SLLs) and reduce the total motion energy consumptions of MSNs by jointly optimizing the positions and excitation current weights of MSN-enabled VAA, and the order of communicating with different ABSs. Then, we propose an improved non-dominated sorting genetic algorithm-III (INSGA-III) with chaos initialization, average grade mechanism and hybrid-solution generate strategy to solve the problem. Simulation results verify that the proposed algorithm can effectively solve the formulated HDTRMOP and it has better performance than some other benchmark methods.

Unmanned aerial vehicle (UAV) communications and networks are promising technologies in the forthcoming fifth-generation wireless communications. However, they have the challenges for realizing secure communications. In this paper, we consider to construct a virtual antenna array consists UAV elements and use collaborative beamforming (CB) to achieve the UAV secure communications with different base stations (BSs), subject to the known and unknown eavesdroppers on the ground. To achieve a better secure performance, the UAV elements can fly to optimal positions with optimal excitation current weights for performing CB transmissions. However, this leads to extra motion energy consumptions. We formulate a secure communication multi-objective optimization problem (MOP) of UAV networks to simultaneously improve the total secrecy rates, total maximum sidelobe levels (SLLs) and total motion energy consumptions of UAVs by jointly optimizing the positions and excitation current weights of UAVs, and the order of communicating with different BSs. Due to the complexity and NP-hardness of the formulated MOP, we propose an improved multi-objective dragonfly algorithm with chaotic solution initialization and hybrid solution update operators (IMODACH) to solve the problem. Simulation results verify that the proposed IMODACH can effectively solve the formulated MOP and it has better performance than some other benchmark approaches.

Unmanned aerial vehicles (UAVs) are usually resource constrained, and have the limited communication and energy storage capacity. Collaborative beamforming (CB) in UAV networks based on a virtual node antenna array (VNAA) can enhance the signal-to-noise-ratio (SNR) and energy efficiency of a single UAV node. The UAV nodes can move to better locations for constructing the VNAA to achieve a maximum SNR of CB. However, this will result in an extra motion energy consumption. In this paper, we formulate a joint optimization problem to simultaneously optimize the received SNR and motion energy consumption of UAVs for CB. Then, a mended particle swarm optimization with weed optimization mechanism (PSOWOM) algorithm is proposed to solve the formulated joint optimization problem. Simulation results verify the effectiveness of the proposed algorithm.

Unmanned aerial vehicle (UAV) communications and networks are of utmost concern. However, they have challenges such as the limited on-board energy and restricted transmit power. In this paper, we study a UAV-enabled communication scenario that a set of UAVs perform a virtual antenna array (VAA) to communicate with different remote base stations (BSs) by using collaborative beamforming (CB). To achieve a better transmission performance, the UAV elements can fly to optimal positions by using optimal speeds and adjust to optimal excitation current weights for performing CB transmissions. However, there are some trade-offs between energy consumption and transmission performance. Thus, we formulate a time and energy minimization communication multi-objective optimization problem (TEMCMOP) of CB in UAV networks to simultaneously minimize the total transmission time, total performing time of VAAs and total motion and hovering energy consumptions of UAVs by jointly optimizing the positions, flight speeds and excitation current weights of UAVs, as well as the order of communicating with different BSs. Due to the complexity and NP-hardness of the formulated TEMCMOP, we propose an improved multi-objective ant lion optimization (IMOALO) algorithm with chaos-opposition based learning solution initialization and hybrid solution update operators to solve the problem. Simulation results verify that the proposed IMOALO can effectively solve the formulated TEMCMOP and it has better performance than some other benchmark approaches.