Search Results (703)

The integration of unmanned aerial vehicles (UAVs) as aerial base stations has emerged as a key enabler for next-generation wireless networks, particularly in disaster recovery, temporary events, and infrastructure-deficient regions. However, multi-UAV deployments introduce severe co-channel interference due to spectrum reuse and overlapping coverage areas, while existing spectrum allocation methods either rely on centralized optimization with limited scalability or on reinforcement learning frameworks that lack spatial awareness of interference sources. To address these challenges, this paper proposes a Hybrid DeepMUSIC-assisted Cooperative Multi-Agent Deep Reinforcement Learning (MADRL) framework for intelligent spectrum allocation and interference management in multi-UAV 6G networks. The proposed framework integrates a hybrid interference localization module, which fuses the classical MUltiple SIgnal Classification (MUSIC) algorithm with a deep neural network to accurately estimate the direction of arrival (DoA) of interference sources, into a DeepMUSIC-enhanced state representation used by cooperative Deep Q-Network (DQN) agents trained under a Centralized Training and Decentralized Execution (CTDE) paradigm, enabling coordinated yet fully distributed spectrum allocation decisions. Extensive simulations demonstrate that the proposed Hybrid DeepMUSIC module reduces the mean DoA estimation error to approximately 0.105°, more than an order of magnitude better than classical MUSIC and standalone DeepMUSIC estimators. Compared with seven baseline algorithms spanning heuristic, optimization-based, single-agent, and cooperative multi-agent reinforcement learning approaches, the proposed framework achieves the highest network throughput, SINR, spectrum efficiency, and energy efficiency, together with the fastest and most stable training convergence, reaching a stable cooperative reward of 76.246 within approximately 371 training epochs. The framework further maintains near-linear computational scaling with the number of UAV agents, confirming its suitability for real-time deployment in dense, AI-native multi-UAV 6G wireless communication systems. Full article

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a practical means of providing computation and communication support for geographically dispersed Internet of Things (IoT) terminals. However, the broadcast nature of wireless links makes offloading data vulnerable to cooperative eavesdropping. Moreover, the limited onboard energy of UAVs and the latency-sensitive characteristics of MEC services lead to a challenging trade-off between the secrecy rate, energy consumption, and latency. To address this issue, we formulate a utility maximization problem to jointly optimize UAV trajectory and task-offloading decisions in UAV-assisted MEC systems against multiple eavesdroppers. Due to the strong coupling among optimization variables and the non-convexity of the problem, an enhanced twin-delayed deep deterministic policy gradient (TD3) framework integrating Hindsight Experience Replay (HER) and Prioritized Experience Replay (PER) is proposed to improve convergence efficiency and learning stability. Furthermore, a system utility-driven reward function is designed to balance the secrecy rate, energy consumption, and processing latency under different application requirements. The simulation results demonstrate that the proposed approach consistently outperforms DDQN, DDPG, and conventional TD3 in terms of system utility, secrecy performance, convergence speed, and adaptability to different scenarios. Full article

Rapid, reliable, and energy-efficient data collection is essential for disaster response, where terrestrial communication networks may be disrupted or unavailable. Unmanned Aerial Vehicles (UAVs) provide a flexible means of collecting critical sensing data, but their operation is constrained by limited onboard energy, stochastic wireless conditions, complex three-dimensional environments, and stringent latency requirements. This paper presents a structured multi-UAV framework that separates mission optimisation into spatial, temporal, and safety layers. In the spatial layer, a three-dimensional Travelling Salesman Problem with Neighbourhoods (3D-TSPN) formulation enables UAVs to collect data by entering valid sensing regions rather than visiting exact sensor coordinates. An Age of Information (AoI)-aware Genetic Algorithm (GA) optimises the sensor-visitation sequence, while Rapidly Exploring Random Tree Connect (RRT-Connect) generates obstacle-aware feasible paths in the three-dimensional environment. In the temporal layer, a Lyapunov-based controller selects between local processing and binary offloading to a single Mobile Edge Computing (MEC) node according to queue backlog, processing delay, energy consumption, information freshness, wireless-link feasibility, and task deadlines. In the safety layer, continuous-time conflict detection and bounded temporal or spatial adjustments are used to monitor and mitigate inter-UAV and obstacle-related risks. The framework is evaluated under stochastic wireless, mobility, computation, and obstacle conditions using 20 independent random seeds. Across the corresponding 20 proposed-policy runs, it achieves a 100% mission-validity rate, complete sensor coverage, no dropped tasks, and zero final collision or near-miss events. Compared with planning-oriented and MEC-oriented baselines, the proposed framework achieves lower information age, average delay, processing delay, energy consumption, and system cost under the evaluated conditions, while maintaining reliable multi-UAV coordination. The layered design also clarifies the contribution of each component: 3D-TSPN provides spatial flexibility, the AoI-aware GA improves route sequencing, RRT-Connect supports obstacle-aware path feasibility, Lyapunov control enables queue-aware processing decisions, and safety monitoring supports coordinated multi-UAV operation. These results indicate that integrating spatial planning, computation control, and safety coordination within a clearly separated layered architecture can provide an effective solution for multi-UAV disaster-response data collection in complex three-dimensional environments. Full article

Unmanned Aerial Vehicles (UAVs) have emerged as a flexible, cost-effective solution for connecting Internet of Things (IoT) devices where traditional infrastructure falls short. However, managing their limited energy alongside the diverse demands of densely deployed devices makes resource allocation a genuinely hard problem. This paper presents a Deep Reinforcement Learning (DRL) framework that jointly optimizes user scheduling, IoT device transmit power, bandwidth, and UAV movement in a 6G-enabled UAV-relay uplink network, using a deterministic large-scale air-to-ground path-loss channel model. The UAV acts as an aerial decode-and-forward relay between IoT devices and a Base Station (BS), with a Deep Q-Network (DQN) making decisions based on queue backlogs, channel conditions, UAV position, and remaining battery. The reward function balances Energy Efficiency (EE), queue stability, fairness, and battery longevity. We benchmark the DQN against six baselines; Round Robin (RR), Random Allocation (RA), the Single-to-Noise Ratio (Max-SNR), Proportional Fair (PF), a Lyapunov heuristic, and a GreedyEE scheme; across a range of device counts, traffic loads, battery budgets, and flight altitudes. Simulations consistently show that the DQN outperforms all baselines, including a RA baseline with equal access to UAV mobility; in EE, throughput, delay, and fairness, confirming that the gain stems from the learned joint control policy rather than from UAV mobility being available. Full article

Infrared defects in photovoltaic (PV) modules captured during unmanned aerial vehicle (UAV) inspection are typically small and low-contrast, while onboard edge platforms are constrained by memory, logic resources, and power consumption. To address these challenges, this paper proposes KD-PH-YOLO for PV infrared defect detection and deployment on the Zynq-7020 platform. Based on YOLOv8, PH-YOLO removes redundant deep-layer computation and introduces a P2 detection head to preserve fine-grained information for small defects. Hardware-friendly Weighted Feature Fusion (HWFF) and Lightweight Attention-CBAM (LA-CBAM) are incorporated to enhance multiscale feature fusion and defect responses. Soft-label and multiscale feature distillation are further employed to improve the lightweight student model without increasing inference complexity. For edge deployment, INT8 quantization and hardware-aware acceleration are applied to map the model onto the Zynq-7020. Experimental results show that KD-PH-YOLO achieves an mAP@0.5 of 92.4% with only 1.48 M parameters and 6.9 GFLOPs. After hardware deployment, the model retains an mAP@0.5 of 91.8%. The Zynq-7020 implementation achieves an average latency of 184.3 ms per frame and a throughput of 5.43 FPS, with power consumption of 3.2 W and an energy efficiency of 1.7 FPS/W. The proposed method therefore provides a favorable accuracy–complexity–energy-efficiency trade-off for resource-constrained PV edge inspection. Full article

Integrating uncrewed aerial vehicles (UAVs) and high-altitude platform stations (HAPS) into terrestrial cellular wireless networks is central to emerging sixth-generation (6G) communication systems. However, the energy budgets of both the ground network and the power-constrained aerial platforms are a serious concern. Cell switching, which selectively places lightly loaded small base stations (SBSs) into sleep mode, is an important energy-saving mechanism, but existing feasibility criteria are typically based either on maintaining a target data rate or merely preserving coverage for the users originally served by the switched-off SBSs (i.e., displaced users). These two approaches represent opposite ends of the quality-energy tradeoff: rate-based policies require a high signal-to-interference-plus-noise ratio (SINR), limiting energy savings, whereas coverage-based policies permit more aggressive sleeping at the expense of quality of service (QoS). This work proposes a novel semantic-aware cell switching policy whose feasibility criterion is a semantic-service requirement rather than a bit-centric target: an SBS is put into a sleep mode if and only if its displaced users still satisfy an assumed semantic-service SINR criterion. The policy is tested in a multi-tier heterogeneous network comprising an always-on macro base station (MBS), switchable SBSs, a tier of UAV base stations (UAV-BSs), and a HAPS-mounted International Mobile Telecommunications (IMT) base station (HIBS), and it is solved by a greedy algorithm. For the deployment and parameter set considered, and with the HIBS present, the semantic criterion deactivates the entire SBS layer while every user continues to satisfy the assumed semantic-service SINR criterion, whereas the rate criterion deactivates none. Full article

Cross-view geo-localization (CVGL) between unmanned aerial vehicle (UAV) imagery and satellite imagery is a key technique for autonomous UAV navigation in Global Navigation Satellite System (GNSS)-denied environments. However, most existing methods rely on energy-intensive Artificial Neural Networks (ANNs), making them difficult to deploy on resource-constrained edge computing platforms. Spiking Neural Networks (SNNs) provide a promising alternative for energy-efficient inference, but their application to CVGL still faces two challenges that remain insufficiently addressed. First, the isotropic computation used by existing SNN backbones is mismatched with the directional characteristics of spike activations. Spike activations tend to form oriented aggregation patterns along elongated geographic structures, and isotropic computation can therefore dilute directional signals. Second, the limited representational capacity of SNNs further increases the sensitivity during training optimization. However, the standard triplet loss adopts a static weighting strategy and assigns the same weight to all triplets that violate the margin constraint, which is unfavorable for learning from hard negatives. To address these challenges, we propose a framework with two core contributions. At the feature extraction level, the Directional Adaptive Convolution Module (DACM) processes spike feature maps by sequentially performing horizontal strip convolution and vertical strip convolution, thereby capturing a more complete geometric structure of directional spike clusters. At the training supervision level, we propose a Dual-dimensional Progressive Reweighting (DPR) loss, which jointly characterizes sample difficulty from pairwise difficulty and positive-pair quality difficulty. A learnable fusion parameter is used to adaptively balance these two types of difficulty information. Experimental results on the University-1652 and SUES-200 benchmarks show that the proposed framework, when equipped with the same representation learning head as its ANN counterparts, achieves competitive and, in many settings, superior performance. In terms of energy efficiency, its estimated theoretical energy consumption is over $8.8\times$ lower than that of published ANN methods under their original configurations. Under a more rigorous matched ANN control that shares the identical architecture, the estimated energy is reduced from 29.84 mJ to 6.36 mJ, an approximately $4.7\times$ reduction obtained at a cost of only 2.29 percentage points in R@1. Full article

The automatic inspection of airport infrastructure, specifically horizontal surface markings and Airfield Ground Lighting (AGL), is a critical task for maintaining aviation safety and operational efficiency. As the aviation industry shifts from manual surveys to automated visual inspections utilizing unmanned aerial vehicles (UAVs) and smart service vehicles, the demand for robust, real-time computer vision systems has surged. However, deploying computationally intensive deep learning models in the field introduces severe Size, Weight, and Power (SWaP) constraints. This paper presents a comprehensive framework for the semantic segmentation of runway/taxiway markings and the point-localization of AGL lamps, specifically focusing on the deployment paradigm shift from the expensive, GPU-accelerated heterogeneous system on chip (SoC) to highly efficient, dedicated Neural Processing Units (NPUs). We evaluate the performance of U-Net, LinkNet, U-Net-Point and HRNet-Lite-Point architectures trained on a custom dataset from the Poznań-Ławica Airport. Crucially, this study conducts a rigorous comparative hardware analysis between the flagship NVIDIA Jetson AGX Orin and a highly cost-effective heterogeneous setup comprising a Raspberry Pi 5 augmented with an NPU Hailo-8 AI accelerator. Experimental results demonstrate that while both platforms achieve real-time inference, the Hailo-8 integration fundamentally disrupts the traditional cost-to-performance ratio. Furthermore, the Hailo-8 configuration consumed less electrical power and memory footprint required by the Jetson, proving that dedicated NPUs are vastly superior for continuous, battery-operated edge deployment in autonomous airport maintenance systems. Specifically, the Raspberry Pi setup with the Hailo-8 accelerator demonstrated superior energy efficiency, requiring a significantly lower energy consumption per processed video frame compared to the Jetson platform. Full article

Despite the rapid proliferation of unmanned aerial vehicles (UAVs) across industrial, agricultural, and scientific domains, their deployment remains constrained by limited endurance, aerodynamic inefficiency, and acoustic emissions, all mediated by a shared onboard energy budget. Existing biomimetic UAV reviews have generally treated aerodynamics, structures, sensing, control, and energy systems as parallel topics rather than as interacting components of a unified aerial architecture. Drawing primarily on literature published between 2015 and June 2026 and identified through searches of Web of Science, Scopus, and Google Scholar, this review addresses this gap by examining bio-inspired technologies across six principal domains: aeroacoustic and passive flow control, aerodynamic efficiency, multifunctional structural composites, neuromorphic sensing and control, ionic energy storage, and energy harvesting. Its principal contribution is a cross-domain synergy analysis identifying five performance couplings and one structural enabling architecture through which these domains interact physically and functionally. Representative examples include serration-based propeller geometries that can simultaneously reduce noise and power demand; morphing wing surfaces that serve as both aerodynamic structures and triboelectric harvesting substrates; and neuromorphic spiking neural networks that have been reported, in specific event-vision inference benchmarks, to reduce inference energy by three to four orders of magnitude relative to embedded graphics processing unit (GPU)-based implementations. Mechanical harvesting outputs nonetheless remain orders of magnitude below propulsion requirements and are thus positioned as supplementary. Four systemic barriers (unquantified mass–energy balance, undocumented durability, aeroelastic co-design gaps, and heterogeneous metrics) are evaluated, and the resulting synthesis indicates that advancing bio-inspired UAVs requires a transition from structural imitation to functional, system-level biomimetics. Full article

Large-scale maritime activity, persistent shipping incidents, and complex marine environments continue to place substantial demands on maritime search and rescue (MSAR). Current MSAR systems do not fully capitalize on the complementary strengths of unmanned aerial vehicles (UAVs) and satellites for collaborative tracking and rescue support. Existing air-space collaboration technologies suffer from two critical limitations: (1) rigid processes, including fixed task allocation, pre-determined path planning without real-time environmental adaptation, and isolated satellite–UAV decision-making, and (2) long task completion cycles, mainly because many methods are adapted to wide-area, long-duration military tracking scenarios. They therefore provide limited support for the dynamic flexibility required in MSAR. This study proposes a Proximal Policy Optimization (PPO)-based air-space collaborative tracking method for maritime moving targets to address these shortcomings and enhance air-space cooperation in MSAR operations. The core implementation of the method includes: (1) integration of target drift forecasting, satellite orbit prediction, UAV task allocation, and path planning into a unified reinforcement learning framework to reduce isolated single-platform decision-making; (2) the adoption of PPO to generate dynamic and flexible air-space collaborative tracking strategies that adjust satellite observation angles and scanning ranges, as well as UAV altitude, speed, and heading according to real-time target, environmental, and platform states; and (3) the design of a multi-dimensional reward function that balances target proximity, energy efficiency, coverage overlap, and inter-platform cooperation to guide strategy optimization. Simulation experiments include system-feasibility verification, baseline-controller comparison, PPO hyperparameter screening, and cross-scenario evaluation. Under idealized communication and payload-matching assumptions, the method enables coordinated tracking of maritime moving targets in simulated MSAR scenarios. In the standardized evaluation, PPO achieved an 11.9% higher mean evaluation episode return, 11.2% lower aggregate UAV energy consumption, and a 9.92-percentage-point greater endurance margin than DDPG. Hyperparameter screening compared candidate learning rates, discount factors, and training budgets, informing the PPO configuration for the subsequent six-scenario evaluation. Across the six controlled scenarios, rewards stabilized after approximately 1400 steps, while action magnitudes varied among regions. These results indicate that the proposed method has potential to enhance air-space collaborative tracking for MSAR decision support. Full article

Owing to its inherent flight safety, low takeoff and landing requirements, and favorable economic efficiency, the autogyro UAV, especially its electric and hybrid-electric variants, has become a core platform for low-altitude aviation missions such as transportation, inspection, and surveillance in plateau and offshore regions. However, the low air density and low Reynolds number conditions encountered in plateau regions can induce aerodynamic issues such as premature laminar flow separation, dynamic stall, and increased induced drag, which directly reduce payload capacity and endurance of small electric autogyro UAVs. In offshore environments, strong winds, turbulence, and gust disturbances intensify rotor–wake interactions, cause abrupt variations in aerodynamic loads, and reduce control margins, which severely restricts the mission reliability and flight safety of low-altitude unmanned platforms. These environmental effects collectively degrade rotor performance, including reduced aerodynamic efficiency and insufficient lift generation, and further amplify the energy constraint of electric/hybrid-electric propulsion systems. In response to bottlenecks that restrict the practical application of autogyro UAVs in extreme environments, this paper systematically reviews research progress on lift vector control for autogyro UAV rotors operating under such conditions. First, the typical aerodynamic problems encountered by autogyro UAVs in plateau and offshore environments are summarized, and their underlying physical mechanisms are analyzed from both system-level and local-flow perspectives, with a focus on how environmental factors affect the autorotation stability of unmanned platforms. Subsequently, the development of passive lift vector control technologies is reviewed, with an emphasis on the aerodynamic benefits of passive pitch mechanisms, vortex generators, and blade-tip winglets, as well as their engineering feasibility for small autogyro UAV blades. Active lift vector control technologies are then examined, including air-jet flow control, synthetic jets, and trailing-edge flaps, with discussions of their potential to delay flow separation and stall, enhance rotor aerodynamic efficiency, and an assessment of their adaptability to the energy and structural constraints of unmanned platforms. Finally, a lift vector control strategy suitable for autorotating rotors of autogyro UAVs is proposed, based on careful consideration of energy consumption, structural constraints, and control effectiveness. It provides a reference for aerodynamic optimization and flight control research on electric and hybrid-electric autogyro UAVs operating in extremely low-altitude environments. Full article

Flying Ad Hoc Networks let unmanned aerial vehicles communicate directly, without relying on fixed ground infrastructure, in time-critical settings such as disaster response, search and rescue, border surveillance, precision agriculture, and military reconnaissance. UAV mobility, however, causes frequent topology changes and intermittent connectivity, so nodes typically store data locally and replicate it across the network to keep it available. The resulting challenge is consistency: independently evolving copies must be reconciled without a central coordinator, and strong consistency is not realistic in a network this prone to partitioning. We address this with the Decentralized Mobile Reconciliation Protocol (DMRP), which provides eventual consistency among UAV nodes with no external coordination, with convergence formally guaranteed whenever the swarm’s synchronisation graph is eventually connected. DMRP combines immediate local validation and convergence guarantees grounded in conflict-free replicated data type properties; hysteresis-based memory management with dual thresholds to cap journal storage overhead; adaptive delta or full-state synchronisation based on receiver lag; and epidemic propagation for transitive update dissemination. Energy efficiency guided the design throughout, through wireless broadcast and the avoidance of redundant transmissions. DMRP was implemented and evaluated through extensive OMNeT++/INET simulations of three-dimensional FANET scenarios. Results demonstrate that the protocol maintains a strictly bounded reconciliation journal, whereas the reference $\delta$-CRDT log grows without bound, reducing reconciliation-journal storage by up to 75% at the largest workload evaluated, while achieving near-complete consistency after node isolation and network partitioning. Full article

Multi-agent reinforcement learning has emerged as a promising paradigm for cooperative unmanned aerial vehicle (UAV) swarm coordination. However, existing communication-aware MARL methods primarily focus on communication topology, message routing, and message aggregation, while the information content of the exchanged messages is often only implicitly controlled. In realistic UAV networks, inter-agent communication is constrained by limited bandwidth, communication range, energy consumption, and packet loss. It is therefore desirable for each UAV to transmit compact and task-relevant information rather than dense and redundant latent features. In this paper, we propose IB-CEMARL, an information-bottleneck-guided, communication-efficient multi-agent reinforcement learning framework for UAV swarms. We formulate inter-UAV communication as a minimal sufficient message-learning problem in which each UAV encodes its local observation into a stochastic bottleneck message before exchanging information with its neighbors. Cauchy–Schwarz divergence-based quadratic mutual information is adopted as a unified dependence measure to jointly regularize message compression, preserve decision-relevant information, and reduce statistical redundancy among neighboring UAV messages. Extensive experiments demonstrate that IB-CEMARL achieves superior cooperative performance, reduced message redundancy, and stronger robustness compared with representative communication-aware MARL baselines. In particular, IB-CEMARL improves the average return by $4.9\%$ and reduces inter-message dependence by $29.0\%$ compared with the KL-IB-MARL baseline while maintaining efficient communication under constrained bandwidth settings. Full article

Unmanned Aerial Vehicles (UAVs) are used in numerous practical applications in industry, science, and ecology; however, the large-scale use of UAVs is hampered by the limited onboard energy capacity. Increasing the energy efficiency of UAVs has thus become one of the most important tasks in UAV research. This review examines approaches to energy optimization of UAVs using algorithms and artificial intelligence (AI). It evaluates and compares algorithms and methods used to optimize energy efficiency of trajectory planning, adaptive speed control, battery management in mission planning, and navigation that accounts for environmental characteristics. It differs from those focusing on hardware solutions by highlighting optimization problems where energy usage is considered the key target for optimization and not a limiting constraint. The analyzed methods have been categorized into five groups, including classical optimization, metaheuristics, machine learning (ML), reinforcement learning (RL), and hybrid approaches. Key approaches such as RL, Model Predictive Control, evolutionary algorithms, and data-driven energy modeling have been outlined and compared with regard to energy-model accuracy, type of validation, scalability, and deployment readiness. Additionally, it emphasizes practical aspects such as the accuracy of energy modeling, real-time capabilities, scalability to multiple UAVs, and robustness to environmental uncertainty. Finally, this review provides directions for future research that will help develop sustainable, intelligent, and energy-efficient UAVs. Full article

The rapid growth of computing-intensive and latency-sensitive applications, including augmented reality, virtual reality, and self-driving systems, has increased the demand for low-latency and energy-efficient processing solutions. Mobile edge computing (MEC) has evolved as a transformative paradigm by relocating computation to the network edge, closer to end users. Unmanned aerial vehicles (UAVs) further strengthen MEC by offering flexible deployment, mobility, and reliable line-of-sight communication, making them suitable for temporary high-demand scenarios. Moreover, such latency-sensitive applications often generate numerous repetitive tasks and, thus, storing the results of these tasks can reduce both communication overhead and computational workload. However, jointly addressing the caching of task-results alongside offloading and resource allocation decisions in UAV-aided MEC networks remains a non-trivial challenge. In this study, an integrated task offloading and resource allocation with data caching (JORC) framework is proposed to address these challenges. The offloading and resource allocation problems are formulated as a Markov decision process and solved using the soft actor–critic reinforcement learning algorithm. In addition, dynamic and adaptive caching manages limited storage and reduces redundant computations by using a hybrid strategy that integrates the least-frequently used and least-recently used policies to reduce computational redundancy. Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches. Full article