Search Results (101)

The swift growth of the low-altitude economic ecosystem and Unmanned Aerial Vehicle (UAV) swarm applications across diverse sectors presents significant challenges for engineering managers in terms of effective performance evaluation and operational optimization. Traditional evaluation methods often struggle with the inherent complexities, dynamic nature, and multi-faceted performance criteria of UAV swarms. This study introduces a novel Bayesian Network (BN)-based multicriteria decision-making framework that systematically integrates expert intuition with real-time data. By employing variance decomposition, the framework establishes theoretically grounded, bidirectional mapping between expert-assigned weights and the network’s probabilistic parameters, creating a unified model of subjective expertise and objective data. Comprehensive validation demonstrates the framework’s efficacy in identifying critical performance drivers, including environmental awareness, communication ability, and a collaborative decision. Ultimately, our work provides engineering managers with a transparent and adaptive tool, offering actionable insights to inform resource allocation, guide technology adoption, and enhance the overall operational effectiveness of complex UAV swarm systems. Full article

With the continuous development of quantum computing technology, the traditional public key cryptosystem is facing severe security challenges, especially in the resource-constrained UAV swarm communication scenario. To deal with this problem, this paper proposes a secure communication scheme for the post-quantum era, which combines the Kyber-based group key agreement mechanism and the lightweight identity authentication system constructed by sparse Merkle tree (SMT). The system is initialized by the edge node, and supports the dynamic joining and leaving of the UAV through the authentication and key management mechanism. To meet the security and performance requirements in different application scenarios, we design and integrate two mainstream post-quantum signature schemes to provide flexible identity authentication options. Experimental results show that the scheme has low resource overhead while ensuring security, which is suitable for the actual communication deployment of post-quantum UAV swarm. Full article

The main purpose of this paper is to explore the benefits of combining two radio interfaces onboard an unmanned aerial vehicle (UAV) to communicate with a ground control station (GCS) and other UAVs inside a swarm. The goals are to use the IEEE 802.11ah standard (Wi-Fi HaLow) combined with the IEEE 802.11ax specification (Wi-Fi 6/6E) to enable real-time video transmission from UAVs to the GCS. While airport runway inspection serves as the proof-of-concept use case, the proposed multi-hop architectures apply to other medium-range UAV operations (i.e., a few kilometers) requiring real-time video transmission, such as natural disaster relief and agricultural monitoring. Several scenarios in which a UAV swarm performs infrastructure inspection are emulated. During the missions, UAVs have to send real-time video to the GCS through a multi-hop network when some damage in the infrastructure is found. The different scenarios are studied by means of emulation. Emulated scenarios are defined using different network architectures and radio technologies. Once the emulations finish, different performance metrics related to time, energy and the multi-hop video transmission network are analyzed. The capacity of a multi-hop network is a limiting factor for the transmission of high-quality video. As a first contribution, an expression to find this capacity from distances between UAVs in the emulated scenario is found using the NS-3 simulator. Then, this expression is applied in the algorithms in charge of composing the multi-hop network to offer on-demand quality video. However, the main contribution of this work lies in the development of efficient mechanisms for exchanging control information between UAVs and the GCS, and for forming a multi-hop network to transmit video. Full article

Deployment of Unmanned Aerial Vehicles (UAVs) continues to expand rapidly across a wide range of applications, including environmental monitoring, precision agriculture, and disaster response. Despite their increasing ubiquity, UAVs remain inherently vulnerable to security threats due to resource-constrained hardware, energy limitations, and reliance on open wireless communication channels. These factors render traditional cryptographic solutions impractical, thereby necessitating the development of lightweight, UAV-specific security mechanisms. This review article presents a comprehensive analysis of lightweight encryption techniques and key management strategies designed for energy-efficient and secure UAV communication. Special emphasis is placed on recent cryptographic advancements, including the adoption of the ASCON family of ciphers and the emergence of post-quantum algorithms that can secure UAV networks against future quantum threats. Key management techniques such as blockchain-based decentralized key exchange, Physical Unclonable Function (PUF)-based authentication, and hierarchical clustering schemes are evaluated for their performance and scalability. To ensure comprehensive protection, this review introduces a multilayer security framework addressing vulnerabilities from the physical to the application layer. Comparative analysis of lightweight cryptographic algorithms and multiple key distribution approaches is conducted based on energy consumption, latency, memory usage, and deployment feasibility in dynamic aerial environments. Unlike design- or implementation-focused studies, this work synthesizes existing literature across six interconnected security dimensions to provide an integrative foundation. Our review also identifies key research challenges, including secure and efficient rekeying during flight, resilience to cross-layer attacks, and the need for standardized frameworks supporting post-quantum cryptography in UAV swarms. By highlighting current advancements and research gaps, this study aims to guide future efforts in developing secure communication architectures tailored to the unique operational constraints of UAV networks. Full article

Unmanned aerial vehicles (UAVs) with high mobility and self-organization capabilities can establish highly connected networks to cache popular content for edge users, which improves network stability and significantly reduces access time. However, an uneven distribution of demand and storage capacity may reduce the utilization of the storage capacity of UAVs without a proper UAV coordination mechanism. This work proposes a multi-UAV-enabled caching socially aware network (SAN) where UAVs can switch roles by adjusting the social attributes, effectively enhancing data interaction within the UAVs. The proposed network breaks down communication barriers at the UAV layer and integrates the collective storage resources by incorporating social awareness mechanisms to mitigate these imbalances. Furthermore, we formulate a multi-objective optimization problem (MOOP) with the objectives of maximizing both the diversity of cached content and the total request probability (RP) of the network, while employing a multi-objective particle swarm optimization (MOPSO) algorithm with a mutation strategy to approximate the Pareto front. Finally, the impact of key parameters on the Pareto front is analyzed under various scenarios. Simulation results validate the benefits of leveraging social attributes for resource allocation and demonstrate the effectiveness and convergence of the proposed algorithm for the multi-UAV caching strategy. Full article

Unmanned Aerial Vehicles (UAVs), commonly known as drones, are becoming increasingly important in multiple areas and various applications, including communication, detection, and monitoring. This review paper examines the development of antennas for UAVs, with a particular focus on miniaturization techniques, polarization strategies, and beamforming solutions. It explores both structural and material-based methods, such as meander lines, slots, high-dielectric substrates, and metasurfaces, which aim to make the antenna more compact without compromising performance. Different antenna types including dipole, monopole, horn, vivaldi, and microstrip patch are explored to identify solutions that meet performance standards while respecting UAV constraints. In terms of polarization strategies, these are often implemented in the feeding network to achieve linear or circular polarization, and beamforming techniques like beam-steering and beam-switching enhance communication efficiency by improving signal directionality. Future research should focus on more lightweight, structurally integrated, and reconfigurable apertures that push miniaturization through conformal substrates and programmable metasurfaces, extending efficient operation from 5/6 GHz into the sub-THz regime and supporting agile beamforming for dense UAV swarms. Full article

This study investigates the scheduling problem of return communication tasks for unmanned aerial vehicle (UAV) swarms, where disaster relief environmental global positioning is hampered. To characterize the utility of these tasks and optimize scheduling decisions, we developed a time window-constrained scheduling model that operates under constraints, including communication base station time windows, battery levels, and task uniqueness. To solve the above model, we propose an enhanced algorithm through integrating Dueling Deep Q-Network (Dueling DQN) into adaptive large neighborhood search (ALNS), referred to as Dueling DQN-ALNS. The Dueling DQN component develops a method to update strategy weights, while the action space defines the destruction and selection strategies for the ALNS scheduling solution across different time windows. Meanwhile, we design a two-stage algorithm framework consisting of centralized offline training and decentralized online scheduling. Compared to traditionally optimized search algorithms, the proposed algorithm could continuously and dynamically interact with the environment to acquire state information about the scheduling solution. The solution ability of Dueling DQN is 3.75% higher than that of the Ant Colony Optimization (ACO) algorithm, 5.9% higher than that of the basic ALNS algorithm, and 9.37% higher than that of the differential evolution algorithm (DE). This verified its efficiency and advantages in the scheduling problem of return communication tasks for UAVs. Full article

Collaborative unmanned aerial vehicle (UAV) swarm operations using the open-source PX4–ROS2 system have been extensively studied for reconnaissance and autonomous missions. PX4–ROS2 utilizes data distribution service (DDS) middleware to ensure network flexibility and support scalable operations. DDS enables decentralized information exchange through its discovery protocol. However, in dense swarm environments, the default initialization process of this protocol generates considerable communication overhead, which hinders reliable peer detection among UAVs. This study introduces an optimized DDS discovery scheme incorporating two key strategies: a preloading method that embeds known participant data before deployment, and a dynamic network awareness approach that regulates discovery behavior based on real-time connectivity. Integrated into PX4–ROS2, the proposed scheme was assessed through both simulations and real-world testing. Results demonstrate that the optimized discovery process reduced peak packet traffic by over 90% during the initial exchange phase, thereby facilitating more stable and scalable swarm operations in wireless environments. Full article

Unmanned Aerial Vehicles (UAVs) exhibit significant potential in enhancing the wireless communication coverage and service quality of Mobile Edge Computing (MEC) systems due to their superior flexibility and ease of deployment. However, the rapid growth of tasks leads to transmission queuing in edge networks, while the uneven distribution of user nodes and services causes network load imbalance, resulting in increased user waiting delays. To address these issues, we propose a multi-UAV collaborative MEC network model based on Non-Orthogonal Multiple Access (NOMA). In this model, UAVs are endowed with the capability to dynamically offload tasks among one another, thereby fostering a more equitable load distribution across the UAV swarm. Furthermore, the integration of NOMA is strategically employed to alleviating the inherent queuing delays in the communication infrastructure. Considering delay and energy consumption constraints, we formulate a task offloading strategy optimization problem with the objective of minimizing the overall system delay. To solve this problem, we design a delay-optimized offloading strategy based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. By jointly optimizing task offloading decisions and UAV flight trajectories, the system delay is significantly reduced. Simulation results show that, compared to traditional approaches, the proposed algorithm achieves a delay reduction of 20.2%, 9.8%, 17.0%, 12.7%, 15.0%, and 11.6% under different scenarios, including varying task volumes, the number of IoT devices, UAV flight speed, flight time, IoT device computing capacity, and UAV computing capability. These results demonstrate the effectiveness of the proposed solution and offloading decisions in reducing the overall system delay. Full article

To enable distributed target searches by unmanned aerial vehicle (UAV) swarms, it is essential to collaboratively process multi-source sensing data and construct a globally consistent map. In response to the challenges posed by constrained communication and multi-hop transmission delays, this paper proposes a symmetry-oriented dynamic routing planning algorithm for reliable map fusion. The algorithm introduces a framework for the transmission and fusion of local perception maps, formulating routing tasks as an integer programming problem to determine latency-minimized transmission paths. When packet loss occurs, a dynamic re-routing strategy is triggered to ensure the continued reliability of the fusion process. The routing design preserves latency symmetry, aiming to keep transmission delays under packet loss conditions close to those under ideal, lossless scenarios. To improve scalability in large-scale UAV swarms, an approximate algorithm based on L-step forward prediction is further introduced to reduce computational complexity. The simulation results demonstrate that the proposed algorithm achieves low latency, strong robustness, and stable performance under varying communication conditions. Full article

Ultraviolet (UV) communication, characterized by non-line-of-sight (NLOS) scattering, holds substantial potential for enabling communication networking in unmanned aerial vehicle (UAV) formations within strong electromagnetic interference environments. This paper proposes a networking protocol for large-scale mobile ultraviolet communication networks (LSM-UVCN). In large-scale networks, the proposed protocol establishes multiple non-interfering transmission paths based on a connection matrix simultaneously, ensuring reliable space division multiplexing (SDM) and optimizing the utilization of network channel resources. To address frequent network topology changes in mobile scenarios, the protocol employs periodic maintenance of the connection matrix, significantly reducing the adverse impacts of node mobility on network performance. Simulation results demonstrate that the proposed protocol achieves superior performance in large-scale mobile UV communication networks. By dynamically adjusting the connection matrix update frequency, it adapts to varying node mobility intensities, effectively minimizing control overhead and data loss rates while enhancing network throughput. This work underscores the protocol’s adaptability to dynamic network environments, providing a robust solution for high-reliability communication requirements in complex electromagnetic scenarios, particularly for UAV swarm applications. The integration of SDM and adaptive matrix maintenance highlights its scalability and efficiency, positioning it as a viable technology for next-generation wireless communication systems in challenging operational conditions. Full article

The integration of reconfigurable intelligent surfaces (RISs) with non-terrestrial networks (NTNs), particularly those enabled by unmanned aerial vehicles (UAVs) or drone-based platforms, has emerged as a transformative approach to enhance 6G connectivity in high-mobility scenarios. UAV-assisted NTNs offer flexible deployment, dynamic altitude control, and rapid network reconfiguration, making them ideal candidates for RIS-based signal optimization. However, the high mobility of UAVs and their three-dimensional trajectory dynamics introduce unique challenges in maintaining robust, low-latency links and seamless handovers. This paper presents a comprehensive performance analysis of RIS-assisted UAV-based NTNs, focusing on optimizing RIS phase shifts to maximize the signal-to-interference-plus-noise ratio (SINR), throughput, energy efficiency, and reliability under UAV mobility constraints. A joint optimization framework is proposed that accounts for UAV path loss, aerial shadowing, interference, and user mobility patterns, tailored specifically for aerial communication networks. Extensive simulations are conducted across various UAV operation scenarios, including urban air corridors, rural surveillance routes, drone swarms, emergency response, and aerial delivery systems. The results reveal that RIS deployment significantly enhances the SINR and throughput while navigating energy and latency trade-offs in real time. These findings offer vital insights for deploying RIS-enhanced aerial networks in 6G, supporting mission-critical drone applications and next-generation autonomous systems. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly used in the new generation of communication systems. They serve as access points, base stations, relays, and gateways to extend network coverage, enhance connectivity, or offer communications services in places lacking telecommunication infrastructure. However, optimizing UAV placement in three-dimensional (3D) environments with diverse user distributions and uneven terrain conditions is a crucial challenge. Therefore, this paper proposes a novel framework to minimize UAV transmission power while ensuring a guaranteed data rate in realistic and complex scenarios. To this end, using the particle swarm optimization evolution (PSO-E) algorithm, this paper analyzes the impact of user-truncated distribution models for suburban, urban and dense urban environments. Extensive simulations demonstrate that dense urban environments demand higher power than suburban and urban environments, with uniform user distributions requiring the most power in all scenarios. Conversely, Gaussian and exponential distributions exhibit lower power requirements, particularly in scenarios with concentrated user hotspots. The proposed model provides insight into achieving efficient network deployment and power optimization, offering practical solutions for future communication networks in complex 3D scenarios. Full article

Swarm-based unmanned aerial vehicle (UAV) systems offer enhanced spatial coverage, collaborative intelligence, and mission scalability for various applications, including environmental monitoring and emergency response. However, their onboard processing is limited by stringent size, weight, and power constraints, posing challenges for real-time computation and autonomous control. This paper presents an integrated communication and computation framework that combines cloud–edge–end collaboration with cell-free massive multiple-input multiple-output (CF-mMIMO) to enable scalable and efficient task offloading in UAV swarms. Furthermore, we implement a prototype system testbed with nine UAVs and validate the proposed framework through real-time object detection tasks. Results demonstrate over $30\%$ reduction in onboard computation and significant improvements in communication reliability, highlighting the framework’s potential for enabling intelligent, cooperative aerial systems. Full article

Unmanned aerial vehicle (UAV) swarms are widely applied in various fields, including military and civilian domains. However, due to the scarcity of spectrum resources, UAV swarm clustering technology has emerged as an effective method for achieving spectrum sharing among UAV swarms. This paper introduces a distributed heterogeneous multi-agent deep reinforcement learning algorithm, named HMDRL-UC, which is specifically designed to address the cluster-based spectrum sharing problem in heterogeneous UAV swarms. Heterogeneous UAV swarms consist of two types of UAVs: cluster head (CH) and cluster member (CM). Each UAV is equipped with an intelligent agent to execute the deep reinforcement learning (DRL) algorithm. Correspondingly, the HMDRL-UC consists of two parts: multi-agent proximal policy optimization for cluster head (MAPPO-H) and independent proximal policy optimization for cluster member (IPPO-M). The MAPPO-H enables the CHs to decide cluster selection and moving position, while CMs utilize IPPO-M to cluster autonomously under the condition of certain partial channel distribution information (CDI). Adequate experimental evidence has confirmed that the HMDRL-UC algorithm proposed in this paper is not only capable of managing dynamic drone swarm scenarios in the presence of partial CDI, but also has a clear advantage over the other existing three algorithms in terms of average throughput, intra-cluster communication delay, and minimum signal-to-noise ratio (SNR). Full article