Search Results (1,551)

Backscatter communication (BackCom) has emerged as an energy-efficient and low-cost communication paradigm, in which wireless devices transmit information by reflecting incident signals rather than actively generating radio frequency signals. Owing to the extremely low power consumption and hardware cost, BackCom is particularly suitable for Internet of Things (IoT) devices with stringent low energy and cost constraints. However, due to the severe double channel attenuation inherent in backscatter links, conventional ground-based deployment of transmitters and receivers often suffers from poor communication quality and low energy efficiency. Unmanned aerial vehicles (UAVs), with their high mobility and favorable line-of-sight (LoS) links, can act as dynamic aerial transmitters and receivers in BackCom, thereby mitigating channel attenuation and improving both communication reliability and energy efficiency. To enhance the data collection efficiency of UAV-assisted BackCom systems under a limited mission duration, this paper proposes a joint optimization method for communication resource allocation and UAV trajectory design under task time constraints. Specifically, a mixed-integer non-convex optimization problem is formulated to maximize the number of devices served by the UAV within a given task duration. The original problem is then decomposed into two subproblems, namely communication resource allocation optimization and UAV trajectory optimization. An iterative algorithm based on Block Coordinate Descent (BCD) and Successive convex approximation (SCA) is developed to obtain an efficient solution. Simulation results demonstrate that the proposed method can effectively increase the number of served devices within the specified mission time limit. Full article

In today’s densely populated and technology-driven smart cities, natural and human-made disasters increasingly threaten the resilience of communication infrastructures, creating critical challenges for maintaining reliable connectivity. The failure of conventional networks during crises significantly hampers emergency response, coordination, and information dissemination. To address these challenges, this paper presents Weighted Average Algorithm-based Clustering and Routing (WAA-CR), a novel, secure, and adaptive UAV-based framework for disaster response and recovery. WAA-CR integrates three key components: shelters or Ground Control Stations (GCSs) as communication anchors and support hubs, survivable clustering and routing using a WAA-based metaheuristic optimizer, and secure and trustworthy drone communication enabled by a lightweight trust evaluation mechanism, and authentication model. The framework formulates a multi-objective optimization model that simultaneously minimizes the number of active UAVs and routing cost, while maximizing trust, communication reliability, and coverage. Cluster head (CH) election and routing decisions are guided by a composite fitness function that considers residual energy, link stability, mobility, and dynamic trust scores. Additionally, an adaptive maintenance mechanism enables dynamic reconfiguration to handle CH failures, trust degradation, or mobility-driven topology changes. Extensive simulations conducted in MATLAB R2020ademonstrate that WAA-CR significantly outperforms existing baseline FANET protocols in terms of energy efficiency, cluster stability, trust accuracy, and end-to-end delivery performance. These results validate the proposed framework’s effectiveness in building resilient, scalable, and secure UAV-based communication networks for post-disaster environments. Full article

Mobile Unmanned Aerial Vehicles (UAVs) forming Flying Ad Hoc Networks (FANETs) offer promising applications, but dynamic network structures, limited resources, and potential single points of failure create security challenges. While cluster-based data aggregation, where data is collected and combined at Cluster Heads (CHs) before transmission, improves efficiency, traditional techniques can compromise data privacy. This paper introduces SSDBFAN, a scalable and secure cluster-based data aggregation framework for Flying Ad Hoc Networks (FANETs). The proposed approach integrates the Frilled Lizard Optimization Algorithm (FLOA) for efficient cluster head selection with blockchain technology and post-quantum cryptographic techniques, including lattice-based homomorphic encryption and the Chinese Remainder Theorem, to ensure privacy-preserving data aggregation. Additionally, a hybrid online/offline signature mechanism is employed to achieve secure and efficient authentication with reduced computational overhead. The performance of the proposed framework is evaluated using NS-3 simulations under varying network sizes. Experimental results demonstrate that SSDBFAN significantly improves communication efficiency, reduces computational cost, and enhances network stability compared to existing schemes. Furthermore, scalability analysis with up to 500 UAV nodes confirms that the proposed framework effectively controls blockchain overhead, including bandwidth consumption, consensus latency, and storage requirements. Comparative evaluation with existing optimization algorithms shows that FLOA achieves superior performance in terms of cluster stability, delay, and throughput. These results validate the effectiveness of SSDBFAN as a scalable and security-aware solution for large-scale FANET environments. Full article

The sixth-generation (6G) mobile networks are envisioned to deliver seamless three-dimensional(3D) coverage from ground to sky and vice versa. In parallel, aerial corridors are emerging to elevate ground-based transportation into the air, enabling smart air mobility for unmanned aerial vehicles (UAVs). The convergence of this intelligent transportation system (ITS) with 6G introduces new challenges: how to ensure reliable, efficient connectivity within aerial corridors, and how these corridors can serve as foundational sky infrastructure to advance the 6G ecosystem. This paper presents a comprehensive survey that systematically presents aerial corridors as integrated 6G sky infrastructure, unifying corridor geometry, network architecture, channel modeling, and key enabling technologies within a single framework. It conceptualizes the aerial corridor as a tube-shaped, multi-lane, bidirectional structure to manage drone-based roles, including user equipment (UE), base stations (BS), and communication relays. To support this vision, key enablers such as air-to-ground channel modeling and integrated sensing and communication (ISAC) are investigated. The proposed infrastructure aligns with the IMT-2030 vision, supporting machine-type communication, ubiquitous connectivity, and immersive services in regulated aerial space. Full article

With the rapid development of the low-altitude economy, constructing an accurate unmanned aerial vehicle (UAV) air-to-ground channel model is crucial for ensuring communication quality. However, due to the significant fluctuations in UAV operation altitudes and the complex propagation environment, traditional empirical models struggle to achieve universal high-precision prediction within a 3D airspace. This paper proposes a Physics-Informed Feature Engineering (PIFE) method and constructs a 3D signal strength prediction model in combination with Gradient Boosting Decision Tree (XGBoost). Unlike traditional purely data-driven methods, this paper explicitly extracts physical propagation features such as three-dimensional Euclidean distance and height-to-angle ratio, and specifically designs a height–path loss interaction term to capture the nonlinear coupling relationship of signal attenuation at different operating heights. The experimental results demonstrate that the model proposed in this paper performs excellently in multi-altitude airspace scenarios ranging from 70 m to 150 m. At the typical operation height of 70 m, the model achieves a high goodness of fit ($R^2$) of 0.843. Ablation experiments further confirm that the introduction of physical interaction features successfully breaks through the performance bottleneck of pure geometric features, proving the necessity of explicitly modeling the height–distance coupling effect in complex three-dimensional airspace. The research in this paper demonstrates the effectiveness of integrating physical priors with machine learning algorithms, providing an important theoretical basis and technical support for future drone network planning and coverage optimization in complex low-altitude environments. Full article

Coupling effects and communication delays present major challenges for distributed formation control of multi-UAV formations. This work characterizes coupling effects and integrates them into cooperative control synthesis under delay conditions. A leader state observer is introduced to reconstruct the leader’s state via neighboring information, reducing reliance on direct links and improving communication robustness. A delay aware cooperative control law with coupling effects is then developed, and Lyapunov–Krasovskii analysis establishes matrix inequality conditions to ensure stability. The key innovation lies in actively exploiting communication coupling to accelerate the error convergence rate and ensure formation tracking under communication delays. Theoretical analysis, grounded in the Lyapunov stability theorem, elucidates the mechanism by which coupling effects accelerate the error convergence rate. The effectiveness of the proposed method is validated through simulations of leader–follower formations. Full article

Communication latency is one of the main factors limiting the practical scalability of unmanned aerial vehicle (UAV) swarms operating with distributed formation control. In real-time UAV missions, such as coordinated swarm navigation, autonomous inspection, and aerial monitoring, delayed information exchange directly affects formation stability and operational safety. In practical aerial networks, inter-UAV communication latency is influenced by stochastic effects including jitter, burst delays, and multi-hop propagation, which are rarely captured by the simplified deterministic delay assumptions commonly adopted in analytical formation-control studies. This paper introduces a measurement-informed stochastic delay model and a communication–control delay-feasibility framework that jointly account for per-link latency behavior, multi-hop delay accumulation, and controller-level delay tolerance. The proposed framework is evaluated using an attractive–repulsive distance-based potential field (ARD–PF) formation controller, for which the maximum admissible end-to-end delay is quantified as a function of swarm size and inter-UAV separation. The delay model is calibrated and validated using more than 15,000 in-flight communication delay samples collected from a multi-UAV LoRa platform operating under realistic flight conditions. The results show that different mechanisms limit swarm operation under different operating scenarios. In some configurations, stochastic communication latency becomes the dominant constraint, whereas in others, formation geometry or network load determines the feasible operating region. Based on these elements, the proposed framework characterizes delay-feasible operating regions and predicts the maximum feasible swarm size under distributed formation control and realistic multi-hop communication latency. Full article

With the deployment and application of the Fifth-Generation (5G) mobile communication technologies and the ongoing research and development of the Sixth-Generation (6G) mobile communication technologies, the space–air–ground–sea integrated network has become the core development vision for future communications. As aerial nodes, Unmanned Aerial Vehicles (UAVs) can be applied in a wide range of scenarios, including emergency rescue, surveying and mapping, environmental monitoring, and communication coverage enhancement. In terms of communication coverage enhancement, the space–air–ground integrated network, with UAVs as a key component, can provide seamless communication coverage for the full-domain three-dimensional space such as remote areas, deserts, and oceans. Benefiting from advantages such as low cost and high flexibility, UAVs have become a critical research focus, and the one-hop Base Station (BS)–relay UAV–slave UAV architecture for communication coverage enhancement has emerged as an important development direction. However, the high mobility and wide coverage characteristics of UAVs also pose significant synchronization challenges. Aiming at the uplink synchronization problem on the sidelink between slave UAVs and the relay UAV, a two-step random-access scheme based on Asynchronous Non-Orthogonal Multiple Access (A-NOMA) is designed to mitigate the Doppler Frequency Offset (DFO), improve access efficiency, reduce resource consumption, and accommodate the asynchrony among different users. This scheme leverages the existing preamble sequences of the Physical Random Access Channel (PRACH) and realizes DFO estimation in combination with the pairing index. On this basis, a Successive Interference Cancellation (SIC) algorithm based on DFO and phase compensation is designed to complete the demodulation of user data. For the downlink synchronization problem on the sidelink between slave UAVs and the relay UAV, the frequency offset estimation performance is improved by redesigning the resource allocation scheme of the Sidelink Synchronization Signal Block (S-SSB). Meanwhile, considering the energy constraint of UAVs, a downsampling-based detection scheme is designed to reduce UAV power consumption, and a full-link algorithm is developed to support the practical implementation of the proposed scheme. Full article

This paper develops a unified finite-bit distributed optimization framework for UAV swarms operating over bandwidth-limited communication graphs. We consider strongly convex and smooth global objectives decomposed over local UAV cost functions and study three communication-efficient algorithmic regimes. First, we design a quantized distributed gradient-tracking descent scheme with fixed finite-bit communication and show that, under bounded quantization errors, the method converges R-linearly to a quantization-dependent neighborhood of the global optimizer. Second, we introduce an adaptive quantization strategy that dynamically adjusts the number of transmitted bits according to the current convergence stage. By forcing the quantization distortion to decay proportionally to the optimization error, the proposed adaptive scheme recovers exact linear convergence to the optimal solution while substantially reducing the cumulative communication load. Third, we develop a fully distributed 1-bit communication mode in which UAVs exchange only sign information and use coordinate-wise majority voting to aggregate both descent and consensus directions. The robust linear-contraction property is proved to a small neighborhood under a sign-Polyak–Lojasiewicz condition and a probabilistic majority-correctness assumption. Full article

This paper investigates the application of cooperative relaying systems with non-orthogonal multiple access (NOMA) in low-altitude intelligent networking-enabled medical Internet of Things (IoT) and analyzes their transmission performance. First, to enhance the communication quality of remote base stations, we deploy a relaying unmanned aerial vehicle (UAV). A two-slot NOMA cooperative transmission mechanism is proposed accordingly. Next, for the NOMA-enhanced UAV-relayed smart healthcare system under Rician fading channels, an exact closed-form expression for the achievable rate is derived using the incomplete Gamma function. Then, to improve computational efficiency, a low-complexity approximation method based on Gauss–Chebyshev quadrature is designed, overcoming the high complexity of the exact expression. Finally, the simulation results validate a close match between the proposed approximation and the exact values (average approximation error below 6.17%), and demonstrate superior achievable rate performance compared to three state-of-the-art schemes. Full article

Air–ground collaborative systems leverage the complementary strengths of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) and hold significant potential for logistics in complex, unstructured environments. However, trajectory planning in infrastructure-free mountainous regions remains challenging owing to the need for continuous tight coupling, obstacle avoidance, and reliable communication-link maintenance. To address these challenges, this study proposes a cooperative trajectory planning framework that enforces strict inter-vehicle distance constraints to maintain communication connectivity. By formulating the coordination problem in terms of relative configurations between air and ground vehicles, the proposed framework exhibits translational invariance, reflecting an underlying symmetry with respect to global position shifts. This symmetry-aware formulation reduces reliance on absolute coordinates and promotes consistent cooperative behavior under environmental variability. The trajectory planning problem is mathematically formulated as a constrained multi-objective nonlinear programming (MONLP) model that balances energy consumption and trajectory smoothness. An adaptive inertia weight particle swarm optimization (AIWPSO) algorithm is developed to efficiently solve the resulting optimization problem. Simulation results demonstrate that the proposed approach generates smooth, collision-free trajectories while maintaining stable air–ground coordination, demonstrating improved feasibility and robustness over conventional planning methods in unstructured mountainous environments. Full article

Considering that the unmanned aerial vehicles (UAVs) are susceptible to both co-channel interference and malicious jamming with limited onboard battery energy, this paper proposes an energy-harvesting-assisted anti-jamming communication framework for UAV swarm networks. Specifically, we first model the problem as a decentralized partially observable Markov decision process (Dec-POMDP), aiming to achieve a long-term trade-off between data transmission success rate and energy consumption. Then we propose a multi-agent independent advantage actor–critic (IA2C)-based energy-harvesting-assisted anti-jamming communication solution, which enables each cluster head (CH) to learn its transmit channel, power, and energy harvesting time policy independently. By constructing a time-space-based extended Dec-POMDP, the spatiotemporal correlations among neighboring nodes are learned by allowing adjacent agents to share discounted local observations. Extensive simulations show that, compared with the benchmark schemes, the proposed scheme improves the average cumulative reward and average cumulative success rate by 17.26% and 10.37%, respectively, while achieving a higher transmission success rate with lower energy consumption under different numbers of available channels. Full article

Unmanned aerial vehicles (UAVs) are increasingly deployed in missions that require high autonomy and reliable decision-making; however, many operational concepts still assume access to GNSS and stable communication with a human operator. In contested environments, this assumption may no longer hold because GNSS degradation, radio-frequency interference, and intentional jamming can disrupt positioning and communication, thereby reducing mission effectiveness and safety. Recent surveys show that operation in GNSS-denied environments remains a major challenge and often requires alternative perception, localization, and control strategies. In response, this article investigates a reinforcement learning (RL)-based decision-support system for the autonomous control of a quadrotor UAV in a three-dimensional simulated environment. Rather than following pre-programmed waypoints, the UAV learns a control policy through interaction with the environment and reward-driven adaptation. The proposed system is designed for mission execution under uncertainty, limited external guidance, and partial observability. Two policy-gradient approaches are implemented and compared: classical REINFORCE and Proximal Policy Optimization (PPO) with an Actor–Critic architecture. The study presents the simulation environment, state and action representation, reward formulation, staged training procedure, and comparative evaluation. The results indicate that, within the considered unseen test scenario, the PPO-based configuration achieved higher mission effectiveness than REINFORCE in the final unseen test scenario, supporting the practical relevance of structured deep reinforcement learning for UAV operation in GPS-denied and communication-constrained environments. Full article

Advancing environmental monitoring through coordinated autonomous systems is central to sustainable smart region governance and data-driven territorial management. The article presents an engineering-oriented architecture and deployment methodology for an integrated wildfire monitoring and response system that combines unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and stationary sensor networks (SNs). We formalise hub-and-spoke infrastructure placement as a mixed-integer optimisation problem that accounts for platform types, endurance, travel times and logistical constraints, and propose a practical pre-processing pipeline (confidence scoring, resampling, Kalman/median filtering, strategy fusion) for heterogeneous telemetry and imagery. The system couples multimodal neural network processing (image backbones, clustering and time-series models) with online resource-allocation and mission-planning mechanisms to prioritise UAV/UGV sorties and dynamically select launch sites. The article describes scenario-driven operational modes (early warning, alarm verification, autonomous local extinguishing, post-fire recovery, sensor-gap compensation, and inter-hub reinforcement), defines validation protocols (synthetic experiments, precision/recall/F1, and hardware-in-the-loop testing), and proposes KPIs to assess environmental, social, and economic impacts for smart regions. The contribution is a reproducible, deployment-focused blueprint that bridges conceptual UAV–UGV–SN research and practical implementation, highlighting trade-offs in reliability, communication redundancy, and sustainability, and outlining directions for simulation, field pilots and algorithmic refinement. Full article

This study presents a leader–follower flight control architecture for a small-scale UAV swarm, demonstrated using a three-UAV system built on heterogeneous autopilots, GPS positioning, Raspberry Pi 3B+ units, and Wi-Fi communication. The follower UAVs autonomously maintain predefined formations while tracking the leader’s trajectory. During flight, each Raspberry Pi establishes inter-UAV communication via a Wi-Fi network using the UDP protocol, enabling real-time data exchange and attitude adjustments. An outer-loop proportional–integral control design implemented on the Raspberry Pi generates corrective commands to the corresponding autopilot to reduce the followers’ position errors. Under the tested conditions, the framework achieves formation tracking with horizontal and vertical errors of approximately 60 and 20 cm, respectively, providing initial experimental validation in a small-scale setting. In addition, a simulation environment based on pre-recorded UAV and environmental data with 3D visualization is developed to support behavior prediction, performance evaluation, and control tuning prior to real-world deployment, although its applicability beyond the tested scenarios remains to be established. Full article