Search Results (1,328)

The diverse usage of Unmanned Aerial Vehicles (UAVs) across commercial, military, and civil domains has significantly heightened the need for robust cybersecurity mechanisms. Given their reliance on wireless communications, real-time control systems, and sensor integration, UAVs are highly susceptible to cyber intrusions that can disrupt missions, compromise data integrity, or cause physical harm. This paper presents a comprehensive literature review of Intrusion Detection Systems (IDSs) that leverage artificial intelligence (AI) to enhance the security of UAV and UAV swarm environments. Through rigorous analysis of recent peer-reviewed publications, we have examined the studies in terms of AI model algorithm, dataset origin, deployment mode: centralized, distributed or federated. The classification also includes the detection strategy: online versus offline. Results show a dominant preference for centralized, supervised learning using standard datasets such as CICIDS2017, NSL-KDD, and KDDCup99, limiting applicability to real UAV operations. Deep learning (DL) methods, particularly Convolutional Neural Networks (CNNs), Long Short-term Memory (LSTM), and Autoencoders (AEs), demonstrate strong detection accuracy, but often under ideal conditions, lacking resilience to zero-day attacks and real-time constraints. Notably, emerging trends point to lightweight IDS models and federated learning frameworks for scalable, privacy-preserving solutions in UAV swarms. This review underscores key research gaps, including the scarcity of real UAV datasets, the absence of standardized benchmarks, and minimal exploration of lightweight detection schemes, offering a foundation for advancing secure UAV systems. Full article

Excessive energy consumption of communication protocols in IoT/IIoT systems constitutes one of the key constraints for the operational longevity of remote sensor nodes, where radio transmission often incurs higher energy costs than data acquisition or local computation. Previous studies have remained fragmented, typically focusing on selected technologies or specific layers of the communication stack, which has hindered the development of comparable quantitative metrics across protocols. The aim of this study is to design and validate a unified evaluation framework enabling consistent assessment of both wired and wireless protocols in terms of energy efficiency, reliability, and maintenance costs. The proposed approach employs three complementary research methods: laboratory measurements on physical hardware, profiling of SBC devices, and simulations conducted in the COOJA/Powertrace environment. A Unified Comparative Method was developed, incorporating bilinear interpolation and weighted normalization, with its robustness confirmed by a Spearman rank correlation coefficient exceeding 0.9. The analysis demonstrates that MQTT-SN and CoAP (non-confirmable mode) exhibit the highest energy efficiency, whereas HTTP/3 and AMQP incur the greatest energy overhead. Results are consolidated in the ICoPEP matrix, which links protocol characteristics to four representative RS-IoT scenarios: unmanned aerial vehicles (UAVs), ocean buoys, meteorological stations, and urban sensor networks. The framework provides well-grounded engineering guidelines that may extend node lifetime by up to 35% through the adoption of lightweight protocol stacks and optimized sampling intervals. The principal contribution of this work is the development of a reproducible, technology-agnostic tool for comparative assessment of IoT/IIoT communication protocols. The proposed framework addresses a significant research gap in the literature and establishes a foundation for further research into the design of highly energy-efficient and reliable IoT/IIoT infrastructures, supporting scalable and long-term deployments in diverse application environments. Full article

This paper studies joint data collection and wireless power transfer in a UAV-assisted IoT network. A rotary-wing UAV follows a fly–hover–communicate cycle. At each hover, it simultaneously receives uplink data in full-duplex mode while delivering radio-frequency energy to nearby devices. Using a realistic propulsion-power model and a nonlinear energy-harvesting model, we formulate trajectory and hover control as a multi-objective optimization problem that maximizes the aggregate data rate and total harvested energy while minimizing the UAV’s energy consumption over the mission. To enable flexible trade-offs among these objectives under time-varying conditions, we propose a dynamic, state-adaptive weighting mechanism that generates environment-conditioned weights online, which is integrated into an enhanced deep deterministic policy gradient (DDPG) framework. The resulting dynamic-weight MODDPG (DW-MODDPG) policy adaptively adjusts the UAV’s trajectory and hover strategy in response to real-time variations in data demand and energy status. Simulation results demonstrate that DW-MODDPG achieves superior overall performance and a more favorable balance among the three objectives. Compared with the fixed-weight baseline, our algorithm increases total harvested energy by up to 13.8% and the sum data rate by up to 5.4% while maintaining comparable or even lower UAV energy consumption. Full article

In this paper, a UAV target tracking method with 6G integrated sensing and communication (ISAC) is proposed to address the surveillance requirements for unmanned aerial vehicle (UAV) targets in the context of the rapid development of low-altitude economy. Firstly, a target tracking system model for UAVs is established based on the ISAC base station transceiver architecture. Then, an unscented Kalman filter (UKF) target tracking framework is designed to tackle the occlusion effect during UAV navigation. Specifically, the measurement position information of the UAV is obtained through a spatial rotation-based parameter estimation method. Subsequently, occlusion is detected by analyzing the Line-of-Sight (LoS) visibility between the UAV and the base station. On this basis, the problem of short-term and long-term trajectory loss under occlusion is solved by integrating cubic interpolation with a constant velocity (CV) model, which enables real-time UAV trajectory tracking. Finally, simulation results demonstrate that: (1) under no occlusion, the average estimation errors of the X/Y/Z axes are 0.82 m, 0.79 m, and 0.68 m, respectively; (2) under short-term occlusion, the average errors of the X/Y/Z axes are 1.25 m, 2.18 m, and 1.05 m, with a convergence time of 1 s after LoS recovery; (3) under long-term occlusion, the average errors of the X/Y/Z axes are 2.87 m, 3.79 m, and 1.85 m, with a convergence time of 5 s after LoS recovery; (4) the velocity estimation error can quickly converge to within 0.2 m/s after re-acquiring observations. The proposed method exhibits small trajectory and velocity estimation errors in different occlusion scenarios, effectively meeting the requirements for UAV target tracking. Full article

Multiple unmanned aerial vehicle (UAV) systems have attracted considerable research interest due to their broad applications, such as formation control. However, decentralized UAV formation faces challenges stemming from limited local observations, which may lead to consistency conflicts, and excessive communication. To address these issues, this paper proposes SemanticBC-DecAF, a decentralized adaptive formation (DecAF) framework under a leader–follower architecture, incorporating a semantic broadcast communication (SemanticBC) mechanism. The framework consists of three modules: (1) a proximal policy optimization (PPO)-based semantic broadcast module, where the leader UAV transmits semantically encoded global obstacle images to followers to enhance their perception; (2) a YOLOv5-based detection and position estimation module, enabling followers to infer obstacle locations from recovered images; and (3) a multi-agent proximal policy optimization (MAPPO)-based formation module, which fuses global and local observations to achieve adaptive formation and obstacle avoidance. Experiments in the multi-agent simulation environment MPE show that the proposed framework significantly improves global perception and formation efficiency compared with methods that rely on local observations. Full article

The emergence of 6G wireless systems marks a paradigm shift toward intelligent, context-aware networks that can adapt in real-time to their environment. Within this landscape, Sensing-Assisted Communication (SAC) emerges as a key enabler, integrating perception into the communication control loop to enhance reliability, beamforming accuracy, and system responsiveness. Unlike prior surveys that treat SAC as a subfunction of Integrated Sensing and Communication (ISAC), this work offers the first dedicated review of SAC in Millimeter-Wave (mmWave) and Sub-Terahertz (Sub-THz) systems, where directional links and channel variability present core challenges. SAC encompasses a diverse set of methods that enable wireless systems to dynamically adapt to environmental changes and channel conditions in real time. Recent studies demonstrate up to 80% reduction in beam training overhead and significant gains in latency and mobility resilience. Applications include predictive beamforming, blockage mitigation, and low-latency Unmanned Aerial Vehicle (UAV) and vehicular communication. This review unifies the SAC landscape and outlines future directions in standardization, Artificial Intelligence (AI) integration, and cooperative sensing for next-generation wireless networks. Full article

The development of efficient and reliable trajectory-planning strategies for swarms of unmanned aerial vehicles (UAVs) is an increasingly important area of research, with applications in surveillance, search and rescue, smart agriculture, defence operations, and communication networks. This article provides a comprehensive and critical review of the various techniques available for UAV swarm trajectory planning, which can be broadly categorised into three main groups: traditional algorithms, biologically inspired metaheuristics, and modern artificial intelligence (AI)-based methods. The study examines cutting-edge research, comparing key aspects of trajectory planning, including computational efficiency, scalability, inter-UAV coordination, energy consumption, and robustness in uncertain environments. The strengths and weaknesses of these algorithms are discussed in detail, particularly in the context of collision avoidance, adaptive decision making, and the balance between centralised and decentralised control. Additionally, the review highlights hybrid frameworks that combine the global optimisation power of bio-inspired algorithms with the real-time adaptability of AI-based approaches, aiming to achieve an effective exploration–exploitation trade-off in multi-agent environments. Lastly, the article addresses the major challenges in UAV swarm trajectory planning, including multidimensional trajectory spaces, nonlinear dynamics, and real-time adaptation. It also identifies promising directions for future research. This study serves as a valuable resource for researchers, engineers, and system designers working to develop UAV swarms for real-world, integrated, intelligent, and autonomous missions. Full article

Unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) systems have developed rapidly in the sixth generation (6G) era. However, factors such as the mobility of ground users and malicious jamming pose significant challenges to systems’ performance and reliability. Against this backdrop, this paper designs a multi-UAV-assisted ISAC system model under malicious jamming environments. Under the constraint of sensing accuracy, the total communication rate of the system is maximized through joint optimization of user association, UAV trajectory, and transmit power. The problem is then decomposed into three subproblems, which are solved using the improved auction algorithm (IAA), dream optimization algorithm (DOA), and rapidly-exploring random trees-based optimizer algorithm (RRTOA). The global optimal solution is approached through the alternating optimization-based predictive scheduling algorithm (AOPSA). Meanwhile, this paper also introduces a long short-term memory (LSTM) network to predict users’ dynamic positions, addressing the impact of user mobility and enhancing the system’s real-time performance. Simulation results show that compared with the baseline scheme, the proposed algorithm achieves a 188% improvement in communication rate, which verifies its effectiveness and superiority. Full article

To enhance the efficiency and reduce the operational costs of large-scale Unmanned Aerial Vehicle (UAV) inspection missions limited by endurance, this paper addresses the coupled problem of dynamically positioning landing/takeoff sites and routing the UAVs. A novel Hierarchical Reinforcement Learning (H-DRL) framework is proposed, which decouples the problem into a high-level strategic deployment policy and a low-level tactical routing policy. The primary contribution of this work lies in two architectural innovations that enable globally coordinated, end-to-end optimization. First, a coordinated credit assignment mechanism is introduced, where the high-level policy communicates its strategic guidance to the low-level policy via a learned “intent vector,” facilitating intelligent collaboration. Second, an Energy-Aware Graph Attention Network (Ea-GAT) is designed for the low-level policy. By endogenously embedding an energy feasibility model into its attention mechanism, the Ea-GAT guarantees the generation of dynamically feasible flight paths. Comprehensive simulations and a physical experiment validate the proposed framework. The results demonstrate a significant improvement in mission efficiency, with the makespan reduced by up to 16.3%. This work highlights the substantial benefits of joint optimization for dynamic robotic applications. Full article

This paper addresses secure transmission in a high-occlusion urban environment, where an intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communication system serves a legitimate user while countering an eavesdropper. The UAV signal is reflected to the base station through the IRS. We study the secure energy efficiency optimization problem. The tightly coupled optimization variables make the problem difficult to solve directly. Therefore, we decompose the original problem into three sub-problems. For the UAV active beamforming design, the closed-form solution can be obtained directly. For the IRS phase shift optimization, we propose an optimization algorithm based on Riemannian manifolds to obtain the optimal solution. Due to the non-convex fractional UAV trajectory optimization, it can be solved by successive convex approximation (SCA) and the Dinkelbach algorithm. Different comparison schemes are designed to evaluate the effectiveness of the proposed algorithm. The simulation results show that the proposed algorithm has improved advantages compared with other schemes. Full article

Visible light communication (VLC) aided by unmanned aerial vehicles (UAVs) offers significant advantages in adapting to dynamic network requirements, but the endurance and service capability of UAVs are still the key limiting factors. To overcome this limitation, the UAV array-aided VLC system with an enhanced angle diversity transmitter (ADT) is proposed to improve energy efficiency (EE). Enhanced ADTs with varying LED layers, multiple LEDs per layer, and inter-layer rotation angles are considered. By jointly optimizing the inclination angle of the side LEDs in the enhanced ADT and the hovering height of the UAVs, this research aims to minimize the power consumption of the UAV array-aided VLC system while meeting illumination and communication requirements. The simulation results present that the EE of the centralized single-UAV VLC system can be greatly improved by applying the enhanced ADT structures. More specifically, compared with the single LED transmitter configuration, an EE enhancement of up to 215.7% can be achieved by the enhanced ADT, which employs multi-layer LEDs, inter-layer rotation, and layer-doubled designs. In addition, the EE can be further improved by the deployment of a distributed UAV array. The VLC system with four UAVs is demonstrated to achieve a peak EE performance of 19.9 bits/J/Hz, representing a 298% improvement over the centralized single-UAV configuration. Full article

Differentiation of various crops in small-scale crops is important for food security and economic development in many rural communities. Despite being the oldest and simplest classification technique, thresholding continues to gain popularity for classifying complex images. This study aimed to evaluate the effectiveness of a multilevel thresholding technique in differentiating various crop types in small-scale farms. Three (3) types of crops were identified in the study area, and these were cabbage, maize, and sugar bean. Analytical Spectral Devices (ASD) spectral reflectance data were used to detect subtle differences in the spectral reflectance of crops. Analysis of ASD reflectance data revealed reflectance disparities among the surveyed crops in the Green, red, near-infrared (NIR), and shortwave infrared (SWIR) wavelengths. The ASD reflectance data in the Green, red, and NIR were then used to define thresholds for different crop types. The multilevel thresholding technique was used to classify the surveyed crops on the unmanned aerial vehicle (UAV) imagery, using the defined thresholds as input. Three (3) other machine learning classification techniques were also used to offer a baseline for evaluating the performance of the MLT approach, and these were the multilayer perceptron (MLP) neural network, radial basis function neural network (RBFNN), and the Kohonen’s self-organizing maps (SOM). An analysis of crop cover patterns revealed variations in crop area cover as predicted by the MLT and selected machine learning techniques. The classification results of the surveyed crops revealed the area covered by cabbage crops to be 7.46%, 6.01%, 10.33%, 7.05%, 9.48%, and 7.04% as predicted by the MLT on Blue band, MLT on Green band, MLT on NIR, MLP, RBFNN, and SOM, respectively. The area covered by maize crops as predicted by the MLT on Blue band, MLT on Green band, MLT on NIR, MLP, RBFNN, and SOM were noted to be 13.62%, 26.41%, 12.12%, 11.03%, 12.19% and 15.11%, respectively. Sugar bean was noted to occupy 57.51%, 43.72%, 26.77%, 27.44%, 24.15%, and 16.33% as predicted by the MLT on Blue band, MLT on Green band, MLT on NIR, MLP, RBFNN, and SOM, respectively. Accuracy assessment results generally showed poor crop pattern prediction with all tested classifiers in categorizing the surveyed crops, with the kappa index of agreement (KIA) values of 0.372, 0.307, 0.488, 0.531, 0.616, and 0.659 for the MLT on Blue band, MLT on Green band, MLT on NIR, MLP, RBFNN, and Kohonen’s SOM, respectively. Despite recommendations by recent studies, we noted that the MLT was noted to be unsuitable for classifying complex features such as spectrally overlapping crops. Full article

As UAVs are increasingly deployed in complex scenarios such as disaster monitoring, emergency rescue, and power-line inspection, traditional command and control systems face severe challenges in intelligent decision-making, resource allocation, and elastic scalability. To address these issues, we first propose a distributed UAV command and control system based on large language models of the LLaMA2 family. The system adopts a “cloud–edge–terminal” architecture, using 5G as the backbone network and the Internet of Things as a supplement, with edge computing serving as the computing platform. LLMs of various parameter scales are deployed on demand at different hierarchical levels to support both training and inference, enabling intelligent decision-making and optimal resource allocation. Second, we establish a multidimensional system model that integrates computation, communication, and energy consumption, providing a theoretical analysis of network dynamics, resource constraints, and task heterogeneity. Furthermore, we develop an improved Grey Wolf Optimizer (ILGWO) that incorporates adaptive weights, an elite learning strategy, and Lévy flights to solve the multi-objective optimization problem posed by the system. Experimental results show that the proposed system improves task latency, energy efficiency, and resource utilization by 34.2%, 29.6%, and 31.8%, respectively, compared with conventional methods. Real-world field tests demonstrate that, in urban rescue scenarios, the system reduces response latency by 44.7% and increases coordination efficiency by 39.5%. This work offers a reference for the optimized design and practical deployment of UAV command and control systems in complex environments. Full article

With the rapid growth of unmanned aerial vehicles (UAVs) and IoT users, spectrum resources are becoming increasingly scarce, making cognitive radio (CR) technology a key approach to improving spectrum utilization. However, traditional antennas are difficult to meet the lightweight, compact, and low-drag requirements of small UAVs due to spatial constraints. This paper proposes a tri-mode frequency reconfigurable flexible antenna that can be conformally integrated onto UAV wing arms to enable CR dynamic frequency communication. The antenna uses a polyimide (PI) substrate and has compact dimensions of 31.4 × 58 × 0.05 mm³. A microstrip line-based frequency-selective network is designed, incorporating PIN and varactor diodes to realize three operation modes, dual-band (2.25~3.55 GHz, 5.6~6.75 GHz), single-band (3.35~5.3 GHz), and continuous tuning (4.3~6.1 GHz), covering WLAN, WiMAX, and 5G NR bands. Test results show that the antenna maintains stable performance under conformal conditions, with frequency shifts less than 4%, gain (3.65~4.77 dBi), and radiation efficiency between 67.2% and 82.9%. The tuning ratio reaches 38.8% in the continuous mode. This design offers a new solution for CR communication in compact UAV platforms and shows promising application potential. Full article

Biological swarms communicate through decentralized, adaptive behaviors shaped by local interactions, selective attention, and symbolic signaling. These principles of animal communication enable robust coordination without centralized control or persistent connectivity. This work presents a proof of concept that identifies, evaluates, and translates biological communication strategies into a generative visual language for unmanned aerial vehicle (UAV) swarm agents operating in radio-frequency (RF)-denied environments. Drawing from natural exemplars such as bee waggle dancing, white-tailed deer flagging, and peacock feather displays, we construct a configuration space that encodes visual messages through trajectories and LED patterns. A large language model (LLM), preconditioned using retrieval-augmented generation (RAG), serves as a generative translation layer that interprets perception data and produces symbolic UAV responses. Five test cases evaluate the system’s ability to preserve and adapt signal meaning through within-modality fidelity (maintaining symbolic structure in the same modality) and cross-modal translation (transferring meaning across motion and light). Covariance and eigenvalue-decomposition analysis demonstrate that this bio-agentic approach supports clear, expressive, and decentralized communication, with motion-based signaling achieving near-perfect clarity and expressiveness (0.992, 1.000), while LED-only and multi-signal cases showed partial success, maintaining high expressiveness (~1.000) but with much lower clarity (≤0.298). Full article