Deployment Of Unmanned Aerial Vehicles Research Articles

A Low-Cost and Lightweight Real-Time Object-Detection Method Based on UAV Remote Sensing in Transportation Systems

Accurate detection of transportation objects is pivotal for enhancing driving safety and operational efficiency. In the rapidly evolving domain of transportation systems, the utilization of unmanned aerial vehicles (UAVs) for low-altitude detection, leveraging remotely-sensed images and videos, has become increasingly vital. Addressing the growing demands for robust, real-time object-detection capabilities, this study introduces a lightweight, memory-efficient model specifically engineered for the constrained computational and power resources of UAV-embedded platforms. Incorporating the FasterNet-16 backbone, the model significantly enhances feature-processing efficiency, which is essential for real-time applications across diverse UAV operations. A novel multi-scale feature-fusion technique is employed to improve feature utilization while maintaining a compact architecture through passive integration methods. Extensive performance evaluations across various embedded platforms have demonstrated the model’s superior capabilities and robustness in real-time operations, thereby markedly advancing UAV deployment in crucial remote-sensing tasks and improving productivity and safety across multiple domains.

INVESTIGATION OF DEEP LEARNING MODELS BASED ON SINGLE-LAYER SimpleRNN, LSTM AND GRU NETWORKS FOR RECOGNIZING SOUNDS OF UAV DISTANCES

In recent years, the potential risks posed by easily moving objects have highlighted the need for intelligent surveillance systems in protected areas, primarily to ensure the safety of human lives. Among the most common of these objects are unmanned aerial vehicles (UAVs). Recent advances in deep learning techniques for recognizing audio signals have made these techniques effective in identifying moving or aerial objects, especially those powered by engines. And the growing deployment of UAVs has made their rapid recognition in various suspicious or unauthorized circumstances critical. Detecting suspicious drone flights, especially in restricted areas, remains a significant research challenge. It is vital to perform the task of determining their distance in order to quickly detect drones approaching people in such protected areas. Therefore, this paper aims to study the research question of recognizing UAV audio data from different distances. That is, recognizing drone audio at different distances was experimentally studied using Simple RNN, LSTM and GRU based deep learning models. The main objective of this study is based on finding one of the capable types of recurrent network for the task of recognizing UAV audio data at different distances. During the experimental study, the recognition abilities of Single-layer Simple RNN, LSTM and GRU recurrent network types were studied from two basic directions: with recognition accuracy curves and classification reports. As a result, LSTM and GRU based models showed high recognition ability for these types of audio signals. It was noted that UAVs can reliably predict distances greater than 10 meters based on the proposed deep learning architecture.

Dynamic environment UAV deployment algorithm based on potential game theory

AbstractTo address the issue of low coverage resulting from the challenge of acquiring the optimal deployment position in commonly used distributed deployment algorithms, this study presents a three‐dimensional deployment algorithm for Unmanned Aerial Vehicles (UAVs) based on potential games. First, a local mutually beneficial game model is designed to demonstrate the existence of exact potential games and Nash equilibrium. The Nash equilibrium solution corresponds to the maximum coverage. Next, drawing inspiration from exploration, a solution method called Exploration Spatial Adaptive Play is proposed. It utilizes the maximum utility function value from multiple step sizes in the exploration direction to update the action selection probability, thereby ensuring the optimal deployment position in each decision cycle. To address the issue of sensor position error, a method for processing sensor position errors is proposed. The simulation results demonstrate that the proposed distributed deployment algorithm achieves higher coverage compared to commonly used methods.

Distributed beamforming design for UAV‐based integrated communication and jamming systems

AbstractThe integrated communication and jamming system is proposed by exploiting the diversity gain and flexible deployment of unmanned aerial vehicle (UAV)‐based distributed multiple input multiple output technology, which is expected to gain higher spectral efficiency and system scalability. This paper designs transmit beamforming of multiple UAVs so that they can simultaneously impose jamming signal to jamming targets and communication signal to communication users at the same frequency. The aim is to maximize the total jamming effect of all jamming targets while achieving a certain communication rate of communication users by optimizing the transmit beamforming matrices. Two cases are considered in which the UAV swarm works in clock synchronization and clock asynchronization. Specifically, the problem is formulated into a non‐convex optimization in the synchronous case. A near optimal solution is derived by decoupling the beamforming matrix into multi‐user interference suppression matrix and single‐user beamforming matrix. In the asynchronous case, the transmitter–receiver matching relation needs to consider and multiple optimization variables are highly coupled, penalty factors and intermediate variables to convert the optimization problem to convex programming are introduced. The simulation results show that the proposed beamforming design algorithm coordinates the communication and jamming signal rationally and outperforms the conventional beamforming design algorithms.

An ellipse-based locating method for flexible deployment of emergency UAVs

Unmanned Aerial Vehicles (UAVs), or drones, are gaining attention in emergency response for their rapid mobility in dynamic scenarios. Constrained by limited endurance and payload, UAVs typically operate in a ”depot-customer-depot” paradigm. Thus, optimally locating multiple depots is critical to achieving operational efficiency and flexibility. Traditional location models, which rely on circular coverage, fail to capture the actual reachable area for UAV round-trips between multiple depots within a given endurance range. This drawback restricts deployment flexibility or results in excessive redundancy, even making it impractical. To address this limitation, we introduce an ellipse-based locating method for flexible UAV deployment, inspired by UAV reachability and process flexibility in manufacturing. This approach attempts to optimize the redundancy of multi-depot coverage for demand points to achieve a better balance between deployment flexibility and resource requirements. To tackle the model’s computational challenge, we present an improved Benders decomposition algorithm that speeds up the solution process by analytically addressing subproblems and implementing dominance rules to manage the master problem’s size. Simulations show that the proposed model greatly improves the ability to handle uncertainties by incorporating slight redundancy in emergency resources, and the fulfillment rate of demand fluctuations is increased by 5%–20%, which shows the superiority of enhancing the mobility and flexibility of UAV deployment.

Joint differential evolution algorithm in RIS-assisted multi-UAV IoT data collection system

This paper investigates a Reconfigurable Intelligent Surface (RIS)-assisted multi-UAV data collection system, in which unmanned aerial vehicles (UAVs) collect data from Internet of Things (IoT) devices. The RIS, mounted on building surfaces, plays a vital role in preventing obstruction and improving the communication quality of the IoT-UAV transmission link. Our aim is to minimize the energy consumption of this system, including the transmission energy consumption of IoT devices and the hovering energy consumption of UAVs, by optimizing the deployment of UAVs and the phase shifts of RIS. To achieve this goal, a multi-UAV deployment and phase shift of RIS optimization algorithm (MUDPRA) is proposed that consists of two phases. In the first phase, a joint differential evolution (DE) algorithm with a two-layer structure featuring a variable population size, namely DEC-ADDE, is proposed to optimize the UAV deployment. Specifically, each UAV’s location is encoded as an individual, with the whole UAV deployment is considered as the population in DEC-ADDE. Thus, a differential evolution clustering (DEC) algorithm is employed initially to initialize the population, which allows for obtaining better initial UAV deployment without the need for a predefined number of UAVs. Subsequently, an adaptive and dynamic DE algorithm (ADDE) is employed to produce offspring population to further optimize UAV deployment. Finally, an adaptive updating strategy is adopted to adjust the population size to optimize the number of UAVs. In the second phase, a low-complexity method is proposed to optimize the phase shift of RIS with the aim of enhancing the IoT-UAV data transmission rate. Experimental results conducted on eight instances involving IoT devices ranging from 60 to 200 demonstrate the effectiveness of MUDPRA in minimizing energy consumption of this system compared to six alternative algorithms and three benchmark systems.

Towards an optimal 3-D design and deployment of 6G UAVs for interference mitigation under terrestrial networks

Unmanned aerial vehicles (UAVs) have opened new communication possibilities by being able to access remote areas. Their ability to serve a large number of users based on demand and adaptability is a key strength. In Sixth Generation (6G) networks, UAVs are highly valued for their cost-efficiency and versatile deployment. However, the mobility of UAVs introduces different types of interference issues, resulting in a decrease in network performance and quality of service (QoS) for edge users. To address these challenges, this paper introduces a clustering-based solution involving three main steps. Firstly, UAVs are deployed in three-dimension (3D) space based on user requests using mini-batch K-mean clustering Subsequently, re-clustering is explored to tackle load balancing within clusters. Finally, outliers and boundary users are classified to enhance QoS for edge users. This model effectively reduces interference and boosts UAV reliability in terrestrial networks. Also, a case study is presented to show how UAVs can mitigate interference in maritime communication within terrestrial networks. Numerical results demonstrated that the proposed scheme increases throughput by 33.06% and reduces energy consumption and time delay by 73.15% and 9.15%, respectively, as compared to the existing baseline schemes.

Adaptive Aberrance Repressed Correlation Filters with Cooperative Optimization in High-Dimensional Unmanned Aerial Vehicle Task Allocation and Trajectory Planning

In the rapidly evolving field of unmanned aerial vehicle (UAV) applications, the complexity of task planning and trajectory optimization, particularly in high-dimensional operational environments, is increasingly challenging. This study addresses these challenges by developing the Adaptive Distortion Suppression Correlation Filter Cooperative Optimization (ARCF-ICO) algorithm, designed for high-dimensional UAV task allocation and trajectory planning. The ARCF-ICO algorithm combines advanced correlation filter technologies with multi-objective optimization techniques, enhancing the precision of trajectory planning and efficiency of task allocation. By incorporating weather conditions and other environmental factors, the algorithm ensures robust performance at low altitudes. The ARCF-ICO algorithm improves UAV tracking stability and accuracy by suppressing distortions, facilitating optimal path selection and task execution. Experimental validation using the UAV123@10fps and OTB-100 datasets demonstrates that the ARCF-ICO algorithm outperforms existing methods in Area Under the Curve (AUC) and Precision metrics. Additionally, the algorithm’s consideration of battery consumption and endurance further validates its applicability to current UAV technologies. This research advances UAV mission planning and sets new standards for UAV deployment in both civilian and military applications, where adaptability and accuracy are critical.

Towards 6G Satellite–Terrestrial Networks: Analysis of Air Mobility Operations

This paper presents an analytical exploration of sixth-generation (6G) satellite–terrestrial integrated networks, focusing specifically on their applications within air mobility operations, such as those involving unmanned aerial vehicles (UAVs). As the integration of satellite and terrestrial networks promises to revolutionize mobile communication by extending coverage and enhancing connectivity, this study delves into two critical aspects: link budget analysis and handover and mobility analysis for UAVs. The link budget analysis assesses the communication requirements necessary to ensure robust and consistent connectivity between satellites and UAVs, accounting for factors such as path loss, antenna gains, and power transmission. Meanwhile, the handover and mobility analysis investigates the challenges and solutions associated with UAVs transitioning between different network nodes and layers in a dynamic aerial environment. This paper utilizes theoretical models and simulations to provide insights into the design and optimization of these networks, aiming to enhance the reliability and efficiency of UAV operations in the context of the emerging 6G landscape. The findings propose not only technological advancements in network architecture but also practical guidelines for the deployment of UAVs in complex environments, marking a significant step toward the realization of a fully integrated, satellite-terrestrial ecosystem.

An efficient multi-objective UAV assisted RSU deployment (MOURD) scheme for VANET

A Roadside Unit (RSU) serves as essential infrastructure in Vehicular Ad Hoc Networks (VANETs) that supports the goals of Intelligent Transportation Systems (ITS) by providing safety services, shared storage, and enhanced internet connectivity to vehicular users, drivers, and pedestrians. Additionally, the efficiency of VANETs, concerning network service utility and latency, depends on the relative positioning of these RSUs within the network topology. Most existing RSU deployment approaches deal with a single objective, either enhancing network service utility or minimizing the latency. For instance, some studies suggest deploying RSUs in high-traffic road segments that enhance network service utility but lead to higher latency. Conversely, some suggest deploying the RSUs in low-traffic road segments that minimize the network latency, but there will be low network service utility. Hence, there exists a trade-off between these two conflicting objectives in VANETs, and none of the studies address both objectives simultaneously. To achieve the balance between these two objectives, this paper proposes a Multi-Objective UAV assisted RSU Deployment (MOURD) scheme that leverages the Unmanned Aerial Vehicles (UAVs) for VANET efficiency. The MOURD scheme statically places RSUs in high-traffic road segments and dynamically dispatches the UAVs in low-traffic road segments to facilitate seamless network coverage and minimize the overall network latency. The simulation results on the road network of Delhi, India, demonstrate the effectiveness of the proposed MOURD scheme compared to other benchmark RSU & UAV deployment approaches. MOURD scheme outperforms on an average of 17.42%, 13.29%, 15.67% and 6.23% in terms of vehicle connection time, packet delivery ratio, throughput, and latency, respectively.

Effectiveness of Unmanned Aerial Vehicle on Pipeline Surveillance in Nigeria: A Preliminary Survey

This study examines the usefulness of Unmanned Aerial Vehicle (UAV) on pipeline surveillance in Nigeria over the traditional method. The purpose of this study is to investigate whether there is an effective role occasioned with the use of UAV against traditional method of pipeline surveillance. The population of this study is 4648, from which the sample size of 369 was selected using a combination of cluster sampling and deliberate sampling. Questionnaires were the major instrument used in data collection. Simple percentages were used in analyzing the data collected from the questionnaire. Hypothesis were formulated, statistics was applied to test the hypothesis. Results show that there are challenges occasioned by the use the traditional pipeline surveillance method; that there is a relationship between asset safety and employee’s safety by the deployment of a UAV against the traditional method; that the deployment of a UAV optimize profitability and reduce loss occasioned with the use of traditional surveillance method, and that UAV is a preferred for pipeline surveillance compared to traditional surveillance method. The study recommended that oil and gas companies should as a matter of urgency embrace the deployment of UAV on pipeline safety to promote sustainability, efficient service delivery, real time imagery and more. The given solution is effective, cost-efficient and very reliable as it promotes health, safety and environmental best practice all over developed countries of the world.

Dynamic UAV Deployment Scheme Based on Edge Computing for Forest Fire Scenarios.

This study investigates the dynamic deployment of unmanned aerial vehicles (UAVs) using edge computing in a forest fire scenario. We consider the dynamically changing characteristics of forest fires and the corresponding varying resource requirements. Based on this, this paper models a two-timescale UAV dynamic deployment scheme by considering the dynamic changes in the number and position of UAVs. In the slow timescale, we use a gate recurrent unit (GRU) to predict the number of future users and determine the number of UAVs based on the resource requirements. UAVs with low energy are replaced accordingly. In the fast timescale, a deep-reinforcement-learning-based UAV position deployment algorithm is designed to enable the low-latency processing of computational tasks by adjusting the UAV positions in real time to meet the ground devices' computational demands. The simulation results demonstrate that the proposed scheme achieves better prediction accuracy. The number and position of UAVs can be adapted to resource demand changes and reduce task execution delays.

Drone-Borne Magnetic Gradiometry in Archaeological Applications.

The use of magnetometers arranged in a gradiometer configuration offers a practical and widely used solution, particularly in archaeological applications where the sources of interest are generally shallow. Since magnetic anomalies due to archaeological remains often have low amplitudes, highly sensitive magnetic sensors are kept very close to the ground to reveal buried structures. However, the deployment of Unmanned Aerial Vehicles (UAVs) is increasingly becoming a reliable and valuable tool for the acquisition of magnetic data, providing uniform coverage of large areas and access to even very steep terrain, saving time and reducing risks. However, the application of a vertical gradiometer for drone-borne measurements is still challenging due to the instability of the system drone magnetometer in flight and noise issues due to the magnetic interference of the mobile platform or related to the oscillation of the suspended sensors. We present the implementation of a magnetic vertical gradiometer UAV system and its use in an archaeological area of Southern Italy. To reduce the magnetic and electromagnetic noise caused by the aircraft, the magnetometer was suspended 3m below the drone using ropes. A Continuous Wavelet Transform analysis of data collected in controlled tests confirmed that several characteristic power spectrum peaks occur at frequencies compatible with the magnetometer oscillations. This noise was then eliminated with a properly designed low-pass filter. The resulting drone-borne vertical gradient data compare very well with ground-based magnetic measurements collected in the same area and taken as a control dataset.

Joint UAV Deployment and Task Offloading in Large-Cale UAV-Assisted MEC: A Multiobjective Evolutionary Algorithm

With the development of digital economy technologies, mobile edge computing (MEC) has emerged as a promising computing paradigm that provides mobile devices with closer edge computing resources. Because of high mobility, unmanned aerial vehicles (UAVs) have been extensively utilized to augment MEC to improve scalability and adaptability. However, with more UAVs or mobile devices, the search space grows exponentially, leading to the curse of dimensionality. This paper focus on the combined challenges of the deployment of UAVs and the task of offloading mobile devices in a large-scale UAV-assisted MEC. Specifically, the joint UAV deployment and task offloading problem is first modeled as a large-scale multiobjective optimization problem with the purpose of minimizing energy consumption while improving user satisfaction. Then, a large-scale UAV deployment and task offloading multiobjective optimization method based on the evolutionary algorithm, called LDOMO, is designed to address the above formulated problem. In LDOMO, a CSO-based evolutionary strategy and a MLP-based evolutionary strategy are proposed to explore solution spaces with different features for accelerating convergence and maintaining the diversity of the population, and two local search optimizers are designed to improve the quality of the solution. Finally, simulation results show that our proposed LDOMO outperforms several representative multiobjective evolutionary algorithms.

Mountain search and recovery: An unmanned aerial vehicle deployment case study and analysis

AbstractMountain search and rescue (MSAR) seeks to assist people in extreme remote environments. This method of emergency response often relies on crewed aircraft to perform aerial visual search. Many MSAR teams use low‐cost, consumer‐grade unmanned aerial vehicles (UAVs) to augment the crewed aircraft operations. These UAVs are primarily developed for aerial photography and lack many features critical (e.g., probability‐prioritized coverage path planning) to support MSAR operations. As a result, UAVs are underutilized in MSAR. A case study of a recent mountain search and recovery scenario that did not use, but may have benefited from, UAVs is provided. An overview of the mission is augmented with a subject matter expert‐informed analysis of how the mission may have benefited from current UAV technology. Lastly, mission relevant requirements are presented along with a discussion of how future UAV development can seek to bridge the gap between state‐of‐the‐art robotics and MSAR.

Max–min fairness beamforming design for UAV‐enabled integrated sensing and communication

AbstractThis paper investigates the design of transmit beamforming in an integrated sensing and communication system, where an unmanned aerial vehicle (UAV) equipped with a uniform linear array (ULA) transmits combined information‐carrying and dedicated sensing signals to perform multi‐user communication and simultaneously sense potential targets. First, the transmit beamforming design is formulated as a series of non‐convex optimisation problems, and then jointly design the information and sensing beamforming to maximise the minimum communication rate under an optimised UAV deployment location, subject to the sensing beampattern gain requirements and the limited transmit power of the UAV. However, due to the application of ULA on the UAV, the formulated problems are highly non‐convex and difficult to solve optimally. To address the aforementioned issue, an efficient algorithm is proposed to find suboptimal yet high‐quality solutions. This is achieved by adopting the successive convex approximation and semidefinite relaxation. The numerical results confirm the effectiveness of the proposed algorithm and demonstrate that the transmit beamforming design achieves a balance between communication and sensing performance.

Integrating Bio-Inspired Approaches with UAV-FSN Systems for Clustered Target Area Detection in Agriculture

Abstract: Target area detection in agriculture plays a crucial role in optimizing crop management and resource allocation. Traditional methods often lack the precision and efficiency required for modern farming practices. Integrating bio-inspired approaches with UAV-FSN systems offers a promising solution to this challenge. By harnessing principles from nature, such as swarm intelligence and reinforcement learning, it becomes possible to optimize the deployment and coordination of UAVs within FSN for clustered target area detection in agriculture. This research explores the integration of bio-inspired approaches with UAV-FSN systems for clustered target area detection in agriculture. The increasing demand for precision agriculture necessitates efficient methods for identifying target areas affected by various factors. In this study, we introduce a novel algorithm, the QL-ABC Algorithm, which combines the Artificial Bee Colony (ABC) Algorithm with Q-Learning to optimize the deployment and movement of unmanned aerial vehicles (UAVs) within flying sensor networks (FSN) for target area detection. The performance outcomes of the proposed QL-ABC Algorithm on comparison with the existing algorithms are analysed using extensive simulation analysis with the appropriate simulation metrics: packet delivery ratio, mean end-to-end delay, and energy consumption. Results validate the usefulness of the proposed QL-ABC algorithm in accurately detecting clustered target areas in agricultural landscapes, thus contributing to advancements in precision farming practices and sustainable agriculture. Keywords: Flying Sensor Networks, Artificial Bee Colony Algorithm, Clustered Target Area Detection, Q-Learning, Unmanned Aerial Vehicles.

Reinforcement learning vs rule-based dynamic movement strategies in UAV assisted networks

Since resource allocation of cellular networks is not dynamic, some cells may experience unplanned high traffic demands due to unexpected events. Unmanned aerial vehicles (UAV) can be used to provide the additional bandwidth required for data offloading.Considering real-time and non-real-time traffic classes, our work is dedicated to optimize the placement of UAVs in cellular networks by two approaches. A first rule-based, low complexity method, that can be embedded in the UAV, while the other approach uses Reinforcement Learning (RL). It is based on Markov Decision Processes (MDP) for providing optimal results. The energy of the UAV battery and charging time constraints have been taken into account to cover a typical cellular environment consisting of many cells.We used an open dataset for the Milan cellular network provided by Telecom Italia to evaluate the performance of both proposed models. Considering this dataset, the MDP model outperforms the rule-based algorithm. Nevertheless, the rule-based one requires less processing complexity and can be used immediately without any prior data. This work makes a notable contribution to developing practical and optimal solutions for UAV deployment in modern cellular networks.

UAV-Mounted RIS-Aided Mobile Edge Computing System: A DDQN-Based Optimization Approach

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are increasingly employed in mobile edge computing (MEC) systems to flexibly modify the signal transmission environment. This is achieved through the active manipulation of the wireless channel facilitated by the mobile deployment of UAVs and the intelligent reflection of signals by RISs. However, these technologies are subject to inherent limitations such as the restricted range of UAVs and limited RIS coverage, which hinder their broader application. The integration of UAVs and RISs into UAV–RIS schemes presents a promising approach to surmounting these limitations by leveraging the strengths of both technologies. Motivated by the above observations, we contemplate a novel UAV–RIS-aided MEC system, wherein UAV–RIS plays a pivotal role in facilitating communication between terrestrial vehicle users and MEC servers. To address this challenging non-convex problem, we propose an energy-constrained approach to maximize the system’s energy efficiency based on a double-deep Q-network (DDQN), which is employed to realize joint control of the UAVs, passive beamforming, and resource allocation for MEC. Numerical results demonstrate that the proposed optimization scheme significantly enhances the system efficiency of the UAV–RIS-aided time division multiple access (TDMA) network.

An Improved Lightweight Deep Learning Model and Implementation for Track Fastener Defect Detection with Unmanned Aerial Vehicles

Track fastener defect detection is an essential component in ensuring railway safety operations. Traditional manual inspection methods no longer meet the requirements of modern railways. The use of deep learning image processing techniques for classifying and recognizing abnormal fasteners is faster, more accurate, and more intelligent. With the widespread use of unmanned aerial vehicles (UAVs), conducting railway inspections using lightweight, low-power devices carried by UAVs has become a future trend. In this paper, we address the characteristics of track fastener detection tasks by improving the YOLOv4-tiny object detection model. We improved the model to output single-scale features and used the K-means++ algorithm to cluster the dataset, obtaining anchor boxes that were better suited to the dataset. Finally, we developed the FPGA platform and deployed the transformed model on this platform. The experimental results demonstrated that the improved model achieved an mAP of 95.1% and a speed of 295.9 FPS on the FPGA, surpassing the performance of existing object detection models. Moreover, the lightweight and low-powered FPGA platform meets the requirements for UAV deployment.