Journal articles on the topic 'The UAV placement problem'
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1
Goehar, Huda, Ahmed S. Khwaja, Ali A. Alnoman, Alagan Anpalagan, and Muhammad Jaseemuddin. "Investigation of a HAP-UAV Collaboration Scheme for Throughput Maximization via Joint User Association and 3D UAV Placement." Sensors 23, no. 13 (July 2, 2023): 6095. http://dx.doi.org/10.3390/s23136095.
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In this paper, a collaboration scheme between a high-altitude platform (HAP) and several unmanned aerial vehicles (UAVs) for wireless communication networks is investigated. The main objective of this study is to maximize the total downlink throughput of the ground users by optimizing the UAVs’ three-dimensional (3D) placements and user associations. An optimization problem is formulated and a separate genetic-algorithm-based approach is proposed to solve the problem. The K-means algorithm is also utilized to find the initial UAV placement to reduce the convergence time of the proposed genetic-algorithm-based allocation. The performance of the proposed algorithm is analyzed in terms of convergence time, complexity, and fairness. Finally, the simulation results show that the proposed HAP-UAV integrated network achieves a higher total throughput through joint user association and UAV placement schemes compared to a scheme with a single HAP serving all users.
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Lee, Chunghyun, Gunhee Jang, Nhu-Ngoc Dao, Demeke Shumeye Lakew, Cheol Lee, and Sungrae Cho. "Competitive Game Theoretic Clustering-Based Multiple UAV-Assisted NB-IoT Systems." Electronics 10, no. 3 (February 2, 2021): 356. http://dx.doi.org/10.3390/electronics10030356.
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Unmanned aerial vehicle (UAV) communication is regarded as a promising technology for lightweight Internet of Things (IoT) communications in narrowband-IoT (NB-IoT) systems deployed in rugged terrain. In such UAV-assisted NB-IoT systems, the optimal UAV placement and resource allocation play a critical role. Consequently, the joint optimization of the UAV placement and resource allocation is considered in this study to improve the system capacity. Because the considered optimization problem is an NP-hard problem and owing to its non-convex property, it is difficult to optimize both the UAV placement and resource allocation simultaneously. Therefore, a competitive clustering algorithm has been developed by exchanging strategies between the UAV and the adjacent IoT devices to optimize the UAV placement. With multiple iterations, the UAV and the IoT devices within the coverage area of the UAV, converge their clustering strategies, which are suboptimal, to satisfy both sides. The bordering IoT devices of the adjacent clusters are then migrated heuristically toward each other to obtain the optimal system capacity maximization. Finally, the transmission throughput is optimized using the Nash equilibrium. The simulation results demonstrate that the algorithms proposed in this study exhibit rapid convergence, within 10 iterations, even in a large environment. The performance evaluation demonstrates that the proposed scheme improves the system capacity of the existing schemes by approximately 28%.
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Xue, Yishi, Bo Xu, Wenchao Xia, Jun Zhang, and Hongbo Zhu. "Backhaul-Aware Resource Allocation and Optimum Placement for UAV-Assisted Wireless Communication Network." Electronics 9, no. 9 (August 28, 2020): 1397. http://dx.doi.org/10.3390/electronics9091397.
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Driven by its agile maneuverability and deployment, the unmanned aerial vehicle (UAV) becomes a potential enabler of the terrestrial networks. In this paper, we consider downlink communications in a UAV-assisted wireless communication network, where a multi-antenna UAV assists the ground base station (GBS) to forward signals to multiple user equipments (UEs). The UAV is associated with the GBS through in-band wireless backhaul, which shares the spectrum resource with the access links between UEs and the UAV. The optimization problem is formulated to maximize the downlink ergodic sum-rate by jointly optimizing UAV placement, spectrum resource allocation and transmit power matrix of the UAV. The deterministic equivalents of UE’s achievable rate and backhaul capacity are first derived by utilizing large-dimensional random matrix theory, in which, only the slowly varying large-scale channel state information is required. An approximation problem of the joint optimization problem is then introduced based on the deterministic equivalents. Finally, an algorithm is proposed to obtain the optimal solution of the approximate problem. Simulation results are provided to validate the accuracy of the deterministic equivalents, and the effectiveness of the proposed method.
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Safwat, Nour El-Din, Ismail Mohammed Hafez, and Fatma Newagy. "3D Placement of a New Tethered UAV to UAV Relay System for Coverage Maximization." Electronics 11, no. 3 (January 27, 2022): 385. http://dx.doi.org/10.3390/electronics11030385.
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In this paper, a new relay system that uses the UAV as a relay station between the tethered UAV and ground user (TU2U2G) is proposed. The TU2U2G system uses a TUAV as a viable alternative to replace BS and provide seamless service over a cable that simultaneously supplies stable power and a reliable wired data-link connection from a ground control station. Compared to the BS, TUAV improves system coverage due to its high altitude. Also, it overcomes the antenna down-tilting, which increases the path loss between BS and UAV in the cellular system. In addition, it overcomes the UAV drawback of the batteries’ limited capacity. Therefore, TUAV can achieve the main requirements of a reliable cellular BS in terms of endurance, backhaul link quality, and the advantage of the UAV’s high altitude. After that, the optimization problem is formulated to maximize UAV relay station coverage under the power budget and maximum UAV height constraints. For simplicity, the 3D placement of the UAV is decoupled to the vertical and horizontal placement. Then, a 3D placement algorithm for the system is proposed. The UAV placement in the TU2U2G system compared to the cellular system shows better results in terms of optimum UAV height, maximum coverage radius, and maximum relaying distance.
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Liu, Chaoyi, and Qi Zhu. "Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning." Applied Sciences 13, no. 6 (March 15, 2023): 3771. http://dx.doi.org/10.3390/app13063771.
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Aiming at the unmanned aerial vehicle (UAV)-assisted federated learning wireless-network scenario, and considering the influence of the UAV altitude on the coverage area, we propose a joint optimization algorithm of UAV placement, computation and communication resources. Considering the energy efficiency and federated learning performance, we defined the cost function of the system. Under the constraint of the total delay of federated learning completion, we formulated an optimization problem of minimizing the cost function to achieve the balance between the total energy consumption of users and the federated learning performance. Since the formulated optimization problem is a non-convex problem, in order to solve this problem, we decomposed it into three optimization subproblems: UAV horizontal placement, local accuracy and computation and communication resources. We used successive convex approximation (SCA), the Dinkelbach Method, the Bisection method and KKT condition, respectively, to solve the three subproblems, and finally obtain the optimal solutions through iteration of the three subproblems. Simulation results show that compared with the federated learning scenario under fixed UAV altitude, our proposed algorithm not only guarantees the learning performance, but also reduces more users’ total energy consumption.
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Lan, Tingting, Danyang Qin, and Guanyu Sun. "Joint Optimization on Trajectory, Cache Placement, and Transmission Power for Minimum Mission Time in UAV-Aided Wireless Networks." ISPRS International Journal of Geo-Information 10, no. 7 (June 23, 2021): 426. http://dx.doi.org/10.3390/ijgi10070426.
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In recent years, due to the strong mobility, easy deployment, and low cost of unmanned aerial vehicles (UAV), great interest has arisen in utilizing UAVs to assist in wireless communication, especially for on-demand deployment in emergency situations and temporary events. However, UAVs can only provide users with data transmission services through wireless backhaul links established with a ground base station, and the limited capacity of the wireless backhaul link would limit the transmission speed of UAVs. Therefore, this paper designed a UAV-assisted wireless communication system that used cache technology and realized the transmission of multi-user data by using the mobility of UAVs and wireless cache technology. Considering the limited storage space and energy of UAVs, the joint optimization problem of the UAV’s trajectory, cache placement, and transmission power was established to minimize the mission time of the UAV. Since this problem was a non-convex problem, it was decomposed into three sub-problems: trajectory optimization, cache placement optimization, and power allocation optimization. An iterative algorithm based on the successive convex approximation and alternate optimization techniques was proposed to solve these three optimization problems. Finally, in the power allocation optimization, the proposed algorithm was improved by changing the optimization objective function. Numerical results showed that the algorithm had good performance and could effectively reduce the task completion time of the UAV.
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Zhang, Ziyong, Xiaoling Xu, Jinqiang Cui, and Wei Meng. "Multi-UAV Area Coverage Based on Relative Localization: Algorithms and Optimal UAV Placement." Sensors 21, no. 7 (March 31, 2021): 2400. http://dx.doi.org/10.3390/s21072400.
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This paper is concerned with relative localization-based optimal area coverage placement using multiple unmanned aerial vehicles (UAVs). It is assumed that only one of the UAVs has its global position information before performing the area coverage task and that ranging measurements can be obtained among the UAVs by using ultra-wide band (UWB) sensors. In this case, multi-UAV relative localization and cooperative coverage control have to be run simultaneously, which is a quite challenging task. In this paper, we propose a single-landmark-based relative localization algorithm, combined with a distributed coverage control law. At the same time, the optimal multi-UAV placement problem was formulated as a quadratic programming problem by compromising between optimal relative localization and optimal coverage control and was solved by using Sequential Quadratic Programming (SQP) algorithms. Simulation results show that our proposed method can guarantee that a team of UAVs can efficiently localize themselves in a cooperative manner and, at the same time, complete the area coverage task.
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Zhong, Tianyao, Ducheng Wu, Guoxin Li, Haichao Wang, Runfeng Chen, and Jihao Cai. "Joint Optimization of Spectrum Resource Management and Position Placement for UAV Base Station Networks." Wireless Communications and Mobile Computing 2023 (April 27, 2023): 1–14. http://dx.doi.org/10.1155/2023/2328249.
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The unmanned aerial vehicle (UAV) base station plays a significant role in enhancing the terrestrial network, when the ground base station (GBS) is destroyed in emergent cases or its load exceeds the capacity of the terrestrial network. Presently, many papers focus on optimizing the UAV position deployment and user access, while ignoring the optimization about the spectrum resource management. To solve this problem, we formulate a joint optimization problem of the spectrum resource management and the position placement for UAVs with the constraint of the limited backhaul capacity. Later, the joint optimization problem is modeled as a hierarchical game decision architecture comprised of a UAV position placement game and a spectrum resource management game. Further, we analyze the equilibrium property of the two games and propose two best response- (BR) based optimization algorithms to reach the Nash Equilibriums (NEs) of the two games, respectively. Specifically, the proposed algorithm about the UAV deployment considers the variable granularity local exploration and global random exploration. Simulation results show that the proposed UAV deployment algorithm can improve the total throughput by 7% and 20% at least in comparison with the K-means deployment algorithm and the fixed granularity exploration algorithm, respectively.
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Waheed, Maham, Rizwan Ahmad, Waqas Ahmed, Muhammad Mahtab Alam, and Maurizio Magarini. "On Coverage of Critical Nodes in UAV-Assisted Emergency Networks." Sensors 23, no. 3 (February 1, 2023): 1586. http://dx.doi.org/10.3390/s23031586.
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Unmanned aerial vehicle (UAV)-assisted networks ensure agile and flexible solutions based on the inherent attributes of mobility and altitude adaptation. These features render them suitable for emergency search and rescue operations. Emergency networks (ENs) differ from conventional networks. They often encounter nodes with vital information, i.e., critical nodes (CNs). The efficacy of search and rescue operations highly depends on the eminent coverage of critical nodes to retrieve crucial data. In a UAV-assisted EN, the information delivery from these critical nodes can be ensured through quality-of-service (QoS) guarantees, such as capacity and age of information (AoI). In this work, optimized UAV placement for critical nodes in emergency networks is studied. Two different optimization problems, namely capacity maximization and age of information minimization, are formulated based on the nature of node criticality. Capacity maximization provides general QoS enhancement for critical nodes, whereas AoI is focused on nodes carrying critical information. Simulations carried out in this paper aim to find the optimal placement for each problem based on a two-step approach. At first, the disaster region is partitioned based on CNs’ aggregation. Reinforcement learning (RL) is then applied to observe optimal placement. Finally, network coverage over optimal UAV(s) placement is studied for two scenarios, i.e., network-centric and user-centric. In addition to providing coverage to critical nodes, the proposed scheme also ensures maximum coverage for all on-scene available devices (OSAs).
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Shalaby, Abdulrahman M., and Noor Shamsiah Othman. "The Effect of Rainfall on the UAV Placement for 5G Spectrum in Malaysia." Electronics 11, no. 5 (February 23, 2022): 681. http://dx.doi.org/10.3390/electronics11050681.
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In this paper, the influence of rainfall on the deployment of UAV as an aerial base station in the Malaysia 5G network is studied. The outdoor-to-outdoor and outdoor-to-indoor path loss models are derived by considering the user’s antenna height, rain attenuation, and the wall penetration loss at high frequencies. The problem of finding the UAV 3D placement is formulated with the objective to minimize the total path loss between the UAV and all users. The problem is solved by invoking two algorithms, namely Particle Swarm Optimization (PSO) and Gradient Descent (GD) algorithms. The performance of the proposed algorithms is evaluated by considering two scenarios to determine the optimum location of the UAV, namely outdoor-to-outdoor and outdoor-to-indoor scenarios. The simulation results show that, for the outdoor-to-outdoor scenario, both algorithms resulted in similar UAV 3D placement unlike for the outdoor-to-indoor scenario. Additionally, in both scenarios, the proposed algorithm that invokes PSO requires less iterations to converge to the minimum transmit power compared to that of the algorithm that invokes GD. Moreover, it is also observed that the rain attenuation increases the total path loss for high operating frequencies, namely at 24.9 GHz and 28.1 GHz. Hence, this resulted in an increase of UAV required transmit power. At 28.1 GHz, the presence of rain at the rate of 250 mm/h resulted in an increase of UAV required transmit power by a factor of 4 and 15 for outdoor-to-outdoor and outdoor-to-indoor scenarios, respectively.
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Moon, Inseok, Le The Dung, and Taejoon Kim. "Optimal 3D Placement of UAV-BS for Maximum Coverage Subject to User Priorities and Distributions." Electronics 11, no. 7 (March 25, 2022): 1036. http://dx.doi.org/10.3390/electronics11071036.
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The usage of unmanned aerial vehicle (UAV) as a base station is in the spotlight to overcome the severe attenuation characteristics of short-wavelength radio in high-speed wireless networks. In this paper, we propose an optimal UAV deployment algorithm, considering the priority of ground nodes (GNs) in different wireless communication environments. Specifically, the optimal position of a UAV is determined so that as many high-priority GNs can be served rather than covering as many GNs as possible. The proposed optimization problem deals with two groups of GNs with different priorities and finds the optimal position of the UAV by solving the mixed-integer second-order cone problem (MISOCP). To verify the effectiveness of the proposed optimal UAV deployment algorithm, we conduct various evaluating scenarios with different urban environments and GN spatial distributions. We also compare the performance of the proposed algorithm with the conventional one. Simulation results show that the proposed scheme achieves superior coverage efficiency, throughput, and delay performance compared to the conventional algorithm, even when the environment and the spatial distribution of GNs are changed.
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Hulianytskyi, Leonid, and Oleg Rybalchenko. "Formalization of the Problem of Optimization of Base Places and Routes of the UAV Group." Cybernetics and Computer Technologies, no. 4 (December 30, 2021): 12–26. http://dx.doi.org/10.34229/2707-451x.21.4.2.
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Introduction. The problem of planning the mission of a set of heterogeneous unmanned aerial vehicles (UAVs)is considered, which is to survey and/or service a given set of targets in the field. A mathematical model of the problem and algorithms for its solving that is based on deterministic local search, as well as optimization by ant colonies are proposed. The efficiency of algorithms is investigated based on the results of solving problems with real objects in the field. The relative error of the results of each algorithm was obtained, which allowed to compare their efficiency. The purpose of the paper is to solve a routing problem in different ways to reduce overall mission cost and compare the efficiency. The problem statement considers multiple starting points and destinations (depots) for UAVs with determined capacity, so algorithms proposed in the paper are designed to optimize the initial placement. Each UAV has a maximum flight distance because of an energy limit, though vehicles can be recharged by visiting one of previously placed depots. The mission goal is to visit all the given targets while minimizing the overall cost, so fuel consumption over distance, depot placement, and resources needed to survey and/or service of the target by each UAV are considered as components of the final cost metric to be minimized considering a set of specific constraints. Results. To solve the given UAV routing problem, a max-min algorithm of ant systems was developed, which features step-by-step interaction of ants to form solutions, a hybrid taboo search algorithm and a deterministic local search algorithm - the decay vector method. The developed algorithms were tested both on the known travelling salesman problems, and on specially developed problems with multiple depots and additional restrictions. Conclusions. The proposed algorithms which are based on ant colony optimization are compared both in terms of accuracy and computation time. A hybrid algorithm achieved slightly better score, though computation time has increased. Keywords: routing, combinatorial optimization, UAV, local search, ant colony optimization algorithms.
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Fernandez, Stephanie Alvarez, Marcelo M. Carvalho, and Daniel G. Silva. "A Hybrid Metaheuristic Algorithm for the Efficient Placement of UAVs." Algorithms 13, no. 12 (December 3, 2020): 323. http://dx.doi.org/10.3390/a13120323.
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This work addresses the problem of using Unmanned Aerial Vehicles (UAV) to deploy a wireless aerial relay communications infrastructure for stations scattered on the ground. In our problem, every station in the network must be assigned to a single UAV, which is responsible for handling all data transfer on behalf of the stations that are assigned to it. Consequently, the placement of UAVs is key to achieving both network coverage and the maximization of the aggregate link capacities between UAVs and stations, and among the UAVs themselves. Because the complexity of this problem increases significantly with the number of stations to cover, for a given fixed number p of available UAVs, we model it as a single allocation p-hub median optimization problem, and we propose a hybrid metaheuristic algorithm to solve it. A series of numerical experiments illustrate the efficiency of the proposed algorithm against traditional optimization tools, which achieves high-quality results in very short time intervals, thus making it an attractive solution for real-world application scenarios.
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Abu-Baker, Amjad, Hazim Shakhatreh, Ahmad Sawalmeh, and Ali H. Alenezi. "Efficient Data Collection in UAV-Assisted Cluster-Based Wireless Sensor Networks for 3D Environment: Optimization Study." Journal of Sensors 2023 (April 12, 2023): 1–21. http://dx.doi.org/10.1155/2023/9513868.
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Unmanned aerial vehicles (UAVs) have been recently employed in combination with wireless sensor networks (WSNs) to collect data efficiently and improve surveillance effectiveness. This integration enhances the WSN infrastructure where UAVs are used as aerial base stations from which to access wireless sensors in hard-to-reach places within surveillance area. Consequently, the UAVs have become a promising solution to maintain reliability for the communication between wireless sensors and base station particularly in cases where infrastructure becomes unavailable such as hilly terrains and emergencies. However, UAVs encounter many challenges which mainly focus on their lifespan and efficient placement that improves the coverage and data collection. In this paper, a novel optimization study is presented to improve the lifespan of UAV-assisted cluster-based WSNs deployed in 3D environment. This optimization study is based on two algorithms: (1) Particle Swarm Optimization (PSO) which is employed to address the clustering problem in the WSN and (2) Genetic Algorithm (GA) which is employed to locate an efficient UAV placement to maximize the lifetime. The UAV-WSN system is evaluated by considering two metrics: lifetime and throughput. The simulation results show that varying UAV altitude has significant impact on both lifetime and throughput especially in the presence of different terrain. With increasing altitude, lifetime and throughput decrease as this loss can be as high as 94%. However, the proposed optimization plays a major role in combating these losses by redirecting the UAV to efficient placement corresponding to the new altitude level to maintain maximum lifetime and throughput. Moreover, the system lifetime concerning efficient UAV placement outperforms the one concerning centered placement at lower altitude, while the difference between two cases becomes less at higher altitude. Thereby, these outcomes may provide interesting measures for designing such integrated systems to achieve efficient data collection.
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Leichenko, Kyrylo, Herman Fesenko, Vyacheslav Kharchenko, and Oleg Illiashenko. "Deployment of a UAV swarm-based LiFi network in the obstacle-ridden environment: algorithms of finding the path for UAV placement." Radioelectronic and Computer Systems 2024, no. 1 (February 28, 2024): 176–95. http://dx.doi.org/10.32620/reks.2024.1.14.
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The subject of this study is unmanned aerial vehicle (UAV)-based wireless networks in an obstacle-ridden environment. The aim of this study is to develop methods and software to ensure reliable LiFi communication using swarm UAVs in an obstacle-ridden environment. The objectives are as follows: 1) to describe the problem of providing a reliable UAV swarm-based LiFi network, requirements for the composition and use of UAVs, and assumptions; 2) to develop the methodology for solving research tasks; 3) to develop the method and algorithms for solving the problem, considering the requirements, assumptions, and practical limitations; 4) to explore the algorithms by developing software for modeling and searching for rational UAV placement to ensure the required UAV-based LiFi network characteristics; 5) to provide experiments and illustrative examples of the developed tool’s application. The following results were obtained. 1) The requirements for the composition and use of UAVs for creating LiFi networks, as well as assumptions and limitations for the methodology development and research task solving. 2) An obstacle avoidance method using the left and right angles algorithm. 3) A method for obstacle avoidance using the controlled waterfall algorithm. 4) A software tool for modeling and searching for rational UAV placement to ensure the required LiFi network characteristics. The tool allows route construction under obstacles in 2D space and a comparison of the developed algorithms for various variants of obstacle placement. Conclusions. The main contribution of this research is a set of methods, algorithms, and software tools for providing communications between two points using LiFi technologies and a swarm of UAVs supporting these communications as transmitters in conditions of mechanical obstacles.
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Shakhatreh, Hazim, Khaled Hayajneh, Khaled Bani-Hani, Ahmad Sawalmeh, and Muhammad Anan. "Cell on Wheels-Unmanned Aerial Vehicle System for Providing Wireless Coverage in Emergency Situations." Complexity 2021 (November 22, 2021): 1–9. http://dx.doi.org/10.1155/2021/8669824.
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Due to natural disasters, unmanned aerial vehicles (UAVs) can be deployed as aerial wireless base stations when conventional cellular networks are out of service. They can also supplement the mobile ground station to provide wireless devices with improved coverage and faster data rates. Cells on wheels (CoWs) can also be utilized to provide enhanced wireless coverage for short-term demands. In this paper, a single CoW cooperates with a single UAV in order to provide maximum wireless coverage to ground users. The optimization problem is formulated to find the following: (1) the optimal 2D placement of the CoW, (2) the optimal 3D placement of the UAV, (3) the optimal bandwidth allocation, (4) the percentage of the available bandwidth that must be provided to the CoW and UAV, and (5) the priority of wireless coverage; which maximizes the number of covered users. We utilize the exhaustive search (ES) and particle swarm optimization (PSO) algorithms to solve the optimization problem. The effectiveness of the proposed algorithms is validated using simulation results.
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Ahmed, Ashfaq, Muhammad Awais, Tallha Akram, Selman Kulac, Musaed Alhussein, and Khursheed Aurangzeb. "Joint Placement and Device Association of UAV Base Stations in IoT Networks." Sensors 19, no. 9 (May 9, 2019): 2157. http://dx.doi.org/10.3390/s19092157.
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Drone base stations (DBSs) have received significant research interest in recent years. They provide a flexible and cost-effective solution to improve the coverage, connectivity, quality of service (QoS), and energy efficiency of large-area Internet of Things (IoT) networks. However, as DBSs are costly and power-limited devices, they require an efficient scheme for their deployment in practical networks. This work proposes a realistic mathematical model for the joint optimization problem of DBS placement and IoT users’ assignment in a massive IoT network scenario. The optimization goal is to maximize the connectivity of IoT users by utilizing the minimum number of DBS, while satisfying practical network constraints. Such an optimization problem is NP-hard, and the optimal solution has a complexity exponential to the number of DBSs and IoT users in the network. Furthermore, this work also proposes a linearization scheme and a low-complexity heuristic to solve the problem in polynomial time. The simulations are performed for a number of network scenarios, and demonstrate that the proposed heuristic is numerically accurate and performs close to the optimal solution.
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Shakhatreh, Hazim, Ali Alenezi, Ahmad Sawalmeh, Muhannad Almutiry, and Waed Malkawi. "Efficient Placement of an Aerial Relay Drone for Throughput Maximization." Wireless Communications and Mobile Computing 2021 (June 3, 2021): 1–11. http://dx.doi.org/10.1155/2021/5589605.
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Unmanned aerial vehicle (UAV) communication can be used in overcrowded areas and either during or postdisaster situations as an evolving technology to provide ubiquitous connections for wireless devices due to its flexibility, mobility, and good condition of the line of sight channels. In this paper, a single UAV is used as an aerial relay node to provide connectivity to wireless devices because of the considerable distance between wireless devices and the ground base station. Specifically, two path loss models have been utilized; a cellular-to-UAV path loss for a backhaul connection and an air-to-ground path loss model for a downlink connection scenario. Then, the tradeoff introduced by these models is discussed. The problem of efficient placement of an aerial relay node is formulated as an optimization problem, where the objective is to maximize the total throughput of wireless devices. To find an appropriate location for a relay aerial node that maximizes the overall throughput, we first use the particle swarm optimization algorithm to find the drone location; then, we use three different approaches, namely, (1) the equal power allocation approach, (2) water filling approach, and (3) modified water filling approach to maximize the total users’ throughput. The results show that the modified water filling outperforms the other two approaches in terms of the average sum rate of all users and the total number of served users. More specifically, in the best-case scenario, it was observed that the average sum rate of the modified water filling is better than the equal power allocation and ensuring 100% coverage. In contrast, the water filling provides a very close average sum rate to the modified water filling, but it only provides a 28% user coverage.
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Pinto, Luis Ramos, and Luis Almeida. "Optimal Relay Network for Aerial Remote Inspections." Sensors 22, no. 4 (February 11, 2022): 1391. http://dx.doi.org/10.3390/s22041391.
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Unmanned aerial vehicles (UAVs), in particular multirotors, are becoming the de facto tool for aerial sensing and remote inspection. In large industrial facilities, a UAV can transmit an online video stream to inspect difficult-to-access structures, such as chimneys, deposits, and towers. However, the communication range is limited, constraining the UAV operation range. This limitation can be overcome with relaying UAVs placed between the source UAV and the control station, creating a line of communication links. In this work, we assume the use of a digital data packet network technology, namely WiFi, and tackle the problem of defining the exact placement for the relaying UAVs that creates an end-to-end channel with maximal delivery of data packets. We consider asymmetric communication links and we show an increase as large as 15% in end-to-end packet delivery ratio when compared to an equidistant placement. We also discuss the deployment of such a network and propose a fully distributed method that converges to the global optimal relay positions taking, on average, 1.4 times the time taken by a centralized method.
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Bian, Yuan, Jianbo Hu, Shuo Wang, Yukai Hao, Wenjie Liu, and Chaoqi Fu. "Two-Hop Cooperative Caching and UAVs Deployment Based on Potential Game." Drones 7, no. 7 (July 11, 2023): 465. http://dx.doi.org/10.3390/drones7070465.
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This paper explores the joint cache placement and 3D deployment of Unmanned Aerial Vehicle (UAV) groups, utilizing potential game theory and a two-hop UAV cooperative caching mechanism, which could create a tradeoff between latency and coverage. The proposed scheme consists of three parts: first, the initial 2D location of UAV groups is determined through K-means, with the optimal altitude based on the UAV coverage radius. Second, to balance the transmission delay and coverage, the MOS (Mean Opinion Score) and coverage are designed to evaluate the performance of UAV-assisted networks. Then, the potential game is modeled, which transfers the optimization problem into the maximization of the whole network utility. The locally coupling effect resulting from action changes among UAVs is considered in the design of the potential game utility function. Moreover, a log-linear learning scheme is applied to solve the problem. Finally, the simulation results verify the superiority of the proposed scheme in terms of the achievable transmission delay and coverage performance compared with two other tested schemes. The coverage ratio is close to 100% when the UAV number is 25, and the user number is 150; in addition, this game outperforms the benchmarks when it comes to maximizing MOS of users.
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Kholostov, K. M., A. V. Betskov, and A. S. Ovchinsky. "THE PROBLEM SOLUTION OF RATIONAL PLACEMENT OF UNMANNED AIRCRAFT GROUPS IN URBAN DEVELOPMENT AS PART OF AN OPERATIONAL RESPONSE SYSTEM." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 232 (October 2023): 12–21. http://dx.doi.org/10.14489/vkit.2023.10.pp.012-021.
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A method of rational placement in urban development of a grouping of unmanned aerial vehicles (aircraft) intended for information support of the police rapid response forces is proposed. By placement, in this case, it is meant to determine the composition of the grouping and the coordinates of the locations (take-off points) of unmanned aircraft that are part of it. As geographical objects for the placement of the aviation grouping, cities and urban areas are assumed, in the construction of which residential quarters, places of recreation and mass stay of people, transport and commercial facilities predominate. The method allows, with the specified requirements for the maximum time of arrival of an unmanned aircraft (UAV) to the scene of the incident, to determine the rational composition and location of the elements of the UAV grouping in the city (village, urban district, village), including having a complex shape of the building area. To search for take-off points, fragments of a triangular lattice are used as a basic geometric model, which are virtually and conformally placed on the city plan in accordance with the developed algorithm. The proposed method allows, under conditions of resource constraints, to solve the problem of placing a group of UAVs in urban development, from the point of view of minimizing the material costs associated with its creation and operation, by reducing the number of UAVs used.
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Xu, Woping, Junhui Tian, Li Gu, and Shaohua Tao. "Joint Placement and Power Optimization of UAV-Relay in NOMA Enabled Maritime IoT System." Drones 6, no. 10 (October 18, 2022): 304. http://dx.doi.org/10.3390/drones6100304.
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In this paper, an unmanned aerial vehicle is utilized as an aerial relay to connect onshore base station with offshore users in a maritime IoT system with uplink non-orthogonal multiple access enabled. A coordinated direct and relay transmission scheme is adopted in the proposed system, where close shore maritime users directly communicate with onshore BS and offshore maritime users need assistance of an aerial relay to communicate with onshore BS. We aim to minimize the total transmit energy of the aerial relay by jointly optimizing the UAV hovering position and transmit power allocation. The minimum rate requirements of maritime users and transmitters’ power budgets are considered. The formulated optimization problem is non-convex due to its non-convex constraints. Therefore, we introduce successive convex optimization and block coordinate descent to decompose the original problem into two subproblems, which are alternately solved to optimize the UAV energy consumption with satisfying the proposed constraints. Numerical results indicate that the proposed algorithm outperformed the benchmark algorithm, and shed light on the potential of exploiting the energy-limited aerial relay in IoT systems.
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Quan, Xiaoya. "QoS-Aware Power Allocation for Multi-UAV Aided Networks." Journal of Physics: Conference Series 2113, no. 1 (November 1, 2021): 012012. http://dx.doi.org/10.1088/1742-6596/2113/1/012012.
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Abstract UAV base stations (UAVBS’s) have been proposed as a revolution for the new architecture of 5G networks. The UAVBS’s can be deployed as access points to provide wireless services to users in emergency scenarios. However, it is challenging to solve the highly coupled problem for UAVBS deployment and power allocation. In the meanwhile, the hybrid analog and digital beamforming is leverage to reduce the hardware cost for beamforming in 5G networks. In this work, we first use k-means algorithm to solve the 3D placement of UAVBS’s by exploiting the optimal coverage altitude. Next, power allocation problem is resolved using the difference-of-two-convex functions (D.C.) programming algorithm. Furthermore, the quality of service (QoS) for each user is guaranteed by adjusting the transmitted power. Finally, extensive experiments are conducted to demonstrate the feasibility of the proposed algorithm.
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Dubey, Rahul, and Sushil J. Louis. "Genetic Algorithms Optimized Adaptive Wireless Network Deployment." Applied Sciences 13, no. 8 (April 12, 2023): 4858. http://dx.doi.org/10.3390/app13084858.
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Advancements in UAVs have enabled them to act as flying access points that can be positioned to create an interconnected wireless network in complex environments. The primary aim of such networks is to provide bandwidth coverage to users on the ground in case of an emergency or natural disaster when existing network infrastructure is unavailable. However, optimal UAV placement for creating an ad hoc wireless network is an NP-hard and challenging problem because of the UAV’s communication range, unknown users’ distribution, and differing user bandwidth requirements. Many techniques have been presented in the literature for wireless mesh network deployment, but they lack either generalizability (with different users’ distributions) or real-time adaptability as per users’ requirements. This paper addresses the UAV placement and control problem, where a set of genetic-algorithm-optimized potential fields guide UAVs for creating long-lived ad hoc wireless networks that find all users in a given area of interest (AOI) and serve their bandwidth requirements. The performance of networks deployed using the proposed algorithm was compared with the current state of the art on several experimental simulation scenarios with different levels of communication among UAVs, and the results show that, on average, the proposed algorithm outperforms the state of the art by 5.62% to 121.73%.
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Shakhatreh, Majd, Hazim Shakhatreh, and Ahmad Ababneh. "Efficient 3D Positioning of UAVs and User Association Based on Hybrid PSO-K-Means Clustering Algorithm in Future Wireless Networks." Mobile Information Systems 2023 (January 27, 2023): 1–11. http://dx.doi.org/10.1155/2023/6567897.
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Unmanned aerial vehicles (UAVs) play an important role in the future of 5G and 6G communication networks. UAV-assisted communication offers the benefits of improved network capacity and coverage. A typical communication setup is for UAVs to connect users to the core network via a backhaul channel. Some of the challenges in such a setup include user-UAV association and management of the backhaul channel. These two challenges are greatly impacted by the positioning of the UAVs in the network. In this article, we address these challenges by considering a joint UAV placement and user association problem under data rate, signal to interference and noise ratio, and bandwidth constraints. To overcome this problem, a hybrid PSO-K-means clustering algorithm is used in two stages. In the first stage, we use a K-means algorithm to cluster users and determine their horizontal locations. In the second stage, we use particle swarm optimization (PSO) to find the efficient 3D position of UAVs to maximize various network designs, namely, the network-centric approach and the user-centric approach. The performance of the proposed solution is verified using simulation results.
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Song, Ha Yoon. "A Method of Mobile Base Station Placement for High Altitude Platform Based Network with Geographical Clustering of Mobile Ground Nodes." Journal of Telecommunications and Information Technology, no. 2 (June 26, 2023): 22–33. http://dx.doi.org/10.26636/jtit.2009.2.922.
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High altitude platforms (HAPs) such as unmanned aerial vehicles (UAVs) which can be deployed as stratospheric infrastructures enable a sort of new configurations of wireless networks. Ground nodes must be clustered in multiple sets and one dedicated UAV is assigned to each set and act as an mobile base station (MBS). For the intra-set nodes, UAVs must communicate each other in order to establish network links among intra-set nodes. Here we find a geographical clustering problem of networking nodes and a placement problem of MBSs. The clustering technique of mobile ground nodes can identify the geographical location of MBSs as well as the coverage of MBSs. In this paper we proposed a clustering mechanism to build such a configuration and the effectiveness of this solution is demonstrated by simulation. For a selected region with a relatively big island, we modeled mobile ground nodes and showed the result of dynamic placement of MBSs by our clustering algorithm. The final results will be shown graphically with the mobility of ground nodes as well as the placement of MBSs.
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Wu, Chenyu, Shuo Shi, Shushi Gu, Lingyan Zhang, and Xuemai Gu. "Deep Reinforcement Learning-Based Content Placement and Trajectory Design in Urban Cache-Enabled UAV Networks." Wireless Communications and Mobile Computing 2020 (August 14, 2020): 1–11. http://dx.doi.org/10.1155/2020/8842694.
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Cache-enabled unmanned aerial vehicles (UAVs) have been envisioned as a promising technology for many applications in future urban wireless communication. However, to utilize UAVs properly is challenging due to limited endurance and storage capacity as well as the continuous roam of the mobile users. To meet the diversity of urban communication services, it is essential to exploit UAVs’ potential of mobility and storage resource. Toward this end, we consider an urban cache-enabled communication network where the UAVs serve mobile users with energy and cache capacity constraints. We formulate an optimization problem to maximize the sum achievable throughput in this system. To solve this problem, we propose a deep reinforcement learning-based joint content placement and trajectory design algorithm (DRL-JCT), whose progress can be divided into two stages: offline content placement stage and online user tracking stage. First, we present a link-based scheme to maximize the cache hit rate of all users’ file requirements under cache capacity constraint. The NP-hard problem is solved by approximation and convex optimization. Then, we leverage the Double Deep Q-Network (DDQN) to track mobile users online with their instantaneous two-dimensional coordinate under energy constraint. Numerical results show that our algorithm converges well after a small number of iterations. Compared with several benchmark schemes, our algorithm adapts to the dynamic conditions and provides significant performance in terms of sum achievable throughput.
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Sawalmeh, Ahmad, Noor Othman, and Hazim Shakhatreh. "Efficient Deployment of Multi-UAVs in Massively Crowded Events." Sensors 18, no. 11 (October 26, 2018): 3640. http://dx.doi.org/10.3390/s18113640.
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In this paper, the efficient 3D placement of UAV as an aerial base station in providing wireless coverage for users in a small and large coverage area is investigated. In the case of providing wireless coverage for outdoor and indoor users in a small area, the Particle Swarm Optimization (PSO) and K-means with Ternary Search (KTS) algorithms are invoked to find an efficient 3D location of a single UAV with the objective of minimizing its required transmit power. It was observed that a single UAV at the 3D location found using the PSO algorithm requires less transmit power, by a factor of 1/5 compared to that when using the KTS algorithm. In the case of providing wireless coverage for users in three different shapes of a large coverage area, namely square, rectangle and circular regions, the problems of finding an efficient placement of multiple UAVs equipped with a directional antenna are formulated with the objective to maximize the coverage area and coverage density using the Circle Packing Theory (CPT). Then, the UAV efficient altitude placement is formulated with the objective of minimizing its required transmit power. It is observed that the large number of UAVs does not necessarily result in the maximum coverage density. Based on the simulation results, the deployment of 16, 19 and 26 UAVs is capable of providing the maximum coverage density of 78.5%, 82.5% and 80.3% for the case of a square region with the dimensions of 2 km × 2 km, a rectangle region with the dimensions of 6 km × 1.8 km and a circular region with the radius of 1.125 km, respectively. These observations are obtained when the UAVs are located at the optimum altitude, where the required transmit power for each UAV is reasonably small.
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Menéndez, Oswaldo, Marcelo Pérez, and Fernando Auat Cheein. "Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines." Applied Sciences 9, no. 1 (January 4, 2019): 165. http://dx.doi.org/10.3390/app9010165.
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Unmanned aerial vehicles (UAVs) are an emerging and promising alternative for monitoring of transmission lines in terms of flexibility, complexity, working speed, and cost. One of the main challenges is to enable UAVs to become as autonomous as possible. A vital component toward this direction is the robust and accurate estimation of the UAV placement with respect to the transmission grid. This work faces this challenge by developing a transmission line autonomous tracking system, which allows the placement of a commercial drone over a transmission grid using a monocular camera. This feature provides accurate positioning for the vehicle even where the Global navigation satellite system (GNSS) signal is denied, enabling to report the status of transmission lines, at any time. The system isolates transmission grid conductors in each acquired RGB-image using an image-processing algorithm based on Hough transform, morphological operations, and Gabor filters. With this information, the system computes the location of the UAV using a geometric approach that relates transmission lines building parameter and optical geometry. However, it has the problem of gradual error accumulation when the drone moves. In this regards, the estimated position of the drone is computed by the maximum likelihood estimation (MLE) by the position information estimated by visual-system, the inertial measurement unit (IMU) and GNSS. The proposed positioning system showed an efficiency of 91.44% in field experimentation in the extraction of transmission conductor, with a root mean square the error of 0.18 m in the UAV localization.
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Hydher, Hassaan, Dushantha Nalin K. Jayakody, Kasun T. Hemachandra, and Tharaka Samarasinghe. "Intelligent UAV Deployment for a Disaster-Resilient Wireless Network." Sensors 20, no. 21 (October 28, 2020): 6140. http://dx.doi.org/10.3390/s20216140.
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Deployment of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) has been considered to be a feasible solution to provide network coverage in scenarios where the conventional terrestrial network is overloaded or inaccessible due to an emergency situation. This article studies the problem of optimal placement of the UAVs as ABSs to enable network connectivity for the users in such a scenario. The main contributions of this work include a less complex approach to optimally position the UAVs and to assign user equipment (UE) to each ABS, such that the total spectral efficiency (TSE) of the network is maximized, while maintaining a minimum QoS requirement for the UEs. The main advantage of the proposed approach is that it only requires the knowledge of UE and ABS locations and statistical channel state information. The optimal 2-dimensional (2D) positions of the ABSs and the UE assignments are found using K-means clustering and a stable marriage approach, considering the characteristics of the air-to-ground propagation channels, the impact of co-channel interference from other ABSs, and the energy constraints of the ABSs. Two approaches are proposed to find the optimal altitudes of the ABSs, using search space constrained exhaustive search and particle swarm optimization (PSO). The numerical results show that the PSO-based approach results in higher TSE compared to the exhaustive search-based approach in dense networks, consuming similar amount of energy for ABS movements. Both approaches lead up to approximately 8-fold energy savings compared to ABS placement using naive exhaustive search.
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Rodríguez, Wenceslao Eduardo, Ramiro Ibarra, Gerardo Romero, David Lara, Jaime Arredondo, José G. Rivera, and Claude Pegard. "Comparison of Controllers for a UAV with Integral Effect and Kalman Estimator: By Bessel Polynomials and LQR." Applied Mechanics and Materials 436 (October 2013): 54–60. http://dx.doi.org/10.4028/www.scientific.net/amm.436.54.
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This paper presents the development of two different control techniques as an approach having to remove steady-state error present in the response of attitude of a mini unmanned aerial vehicle. A problem that arises when performing pole placement controller is the selection of the poles, the Bessel approximation allows the selection of the eigenvalues in function to a specified response time for a feedback pole placement controller and state estimator (observer). On the other hand presents an optimal control technique combined with Kalman filter to estimate the state affected by perturbations in the system, both cases using the integral effect to eliminate the steady state error.These two control laws has the property of responding to a desired response according to a time or state response desired.
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Atli, İbrahim, Metin Ozturk, Gianluca C. Valastro, and Muhammad Zeeshan Asghar. "Multi-Objective UAV Positioning Mechanism for Sustainable Wireless Connectivity in Environments with Forbidden Flying Zones." Algorithms 14, no. 11 (October 21, 2021): 302. http://dx.doi.org/10.3390/a14110302.
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A communication system based on unmanned aerial vehicles (UAVs) is a viable alternative for meeting the coverage and capacity needs of future wireless networks. However, because of the limitations of UAV-enabled communications in terms of coverage, energy consumption, and flying laws, the number of studies focused on the sustainability element of UAV-assisted networking in the literature was limited thus far. We present a solution to this problem in this study; specifically, we design a Q-learning-based UAV placement strategy for long-term wireless connectivity while taking into account major constraints such as altitude regulations, nonflight zones, and transmit power. The goal is to determine the best location for the UAV base station (BS) while reducing energy consumption and increasing the number of users covered. Furthermore, a weighting method is devised, allowing energy usage and the number of users served to be prioritized based on network/battery circumstances. The suggested Q-learning-based solution is contrasted to the standard k-means clustering method, in which the UAV BS is positioned at the centroid location with the shortest cumulative distance between it and the users. The results demonstrate that the proposed solution outperforms the baseline k-means clustering-based method in terms of the number of users covered while achieving the desired minimization of the energy consumption.
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Mayor, Vicente, Rafael Estepa, and Antonio Estepa. "QoS-Aware Multilayer UAV Deployment to Provide VoWiFi Service over 5G Networks." Wireless Communications and Mobile Computing 2022 (January 28, 2022): 1–13. http://dx.doi.org/10.1155/2022/3110572.
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Drones equipped with wireless network cards can provide communication services in open areas. This paper proposes a hierarchical two-layered network architecture with two types of drones according to their communication equipment: Access and Distribution. While access drones provide WiFi access to ground users, distribution drones act as WiFi-to-5G relay forwarding packets into the 5G Core Network. In this context, we formulate a novel optimization problem for the 3-D initial placement of drones to provide Voice over WiFi (VoWiFi) service to ground users. Our optimization problem finds the minimum number of drones (and their type and location) to be deployed constrained to coverage and minimum voice speech quality. We have used a well-known metaheuristic algorithm (Particle Swarm Optimization) to solve our problem, examining the results obtained for different terrain sizes (from 25 m × 25 m to 100 m × 100 m ) and ground users (from 10 to 100 ). In the most demanding case, we were able to provide VoWiFi service with four distribution drones and five access drones. Our results show that the overall number of UAVs deployed grows with the terrain size (i.e., with users’ sparsity) and the number of ground users.
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Kang, Xu, Yu Shao, Guanbing Bai, He Sun, Tao Zhang, and Dejiang Wang. "Dual-UAV Collaborative High-Precision Passive Localization Method Based on Optoelectronic Platform." Drones 7, no. 11 (October 25, 2023): 646. http://dx.doi.org/10.3390/drones7110646.
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Utilizing the optical characteristics of the target for detection and localization does not require actively emitting signals and has the advantage of strong concealment. Once the optoelectronic platform mounted on the unmanned aerial vehicle (UAV) detects the target, the vector pointing to the target in the camera coordinate system can estimate the angle of arrival (AOA) of the target relative to the UAV in the Earth-centered Earth-fixed (ECEF) coordinate system through a series of rotation transformations. By employing two UAVs and the corresponding AOA measurements, passive localization of an unknown target is possible. To achieve high-precision target localization, this paper investigates the following three aspects. Firstly, two error transfer models are established to estimate the noise distributions of the AOA and the UAV position in the ECEF coordinate system. Next, to reduce estimation errors, a weighted least squares (WLS) estimator is designed. Theoretical analysis proves that the mean squared error (MSE) of the target position estimation can reach the Cramér–Rao lower bound (CRLB) under the condition of small noise. Finally, we study the optimal placement problem of two coplanar UAVs relative to the target based on the D-optimality criterion and provide explicit conclusions. Simulation experiments validate the effectiveness of the localization method.
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Raffo, Guilherme V., and Marcelino M. de Almeida. "A Load Transportation Nonlinear Control Strategy Using a Tilt-Rotor UAV." Journal of Advanced Transportation 2018 (June 27, 2018): 1–20. http://dx.doi.org/10.1155/2018/1467040.
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This paper proposes a nonlinear control strategy to solve the trajectory tracking problem of a tilt-rotor Unmanned Aerial Vehicle (UAV) when transporting a suspended load. For the present study, the aim of the control system is to track a desired trajectory of the aircraft with load’s swing-free, even in the presence of external disturbances, parametric uncertainties, unmodeled dynamics, and noisy position measurements with lower sampling frequency than the controller. The whole system modeling is obtained through the Euler-Lagrange formulation considering the dynamics of the tilt-rotor UAV coupled to the suspended load. As for the nonlinear control strategy, an inner-loop control is designed based on input-output feedback linearization combined with the dynamic extension approach to stabilize the attitude and altitude of the UAV assuming nonlinearities, while an outer-loop control law is designed for guiding the aircraft with reduced load swing. The linearized dynamics are controlled using linear mixed H2/H∞ controllers with pole placement constraints. The solution is compared to two simpler control systems: the first one considers the load as a disturbance to the system but does not avoid its swing; the second one is a previous academic result with a three-level cascade strategy. Finally, in order to deal with the problem of position estimation in presence of unknown disturbances and noisy measurements with low sampling frequency, a Linear Kalman Filter with Unknown Inputs is designed for estimating both the aircraft’s translational position and translational disturbances. Simulation results are carried out to corroborate the proposed control strategy.
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et al., Malkawi. "3D placements of drones in a millimeter-wave network to maximize the lifetime of wireless devices." International Journal of ADVANCED AND APPLIED SCIENCES 8, no. 11 (November 2021): 119–28. http://dx.doi.org/10.21833/ijaas.2021.11.015.
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In the last few years, the use of drones is increasing day by day in wireless networks and the applications of them are rapidly increased on different sides. Now, we can use the drone as an aerial base station (BS) to support cellular networks in emergency cases and in natural disasters. To take the advantage of both drones and fifth-generation (5G) and link between their features, we study an aerial BS considering millimeter waves (mm-waves). In this paper, we optimize the 3D placements for multiple unmanned aerial vehicles (UAVs) in an mm-wave network to achieve maximum time durations of the uplink transmission. First, we present a formulation for the placement problem, where we aim to allocate 3D locations for multiple UAVs to achieve the maximum sum of time durations of uplink transmissions. We propose an efficient algorithm to find the placements of UAVs. We propose an algorithm that starts by grouping the wireless devices into a number of clusters, and each cluster is served by a single UAV. After the clustering process, it applies the gradient projection-based algorithm (GP) or particle swarm optimization (PSO) in each cluster. In the results section, our proposed approach and the center projection algorithm will be compared to prove the efficiency of our approach.
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Kim, Namhoon, Sangho Baek, and Gihong Kim. "Absolute IOP/EOP Estimation Models without Initial Information of Various Smart City Sensors." Sensors 23, no. 2 (January 9, 2023): 742. http://dx.doi.org/10.3390/s23020742.
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In smart cities, a large amount of optical camera equipment is deployed and used. Closed-circuit television (CCTV), unmanned aerial vehicles (UAVs), and smartphones are some examples of such equipment. However, additional information about these devices, such as 3D position, orientation information, and principal distance, is not provided. To solve this problem, the structured mobile mapping system point cloud was used in this study to investigate methods of estimating the principal point, position, and orientation of optical sensors without initial given values. The principal distance was calculated using two direct linear transformation (DLT) models and a perspective projection model. Methods for estimating position and orientation were discussed, and their stability was tested using real-world sensors. When the perspective projection model was used, the camera position and orientation were best estimated. The original DLT model had a significant error in the orientation estimation. The correlation between the DLT model parameters was thought to have influenced the estimation result. When the perspective projection model was used, the position and orientation errors were 0.80 m and 2.55°, respectively. However, when using a fixed-wing UAV, the estimated result was not properly produced owing to ground control point placement problems.
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Погудина, Ольга Константиновна, and Ирина Васильевна Вайленко. "АЛГОРИТМ ОЦЕНКИ ПРОПУСКНОЙ СПОСОБНОСТИ ПРИ УПРАВЛЕНИИ ТРАФИКОМ БЕСПИЛОТНЫХ ЛЕТАТЕЛЬНЫХ АППАРАТОВ." Aerospace technic and technology, no. 3 (June 27, 2018): 69–75. http://dx.doi.org/10.32620/aktt.2018.3.09.
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The subject of the study in the article is the processes of assessing the airship throughput in controlling the unmanned aerial vehicles (UAV) traffic management. The goal is to improve the quality of air traffic control, taking into account the avoidance of conflicts involving three or more UAV. Problems: to develop a mathematical model of the probabilistic traffic map, as well as to formalize the construction of a random geometric graph model for the estimation of alleged UAVs conflicts and collisions; To implement algorithms given models construction for airship throughput automation. The models used: Poisson process whose intensity model is used for building a probabilistic traffic map, random geometric graph model is used for calculate the number of possible conflicts involving the UAV. The following results are obtained. A formalized model of the UAV location map has been created taking into account: the given region with the specified population density and the expected number of operations during the specified time interval. This model was used in the construction of a random geometric graph, in which, taking into account the minimum distance possible for the approximation of two UAVs, an estimation of the probability of conflicts and collisions was conducted. The model is the basis for obtaining an algorithm for estimating the factors limiting the capacity of the airspace, as a result of the occurrence of difficult solvable conflicts. The scientific novelty of the obtained results is as follows: The random geometric graph model is improved by formalizing the position of the vertices. The vertices, taking into account the law of the Poisson process, are placed in the cells of a given region. This allows us to obtain an objective picture of the location of the UAV in the city's airspace. Two-dimensional models of probabilistic traffic maps (Dutch model "Metropolis", model Cal) have been further developed, due to the formalization of the initial UAV placement, taking into account the law of the Poisson process. This will help to determine the technical requirements for ensuring uninterrupted operation of small unmanned aerial vehicles in the urban airspace
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McGuire, John L., Yee Wei Law, Kutluyıl Doğançay, Sook-Ying Ho, and Javaan Chahl. "Optimal Maneuvering for Autonomous Vehicle Self-Localization." Entropy 24, no. 8 (August 22, 2022): 1169. http://dx.doi.org/10.3390/e24081169.
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We consider the problem of optimal maneuvering, where an autonomous vehicle, an unmanned aerial vehicle (UAV) for example, must maneuver to maximize or minimize an objective function. We consider a vehicle navigating in a Global Navigation Satellite System (GNSS)-denied environment that self-localizes in two dimensions using angle-of-arrival (AOA) measurements from stationary beacons at known locations. The objective of the vehicle is to travel along the path that minimizes its position and heading estimation error. This article presents an informative path planning (IPP) algorithm that (i) uses the determinant of the self-localization estimation error covariance matrix of an unscented Kalman filter as the objective function; (ii) applies an l-step look-ahead (LSLA) algorithm to determine the optimal heading for a constant-speed vehicle. The novel algorithm takes into account the kinematic constraints of the vehicle and the AOA means of measurement. We evaluate the performance of the algorithm in five scenarios involving stationary and mobile beacons and we find the estimation error approaches the lower bound for the estimator. The simulations show the vehicle maneuvers to locations that allow for minimum estimation uncertainty, even when beacon placement is not conducive to accurate estimation.
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Mayor, Vicente, Rafael Estepa, Antonio Estepa, and Germán Madinabeitia. "Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service." Sensors 20, no. 16 (August 10, 2020): 4455. http://dx.doi.org/10.3390/s20164455.
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This paper formulates a new problem for the optimal placement of Unmanned Aerial Vehicles (UAVs) geared towards wireless coverage provision for Voice over WiFi (VoWiFi) service to a set of ground users confined in an open area. Our objective function is constrained by coverage and by VoIP speech quality and minimizes the ratio between the number of UAVs deployed and energy efficiency in UAVs, hence providing the layout that requires fewer UAVs per hour of service. Solutions provide the number and position of UAVs to be deployed, and are found using well-known heuristic search methods such as genetic algorithms (used for the initial deployment of UAVs), or particle swarm optimization (used for the periodical update of the positions). We examine two communication services: (a) one bidirectional VoWiFi channel per user; (b) single broadcast VoWiFi channel for announcements. For these services, we study the results obtained for an increasing number of users confined in a small area of 100 m2 as well as in a large area of 10,000 m2. Results show that the drone turnover rate is related to both users’ sparsity and the number of users served by each UAV. For the unicast service, the ratio of UAVs per hour of service tends to increase with user sparsity and the power of radio communication represents 14–16% of the total UAV energy consumption depending on ground user density. In large areas, solutions tend to locate UAVs at higher altitudes seeking increased coverage, which increases energy consumption due to hovering. However, in the VoWiFi broadcast communication service, the traffic is scarce, and solutions are mostly constrained only by coverage. This results in fewer UAVs deployed, less total power consumption (between 20% and 75%), and less sensitivity to the number of served users.
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Alsadik, Bashar, and Fabio Remondino. "Flight Planning for LiDAR-Based UAS Mapping Applications." ISPRS International Journal of Geo-Information 9, no. 6 (June 8, 2020): 378. http://dx.doi.org/10.3390/ijgi9060378.
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In the last two decades, unmanned aircraft systems (UAS) were successfully used in different environments for diverse applications like territorial mapping, heritage 3D documentation, as built surveys, construction monitoring, solar panel placement and assessment, road inspections, etc. These applications were correlated to the onboard sensors like RGB cameras, multi-spectral cameras, thermal sensors, panoramic cameras, or LiDARs. According to the different onboard sensors, a different mission plan is required to satisfy the characteristics of the sensor and the project aims. For UAS LiDAR-based mapping missions, requirements for the flight planning are different with respect to conventional UAS image-based flight plans because of different reasons related to the LiDAR scanning mechanism, scanning range, output scanning rate, field of view (FOV), rotation speed, etc. Although flight planning for image-based UAS missions is a well-known and solved problem, flight planning for a LiDAR-based UAS mapping is still an open research topic that needs further investigations. The article presents the developments of a LiDAR-based UAS flight planning tool, tested with simulations in real scenarios. The flight planning simulations considered an UAS platform equipped, alternatively, with three low-cost multi-beam LiDARs, namely Quanergy M8, Velodyne VLP-16, and the Ouster OS-1-16. The specific characteristics of the three sensors were used to plan flights and acquired dense point clouds. Comparisons and analyses of the results showed clear relationships between point density, flying speeds, and flying heights.
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Dorozhynskyy, O. L. ,., I. Z. Kolb, L. V. Babiy, and L. V. Dychko. "GEODESY, CARTOGRAPHY AND AERIAL PHOTOGRAPHY." GEODESY, CARTOGRAPHY AND AERIAL PHOTOGRAPHY 92,2020, no. 92 (December 24, 2020): 15–23. http://dx.doi.org/10.23939/istcgcap2020.92.015.
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Aim. Determination of the elements of external spatial orientation of the surveying systems at the moment of image acquisition is the fundamental task in photogrammetry. Principally, this problem is solving in two ways. The first way is direct positioning and measuring of directions of camera optical axis in the geodetic space with the help of GNSS/INS equipment. The second way is the analytical solution of the problem using a set of reference information (often such information is a set of ground control points whose geodetic positions are known with sufficient accuracy and which are reliably recognised on aerial images of the photogrammetric block). The authors consider the task of providing reference and control information using the second approach, which has a number of advantages in terms of reliability and accuracy of determining the unknown image exterior orientation parameters. It is proposed to obtain additional images of ground control points by the method of their auxiliary aerial photography using an unmanned aerial vehicle (UAV) on a larger scale compared to the scale of the images of the photogrammetric block. The aim of the presented work is the implementation of the method of creating reference points and experimental confirmation of its effectiveness for photogrammetric processing. Methods and results. For the entire realization of the potential of the analytical way to determine the elements of external orientation of images, it is necessary to have a certain number of ground control points (GCP) and to keep the defined scheme of their location on the photogrammetric block. As the main source of input data authors use UAV aerial images of the terrain, which are obtained separately from the block of aerial survey, and have a better geometric resolution and which clearly depict the control reference points. Application of such auxiliary images gives the possibility of automated transferring of the position of ground control point into images of the main photogrammetric block. In our interpretation, these images of ground control points and their surroundings on the ground are called "control reference images". The basis of the work is to develop a method for obtaining the auxiliary control reference images and transferring of position of GCP depicted on them into aerial or space images of terrain by means of computer stereo matching. To achieve this goal, we have developed a processing method for the creation of control reference images of aerial image or a series of auxiliary multi-scale aerial images obtained by a drone from different heights above the reference point. The operator identifies and measures the GCP once on the auxiliary aerial image of the highest resolution. Then there is an automatic stereo matching of the control reference image in the whole series of auxiliary images in succession with a decrease in the resolution and, ultimately, directly with the aerial images of photogrammetric block. On this stage there are no recognition/cursor targeting by the human operator, and therefore there are no discrepancies, errors or mistakes related to it. In addition, if to apply fairly large size of control reference images, the proposed method can be used on a low-texture terrain, and therefore deal in many cases without the physical marking of points measured by GNSS method. And this is a way to simplify and reduce the cost of photogrammetric technology. The action of the developed method has been verified experimentally to provide the control reference information of the block of archival aerial images of the low-texture terrain. The results of the experimental approbation of the proposed method give grounds to assert that the method makes it possible to perform geodetic reference of photogrammetric projects more efficiently due to the refusal of the physical marking of the area before aerial survey. The proposed method can also be used to obtain the information for checking the quality of photogrammetric survey for provision of check points. The authors argue that the use of additional equipment - UAV of semi-professional class to obtain control reference images is economically feasible. Scientific novelty and practical relevance. The results of approbation of the "control reference image" method with obtaining stereo pairs of aerial images with vertical placement of the base are presented for the first time. There was implemented the study of the properties of such stereo pairs of aerial images to obtain images of reference points. The effectiveness of including reference images in the main block of the digital aerial triangulation network created on UAV’s images is shown.
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Chen, Yunfei, Wei Feng, and Gan Zheng. "Optimum Placement of UAV as Relays." IEEE Communications Letters 22, no. 2 (February 2018): 248–51. http://dx.doi.org/10.1109/lcomm.2017.2776215.
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Majeed, Saqib, Adnan Sohail, Kashif Naseer Qureshi, Saleem Iqbal, Ibrahim Tariq Javed, Noel Crespi, Wamda Nagmeldin, and Abdelzahir Abdelmaboud. "Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks." Sensors 22, no. 16 (August 16, 2022): 6130. http://dx.doi.org/10.3390/s22166130.
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Unmanned Aerial Vehicle (UAV) deployment and placement are largely dependent upon the available energy, feasible scenario, and secure network. The feasible placement of UAV nodes to cover the cellular networks need optimal altitude. The under or over-estimation of nodes’ air timing leads to of resource waste or inefficiency of the mission. Multiple factors influence the estimation of air timing, but the majority of the literature concentrates only on flying time. Some other factors also degrade network performance, such as unauthorized access to UAV nodes. In this paper, the UAV coverage issue is considered, and a Coverage Area Decision Model for UAV-BS is proposed. The proposed solution is designed for cellular network coverage by using UAV nodes that are controlled and managed for reallocation, which will be able to change position per requirements. The proposed solution is evaluated and tested in simulation in terms of its performance. The proposed solution achieved better results in terms of placement in the network. The simulation results indicated high performance in terms of high packet delivery, less delay, less overhead, and better malicious node detection.
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Bacanli, Salih Safa, Enas Elgeldawi, Begümhan Turgut, and Damla Turgut. "UAV Charging Station Placement in Opportunistic Networks." Drones 6, no. 10 (October 9, 2022): 293. http://dx.doi.org/10.3390/drones6100293.
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Unmanned aerial vehicles (UAVs) are now extensively used in a wide variety of applications, including a key role within opportunistic wireless networks. These types of opportunistic networks are considered well suited for infrastructure-less areas, or urban areas with overloaded cellular networks. For these networks, UAVs are envisioned to complement and support opportunistic network performance; however, the short battery life of commercial UAVs and their need for frequent charging can limit their utility. This paper addresses the challenge of charging station placement in a UAV-aided opportunistic network. We implemented three clustering approaches, namely, K-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and random clustering, with each clustering approach being examined in combination with Epidemic, Spray and Wait, and State-Based Campus Routing (SCR) routing protocols. The simulation results show that determining the charging station locations using K-means clustering with three clusters showed lower message delay and higher success rate than deciding the charging station location either randomly or using DBSCAN regardless of the routing strategy employed between nodes.
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Ali, Muntadher A., Yong Zeng, and Abbas Jamalipour. "Software-Defined Coexisting UAV and WiFi: Delay-Oriented Traffic Offloading and UAV Placement." IEEE Journal on Selected Areas in Communications 38, no. 6 (June 2020): 988–98. http://dx.doi.org/10.1109/jsac.2020.2986660.
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Lassoued, Kaïs. "Balanced scorecard implementation in higher education: An Emirati perspective." Corporate Ownership and Control 15, no. 3-1 (2018): 205–16. http://dx.doi.org/10.22495/cocv15i3c1p5.
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Considering the lack of research focusing on the use of the Balanced Scorecard (BSC) as performance evaluation tool in Emirati higher education institutions, the main purpose of the study is to present a basis for a more general BSC model helping higher education managers in UAE environment for evaluating and managing the performance of their institutions. This paper is based on the case study as a research method. However, the relevance of this case study lies in the use of a joint approach combining SWOT analysis and BSC and generating an integrated strategic management system. The study comes up with a strategic evaluation plan considering the 4 BSC perspectives and designs the strategy map for it in the case of the Emirates College of Business. In this research, the traditional customer perspective of Kaplan and Norton is replaced by the students and stakeholders perspective. It is found that there is a limitation in the effective strategic problem that leads to the recruitment and placement issues, increased costs, student retention, lack of partnerships, a decrease in annual growth of income and poor performance management that can be managed through effective strategic planning. The study also reflects that there is a range of opportunities that can be exploited using the strengths in order to achieve the goals. The outcomes of this study case can be employed in the strategic planning of ECB and all other Emirati business institutions can be inspired.
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Lyu, Jiangbin, Yong Zeng, Rui Zhang, and Teng Joon Lim. "Placement Optimization of UAV-Mounted Mobile Base Stations." IEEE Communications Letters 21, no. 3 (March 2017): 604–7. http://dx.doi.org/10.1109/lcomm.2016.2633248.
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Liu, Xiaonan, Jingjing Wang, Nan Zhao, Yunfei Chen, Shun Zhang, Zhiguo Ding, and F. Richard Yu. "Placement and Power Allocation for NOMA-UAV Networks." IEEE Wireless Communications Letters 8, no. 3 (June 2019): 965–68. http://dx.doi.org/10.1109/lwc.2019.2904034.
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Akram, Tallha, Muhammad Awais, Rameez Naqvi, Ashfaq Ahmed, and Muhammad Naeem. "Multicriteria UAV Base Stations Placement for Disaster Management." IEEE Systems Journal 14, no. 3 (September 2020): 3475–82. http://dx.doi.org/10.1109/jsyst.2020.2970157.
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