UAV Deployment Research Articles

Joint UAV Deployment and Precoder Optimization for Multicasting and Target Sensing in UAV-Assisted ISAC Networks

Joint UAV Deployment and Precoder Optimization for Multicasting and Target Sensing in UAV-Assisted ISAC Networks

Energy optimization and age of information enhancement in multi‐UAV networks using deep reinforcement learning

AbstractThis letter introduces an innovative approach for minimizing energy consumption in multi‐unmanned aerial vehicles (multi‐UAV) networks using deep reinforcement learning, with a focus on optimizing the age of information (AoI) in disaster environments. A hierarchical UAV deployment strategy that facilitates cooperative trajectory planning, ensuring timely data collection and transmission while minimizing energy consumption is proposed. By formulating the inter‐UAV network path planning problem as a Markov decision process, a deep Q‐network (DQN) strategy is applied to enable real‐time decision making that accounts for dynamic environmental changes, obstacles, and UAV battery constraints. The extensive simulation results, conducted in both rural and urban scenarios, demonstrate the effectiveness of employing a memory access approach within the DQN framework, significantly reducing energy consumption up to 33.25% in rural settings and 74.20% in urban environments compared to non‐memory approaches. By integrating AoI considerations with energy‐efficient UAV control, this work offers a robust solution for maintaining fresh data in critical applications, such as disaster response, where ground‐based communication infrastructures are compromised. The use of replay memory approach, particularly the online history approach, proves crucial in adapting to changing conditions and optimizing UAV operations for both data freshness and energy consumption.

Joint Optimization of UAV Deployment and Directional Antenna Orientation for Multi-UAV Cooperative Sensing System

Joint Optimization of UAV Deployment and Directional Antenna Orientation for Multi-UAV Cooperative Sensing System

System Cost-Oriented UAV Deployment for Integrated Access and Backhaul Networks

System Cost-Oriented UAV Deployment for Integrated Access and Backhaul Networks

Joint optimization for on-demand deployment of UAVs and spectrum allocation in UAVs-assisted communication

Joint optimization for on-demand deployment of UAVs and spectrum allocation in UAVs-assisted communication

A joint optimization method for multi-UAV deployment and task scheduling in mobile edge computing with large-scale mobile users

In the realm of multi-UAV-assisted mobile edge computing (MEC), the joint optimization of UAV deployment and task scheduling has emerged as a potent approach for mitigating energy consumption while ensuring high-quality services for mobile users. Nevertheless, the intricate interdependence between UAV deployment and task scheduling presents formidable challenges when implementing this scheme. In response, we introduce a novel methodology called the Dual-Stage Hybrid Strategy with Genetic-Simulated Annealing and Knowledge Sharing (DSHSGSK). DSHSGSK employs an alternating optimization strategy known as HS-GSK, supplemented by an elimination operator, to simultaneously optimize UAV deployment and task scheduling. In pursuit of heightened efficiency in UAV deployment and a more effective task scheduling strategy, we propose an innovative location encoding scheme for UAVs. To harness the local search capabilities of the Harmony search (HS) algorithm and the global search capabilities of the Gaining Sharing Knowledge based Algorithm (GSK), we introduce a coordination parameter (CP) to modulate the frequency of alternation between these two optimization techniques. Experimental results validate the superiority of our proposed method over other state-of-the-art algorithms for jointly optimizing multi-UAV deployment and task scheduling. Notably, our approach excels in terms of energy consumption, underscoring its exceptional performance.

Joint UAV Deployment and User Scheduling for Wireless Powered Wearable Networks

Joint UAV Deployment and User Scheduling for Wireless Powered Wearable Networks

Multi-Objective Deployment of UAVs for Multi-Hop FANET: UAV-Assisted Emergency Vehicular Network

Multi-Objective Deployment of UAVs for Multi-Hop FANET: UAV-Assisted Emergency Vehicular Network

Terrain-Based UAV Deployment: Providing Coverage for Outdoor Users

Terrain-Based UAV Deployment: Providing Coverage for Outdoor Users

Seamline Optimization Based on Triangulated Irregular Network of Tiepoints for Fast UAV Image Mosaicking

UAV remote sensing is suitable for urgent image monitoring and periodic observation of an area of interest. To observe a target area using UAVs, many images must be acquired because of the narrow image coverage of UAVs. To increase the efficiency of UAV remote sensing, UAV mosaicking is used to create a single image from multiple UAV images. In order to maintain the strength of rapid UAV deployment, UAV mosaicked images have to be quickly generated through image-based mosaicking techniques. In addition, it is necessary to improve the mosaic errors of image-based techniques that often occur in contrast to terrain-based techniques. Relief displacement is a major source of mosaic error and can be detected by utilizing a terrain model. We have proposed an image-based mosaicking technique utilizing TIN, which is a model that can represent terrain with discontinuously acquired height information of ground points. Although the TIN is less accurate than DSM, it is simpler and faster to utilize for image mosaicking. In our previous work, we demonstrated fast processing speed of mosaicking using TIN-based image tiepoints. In this study, we improve the quality of image-based mosaicking techniques by optimizing seamline-based TIN geometry. Three datasets containing buildings with large relief displacement were used in this study. The experiment results showed that the TIN based on the proposed method improved the mosaic error caused by relief displacement significantly.

Utility-Aware UAV Deployment and Task Offloading in Multi-UAV Edge Computing Networks

Utility-Aware UAV Deployment and Task Offloading in Multi-UAV Edge Computing Networks

Optimal UAV deployment with star topology in area coverage problems

Optimal UAV deployment with star topology in area coverage problems

Joint UAV Deployment and Resource Allocation: A Personalized Federated Deep Reinforcement Learning Approach

Joint UAV Deployment and Resource Allocation: A Personalized Federated Deep Reinforcement Learning Approach

Provisioning edge computing services in urban areas through the chaining of unmanned aerial vehicles: A genetic approach

AbstractEdge computing has been proved an efficient approach to provisioning computation offloading service to vehicles on road through Road‐Side Units (RSUs). However, the traffic volume on road is highly dynamic, while RSU‐based edge servers are static in terms of geographical location and computation capacity. To address this problem, this paper proposes a mobile edge server placement strategy using cruising UAVs along the roads based on the genetic algorithm. A mathematical model is first built to characterize the deployment cost of these UAV‐mounted servers and their routes. Next, a heuristic UAV‐mounted edge server deployment scheme based on K‐medoid clustering and genetic algorithms is designed. Experimental results verify that the proposed UAV deployment scheme satisfies the offloading demand of IoV nodes while reducing the total deployment cost by 17.05–48.94% compared with existing popular approaches.

Computation‐efficiency‐oriented resource optimization in NOMA‐assisted multi‐UAV‐enabled MEC networks

SummaryUnmanned aerial vehicles (UAVs) can serve as aerial mobile edge computing (MEC) servers to provide computing services to Internet of Things smart devices (ISDs) with insufficient computing capacity on the ground. However, how to provide energy‐efficient computing services by designing appropriate resource optimization strategy is still a challenge issue in UAV‐enabled MEC networks. To this end, this paper proposes a computation efficiency (CE)‐oriented partial offloading framework for UAV‐enabled MEC networks, where the ISDs' computation bits and energy consumption are taken into account simultaneously. Specifically, we firstly divide the ISDs into multiple clusters and determine the deployment of UAVs by k‐means method. Meanwhile, ISDs occupying the same subchannel in the same cluster can offload data through non‐orthogonal multiple access (NOMA) technology. Then, the problem of maximizing the CE of the system is formulated by optimizing subchannel allocation, transmit power and computation resources of ISDs. To solve it, we propose a staged optimization approach by using matching theory, Dinkelbach and sequential convex programming (SCP) methods. Numerical results demonstrate the proposed scheme can achieve higher CE compared with other baseline schemes.

Multi-agent based optimal UAV deployment for throughput maximization in 5 G communications

Multi-agent based optimal UAV deployment for throughput maximization in 5 G communications

Multi-UAV-assisted Internet of Remote Things communication within satellite–aerial–terrestrial integrated network

Due to the limited transmission capabilities of terrestrial intelligent devices within the Internet of Remote Things (IoRT), this paper proposes an optimization scheme aimed at enhancing data transmission rate while ensuring communication reliability. This scheme focuses on multi-unmanned aerial vehicle (UAV)-assisted IoRT data communication within the satellite–aerial–terrestrial integrated network (SATIN), which is one of the key technologies for the sixth generation (6G) networks. To optimize the system’s data transmission rate, we introduce a multi-dimensional coverage and power optimization (CPO) algorithm, rooted in the block coordinate descent (BCD) method. This algorithm concurrently optimizes various parameters, including the number and deployment of UAVs, the correlation between IoRT devices and UAVs, and the transmission power of both devices and UAVs. To ensure comprehensive coverage of a large-scale randomly distributed array of terrestrial devices, combined with machine learning algorithm, we present the Dynamic Deployment based on K-means (DDK) algorithm. Additionally, we address the non-convexity challenge in resource allocation for transmission power through variable substitution and the successive convex approximation technique (SCA). Simulation results substantiate the remarkable efficacy of our CPO algorithm, showcasing a maximum 240% improvement in the uplink transmission rate of IoRT data compared to conventional methods.

The Deployment of UAVs Using Machine Learning for Security Surveillances on the Pipelines and Illegal Oil Refining Clusters: A Critical Review of the Nigerian Scenario

The Deployment of UAVs Using Machine Learning for Security Surveillances on the Pipelines and Illegal Oil Refining Clusters: A Critical Review of the Nigerian Scenario

Joint Blockage Prediction and UAV Deployment in Millimeter-Wave Communication Networks

Joint Blockage Prediction and UAV Deployment in Millimeter-Wave Communication Networks

Energy Efficient Deployment of VLC-Enabled UAV Using Particle Swarm Optimization

Energy Efficient Deployment of VLC-Enabled UAV Using Particle Swarm Optimization