scholarly journals UAV-Enabled Data Collection: Multiple Access, Trajectory Optimization, and Energy Trade-Off

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Lin Xiao ◽  
Yipeng Liang ◽  
Chenfan Weng ◽  
Dingcheng Yang ◽  
Qingmin Zhao
Keyword(s):  
Resource Allocation ◽  
Data Collection ◽  
Unmanned Aerial Vehicle ◽  
Trajectory Optimization ◽  
Minimum Energy ◽  
Trajectory Design ◽  
Energy Trade ◽  
Significant Performance ◽  
Aerial Vehicle ◽  
Resource Allocation Scheme

In this paper, we consider a ground terminal (GT) to an unmanned aerial vehicle (UAV) wireless communication system where data from GTs are collected by an unmanned aerial vehicle. We propose to use the ground terminal-UAV (G-U) region for the energy consumption model. In particular, to fulfill the data collection task with a minimum energy both of the GTs and UAV, an algorithm that combines optimal trajectory design and resource allocation scheme is proposed which is supposed to solve the optimization problem approximately. We initialize the UAV’s trajectory firstly. Then, the optimal UAV trajectory and GT’s resource allocation are obtained by using the successive convex optimization and Lagrange duality. Moreover, we come up with an efficient algorithm aimed to find an approximate solution by jointly optimizing trajectory and resource allocation. Numerical results show that the proposed solution is efficient. Compared with the benchmark scheme which did not adopt optimizing trajectory, the solution we propose engenders significant performance in energy efficiency.

scholarly journals Multi-UAV Enabled Data Collection with Efficient Joint Adaptive Interference Management and Trajectory Design

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 547
Author(s):  
Weichao Pi ◽  
Jianming Zhou
Keyword(s):  
Resource Allocation ◽  
Data Collection ◽  
Trajectory Optimization ◽  
Completion Time ◽  
Interference Management ◽  
Optimal Solution ◽  
Descent Method ◽  
Trajectory Design ◽  
Distributed Sensors ◽  
Joint Resource Allocation

This paper studies interference in a data collection scenario in which multiple unmanned aerial vehicles (UAVs) are dispatched to wirelessly collect data from a set of distributed sensors. To improve the communication throughput and minimize the completion time, we design a joint resource allocation and trajectory optimization framework that not only is compatible with the traditional time-division scheme and interference coordination scheme but also combines their advantages. First, we analyse a basic quasi-stationary scenario with two UAVs and four devices, in which the two UAVs hover at optimal displacements to execute the data collection mission, and it is proven that the proposed optimal resource allocation and trajectory solution is adaptively adjustable according to the severity of the interference and that the common throughput of the network is non-decreasing. Second, for the general mobile case, we design an efficient algorithm to jointly address resource allocation and trajectory optimization, in which we first apply the block coordinate descent method to decompose the original non-convex problem into three non-convex sub-problems and then employ a dedicated genetic algorithm, a penalty function and the sequential convex approximation (SCA) technique to efficiently solve the individual sub-problems and obtain a satisfactory locally optimal solution with an adaptive initialization scheme. Subsequently, numerical experiments are presented to demonstrate that the completion time of the data collection task with our proposed method is at least 25% shorter than those with several baseline dynamic orthogonal schemes when 4 UAVs are deployed. Finally, we provide a practical application principle concerning the maximum suitable number of UAVs to avoid the inherent deficiencies of the proposed algorithm.

scholarly journals UNMANNED AERIAL VEHICLE USE FOR WOOD CHIPS PILE VOLUME ESTIMATION

2016 ◽  
Vol XLI-B1 ◽  
pp. 953-956
Author(s):  
M. Mokroš ◽  
M. Tabačák ◽  
M. Lieskovský ◽  
M. Fabrika
Keyword(s):  
Data Collection ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Applied Research ◽  
Rapid Development ◽  
Volume Estimation ◽  
Wood Chips ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Gnss Data

The rapid development of unmanned aerial vehicles is a challenge for applied research. Many technologies are developed and then researcher are looking up for their application in different sectors. Therefore, we decided to verify the use of the unmanned aerial vehicle for wood chips pile monitoring. <br><br> We compared the use of GNSS device and unmanned aerial vehicle for volume estimation of four wood chips piles. We used DJI Phantom 3 Professional with the built-in camera and GNSS device (geoexplorer 6000). We used Agisoft photoscan for processing photos and ArcGIS for processing points. <br><br> Volumes calculated from pictures were not statistically significantly different from amounts calculated from GNSS data and high correlation between them was found (p = 0.9993). We conclude that the use of unmanned aerial vehicle instead of the GNSS device does not lead to significantly different results. Tthe data collection consumed from almost 12 to 20 times less time with the use of UAV. Additionally, UAV provides documentation trough orthomosaic.

scholarly journals FORMATION OF OPTIMAL PARAMETRS OF THE TRAJECTORY OF THE OVERFLIGHT OF UNMANNED AERIAL VEHICLE THROUGH THE SPECIFIED POINTS OF SPACE

Doklady BGUIR ◽  
2019 ◽  
pp. 50-57
Author(s):  
A. A. Lobaty ◽  
A. Y. Bumai ◽  
Du Jun
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Mathematical Problem ◽  
Minimum Energy ◽  
Optimization Criterion ◽  
Flight Path ◽  
Reference Points ◽  
Control Synthesis ◽  
Control Law ◽  
Analytical Control ◽  
Aerial Vehicle

The purpose of the scientific research, results are determinated in the article, is to analytically synthesize the control law of an unmanned aerial vehicle while guiding one along the trajectory that specified by the reference points of space in an inertial coordinate system. The analysis of various existing approaches of the formation of a given flight path of an unmanned aerial vehicle based on various mathematical formulations of the problem is carried out. To achieve the goal, the flight path is considered as separate intervals, where the control optimization problem is solved. The optimization criterion in general form is substantiated and its presentation in the form of a minimized quadratic quality functional is convenient for analytical control synthesis. As components of the functional, the parameters of the deviation of the flight path of the aircraft from the specified points of space are considered, as well as the predicted parameters of the velocity vector and the control normal acceleration. Moreover, at each specified point in space, the direction of the trajectory to the subsequent point is taken into account, that ensures optimal curvature of the trajectory by specified flight speed of the unmanned aerial vehicle. As a result of analytical synthesis, mathematical dependences are obtained to determine control acceleration, which allow us to get a specified optimal control law on board an unmanned aerial vehicle, which ultimately ensures minimum energy consumption. The validity of the proposed theoretical provisions is confirmed by a clear example, where for a simplified mathematical problem statement the optimal laws of change in control acceleration and the trajectory parameters of an unmanned aerial vehicle are calculated by computer simulation.

scholarly journals Collaborative Offloading for UAV-enabled Time-Sensitive MEC Networks

2020 ◽  
Author(s):  
Wentao Li ◽  
Mingxiong Zhao ◽  
Yuhui Wu ◽  
Junjie Yu ◽  
Lingyan Bao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Trajectory Optimization ◽  
Policy Design ◽  
Descent Method ◽  
Total Energy Consumption ◽  
Aerial Vehicle ◽  
Iot Devices ◽  
Mutually Independent

Abstract Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV is rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization ($\mathbf{P_T}$), resource allocation at UAV ($\mathbf{P_R}$) and offloading decisions at IoT devices ($\mathbf{P_O}$), then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.

Sign in / Sign up

Export Citation Format

Share Document