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 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.

The Applications of Improved Genetic Algorithm on Hypersonic Vehicle Trajectory Optimization

2012 ◽  
Vol 466-467 ◽  
pp. 1095-1099
Author(s):  
Liu Xu ◽  
Wei Min Li ◽  
Lin Zhang ◽  
An Tang Zhang
Keyword(s):  
Genetic Algorithm ◽  
Trajectory Optimization ◽  
Optimal Trajectory ◽  
Optimal Solution ◽  
High Sensitivity ◽  
Initial Guess ◽  
Cruise Missile ◽  
Trajectory Design ◽  
Improved Genetic Algorithm ◽  
Vehicle Trajectory

The Optimal trajectory design for hypersonic cruise missile is an optimal control problem with strict terminal constraints and variable final time. The classical algorithms always encounter the problems of high sensitivity to initial guess and local convergence in solving this problem. Aiming at these problems, genetic algorithm (GA) which is of good global convergence is applied to designing the optimal trajectory for hypersonic cruise missile. In order to improve the convergence rate of GA and overcome its premature problems, this text introduces a predatory search (PS) strategy to speed the convergence of genetic algorithms, robust and closer to the optimal solution. This text compares the original genetic algorithm (GA) and improved genetic algorithm by the emulate experiments, and the results show that the PSGA is a more effective method to design the Optimal trajectory for hypersonic cruise missile than the original genetic algorithm.

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.

Joint Energy-Efficient Resource Allocation Scheme for AF-Based Multi-Relay OFDM Networks

2014 ◽  
Vol 610 ◽  
pp. 783-788
Author(s):  
Zhi Kang Zhou ◽  
Qi Zhu
Keyword(s):  
Resource Allocation ◽  
Energy Efficient ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Allocation Scheme ◽  
Joint Resource Allocation ◽  
The Optimizing Model ◽  
Minlp Problem ◽  
Resource Allocation Scheme ◽  
Optimizing Model

In this paper, a joint resource allocation scheme for energy-efficient communication in cooperative orthogonal frequency division multiple (OFDM) networks based on subcarrier pairing (SP) is studied. The problem aimed at maximizing the system energy efficiency (EE) is formulated into a mixed-integer nonlinear programming (MINLP) problem. To solve the complex MINLP problem, we simplify the optimizing model as a typical fractional programming problem by defining the equivalent channel gain, thus Dinkelbach’s method consisting of outer iterations and inner iterations can be used to find the optimal solution to the MINLP problem proposed in polynomial time. Simulation results show that the proposed scheme can improve the system EE and ensure the quality of service (QoS) of users.

scholarly journals Collaborative offloading for UAV-enabled time-sensitive MEC networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wen-Tao Li ◽  
Mingxiong Zhao ◽  
Yu-Hui Wu ◽  
Jun-Jie Yu ◽  
Ling-Yan Bao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Trajectory Optimization ◽  
Policy Design ◽  
Descent Method ◽  
Total Energy Consumption ◽  
Aerial Vehicle ◽  
Iot Devices ◽  
Mutually Independent

AbstractRecently, 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 are 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 ($$\mathbf {P}_{\mathbf {T}}$$ P T ), resource allocation at UAV ($$\mathbf {P}_{\mathbf {R}}$$ P R ) and offloading decisions at IoT devices ($$\mathbf {P}_{\mathbf {O}}$$ P O ) and 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