scholarly journals Energy-Efficient Trajectory Optimization for UAV-Based Hybrid FSO/RF Communications with Buffer Constraints

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1596
Author(s):  
Rong-Rong Lu ◽  
Yang Ma ◽  
Sheng-Hong Lin ◽  
Bingyuan Zhang ◽  
Qinglin Wang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Hybrid System ◽  
Trajectory Optimization ◽  
Theoretical Models ◽  
Buffer Size ◽  
Theoretical Expression ◽  
Free Space Optical ◽  
Rf Communication ◽  
Aerial Vehicle ◽  
Velocity Constraint

This paper focuses on an unmanned aerial vehicle (UAV) assisted hybrid free-space optical (FSO)/radio frequency (RF) communication system. Considering the rate imbalance between the FSO and RF links, a buffer is employed at the UAV. Initially, theoretical models of energy consumption and throughput are obtained for the hybrid system. Based on these models, the theoretical expression of the energy efficiency is derived. Then, a nonconvex trajectory optimization problem is formulated by maximizing the energy efficiency of the hybrid system under the buffer constraint, velocity constraint, acceleration constraint, start–end position constraint, and start–end velocity constraint. By using the sequential convex optimization and first-order Taylor approximation, the nonconvex problem is transformed into a convex one. An iterative algorithm is proposed to solve the problem. Numerical results verify the efficiency of the proposed algorithm and also show the effects of buffer size on a UAV’s trajectory.

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 Wireless Sensor Networks: Towards Resilience Against Weather-Based Disruptions

2019 ◽  
Vol 15 (2) ◽  
pp. 79-87
Author(s):  
Julius Skirelis ◽  
Antons Patlins ◽  
Nadezhda Kunicina ◽  
Andrejs Romanovs ◽  
Anatolijs Zabasta
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocols ◽  
Weather Conditions ◽  
Wireless Sensor ◽  
Free Space Optical ◽  
Rf Communication ◽  
Manet Routing ◽  
Bodies Of Water ◽  
The Impact

AbstractThe article discusses vulnerability of wireless sensors networks to weather-based disruptions considering the opinions of different experts published in a range of scientific materials. The introduction provides a brief overview of wireless signals in real world conditions focusing on how weather affects signals (rain, fog and clouds, snow, hail, lightning, wind, bodies of water, trees and physical obstruction). Information about the effects of weather on wireless sensor networks using Free Space Optical / Radio Frequency (FSO/RF) communication is then provided. Finally, the impact of weather conditions on MANET routing protocols is considered theoretically, and experimental simulations are performed by comparing the sustainability of different protocols to different weather conditions. After analysis of experiment results, ideas on how to decrease vulnerability of wireless networks to weather-based disruptions are discussed.

scholarly journals Proactive Guidance for Accurate UAV Landing on a Dynamic Platform: A Visual-inertial Approach

2021 ◽  
Author(s):  
Ching-Wei Chang ◽  
Li-Yu Lo ◽  
Hiu Ching Cheung ◽  
Yurong Feng ◽  
An-Shik Yang ◽  
...  
Keyword(s):  
Trajectory Optimization ◽  
Control Method ◽  
Guidance System ◽  
Autonomous Landing ◽  
Ground Vehicle ◽  
Ground Station ◽  
Aerial Vehicle ◽  
Promising Solution ◽  
Marine Vessels ◽  
Long Endurance

This work aims to develop an autonomous system for the unmanned aerial vehicle (UAV) to land on a moving platform such as the automobile or marine vessels, providing a promising solution for a long-endurance flight operation, a large mission coverage range, and a convenient recharging ground station. Different from most state-of-the-art UAV landing frameworks which rely on UAV’s onboard computers and sensors, the proposed system fully depends on the computation unit situated on the ground vehicle/marine vessel to serve as a landing guidance system. Such novel configuration can therefore lighten the burden of the UAV and computation power on the ground vehicle/marine vessel could be enhanced. In particular, we exploit a sensor fusion-based algorithm for the guidance system to perform UAV localization, whilst a control method based upon trajectory optimization is integrated. Indoor and outdoor experiments are conducted and the result shows that a precise autonomous landing on a 43 X 43 cm platform could be performed.

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.

scholarly journals Energy Efficiency Optimization of Cognitive UAV-Assisted Edge Communication for Semantic Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yilong Gu ◽  
Yangchao Huang ◽  
Hang Hu ◽  
Weiting Gao ◽  
Yu Pan
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Energy Efficient ◽  
Optimization Problem ◽  
Optimization Scheme ◽  
Battery Capacity ◽  
Aerial Vehicle ◽  
Efficient Alternative ◽  
The Internet Of Things

With the consolidation of the Internet of Things (IoT), the unmanned aerial vehicle- (UAV-) based IoT has attracted much attention in recent years. In the IoT, cognitive UAV can not only overcome the problem of spectrum scarcity but also improve the communication quality of the edge nodes. However, due to the generation of massive and redundant IoT data, it is difficult to realize the mutual understanding between UAV and ground nodes. At the same time, the performance of the UAV is severely limited by its battery capacity. In order to form an autonomous and energy-efficient IoT system, we investigate semantically driven cognitive UAV networks to maximize the energy efficiency (EE). The semantic device model for cognitive UAV-assisted IoT communication is constructed. And the sensing time, the flight speed of UAV, and the coverage range of UAV communication are jointly optimized to maximize the EE. Then, an efficient alternative algorithm is proposed to solve the optimization problem. Finally, we provide computer simulations to validate the proposed algorithm. The performance of the joint optimization scheme based on the proposed algorithm is compared to some benchmark schemes. And the simulation results show that the proposed scheme can obtain the optimal system parameters and can significantly improve the EE.

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