scholarly journals An Energy Efficient Design of Computation Offloading Enabled by UAV

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3363
Author(s):  
Linpei Li ◽  
Xiangming Wen ◽  
Zhaoming Lu ◽  
Wenpeng Jing
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Energy Efficient ◽  
Computing System ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Efficient Design ◽  
Scheme Design ◽  
Computing Services ◽  
Efficient Scheme ◽  
Aerial Vehicle

The data volume is exploding due to various newly-developing applications that call for stringent communication requirements towards 5th generation wireless systems. Fortunately, mobile edge computing makes it possible to relieve the heavy computation pressure of ground users and decrease the latency and energy consumption. What is more, the unmanned aerial vehicle has the advantages of agility and easy deployment, which gives the unmanned aerial vehicle enabled mobile edge computing system opportunities to fly towards areas with communication demand, such as hotspot areas. However, the limited endurance time of unmanned aerial vehicle affects the performance of mobile edge computing services, which results in the incomplete mobile edge computing services under the time limit. Consequently, this paper concerns the energy-efficient scheme design of the unmanned aerial vehicle while providing high-quality offloading services for ground users, particularly in the regions where the ground communication infrastructures are overloaded or damaged after natural disasters. Firstly, the model of energy-efficient design of the unmanned aerial vehicle is set up taking the constraints of the energy limitation of the unmanned aerial vehicle, the data causality, and the speed of the unmanned aerial vehicle into account. Subsequently, aiming at maximizing the energy efficiency of the unmanned aerial vehicle in the unmanned aerial vehicle enabled mobile edge computing system, the bits allocation in each time slot and the trajectory of the unmanned aerial vehicle are jointly optimized. Secondly, a successive convex approximation based alternating algorithm is brought forward to deal with the non-convex energy efficiency maximization problem. Finally, it is proved that the proposed energy efficient scheme design of the unmanned aerial vehicle is superior to other benchmark schemes by the simulation results. Besides, how the performance of proposed scheme design change under different parameters is discussed.

scholarly journals Mobile Edge Computing in Unmanned Aerial Vehicle Networks

2020 ◽  
Vol 27 (1) ◽  
pp. 140-146 ◽  
Author(s):  
Fuhui Zhou ◽  
Rose Qingyang Hu ◽  
Zan Li ◽  
Yuhao Wang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Vehicle Networks ◽  
Aerial Vehicle

scholarly journals Robust Edge Computing in UAV Systems via Scalable Computing and Cooperative Computing

2021 ◽  
Author(s):  
Zhi Liu ◽  
Cheng Zhan ◽  
Ying Cui ◽  
Celimuge Wu ◽  
Han Hu
Keyword(s):  
Video Streaming ◽  
Unmanned Aerial Vehicle ◽  
Edge Computing ◽  
Future Research ◽  
Mobile Edge Computing ◽  
Computing Power ◽  
Aerial Vehicle ◽  
Cooperative Computing ◽  
Computing Performance ◽  
Simulation Results

<div>Unmanned aerial vehicle (UAV) systems are of increasing interest to academia and industry due to their mobility, flexibility and maneuverability, and are an effective alternative to various uses such as surveillance and mobile edge computing (MEC). However, due to their limited computational and communications resources, it is difficult to serve all computation tasks simultaneously. This article tackles this problem by first proposing a scalable aerial computing solution, which is applicable for computation tasks of multiple quality levels, corresponding to different computation workloads and computation results of distinct performances. It opens up the possibility to maximally improve the overall computing performance with limited computational and communications resources. To meet the demands for timely video analysis that exceed the computing power of a UAV, we propose an aerial video streaming enabled cooperative computing solution namely, UAVideo, which streams videos from a UAV to ground servers. As a complement to scalable aerial computing, UAVideo minimizes the video streaming time under the constraints on UAV trajectory, video features, and communications resources. Simulation results reveal the substantial advantages of the proposed solutions. Besides, we highlight relevant directions for future research.</div>

scholarly journals Robust Edge Computing in UAV Systems via Scalable Computing and Cooperative Computing

2021 ◽  
Author(s):  
Zhi Liu ◽  
Cheng Zhan ◽  
Ying Cui ◽  
Celimuge Wu ◽  
Han Hu
Keyword(s):  
Video Streaming ◽  
Unmanned Aerial Vehicle ◽  
Edge Computing ◽  
Future Research ◽  
Mobile Edge Computing ◽  
Computing Power ◽  
Aerial Vehicle ◽  
Cooperative Computing ◽  
Computing Performance ◽  
Simulation Results

<div>Unmanned aerial vehicle (UAV) systems are of increasing interest to academia and industry due to their mobility, flexibility and maneuverability, and are an effective alternative to various uses such as surveillance and mobile edge computing (MEC). However, due to their limited computational and communications resources, it is difficult to serve all computation tasks simultaneously. This article tackles this problem by first proposing a scalable aerial computing solution, which is applicable for computation tasks of multiple quality levels, corresponding to different computation workloads and computation results of distinct performances. It opens up the possibility to maximally improve the overall computing performance with limited computational and communications resources. To meet the demands for timely video analysis that exceed the computing power of a UAV, we propose an aerial video streaming enabled cooperative computing solution namely, UAVideo, which streams videos from a UAV to ground servers. As a complement to scalable aerial computing, UAVideo minimizes the video streaming time under the constraints on UAV trajectory, video features, and communications resources. Simulation results reveal the substantial advantages of the proposed solutions. Besides, we highlight relevant directions for future research.</div>

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