scholarly journals Computation offloading game in multiple unmanned aerial vehicle‐enabled mobile edge computing networks

2021 ◽  
Author(s):  
Yanling Ren ◽  
Zhibin Xie ◽  
Zhenfeng Ding ◽  
Xiyuan Sun ◽  
Jie Xia ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Aerial Vehicle

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>

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 Designated Verifier Proxy Blind Signature Scheme for Unmanned Aerial Vehicle Network Based on Mobile Edge Computing

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Lei He ◽  
Jianfeng Ma ◽  
Ruo Mo ◽  
Dawei Wei
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Network Architecture ◽  
Random Oracle Model ◽  
Edge Computing ◽  
Blind Signature ◽  
Low Latency ◽  
Mobile Edge Computing ◽  
Proxy Blind Signature ◽  
Aerial Vehicle ◽  
Designated Verifier

Unmanned Aerial Vehicle (UAV) has enormous potential in many domains. According to the characteristics of UAV, it is important for UAV network to assure low latency and integrity and authentication of commands sent by command center or command stations to UAV. In this paper, we proposed a UAV network architecture based on mobile edge computing (MEC) which helps guarantee low latency in the UAV network. Afterwards, we proposed a designated verifier proxy blind signature (DVPBS) scheme for UAV network and proved that it is existentially unforgeable under an adaptive chosen message attack in the random oracle model. We compared the efficiency of our DVPBS scheme with other signature schemes by implementing them in jPBC and theoretically analyzing their signature length. The experiment results indicate that our DVPBS scheme is efficient. The signature length of our DVPBS is longer, but it is still short enough compared with the transmission capacity of UAV.

scholarly journals Deep Reinforcement Learning for Computation Offloading and Resource Allocation in Unmanned-Aerial-Vehicle Assisted Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6499
Author(s):  
Shuyang Li ◽  
Xiaohui Hu ◽  
Yongwen Du
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Energy Consumption ◽  
Unmanned Aerial Vehicle ◽  
Learning Algorithm ◽  
End Users ◽  
Edge Computing ◽  
Computation Offloading ◽  
Multiple Users ◽  
Aerial Vehicle

Computation offloading technology extends cloud computing to the edge of the access network close to users, bringing many benefits to terminal devices with limited battery and computational resources. Nevertheless, the existing computation offloading approaches are challenging to apply to specific scenarios, such as the dense distribution of end-users and the sparse distribution of network infrastructure. The technological revolution in the unmanned aerial vehicle (UAV) and chip industry has granted UAVs more computing resources and promoted the emergence of UAV-assisted mobile edge computing (MEC) technology, which could be applied to those scenarios. However, in the MEC system with multiple users and multiple servers, making reasonable offloading decisions and allocating system resources is still a severe challenge. This paper studies the offloading decision and resource allocation problem in the UAV-assisted MEC environment with multiple users and servers. To ensure the quality of service for end-users, we set the weighted total cost of delay, energy consumption, and the size of discarded tasks as our optimization objective. We further formulate the joint optimization problem as a Markov decision process and apply the soft actor–critic (SAC) deep reinforcement learning algorithm to optimize the offloading policy. Numerical simulation results show that the offloading policy optimized by our proposed SAC-based dynamic computing offloading (SACDCO) algorithm effectively reduces the delay, energy consumption, and size of discarded tasks for the UAV-assisted MEC system. Compared with the fixed local-UAV scheme in the specific simulation setting, our proposed approach reduces system delay and energy consumption by approximately 50% and 200%, respectively.

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