Abstract

AbstractThis chapter studies mobile edge computing (MEC) networks assisted by unmanned aerial vehicles (UAVs). According to the application scenarios, we consider three roles for UAVs in MEC networks: exploiting MEC computing capabilities, serving as a computing server, and serving as a relay for computation offloading. Furthermore, the details for resource allocation and optimization are presented in the three scenarios of UAV-assisted MEC networks. In addition, we focus on the situation in which a UAV not only functions as an MEC server to inspect turbines on a wind farm, but also performs task computation. To facilitate wide applications of UAV-assisted MEC in practice, this chapter highlights the main implementation issues of UAV-assisted MEC, including optimal UAV deployment, wind models, and joint trajectory–computation performance optimization.

Highlights

  • This chapter studies mobile edge computing (MEC) networks assisted by unmanned aerial vehicles (UAVs)

  • Serving as a relay for computation offloading: When the UAV is not equipped with an MEC server and the offloading link between the mobile user and the terrestrial base stations (BSs) with the MEC server is poor, the UAV works as a relay to assist the mobile user offload tasks to the terrestrial BS

  • We illustrated the application of UAV-assisted MEC in scenarios in which terrestrial MEC networks cannot be reliably established

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Summary

Unmanned Aerial Vehicle–Assisted Mobile Edge Computing (MEC) Networks

MEC has emerged as a promising solution to enable resource-limited mobile devices to execute real-time applications (e.g., face recognition, augmented reality, unmanned driving) [81]. MEC assisted by unmanned aerial vehicles (UAVs) has drawn significant research interest because of the advantages it offers, such as fully controllable mobility, flexible deployment, and strong line-of-sight channels with ground devices [82]. Under an emergency scenario, terrestrial MEC infrastructures could be destroyed in a disaster, leaving many rescue tasks unable to be computed or executed. Recent research has focused on the advances of employing UAV-assisted MEC to help ground mobile users [83–85, 88, 90, 95, 96]. Exploiting MEC computing capabilities: When the UAV has limited computation capability and needs to execute computation-intensive tasks, it can function as a user to offload tasks to the terrestrial MEC server for computing. Serving as a computing server: When terrestrial MEC networks are not reliably established, the UAV functions as an MEC server to help ground mobile users perform tasks computation. Serving as a relay for computation offloading: When the UAV is not equipped with an MEC server and the offloading link between the mobile user and the terrestrial BS with the MEC server is poor, the UAV works as a relay to assist the mobile user offload tasks to the terrestrial BS

Joint Trajectory and Resource Optimization in UAV-Assisted MEC Networks
Resource Allocation and Optimization in the Scenario of a UAV Exploiting MEC Computing Capabilities
Energy Consumption Minimization
Completion Time Minimization
Utility Maximization
Resource Allocation and Optimization in the Scenario of a UAV Serving as a Computing Server
Computation Bit Maximization
Computation Efficiency Maximization
Resource
Minimum Throughput Maximization
Case Study
A Wind Model
A UAV Model
Deployment of Multiple UAVs at a Wind Farm
Performance Analysis
Joint Trajectory and Resource Optimization of UAV-Aided MEC at a Wind Farm
Findings
Conclusions
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