Towards Flying Mobile Edge Computing

With the development of mobile edge computing (MEC), many approaches are proposed to improve the computation performances, such as network congestion, transmission latency, and quality of Internet of Things (IoTs) services. Although ground MEC system architectures are developed, there are still unsolved problems, such as depending on the ground infrastructure. Therefore, unmanned aerial vehicle (UAV) assisted MEC is considered to address these problems. However, the computation performance is limited by the UAV hover model, which is affected by the UAV’s velocity, acceleration, and a variable altitude. Nowadays, a terrestrial-satellite network (STN) plays a role in important 5G network development. The characteristics of an STN integrated with 5G are a low delay, high bandwidth, and ubiquitous coverage. Hence, 5G high-speed satellite-terrestrial assisted mobile edge computing (SMEC) is considered as a flying mobile edge computing, which will be discussed in terms of reasons, advantages, and open issues in this paper.

[1]  Joonhyuk Kang,et al.  Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning , 2016, IEEE Transactions on Vehicular Technology.

[2]  Wenyu Zhang,et al.  Satellite Mobile Edge Computing: Improving QoS of High-Speed Satellite-Terrestrial Networks Using Edge Computing Techniques , 2019, IEEE Network.

[3]  Tarik Taleb,et al.  UAV-Based IoT Platform: A Crowd Surveillance Use Case , 2017, IEEE Communications Magazine.

[4]  Yue Wang,et al.  Cooperative Task Offloading in Three-Tier Mobile Computing Networks: An ADMM Framework , 2019, IEEE Transactions on Vehicular Technology.

[5]  Pengfei Wang,et al.  Joint Task Assignment, Transmission, and Computing Resource Allocation in Multilayer Mobile Edge Computing Systems , 2019, IEEE Internet of Things Journal.

[6]  Yong Wang,et al.  Joint Deployment and Task Scheduling Optimization for Large-Scale Mobile Users in Multi-UAV-Enabled Mobile Edge Computing , 2020, IEEE Transactions on Cybernetics.

[7]  Wei Cao,et al.  Intelligent Offloading in Multi-Access Edge Computing: A State-of-the-Art Review and Framework , 2019, IEEE Communications Magazine.

[8]  Fuhui Zhou,et al.  Resource Allocation for a UAV-Enabled Mobile-Edge Computing System: Computation Efficiency Maximization , 2019, IEEE Access.

[9]  Kaibin Huang,et al.  Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading , 2016, IEEE Transactions on Wireless Communications.

[10]  Zhisheng Niu,et al.  Tasks scheduling and resource allocation in heterogeneous cloud for delay-bounded mobile edge computing , 2017, 2017 IEEE International Conference on Communications (ICC).

[11]  Shantanu Sharma,et al.  A survey on 5G: The next generation of mobile communication , 2015, Phys. Commun..

[12]  Jie Xu,et al.  UAV-Enabled Wireless Power Transfer: Trajectory Design and Energy Optimization , 2017, IEEE Transactions on Wireless Communications.

[13]  Zdenek Becvar,et al.  Mobile Edge Computing: A Survey on Architecture and Computation Offloading , 2017, IEEE Communications Surveys & Tutorials.

[14]  Khaled Ben Letaief,et al.  Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices , 2016, IEEE Journal on Selected Areas in Communications.

[15]  Li Zhou,et al.  Stochastic Computation Offloading and Trajectory Scheduling for UAV-Assisted Mobile Edge Computing , 2019, IEEE Internet of Things Journal.

[16]  Geoffrey Ye Li,et al.  Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems , 2019, IEEE Internet of Things Journal.

[17]  Yan Zhang,et al.  Mobile Edge Computing: A Survey , 2018, IEEE Internet of Things Journal.

[18]  Oriol Sallent,et al.  SDN/NFV-enabled satellite communications networks: Opportunities, scenarios and challenges , 2016, Phys. Commun..

[19]  K. B. Letaief,et al.  A Survey on Mobile Edge Computing: The Communication Perspective , 2017, IEEE Communications Surveys & Tutorials.