Air-Ground Integrated Mobile Edge Networks: A Survey

Abstract

With proliferation of smart devices and wireless applications, the recent few years have witnessed data surge. These massive data needs to be stored, transmitted, and processed in time to exploit their value for decision making. Conventional cloud computing requires transmission of massive amount of data in and out of core network, which can lead to longer service latency and potential traffic congestion. As a new platform, mobile edge computing (MEC) moves computation and storage resources to edge network in proximity to the data source. With MEC, data can be processed locally, and thus mitigate issues of latency and congestion. However, it is very challenging to reap the benefits of MEC everywhere due to geographic constraints, expensive deployment cost, and immoveable base stations. Because of easy deployment and high mobility of unmanned aerial vehicles (UAVs), air-ground integrated mobile edge networks (AGMEN) is proposed, where UAVs are employed to assist the MEC network. Such an AGMEN expects to provide MEC services ubiquitously and reliably. In this article, we first introduce the characteristics and components of UAV. Then, we will review the applications, key challenges, and current research technologies of AGMEN, from perspectives of communication, computation, and caching, respectively. Finally, we will discuss some essential research directions for AGMEN.

Joint Communication and Computation Resource Scheduling of a UAV-Assisted Mobile Edge Computing System for Platooning Vehicles

“…Additionally, some researchers focus on joining UAVs and ground mobile users into a MEC system. They are engaged in developing innovative system solutions for addressing either the networking or the computing challenges of envisioned aerial-ground cooperative networks [21]- [23]. Different design models and optimization goals have been achieved in current literature.…”

“…In addition to the works [21]- [25], many other studies have also developed their resource optimization frameworks based on the deep reinforcement learning methodology such as the works [26]- [31]. Although deep reinforcement learning (deep RL) has shown success in solving specified resource scheduling problems in the context of UAV-assisted networks to some extent, such deep RL-based solutions may encounter difficulty and impracticality in real system deployment since they rely on the basic assumption that the system dynamics follows a Markovian chain, and they usually require a lot of training data and computation for learning the state space of a real physical system.…”

Mobile Cloud Computing and Wireless Sensor Networks: A review, integration architecture, and future directions

“…Then, from the viewpoints of connectivity, processing and cache, they explored the processes, core problems and existing research developments of AGMEN in particular. At last, certain relevant testing recommendations for AGMEN were addressed [157]. Shafique et al.…”

Hardware Acceleration for Container Migration on Resource-Constrained Platforms

“…One strategy to support ULL applications is to migrate some of the container based micro-service computing to the computationally powerful client devices. Essentially, the cloud and the Multiaccess Edge Computing (MEC) [15]- [17] are extended by the client devices [18]- [21], as illustrated in Fig. 1.…”