A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

A comprehensive survey on reinforcement-learning-based computation offloading techniques in Edge Computing Systems

Producción CientíficaIn recent years, the number of embedded computing devices connected to the Internet has exponentially increased. At the same time, new applications are becoming more complex and computationally demanding, which can be a problem for devices, especially when they are battery powered. In this context, the concepts of computation offloading and edge computing, which allow applications to be fully or partially offloaded and executed on servers close to the devices in the network, have arisen and received increasing attention. Then, the design of algorithms to make the decision of which applications or tasks should be offloaded, and where to execute them, is crucial. One of the options that has been gaining momentum lately is the use of Reinforcement Learning (RL) and, in particular, Deep Reinforcement Learning (DRL), which enables learning optimal or near-optimal offloading policies adapted to each particular scenario. Although the use of RL techniques to solve the computation offloading problem in edge systems has been covered by some surveys, it has been done in a limited way. For example, some surveys have analysed the use of RL to solve various networking problems, with computation offloading being one of them, but not the primary focus. Other surveys, on the other hand, have reviewed techniques to solve the computation offloading problem, being RL just one of the approaches considered. To the best of our knowledge, this is the first survey that specifically focuses on the use of RL and DRL techniques for computation offloading in edge computing system. We present a comprehensive and detailed survey, where we analyse and classify the research papers in terms of use cases, network and edge computing architectures, objectives, RL algorithms, decision-making approaches, and time-varying characteristics considered in the analysed scenarios. In particular, we include a series of tables to help researchers identify relevant papers based on specific features, and analyse which scenarios and techniques are most frequently considered in the literature. Finally, this survey identifies a number of research challenges, future directions and areas for further study.Consejería de Educación de la Junta de Castilla y León y FEDER (VA231P20)Ministerio de Ciencia e Innovación y Agencia Estatal de Investigación (Proyecto PID2020-112675RB-C42, PID2021-124463OBI00 y RED2018-102585-T, financiados por MCIN/AEI/10.13039/501100011033

Service Provisioning in Edge-Cloud Continuum Emerging Applications for Mobile Devices

Disruptive applications for mobile devices can be enhanced by Edge computing facilities. In this context, Edge Computing (EC) is a proposed architecture to meet the mobility requirements imposed by these applications in a wide range of domains, such as the Internet of Things, Immersive Media, and Connected and Autonomous Vehicles. EC architecture aims to introduce computing capabilities in the path between the user and the Cloud to execute tasks closer to where they are consumed, thus mitigating issues related to latency, context awareness, and mobility support. In this survey, we describe which are the leading technologies to support the deployment of EC infrastructure. Thereafter, we discuss the applications that can take advantage of EC and how they were proposed in the literature. Finally, after examining enabling technologies and related applications, we identify some open challenges to fully achieve the potential of EC, and also research opportunities on upcoming paradigms for service provisioning. This survey is a guide to comprehend the recent advances on the provisioning of mobile applications, as well as foresee the expected next stages of evolution for these applications

迅速な災害管理のための即時的,持続可能，かつ拡張的なエッジコンピューティングの研究

本学位論文は、迅速な災害管理におけるいくつかの問題に取り組んだ。既存のネットワークインフラが災害による直接的なダメージや停電によって使えないことを想定し、本論文では、最新のICTを用いた次世代災害支援システムの構築を目指す。以下のとおり本論文は三部で構成される。第一部は、災害発生後の緊急ネットワーキングである。本論文では、情報指向フォグコンピューティング（Information-Centric Fog Computing）というアーキテクチャを提案し、既存のインフラがダウンした場合に臨時的なネットワーク接続を提供する。本論文では、六次の隔たり理論から着想を得て、緊急時向け名前ベースルーティング（Name-Based Routing）を考慮した。まず、二層の情報指向フォグコンピューティングネットワークモデルを提案した。次に、ソーシャルネットワークを元に、情報指向フォグノード間の関係をモデリングし、名前ベースルーティングプロトコルをデザインする。シミュレーション実験では、既存のソリューションと比較し、提案手法はより高い性能を示し、有用性が証明された。第二部は、ネットワークの通信効率の最適化である。本論文は、第一部で構築されたネットワークの通信効率を最適化し、ネットワークの持続時間を延ばすために、ネットワークのエッジで行われるキャッシングストラテジーを提案した。本論文では、まず、第一部で提案した二層ネットワークモデルをベースにサーバー層も加えて、異種ネットワークストラクチャーを構成した。次に、緊急時向けのエッジキャッシングに必要なTime to Live （TTL）とキャッシュ置換ポリシーを設計する。シミュレーション実験では、エネルギー消費とバックホールレートを性能指標とし、メモリ内キャッシュとディスクキャッシュの性能を比較した。結果では、メモリ内ストレージと処理がエッジキャッシングのエネルギーを節約し、かなりのワークロードを共有できることが示された。第三部は、ネットワークカバレッジの拡大である。本論文は、ドローンの関連技術とリアルタイム視覚認識技術を利用し、被災地のユーザ捜索とドローンの空中ナビゲーションを行う。災害管理におけるドローン制御に関する研究を調査し、現在のドローン技術と無人捜索救助に対する実際のニーズを考慮すると、軽量なソリューションが緊急時に必要であることが判明した。そのため本論文では、転移学習を利用し、ドローンに搭載されたオンボードコンピュータで実行可能な空中ビジョンに基づいたナビゲーションアプローチを開発した。シミュレーション実験では、1/150ミニチュアモデルを用いて、空中ナビゲーションの実行可能性をテストした。結果では、本論文で提案するドローンの軽量ナビゲーションはフィードバックに基づいてリアルタイムに飛行の微調整を実現でき、既存手法と比較して性能において大きな進歩を示した。This dissertation mainly focuses on solving the problems in agile disaster management. To face the situation when the original network infrastructure no longer works because of disaster damage or power outage, I come up with the idea of introducing different emerging technologies in building a next-generation disaster response system. There are three parts of my research. In the first part of emergency networking, I design an information-centric fog computing architecture to fast build a temporary emergency network while the original ones can not be used. I focus on solving name-based routing for disaster relief by applying the idea from six degrees of separation theory. I first put forward a 2-tier information-centric fog network architecture under the scenario of post-disaster. Then I model the relationships among ICN nodes based on delivered files and propose a name-based routing strategy to enable fast networking and emergency communication. I compare with DNRP under the same experimental settings and prove that my strategy can achieve higher work performance. In the second part of efficiency optimization, I introduce the idea of edge caching in prolong the lifetime of the rebuilt network. I focus on how to improve the energy efficiency of edge caching using in-memory storage and processing. Here I build a 3-tier heterogeneous network structure and propose two edge caching methods using different TTL designs & cache replacement policies. I use total energy consumption and backhaul rate as the two metrics to test the performance of the in-memory caching method and compare it with the conventional method based on disk storage. The simulation results show that in-memory storage and processing can help save more energy in edge caching and share a considerable workload in percentage. In the third part of coverage expansion, I apply UAV technology and real-time image recognition in user search and autonomous navigation. I focus on the problem of designing a navigation strategy based on the airborne vision for UAV disaster relief. After the survey of related works on UAV fly control in disaster management, I find that in consideration of the current UAV manufacturing technology and actual demand on unmanned search & rescue, a lightweight solution is in urgent need. As a result, I design a lightweight navigation strategy based on visual recognition using transfer learning. In the simulation, I evaluate my solutions using 1/150 miniature models and test the feasibility of the navigation strategy. The results show that my design on visual recognition has the potential for a breakthrough in performance and the idea of UAV lightweight navigation can realize real-time flight adjustment based on feedback.室蘭工業大学 (Muroran Institute of Technology)博士（工学

Relay-enabled task offloading management for wireless body area networks

Inspired by the recent developments of the Internet of Things (IoT) relay and mobile edge computing (MEC), a hospital/home-based medical monitoring framework is proposed, in which the intensive computing tasks from the implanted sensors can be efficiently executed by on-body wearable devices or a coordinator-based MEC (C-MEC). In this paper, we first propose a wireless relay-enabled task offloading mechanism that consists of a network model and a computation model. Moreover, to manage the computation resources among all relays, a task offloading decision model and the best task offloading recipient selection function is given. The performance evaluation considers different computation schemes under the predetermined link quality condition regarding the selected vital quality of service (QoS) metrics. After demonstrating the channel characterization and network topology, the performance evaluation is implemented under different scenarios regarding the network lifetime of all relays, network residual energy status, total number of locally executed packets, path loss (PL), and service delay. The results show that data transmission without the offloading scheme outperforms the offload-based technique regarding network lifetime. Moreover, the high computation capacity scenario achieves better performance regarding PL and the total number of locally executed packets