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

Fog computing security: a review of current applications and security solutions

Fog computing is a new paradigm that extends the Cloud platform model by providing computing resources on the edges of a network. It can be described as a cloud-like platform having similar data, computation, storage and application services, but is fundamentally different in that it is decentralized. In addition, Fog systems are capable of processing large amounts of data locally, operate on-premise, are fully portable, and can be installed on heterogeneous hardware. These features make the Fog platform highly suitable for time and location-sensitive applications. For example, Internet of Things (IoT) devices are required to quickly process a large amount of data. This wide range of functionality driven applications intensifies many security issues regarding data, virtualization, segregation, network, malware and monitoring. This paper surveys existing literature on Fog computing applications to identify common security gaps. Similar technologies like Edge computing, Cloudlets and Micro-data centres have also been included to provide a holistic review process. The majority of Fog applications are motivated by the desire for functionality and end-user requirements, while the security aspects are often ignored or considered as an afterthought. This paper also determines the impact of those security issues and possible solutions, providing future security-relevant directions to those responsible for designing, developing, and maintaining Fog systems

A Case Study of Edge Computing Implementations: Multi-access Edge Computing, Fog Computing and Cloudlet

With the explosive growth of intelligent and mobile devices, the current centralized cloud computing paradigm is encountering difficult challenges. Since the primary requirements have shifted towards implementing real-time response and supporting context awareness and mobility, there is an urgent need to bring resources and functions of centralized clouds to the edge of networks, which has led to the emergence of the edge computing paradigm. Edge computing increases the responsibilities of network edges by hosting computation and services, therefore enhancing performances and improving quality of experience (QoE). Fog computing, multi-access edge computing (MEC), and cloudlet are three typical and promising implementations of edge computing. Fog computing aims to build a system that enables cloud-to-thing service connectivity and works in concert with clouds, MEC is seen as a key technology of the fifth generation (5G) system, and Cloudlet is a micro-data center deployed in close proximity. In terms of deployment scenarios, Fog computing focuses on the Internet of Things (IoT), MEC mainly provides mobile RAN application solutions for 5G systems, and cloudlet offloads computing power at the network edge. In this paper, we present a comprehensive case study on these three edge computing implementations, including their architectures, differences, and their respective application scenario in IoT, 5G wireless systems, and smart edge. We discuss the requirements, benefits, and mechanisms of typical co-deployment cases for each paradigm and identify challenges and future directions in edge computing