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

Evaluation of Storage Systems for Big Data Analytics

abstract: Recent trends in big data storage systems show a shift from disk centric models to memory centric models. The primary challenges faced by these systems are speed, scalability, and fault tolerance. It is interesting to investigate the performance of these two models with respect to some big data applications. This thesis studies the performance of Ceph (a disk centric model) and Alluxio (a memory centric model) and evaluates whether a hybrid model provides any performance benefits with respect to big data applications. To this end, an application TechTalk is created that uses Ceph to store data and Alluxio to perform data analytics. The functionalities of the application include offline lecture storage, live recording of classes, content analysis and reference generation. The knowledge base of videos is constructed by analyzing the offline data using machine learning techniques. This training dataset provides knowledge to construct the index of an online stream. The indexed metadata enables the students to search, view and access the relevant content. The performance of the application is benchmarked in different use cases to demonstrate the benefits of the hybrid model.Dissertation/ThesisMasters Thesis Computer Science 201

Vue d'ensemble du problème de placement de service dans Fog and Edge Computing

To support the large and various applications generated by the Internet of Things(IoT), Fog Computing was introduced to complement the Cloud Computing and offer Cloud-like services at the edge of the network with low latency and real-time responses. Large-scale, geographical distribution and heterogeneity of edge computational nodes make service placement insuch infrastructure a challenging issue. Diversity of user expectations and IoT devices characteristics also complexify the deployment problem. This paper presents a survey of current research conducted on Service Placement Problem (SPP) in the Fog/Edge Computing. Based on a new clas-sification scheme, a categorization of current proposals is given and identified issues and challenges are discussed.Pour prendre en charge les applications volumineuses et variées générées par l'Internet des objets (IoT), le Fog Computing a été introduit pour compléter le Cloud et exploiter les ressources de calcul en périphérie du réseau afin de répondre aux besoins de calcul à faible latence et temps réel des applications. La répartition géographique à grande échelle et l'hétérogénéité des noeuds de calcul de périphérie rendent difficile le placement de services dans une telle infrastructure. La diversité des attentes des utilisateurs et des caractéristiques des périphériques IoT complexifie également le probllème de déploiement. Cet article présente une vue d'ensemble des recherches actuelles sur le problème de placement de service (SPP) dans l'informatique Fog et Edge. Sur la base d'un nouveau schéma de classification, les solutions présentées dans la littérature sont classées et les problèmes et défis identifiés sont discutés

Vue d'ensemble du problème de placement de service dans Fog and Edge Computing

To support the large and various applications generated by the Internet of Things(IoT), Fog Computing was introduced to complement the Cloud Computing and offer Cloud-like services at the edge of the network with low latency and real-time responses. Large-scale, geographical distribution and heterogeneity of edge computational nodes make service placement insuch infrastructure a challenging issue. Diversity of user expectations and IoT devices characteristics also complexify the deployment problem. This paper presents a survey of current research conducted on Service Placement Problem (SPP) in the Fog/Edge Computing. Based on a new clas-sification scheme, a categorization of current proposals is given and identified issues and challenges are discussed.Pour prendre en charge les applications volumineuses et variées générées par l'Internet des objets (IoT), le Fog Computing a été introduit pour compléter le Cloud et exploiter les ressources de calcul en périphérie du réseau afin de répondre aux besoins de calcul à faible latence et temps réel des applications. La répartition géographique à grande échelle et l'hétérogénéité des noeuds de calcul de périphérie rendent difficile le placement de services dans une telle infrastructure. La diversité des attentes des utilisateurs et des caractéristiques des périphériques IoT complexifie également le probllème de déploiement. Cet article présente une vue d'ensemble des recherches actuelles sur le problème de placement de service (SPP) dans l'informatique Fog et Edge. Sur la base d'un nouveau schéma de classification, les solutions présentées dans la littérature sont classées et les problèmes et défis identifiés sont discutés

When Internet of Things meets Metaverse: Convergence of Physical and Cyber Worlds

In recent years, the Internet of Things (IoT) is studied in the context of the Metaverse to provide users immersive cyber-virtual experiences in mixed reality environments. This survey introduces six typical IoT applications in the Metaverse, including collaborative healthcare, education, smart city, entertainment, real estate, and socialization. In the IoT-inspired Metaverse, we also comprehensively survey four pillar technologies that enable augmented reality (AR) and virtual reality (VR), namely, responsible artificial intelligence (AI), high-speed data communications, cost-effective mobile edge computing (MEC), and digital twins. According to the physical-world demands, we outline the current industrial efforts and seven key requirements for building the IoT-inspired Metaverse: immersion, variety, economy, civility, interactivity, authenticity, and independence. In addition, this survey describes the open issues in the IoT-inspired Metaverse, which need to be addressed to eventually achieve the convergence of physical and cyber worlds.info:eu-repo/semantics/publishedVersio