サーバクラスタでの低消費電力化のための移行モデルの研究

博士(工学)法政大学 (Hosei University

Retransmission Reduction using Checkpoint based Sub-Path Routing for Wireless IoT

Wireless IoT has been one of the major breakthroughs of the current decade. It has improved the quality of life and has also aided in several improvements in domains like healthcare. Effective routing and energy conservation has been the major challenges in creating and maintaining a successful IoT network. This work presents a checkpoint based routing model, CSPR, to improve the transmission efficiency by reducing retransmission. This work selects checkpoints in the network prior to transmission. The checkpoints are used to build the final path. This process ensures that the routes created are dynamic and reactive, leading to improved security and increased path reliability. Comparison with existing routing model shows improved network lifetime and reduced selection overhead levels, exhibiting the high efficiency of CSPR

Embedded System Design

A unique feature of this open access textbook is to provide a comprehensive introduction to the fundamental knowledge in embedded systems, with applications in cyber-physical systems and the Internet of things. It starts with an introduction to the field and a survey of specification models and languages for embedded and cyber-physical systems. It provides a brief overview of hardware devices used for such systems and presents the essentials of system software for embedded systems, including real-time operating systems. The author also discusses evaluation and validation techniques for embedded systems and provides an overview of techniques for mapping applications to execution platforms, including multi-core platforms. Embedded systems have to operate under tight constraints and, hence, the book also contains a selected set of optimization techniques, including software optimization techniques. The book closes with a brief survey on testing. This fourth edition has been updated and revised to reflect new trends and technologies, such as the importance of cyber-physical systems (CPS) and the Internet of things (IoT), the evolution of single-core processors to multi-core processors, and the increased importance of energy efficiency and thermal issues

Embedded System Design

A unique feature of this open access textbook is to provide a comprehensive introduction to the fundamental knowledge in embedded systems, with applications in cyber-physical systems and the Internet of things. It starts with an introduction to the field and a survey of specification models and languages for embedded and cyber-physical systems. It provides a brief overview of hardware devices used for such systems and presents the essentials of system software for embedded systems, including real-time operating systems. The author also discusses evaluation and validation techniques for embedded systems and provides an overview of techniques for mapping applications to execution platforms, including multi-core platforms. Embedded systems have to operate under tight constraints and, hence, the book also contains a selected set of optimization techniques, including software optimization techniques. The book closes with a brief survey on testing. This fourth edition has been updated and revised to reflect new trends and technologies, such as the importance of cyber-physical systems (CPS) and the Internet of things (IoT), the evolution of single-core processors to multi-core processors, and the increased importance of energy efficiency and thermal issues

Real-Time Sensor Networks and Systems for the Industrial IoT

The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Software framework for the development of context-aware reconfigurable systems

In this project we propose a new software framework for the development of context-aware and secure controlling software of distributed reconfigurable systems. Context-awareness is a key feature allowing the adaptation of systems behaviour according to the changing environment. We introduce a new definition of the term “context” for reconfigurable systems then we define a new context modelling and reasoning approach. Afterwards, we define a meta-model of context-aware reconfigurable applications that paves the way to the proposed framework. The proposed framework has a three-layer architecture: reconfiguration, context control, and services layer, where each layer has its well-defined role. We define also a new secure conversation protocol between distributed trustless parts based on the blockchain technology as well as the elliptic curve cryptography. To get better correctness and deployment guarantees of applications models in early development stages, we propose a new UML profile called GR-UML to add new semantics allowing the modelling of probabilistic scenarios running under memory and energy constraints, then we propose a methodology using transformations between the GR-UML, the GR-TNCES Petri nets formalism, and the IEC 61499 function blocks. A software tool implementing the methodology concepts is developed. To show the suitability of the mentioned contributions two case studies (baggage handling system and microgrids) are considered.In diesem Projekt schlagen wir ein Framework für die Entwicklung von kontextbewussten, sicheren Anwendungen von verteilten rekonfigurierbaren Systemen vor. Kontextbewusstheit ist eine Schlüsseleigenschaft, die die Anpassung des Systemverhaltens an die sich ändernde Umgebung ermöglicht. Wir führen eine Definition des Begriffs ``Kontext" für rekonfigurierbare Systeme ein und definieren dann einen Kontextmodellierungs- und Reasoning-Ansatz. Danach definieren wir ein Metamodell für kontextbewusste rekonfigurierbare Anwendungen, das den Weg zum vorgeschlagenen Framework ebnet. Das Framework hat eine dreischichtige Architektur: Rekonfigurations-, Kontextkontroll- und Dienste-Schicht, wobei jede Schicht ihre wohldefinierte Rolle hat. Wir definieren auch ein sicheres Konversationsprotokoll zwischen verteilten Teilen, das auf der Blockchain-Technologie sowie der elliptischen Kurven-Kryptographie basiert. Um bessere Korrektheits- und Einsatzgarantien für Anwendungsmodelle zu erhalten, schlagen wir ein UML-Profil namens GR-UML vor, um Semantik umzufassen, die die Modellierung probabilistischer Szenarien unter Speicher- und Energiebeschränkungen ermöglicht. Dann schlagen wir eine Methodik vor, die Transformationen zwischen GR-UML, dem GR-TNCES-Petrinetz-Formalismus und den IEC 61499-Funktionsblöcken verwendet. Es wird ein Software entwickelt, das die Konzepte der Methodik implementiert. Um die Eignung der genannten Beiträge zu zeigen, werden zwei Fallstudien betrachtet

Reinforcement learning for EV charging optimization : A holistic perspective for commercial vehicle fleets

Recent years have seen an unprecedented uptake in electric vehicles, driven by the global push to reduce carbon emissions. At the same time, intermittent renewables are being deployed increasingly. These developments are putting flexibility measures such as dynamic load management in the spotlight of the energy transition. Flexibility measures must consider EV charging, as it has the ability to introduce grid constraints: In Germany, the cumulative power of all EV onboard chargers amounts to ca. 120 GW, while the German peak load only amounts to 80 GW. Commercial operations have strong incentives to optimize charging and flatten peak loads in real-time, given that the highest quarter-hour can determine the power-related energy bill, and that a blown fuse due to overloading can halt operations. Increasing research efforts have therefore gone into real-time-capable optimization methods. Reinforcement Learning (RL) has particularly gained attention due to its versatility, performance and real- time capabilities. This thesis implements such an approach and introduces FleetRL as a realistic RL environment for EV charging, with a focus on commercial vehicle fleets. Through its implementation, it was found that RL saved up to 83% compared to static benchmarks, and that grid overloading was entirely avoided in some scenarios by sacrificing small portions of SOC, or by delaying the charging process. Linear optimization with one year of perfect knowledge outperformed RL, but reached its practical limits in one use-case, where a feasible solution could not be found by the solver. Overall, this thesis makes a strong case for RL-based EV charging. It further provides a foundation which can be built upon: a modular, open-source software framework that integrates an MDP model, schedule generation, and non-linear battery degradationElektrifieringen av transportsektorn är en nödvändig men utmanande uppgift. I kombination med ökande solcellsproduktion och förnybara energikällor skapar det ett dilemma för elnätet som kräver omfattande flexibilitetsåtgärder. Dessa åtgärder måste inkludera laddning av elbilar, ett fenomen som har lett till aldrig tidigare skådade belastningstoppar. Ur ett kommersiellt perspektiv är incitamentet att optimera laddningsprocessen och säkerställa drifttid. Forskningen har fokuserat på realtidsoptimeringsmetoder som Deep Reinforcement Learning (DRL). Denna avhandling introducerar FleetRL som en ny RL-miljö för EV-laddning av kommersiella flottor. Genom att tillämpa ramverket visade det sig att RL sparade upp till 83% jämfört med statiska riktmärken, och att överbelastning av nätet helt kunde undvikas i de flesta scenarier. Linjär optimering överträffade RL men nådde sina gränser i snävt begränsade användningsfall. Efter att ha funnit ett positivt business case för varje kommersiellt användningsområde, ger denna avhandling ett starkt argument för RL-baserad laddning och en grund för framtida arbete via praktiska insikter och ett modulärt mjukvaruramverk med öppen källko