Emerging embedded nonvolatile memory solution for ultra low power microcontroller systems

13301甲第4810号博士（工学）金沢大学博士論文本文Full 以下に掲載および掲載予定：1.IEEE Journal of Solid-State Circuits 27(4) pp.569-573 1992. IEEE. 共著者：M. Hayashikoshi, H. Hidaka, K. Arimoto, K. Fujishima 2.IEEE Transactions on Multi-Scale Computing Systems IEEE. 共著者：M. Hayashikoshi, H. Noda, H. Kawai, Y. Murai, S. Otani, K. Nii, Y. Matsuda, H. Kond

Emerging embedded nonvolatile memory solution for ultra low power microcontroller systems

13301甲第4810号博士（工学）金沢大学博士論文要旨Abstract 以下に掲載および掲載予定：1.IEEE Journal of Solid-State Circuits 27(4) pp.569-573 1992. IEEE. 共著者：M. Hayashikoshi, H. Hidaka, K. Arimoto, K. Fujishima 2.IEEE Transactions on Multi-Scale Computing Systems IEEE. 共著者：M. Hayashikoshi, H. Noda, H. Kawai, Y. Murai, S. Otani, K. Nii, Y. Matsuda, H. Kond

Low-Power Wireless for the Internet of Things: Standards and Applications: Internet of Things, IEEE 802.15.4, Bluetooth, Physical layer, Medium Access Control,coexistence, mesh networking, cyber-physical systems, WSN, M2M

International audienceThe proliferation of embedded systems, wireless technologies, and Internet protocols have enabled the Internet of Things (IoT) to bridge the gap between the virtual and physical world through enabling the monitoring and actuation of the physical world controlled by data processing systems. Wireless technologies, despite their offered convenience, flexibility, low cost, and mobility pose unique challenges such as fading, interference, energy, and security, which must be carefully addressed when using resource-constrained IoT devices. To this end, the efforts of the research community have led to the standardization of several wireless technologies for various types of application domains depending on factors such as reliability, latency, scalability, and energy efficiency. In this paper, we first overview these standard wireless technologies, and we specifically study the MAC and physical layer technologies proposed to address the requirements and challenges of wireless communications. Furthermore, we explain the use of these standards in various application domains, such as smart homes, smart healthcare, industrial automation, and smart cities, and discuss their suitability in satisfying the requirements of these applications. In addition to proposing guidelines to weigh the pros and cons of each standard for an application at hand, we also examine what new strategies can be exploited to overcome existing challenges and support emerging IoT applications

협업 로봇을 위한 서비스 기반과 모델 기반의 소프트웨어 개발 방법론

학위논문(박사)--서울대학교 대학원 :공과대학 전기·컴퓨터공학부,2020. 2. 하순회.가까운 미래에는 다양한 로봇이 다양한 분야에서 하나의 임무를 협력하여 수행하는 모습은 흔히 볼 수 있게 될 것이다. 그러나 실제로 이러한 모습이 실현되기에는 두 가지의 어려움이 있다. 먼저 로봇을 운용하기 위한 소프트웨어를 명세하는 기존 연구들은 대부분 개발자가 로봇의 하드웨어와 소프트웨어에 대한 지식을 알고 있는 것을 가정하고 있다. 그래서 로봇이나 컴퓨터에 대한 지식이 없는 사용자들이 여러 대의 로봇이 협력하는 시나리오를 작성하기는 쉽지 않다. 또한, 로봇의 소프트웨어를 개발할 때 로봇의 하드웨어의 특성과 관련이 깊어서, 다양한 로봇의 소프트웨어를 개발하는 것도 간단하지 않다. 본 논문에서는 상위 수준의 미션 명세와 로봇의 행위 프로그래밍으로 나누어 새로운 소프트웨어 개발 프레임워크를 제안한다. 또한, 본 프레임워크는 크기가 작은 로봇부터 계산 능력이 충분한 로봇들이 서로 군집을 이루어 미션을 수행할 수 있도록 지원한다. 본 연구에서는 로봇의 하드웨어나 소프트웨어에 대한 지식이 부족한 사용자도 로봇의 동작을 상위 수준에서 명세할 수 있는 스크립트 언어를 제안한다. 제안하는 언어는 기존의 스크립트 언어에서는 지원하지 않는 네 가지의 기능인 팀의 구성, 각 팀의 서비스 기반 프로그래밍, 동적으로 모드 변경, 다중 작업(멀티 태스킹)을 지원한다. 우선 로봇은 팀으로 그룹 지을 수 있고, 로봇이 수행할 수 있는 기능을 서비스 단위로 추상화하여 새로운 복합 서비스를 명세할 수 있다. 또한 로봇의 멀티 태스킹을 위해 '플랜' 이라는 개념을 도입하였고, 복합 서비스 내에서 이벤트를 발생시켜서 동적으로 모드가 변환할 수 있도록 하였다. 나아가 여러 로봇의 협력이 더욱 견고하고, 유연하고, 확장성을 높이기 위해, 군집 로봇을 운용할 때 로봇이 임무를 수행하는 도중에 문제가 생길 수 있으며, 상황에 따라 로봇을 동적으로 다른 행위를 수행할 수 있다고 가정한다. 이를 위해 동적으로도 팀을 구성할 수 있고, 여러 대의 로봇이 하나의 서비스를 수행하는 그룹 서비스를 지원하고, 일대 다 통신과 같은 새로운 기능을 스크립트 언어에 반영하였다. 따라서 확장된 상위 수준의 스크립트 언어는 비전문가도 다양한 유형의 협력 임무를 쉽게 명세할 수 있다. 로봇의 행위를 프로그래밍하기 위해 다양한 소프트웨어 개발 프레임워크가 연구되고 있다. 특히 재사용성과 확장성을 중점으로 둔 연구들이 최근 많이 사용되고 있지만, 대부분의 이들 연구는 리눅스 운영체제와 같이 많은 하드웨어 자원을 필요로 하는 운영체제를 가정하고 있다. 또한, 프로그램의 분석 및 성능 예측 등을 고려하지 않기 때문에, 자원 제약이 심한 크기가 작은 로봇의 소프트웨어를 개발하기에는 어렵다. 그래서 본 연구에서는 임베디드 소프트웨어를 설계할 때 쓰이는 정형적인 모델을 이용한다. 이 모델은 정적 분석과 성능 예측이 가능하지만, 로봇의 행위를 표현하기에는 제약이 있다. 그래서 본 논문에서 외부의 이벤트에 의해 수행 중간에 행위를 변경하는 로봇을 위해 유한 상태 머신 모델과 데이터 플로우 모델이 결합하여 동적 행위를 명세할 수 있는 확장된 모델을 적용한다. 그리고 딥러닝과 같이 계산량을 많이 필요로 하는 응용을 분석하기 위해, 루프 구조를 명시적으로 표현할 수 있는 모델을 제안한다. 마지막으로 여러 로봇의 협업 운용을 위해 로봇 사이에 공유되는 정보를 나타내기 위해 두 가지 모델을 사용한다. 먼저 중앙에서 공유 정보를 관리하기 위해 라이브러리 태스크라는 특별한 태스크를 통해 공유 정보를 나타낸다. 또한, 로봇이 자신의 정보를 가까운 로봇들과 공유하기 위해 멀티캐스팅을 위한 새로운 포트를 추가한다. 이렇게 확장된 정형적인 모델은 실제 로봇 코드로 자동 생성되어, 소프트웨어 설계 생산성 및 개발 효율성에 이점을 가진다. 비전문가가 명세한 스크립트 언어는 정형적인 태스크 모델로 변환하기 위해 중간 단계인 전략 단계를 추가하였다. 제안하는 방법론의 타당성을 검증하기 위해, 시뮬레이션과 여러 대의 실제 로봇을 이용한 협업하는 시나리오에 대해 실험을 진행하였다.In the near future, it will be common that a variety of robots are cooperating to perform a mission in various fields. There are two software challenges when deploying collaborative robots: how to specify a cooperative mission and how to program each robot to accomplish its mission. In this paper, we propose a novel software development framework that separates mission specification and robot behavior programming, which is called service-oriented and model-based (SeMo) framework. Also, it can support distributed robot systems, swarm robots, and their hybrid. For mission specification, a novel scripting language is proposed with the expression capability. It involves team composition and service-oriented behavior specification of each team, allowing dynamic mode change of operation and multi-tasking. Robots are grouped into teams, and the behavior of each team is defined with a composite service. The internal behavior of a composite service is defined by a sequence of services that the robots will perform. The notion of plan is applied to express multi-tasking. And the robot may have various operating modes, so mode change is triggered by events generated in a composite service. Moreover, to improve the robustness, scalability, and flexibility of robot collaboration, the high-level mission scripting language is extended with new features such as team hierarchy, group service, one-to-many communication. We assume that any robot fails during the execution of scenarios, and the grouping of robots can be made at run-time dynamically. Therefore, the extended mission specification enables a casual user to specify various types of cooperative missions easily. For robot behavior programming, an extended dataflow model is used for task-level behavior specification that does not depend on the robot hardware platform. To specify the dynamic behavior of the robot, we apply an extended task model that supports a hybrid specification of dataflow and finite state machine models. Furthermore, we propose a novel extension to allow the explicit specification of loop structures. This extension helps the compute-intensive application, which contains a lot of loop structures, to specify explicitly and analyze at compile time. Two types of information sharing, global information sharing and local knowledge sharing, are supported for robot collaboration in the dataflow graph. For global information, we use the library task, which supports shared resource management and server-client interaction. On the other hand, to share information locally with near robots, we add another type of port for multicasting and use the knowledge sharing technique. The actual robot code per robot is automatically generated from the associated task graph, which minimizes the human efforts in low-level robot programming and improves the software design productivity significantly. By abstracting the tasks or algorithms as services and adding the strategy description layer in the design flow, the mission specification is refined into task-graph specification automatically. The viability of the proposed methodology is verified with preliminary experiments with three cooperative mission scenarios with heterogeneous robot platforms and robot simulator.Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Contribution 7 1.3 Dissertation Organization 9 Chapter 2. Background and Existing Research 11 2.1 Terminologies 11 2.2 Robot Software Development Frameworks 25 2.3 Parallel Embedded Software Development Framework 31 Chapter 3. Overview of the SeMo Framework 41 3.1 Motivational Examples 45 Chapter 4. Robot Behavior Programming 47 4.1 Related works 48 4.2 Model-based Task Graph Specification for Individual Robots 56 4.3 Model-based Task Graph Specification for Cooperating Robots 70 4.4 Automatic Code Generation 74 4.5 Experiments 78 Chapter 5. High-level Mission Specification 81 5.1 Service-oriented Mission Specification 82 5.2 Strategy Description 93 5.3 Automatic Task Graph Generation 96 5.4 Related works 99 5.5 Experiments 104 Chapter 6. Conclusion 114 6.1 Future Research 116 Bibliography 118 Appendices 133 요약 158Docto

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

Evaluation of Edge AI Co-Processing Methods for Space Applications

The recent years spread of SmallSats offers several new services and opens to the implementation of new technologies to improve the existent ones. However, the communication link to Earth in order to process data often is a bottleneck, due to the amount of collected data and the limited bandwidth. A way to face this challenge is edge computing, which supposedly discards useless data and fasten up the transmission, and therefore the research has moved towards the study of COTS architectures to be used in space, often organized in co-processing setups. This thesis considers AI as application use case and two devices in a controller-accelerator configuration. It proposes to investigate the performances of co-processing methods such as simple parallel, horizontal partitioning and vertical partitioning, for a set of different tasks and taking advantage of different pre-trained models. The actual experiments regard only simple parallel and horizontal partitioning mode, and they compare latency and accuracy results with single processing runs on both devices. Evaluating the results task-by-task, image classification has the best performance improvement taking advantage of horizontal partitioning, with a clear accuracy improvement, as well as semantic segmentation, which shows almost stable accuracy and potentially higher throughput with smaller models input sizes. On the other hand, object detection shows a drop in performances, especially accuracy, which could maybe be improved with more specifically developed models for the chosen hardware. The project clearly shows how co-processing methods are worth of being investigated and can improve system outcomes for some of the analyzed tasks, making future work about it interesting