Federated Robust Embedded Systems: Concepts and Challenges

The development within the area of embedded systems (ESs) is moving rapidly, not least due to falling costs of computation and communication equipment. It is believed that increased communication opportunities will lead to the future ESs no longer being parts of isolated products, but rather parts of larger communities or federations of ESs, within which information is exchanged for the benefit of all participants. This vision is asserted by a number of interrelated research topics, such as the internet of things, cyber-physical systems, systems of systems, and multi-agent systems. In this work, the focus is primarily on ESs, with their specific real-time and safety requirements. While the vision of interconnected ESs is quite promising, it also brings great challenges to the development of future systems in an efficient, safe, and reliable way. In this work, a pre-study has been carried out in order to gain a better understanding about common concepts and challenges that naturally arise in federations of ESs. The work was organized around a series of workshops, with contributions from both academic participants and industrial partners with a strong experience in ES development. During the workshops, a portfolio of possible ES federation scenarios was collected, and a number of application examples were discussed more thoroughly on different abstraction levels, starting from screening the nature of interactions on the federation level and proceeding down to the implementation details within each ES. These discussions led to a better understanding of what can be expected in the future federated ESs. In this report, the discussed applications are summarized, together with their characteristics, challenges, and necessary solution elements, providing a ground for the future research within the area of communicating ESs

Исследование достижений и перспектив развития технологических инновацийв области интеллектуальной сельскохозяйственной техники в Китае

Agricultural machinery is the key fi eld in modern scientifi c and technological innovation. In recent years, China has made great achievements in the development of high-performance intelligent agricultural machinery with cutting-edge technology, which promotes the effi cient use of agricultural resources and environment-friendly development, and supports 70 percent of China’s agricultural mechanization production. This paper mainly focus on the innovation and progress in the fi eld of intelligent agricultural equipment technology in China from the aspects of information perception and precision production monitoring technology, intelligent operation management technologies, power machinery, farmland operation machinery, intelligent harvesting technology, production technology and agricultural products processing equipment. the paper also summarizes that, in the future, green, intelligence and universality will become the main characteristics of the development of intelligent agricultural machinery technology, and cross integration, extension and expansion will become the main direction of technological innovation. At last by referring to the application basis and cutting-edge technology of China’s intelligent agricultural machinery industry, the innovation and development goals and research direction of future intelligent agricultural equipment, the scientifi c and technological innovation and industrial development trend in the fi eld of agricultural mechanization and intelligent application integration, this paper puts forward some suggestions on the research direction of future intelligent agricultural equipment.Сельскохозяйственная техника стала ключевой областью современных научных и технологических инноваций. В последние годы Китай добился больших успехов в разработке высокопроизводительной интеллектуальной сельскохозяйственной техники и применении передовых технологий, которые способствуют эффективному использованию сельскохозяйственных ресурсов и экологически безопасному развитию, а также обеспечивают 70 процентов производства в области механизации сельского хозяйства в Китае. В этой работе основное внимание уделяется инновациям и достижениям в области технологии интеллектуального сельскохозяйственного оборудования в Китае, а именно вопросам восприятия информации, технологии точного мониторинга производства, проблемам технологий интеллектуального управления операциями,  энергетического оборудования, машин для обработки сельскохозяйственных угодий, интеллектуальной технологии сбора урожая, технологий производства и оборудования для переработки сельхозпродукции. В статье также прогнозируется, что в будущем экологичность, интеллект и универсальность станут основными характеристиками развития технологий интеллектуальной сельскохозяйственной техники, а перекрестная интеграция, рост и расширение неотрывно связаны с технологическими инновациями. Наконец, на основе прикладного характера китайской интеллектуальной сельскохозяйственной техники и передовых технологий, учитывая цели инновационного развития и направления исследований будущего интеллектуального сельскохозяйственного оборудования, принимая во внимание научные и технологические инновации и тенденции промышленного развития в области механизации сельского хозяйства и возможности интеллектуальной интеграции, авторы выдвигают некоторые предложения в направлении исследований будущего интеллектуального сельскохозяйственного оборудования

IoT Applications Computing

The evolution of emerging and innovative technologies based on Industry 4.0 concepts are transforming society and industry into a fully digitized and networked globe. Sensing, communications, and computing embedded with ambient intelligence are at the heart of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Industry 4.0 technologies with expanding applications in manufacturing, transportation, health, building automation, agriculture, and the environment. It is expected that the emerging technology clusters of ambient intelligence computing will not only transform modern industry but also advance societal health and wellness, as well as and make the environment more sustainable. This book uses an interdisciplinary approach to explain the complex issue of scientific and technological innovations largely based on intelligent computing

Federated Learning on Edge Sensing Devices: A Review

The ability to monitor ambient characteristics, interact with them, and derive information about the surroundings has been made possible by the rapid proliferation of edge sensing devices like IoT, mobile, and wearable devices and their measuring capabilities with integrated sensors. Even though these devices are small and have less capacity for data storage and processing, they produce vast amounts of data. Some example application areas where sensor data is collected and processed include healthcare, environmental (including air quality and pollution levels), automotive, industrial, aerospace, and agricultural applications. These enormous volumes of sensing data collected from the edge devices are analyzed using a variety of Machine Learning (ML) and Deep Learning (DL) approaches. However, analyzing them on the cloud or a server presents challenges related to privacy, hardware, and connectivity limitations. Federated Learning (FL) is emerging as a solution to these problems while preserving privacy by jointly training a model without sharing raw data. In this paper, we review the FL strategies from the perspective of edge sensing devices to get over the limitations of conventional machine learning techniques. We focus on the key FL principles, software frameworks, and testbeds. We also explore the current sensor technologies, properties of the sensing devices and sensing applications where FL is utilized. We conclude with a discussion on open issues and future research directions on FL for further studie

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Exploiting the Internet Resources for Autonomous Robots in Agriculture

Autonomous robots in the agri-food sector are increasing yearly, promoting the application of precision agriculture techniques. The same applies to online services and techniques implemented over the Internet, such as the Internet of Things (IoT) and cloud computing, which make big data, edge computing, and digital twins technologies possible. Developers of autonomous vehicles understand that autonomous robots for agriculture must take advantage of these techniques on the Internet to strengthen their usability. This integration can be achieved using different strategies, but existing tools can facilitate integration by providing benefits for developers and users. This study presents an architecture to integrate the different components of an autonomous robot that provides access to the cloud, taking advantage of the services provided regarding data storage, scalability, accessibility, data sharing, and data analytics. In addition, the study reveals the advantages of integrating new technologies into autonomous robots that can bring significant benefits to farmers. The architecture is based on the Robot Operating System (ROS), a collection of software applications for communication among subsystems, and FIWARE (Future Internet WARE), a framework of open-source components that accelerates the development of intelligent solutions. To validate and assess the proposed architecture, this study focuses on a specific example of an innovative weeding application with laser technology in agriculture. The robot controller is distributed into the robot hardware, which provides real-time functions, and the cloud, which provides access to online resources. Analyzing the resulting characteristics, such as transfer speed, latency, response and processing time, and response status based on requests, enabled positive assessment of the use of ROS and FIWARE for integrating autonomous robots and the Internet