Evidences of World`s Technical Revolution 4.0

Over last three centuries industry as it is now has dramatically changed and developed from global urbanization and steam machines to invention of PC with variety of digital devices and spreading of the Internet. The Fourth Industrial Revolution marked by emerging technology breakthroughs in a number of fields, including robotics, artificial intelligence, nanotechnology, biotechnology, the Internet of Things (IoT), 3D printing and autonomous vehicles. The Fourth Industrial Revolution was declared in Davos on “World Economic Forum” in 2016. This statement was built on the Digital Revolution, representing new ways in which technology becomes embedded within societies and even the human body

Sensor function virtualization to support distributed intelligence in the internet of things

It is estimated that-by 2020-billion devices will be connected to the Internet. This number not only includes TVs, PCs, tablets and smartphones, but also billions of embedded sensors that will make up the "Internet of Things" and enable a whole new range of intelligent services in domains such as manufacturing, health, smart homes, logistics, etc. To some extent, intelligence such as data processing or access control can be placed on the devices themselves. Alternatively, functionalities can be outsourced to the cloud. In reality, there is no single solution that fits all needs. Cooperation between devices, intermediate infrastructures (local networks, access networks, global networks) and/or cloud systems is needed in order to optimally support IoT communication and IoT applications. Through distributed intelligence the right communication and processing functionality will be available at the right place. The first part of this paper motivates the need for such distributed intelligence based on shortcomings in typical IoT systems. The second part focuses on the concept of sensor function virtualization, a potential enabler for distributed intelligence, and presents solutions on how to realize it

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

Five challenges in cloud-enabled intelligence and control

The proliferation of connected embedded devices, or the Internet of Things (IoT), together with recent advances in machine intelligence, will change the profile of future cloud services and introduce a variety of new research problems centered around empowering resource-limited edge devices to exhibit intelligent behavior, both in sensing and control. Cloud services will enable learning from data, performing inference, and executing control, all with assurances on outcomes. The paper discusses such emerging services and outlines five resulting new research directions towards enabling and optimizing intelligent, cloud-assisted sensing and control in the age of the Internet of Things

Managed ecosystems of networked objects

Small embedded devices such as sensors and actuators will become the cornerstone of the Future Internet. To this end, generic, open and secure communication and service platforms are needed in order to be able to exploit the new business opportunities these devices bring. In this paper, we evaluate the current efforts to integrate sensors and actuators into the Internet and identify the limitations at the level of cooperation of these Internet-connected objects and the possible intelligence at the end points. As a solution, we propose the concept of Managed Ecosystem of Networked Objects, which aims to create a smart network architecture for groups of Internet-connected objects by combining network virtualization and clean-slate end-to-end protocol design. The concept maps to many real-life scenarios and should empower application developers to use sensor data in an easy and natural way. At the same time, the concept introduces many new challenging research problems, but their realization could offer a meaningful contribution to the realization of the Internet of Things

A Litigator\u27s Guide to the Internet of Things

Maybe you\u27ve heard about the Internet of Things (loT). It\u27s the network of physical objects (or things ) that connect to the Internet and each other and have the ability to collect and exchange data. It includes a variety of devices with sensors, vehicles, buildings, and other items that contain electronics, software, and sensors. Some loT objects have embedded intelligence, which allows them to detect and react to changes in their physical state. Though there is no specific definition of loT, the concept focuses on how computers, sensors, and objects interact with each other and collect information relating to their surroundings

Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems

Agricultural traceability model based on IoT and Blockchain: Application in industrial hemp production

Facilities based on the Internet of Things and embedded systems along with the application of ambient intelligence paradigms offer new scenarios for optimization services in agronomic processes, specifically in the hemp industry. The traceability of products and activities demonstrates the scope of these technologies. However, the technologies themselves introduce integration-related problems that can affect the planned benefits. This article proposes a model that balances agricultural expert knowledge (user-centered design), value chain planning (through blockchain implementation), and digital technology (Internet of Things protocols) for providing tamper proof, transparent, and secure traceability in this agricultural sector. The proposed approach is backed by a proof-of-concept implementation in a realist scenario, using embedded devices and a permissioned blockchain. The model and its deployment fully integrate a set of services that other proposals only partially integrate. On one hand, the design creates a permissioned blockchain that contemplates the different actors in the value chain, and on the other hand, it develops services that use applications with human-machine interfaces. Finally, it deploys a network of embedded devices with Internet of Things protocols and control algorithms with automated access to the blockchain for traceability services. Combining digital systems with interoperable human tasks it has been possible to deploy a model that provides a new approach for the development of value-added services