In recent years, industrial wireless sensor networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems, and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment, and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper, a detailed discussion on design objectives, challenges, and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines, and possible hazards in industrial atmosphere are discussed. This paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. This paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs.

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A Critical Analysis of Research Potential, Challenges, and Future Directives in Industrial Wireless Sensor Networks

Electrical impedance tomography (EIT) is a nondestructive imaging technique used to estimate the internal conductivity distribution of a conductive domain by taking potential measurements only at the domain boundaries. If a thin electrically conductive material that responds to pressure with local changes in conductivity is used as a conductive domain, then EIT can be used to create a large-scale pressure-sensitive artificial skin for robotics applications. This paper presents a review of EIT and its application as a robotics sensitive skin, including EIT excitation and image reconstruction techniques, materials, and skin fabrication techniques. Touch interpretation via EIT-based artificial skins is also reviewed.

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Electrical Impedance Tomography for Artificial Sensitive Robotic Skin: A Review

In this paper, we describe a novel passive RFID system capable of direct tag-to-tag communication in the presence of external radio frequency field. Tags talk by modulating the external field and thus backscattering the commands to each other. We present the system concept and show its hardware implementation based on TI MSP430 microcontroller. We also provide the theoretical model for modulation depth vs. distance which agrees with experimental results (maximum tag-to-tag communication distance). Finally, we discuss possible applications and outline future work.

Passive tag-to-tag communication

The purpose of this paper is twofold: to raise and address an important change for the human capital in the future of Industry 4.0, and to propose a human-focused perspective for companies underneath the new Industrial Revolution.,The research study follows a state-of-the-art literature review process. The nature of the selected approach enables to cover the extensive aim of the paper with sufficient scientific solidity that should support the understanding of every topic.,This work has presented three relevant aspects for Industry 4.0 and its human labour force: a workforce architecture with new interactions, a term to embrace the human capital of the future and a typology for referencing the required competences for Industry 4.0.,The paper sheds light on an important aspect for the emerging Industrial Revolution, the human force. The result and conclusion sections suggest future implications for academia and the private sector, due to changes at the conceptual and practical levels of human operation in the industry – for example, new structural interactions among employees, additional qualities to human capital and different ways to identify the competences for the workforce.,This is an interdisciplinary study that tries to bring together a modern industrial term, a social focus and a company scenario. From this, it was possible to obtain a new social term, a novel typology of competences and a new company-scenario interaction.

Human Capital 4.0: a workforce competence typology for Industry 4.0

Educational activity is increasingly moving online and course contents are becoming available in digital format. This enables data collection and the use of data for analyzing learning process. For the 4th Revolution in Education, an active and interactive presence of students contributes to a higher learning quality. Machine Learning techniques recently have shown impressive development steps of the use of data analysis and predictions. However, it has been far less used for assessing the learning quality. For this paper we conducted analysis based on neural networks, support vector machine, decision trees and cluster analysis to estimate student's performance at examination and shape the next generation's talent for Industry 4.0 skills.

Education 4.0 — Fostering student's performance with machine learning methods

Education 4.0 is being empowered more and more by artificial intelligence (AI) methods. We observe a continuously growing demand for adaptive and personalized Education. In this paper we present an innovative approach to promoting AI in Education 4.0. Our first contribution is AI assisted Higher Education Process with smart sensors and wearable devices for self-regulated learning. Secondly we describe our first results of Education 4.0 didactic methods implemented with learning analytics and machine learning algorithms. The aim of this case study is to predict the final score of students before participating in final examination. We propose an Early Recognition System equipped with real data captured in a blended learning course with a personalized test at the beginning of the semester, an adaptive learning environment based on Auto Tutor by N. A. Crowder theory with adaptive self-assessment feedback. It is obvious that focusing on students' success and their experiences is a win-win scenario for students and professors as well as for the administration.

Education 4.0 - Artificial Intelligence Assisted Higher Education: Early recognition System with Machine Learning to support Students' Success

The rise of digital technology creates disruptive innovation, new opportunities in communication, it transforms industrial production and it changes the way we work, learn and live. That leads to the fourth Industrial Revolution, which changes the working world enormously. New skills, future-ready curricula, new learning and training concepts as well as the flexibility in education are necessary. To deliver that, it needs a fourth revolution in the educational system with a strong active presence and most important Artificial Intelligence (AI) driven features for better performance. Education 4.0 needs also a strong partnership between industry and academicals environment in the formation of human resources for Industry 4.0. We present in this paper our answers of education regarding the challenges of fourth revolution in Industry and the seven AI driven didactic methods identified for the University of Future.

Education 4.0 for tall thin engineer in a data driven society

Artificial Intelligence (AI) will play a key role in Higher Education. Our contribution leads the road to innovation within IoT for Education 4.0, using a system comprising smartwatch data, health data, learning analytics and Artificial Intelligence. By using embedded sensors from wearable devices, we can add value to Education 4.0. Smartwatches are IoT devices, equipped with a multitude of embedded sensors collecting distraction-free huge amounts of real-time data during students’ learning. In our paper we highlight advantages of wrist-based wearables like smartwatches for Education 4.0. We develop a User Experience Questionnaire for the measurement of acceptance of advanced electronic technology in Higher Education. We identify as experiment’s results the most important sensors and protocols for Education 4.0.

Education 4.0 – Jump to Innovation with IoT in Higher Education

We aim to evaluate the performance of a ZigBee wireless sensor network using OPNET. The network is intended for a general application and should be able to collect data from many nodes, every node connected to several sensors. One possible application for such a network is in the medical area. At first the network will be used to display the evolution of blood pressure; the body temperature, the ambient temperature and pressure and the level of the transfusion liquid can be collected as well. The network coordinator will transmit the data to a local server for further processing. The simulation will be used to evaluate the general parameters of the wireless network and to optimise it.

Simulation of a wireless sensor network using OPNET

The paper presents design, simulation, implementation and testing of an inductive displacement sensor based on a Linear Variable Differential Transformer (LVDT) controlled by a single chip electronic module with 16 bit RISC microcontroller. LVDT device is widely used in hydraulic and pneumatic mechatronic systems for measuring physical quantities like displacement, force or pressure; it consists of two magnetic coupled coils with a common moving core; its displacement is converted in reluctance variation of magnetic circuit. Standard electronics for LVDT sensor conditioning is analog - quadrate oscillator, synchronous demodulator and amplifiers - but has its drawbacks – relative complex, hard to adjust, many components and packages, no connection to computer systems. The originality aspects of the work come from the unique method of finding displacement information by switching supply voltage in the coils of inductive device. This method uses only peripherals from a microcontroller so is low cost and easy to adjust. Present paper focuses on modelling and simulation of displacement measuring method but analyzes also implementation aspects for a real application (an electro hydraulic position control system).

Paul Svasta

Papers

Education 4.0 - Artificial Intelligence Assisted Higher Education: Early recognition System with Machine Learning to support Students' Success

Education 4.0 for tall thin engineer in a data driven society

Education 4.0 – Jump to Innovation with IoT in Higher Education

Simulation of a wireless sensor network using OPNET

Modelling and simulation of an inductive displacement sensor for mechatronic systems