The distributed energy system (DES) architecture is subject to confusion about renewable energy limits, primary energy supply and energy carriers' costs. For the grid to use unreliable electricity sources, the end-user's on-demand presence in the intelligent energy management context is essential. The participation of end-users could influence the management of the system and the volatility of energy prices. By delivering auxiliary services using demand side-resource to increase system reliability, robust planning, constraint control and scheduling, consumers may support grid operators. The optimized approach to managing energy resources enhances demand response to renewable energy sources integrally, controls the demand curve with load versatility as the system requires it. The opportunity to adjust/regulate the charging profile by choosing a particular device. This article discusses a literature and policy analysis that looks at the role of energy management system aggregators and the end-users participating in subsidiary systems within Smart Grid programmers and technologies. In the implementation of aggregators for energy management systems, the objective is to understand the patterns, threats, obstacles and potential obstacles.

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Energy Management for Internet of Things via Distributed Systems

One of the main challenges of Industry 4.0 is how advanced sensors and sensing technologies can be applied through the Internet of Things layers of existing manufacturing. This is the so-called Brownfield Industry 4.0, where the different types and ages of machines and processes need to be digitalized. Smart retrofitting is the umbrella term for solutions to show how we can digitalize manufacturing machines. This problem is critical in the case of solutions to support human workers. The Operator 4.0 concept shows how we can efficiently support workers on the shop floor. The key indicator is the readiness level of a company, and the main bottleneck is the technical knowledge of the employees. This study proposes an education framework and a related Operator 4.0 laboratory that prepares students for the development and application of Industry 5.0 technologies. The concept of intelligent space is proposed as a basis of the educational framework, which can solve the problem of monitoring the stochastic nature of operators in production processes. The components of the intelligent space are detailed through the layers of the IoT in the form of a case study conducted at the laboratory. The applicability of indoor positioning systems is described with the integration of machine-, operator- and environment-based sensor data to obtain real-time information from the shop floor. The digital twin of the laboratory is developed in a discrete event simulator, which integrates the data from the shop floor and can control the production based on the simulation results. The presented framework can be utilized to design education for the generation of Industry 5.0.

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Demonstration Laboratory of Industry 4.0 Retrofitting and Operator 4.0 Solutions: Education towards Industry 5.0

The Internet of Things (IoT) is a multidisciplinary field involving different areas of knowledge. The development of IoT solutions depends on the integration and collaborative work between professionals from all of these areas. This has led to the development of methodologies to teach IoT, such as the three‐phase methodology (TpM). This study presents a collaborative system that operationalizes the use of the TpM, aiding in the process of teaching IoT. This system guides and integrates teams developing IoT solutions while providing easy and structured access to information about the project. The system has been used in short and graduation courses. The results have demonstrated its efficiency in assisting students to develop solutions while applying the TpM.

A TpM-based collaborative system to teach IoT

The Internet of Things (IoT) is one of those technologies with a wide range of applications, from planes to cars, from factories to homes. As IoT technology is an interdisciplinary field, it is generally one of the most difficult subjects to learn. Therefore, there is a need to use and develop new teaching methods to facilitate the teaching of IoT technology. This quasi-experimental study aims to examine the effects of synchronous and asynchronous distance courses on students' achievement and motivation in IoT education. The participants, consisting of 35 students from different engineering departments, were randomly assigned to experimental and control groups. The experimental group was instructed asynchronously on the EdPuzzle platform, where interactive videos were presented, and control group was taught synchronously on the Zoom application for 8 weeks. As a result of the research, it was concluded that the students in the asynchronous group were more successful in their academic achievement post-test scores. Although the synchronous group scored higher in terms of motivation, it was concluded that there was no significant difference from the other group.

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The Effect of Internet of Things Education Through Distance Education on Student Success and Motivation

Internet of Things (IoT) is a network-based system of devices exchanging different kinds of data for decision making. It has multi-disciplinary facets which make it tricky to teach as a university course. Students must be provided with practical learning opportunities to understand the complexities of the Internet of Things applications. This can be challenging as IoT has evolved rapidly with time. This paper introduces a collaborative learning activity that allows students to use real sensors and systems in a laboratory environment. A smart farming based agricultural application is a use case for the laboratory coursework. Real sensors and nodes are used to build a working farm IoT system. It covers sensor-based end-node design, communication, system design, data presentations, and collaborative discussions.

Terry Byrne

Papers

Teaching Internet of Things in a Collaborative Laboratory Environment