**Preface to "Teaching and Learning Advances on Sensors for IoT"**

Dear Colleagues,

The Internet of Things (IoT) is widely considered the next step towards a digital society where objects and people are interconnected and interact through communication networks. The IoT not only has a huge social impact, but can also support the employability and boost the competitiveness of companies. It is widely considered one of the most important key drivers for the implementation of so-called Industry 4.0 and for the digital transformation of companies.

Sensing is a fundamental part of IoT environments, where massive amounts of data are constantly sensed. Proper quality data acquisition leads to more accurate decision making. Thus, the importance of good practices when sensing data in IoT environments is growing.

The rapid diffusion of IoT technologies has created an important educational challenge, namely, the need to train a large number of professionals able to design and manage a fast evolving and complex ecosystem. Thus, an important research effort is being carried out in innovative technologies (simulators, virtual and remote labs, mobile apps, robotics, e-learning platforms, learning analytics, etc.) applied to innovative teaching practices.

This book focuses on all the technologies involved in improving the teaching and learning process of some of the sensor-based IoT topics, such as virtual sensors, simulated data acquisition, virtual and remote labs for IoT sensing and innovative teaching materials, among others.

For example, the work presented by Pastor-Vargas et al., Labs of Things at UNED (LoT@UNED), provides remote laboratories for full IoT development, including edge, fog and cloud computing, complemented with communication protocols and cybersecurity. The use of these remote laboratories allows students to acquire complete IoT skills using real devices and platforms from home. The paper also introduces its use in an official master's degree in computer engineering. The work done by Fernandez-Camar ´ es et al. provides an introductory practical guide to IoT cybersecurity assessment ´ and exploitation. On the other hand, Huertas et al. propose a novel multimodal learning analytics architecture that builds on software-defined networks and network function virtualization principles. The provided findings and the proposed architecture can be useful for other researchers in the area of MMLA and educational technologies envisioning the future of smart classrooms. The work done by Tabuenca et al. addresses the educational need to train students on how to design complex sensor-based IoT ecosystems. Hence, a project-based learning approach is followed to explore multidisciplinary learning processes implementing IoT systems that varied in the sensors, actuators, microcontrollers, plants, soils and irrigation systems they used. Finally, the work done by Ruiz-Rube presents several extensions to the block-based programming language used in App Inventor to make the creation of mobile apps for smart learning experiences less challenging. Such apps are used to process and graphically represent data streams from sensors by applying map-reduce operations.

The articles published in this book present only some of the most important topics about IoT learning and teaching. However, the selected papers offer significant studies and promising environments.

> **Sergio Martin** *Editor*
