Teaching and Learning Advances on Sensors for IoT

• Other Authors: Martin, Sergio (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Subjects: History of engineering & technology; Internet of Things (IoT); mobile apps; end-user development; App Inventor; block-based languages; map-reduce; computer-based systems; environmental awareness; Industry 4.0; Internet of Things; irrigation systems; planter; project-based-learning; smart learning environments; teamwork; smart classrooms; educational technology; multimodal learning analytics; internet of things; multisensorial networks; IoT; cybersecurity; Shodan; teaching methodology; use case based learning; security audit; vulnerabilities; cyber-attacks; vulnerability assessment; web of things; IoT learning; cloud computing; protocols; virtualization; instructional design
• Online Access: DOAB: download the publication; DOAB: description of the publication

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, gamification experiences and innovative teaching materials, among others. In particular, the articles inside the book show excellent works about hot topics, such as: - Remote labs for IoT teaching, including the full development cycle. - Practical guides for IoT cybersecurity. - Innovative multimodal learning analytics architecture that builds on software-defined networks and network function virtualization principles. - Problem-based learning experiences using designed complex sensor-based IoT ecosystems with sensors, actuators, microcontrollers, plants, soils and irrigation systems. - Block-based programming extensions to facilitate the creation of mobile apps for smart learning experiences. The articles published in this book present only some of the most important topics about sensor-based IoT learning and teaching. However, the selected papers offer significant studies and promising environments.