A WoT Platform for Supporting Full-Cycle IoT Solutions from Edge to Cloud Infrastructures: A Practical Case

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 13; p. 3770
• Main Authors: Pastor-Vargas, Rafael, Tobarra, Llanos, Robles-Gómez, Antonio, Martin, Sergio, Hernández, Roberto, Cano, Jesús
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 05.07.2020; MDPI
• Subjects: cloud computing; Computer engineering; instructional design; Internet of Things; IoT learning; Laboratories; protocols; Sensors; Software; virtualization; web of things
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20133770

Internet of Things (IoT) learning involves the acquisition of transversal skills ranging from the development based on IoT devices and sensors (edge computing) to the connection of the devices themselves to management environments that allow the storage and processing (cloud computing) of data generated by sensors. The usual development cycle for IoT applications consists of the following three stages: stage 1 corresponds to the description of the devices and basic interaction with sensors. In stage 2, data acquired by the devices/sensors are employed by communication models from the origin edge to the management middleware in the cloud. Finally, stage 3 focuses on processing and presentation models. These models present the most relevant indicators for IoT devices and sensors. Students must acquire all the necessary skills and abilities to understand and develop these types of applications, so lecturers need an infrastructure to enable the learning of development of full IoT applications. A Web of Things (WoT) platform named Labs of Things at UNED (LoT@UNED) has been used for this goal. This paper shows the fundamentals and features of this infrastructure, and how the different phases of the full development cycle of solutions in IoT environments are implemented using LoT@UNED. The proposed system has been tested in several computer science subjects. Students can perform remote experimentation with a collaborative WoT learning environment in the cloud, including the possibility to analyze the generated data by IoT sensors.