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Special Issue "Teaching and Learning Advances on Sensors for IoT"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 May 2020).

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Dr. Sergio Martin
E-Mail Website
Guest Editor
Electrical and Computer Engineering Department, National Distance Education University, 28040 Madrid, Spain
Interests: Internet of Things; technology-enhanced learning; remote laboratories; mobile and ubiquitous technologies
Special Issues and Collections in MDPI journals

Special Issue Information

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 the so-called Industry 4.0 and for the digital transformation of the 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, gamification, learning analytics, etc.) applied to innovative teaching practices.

This Special Issue 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.

Dr. Sergio Martin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Virtual sensors
  • Virtual laboratories for IoT
  • Remote laboratories for IoT
  • IoT simulators
  • IoT demonstrators
  • Mobile apps for IoT learning
  • Innovative teaching practices
  • Gamification
  • Learning analytics
  • Open educational resources and repositories for IoT learning
  • Platforms for IoT learning
  • Big Data
  • DIY/Maker experiences

Published Papers (5 papers)

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Research

Open AccessArticle
A WoT Platform for Supporting Full-Cycle IoT Solutions from Edge to Cloud Infrastructures: A Practical Case
Sensors 2020, 20(13), 3770; https://doi.org/10.3390/s20133770 - 05 Jul 2020
Viewed by 829
Abstract
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 [...] Read more.
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 ([email protected]) 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 [email protected] 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. Full article
(This article belongs to the Special Issue Teaching and Learning Advances on Sensors for IoT)
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Open AccessArticle
Teaching and Learning IoT Cybersecurity and Vulnerability Assessment with Shodan through Practical Use Cases
Sensors 2020, 20(11), 3048; https://doi.org/10.3390/s20113048 - 27 May 2020
Cited by 2 | Viewed by 1463
Abstract
Shodan is a search engine for exploring the Internet and thus finding connected devices. Its main use is to provide a tool for cybersecurity researchers and developers to detect vulnerable Internet-connected devices without scanning them directly. Due to its features, Shodan can be [...] Read more.
Shodan is a search engine for exploring the Internet and thus finding connected devices. Its main use is to provide a tool for cybersecurity researchers and developers to detect vulnerable Internet-connected devices without scanning them directly. Due to its features, Shodan can be used for performing cybersecurity audits on Internet of Things (IoT) systems and devices used in applications that require to be connected to the Internet. The tool allows for detecting IoT device vulnerabilities that are related to two common cybersecurity problems in IoT: the implementation of weak security mechanisms and the lack of a proper security configuration. To tackle these issues, this article describes how Shodan can be used to perform audits and thus detect potential IoT-device vulnerabilities. For such a purpose, a use case-based methodology is proposed to teach students and users to carry out such audits and then make more secure the detected exploitable IoT devices. Moreover, this work details how to automate IoT-device vulnerability assessments through Shodan scripts. Thus, this article provides an introductory practical guide to IoT cybersecurity assessment and exploitation with Shodan. Full article
(This article belongs to the Special Issue Teaching and Learning Advances on Sensors for IoT)
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Open AccessArticle
A Scalable Architecture for the Dynamic Deployment of Multimodal Learning Analytics Applications in Smart Classrooms
Sensors 2020, 20(10), 2923; https://doi.org/10.3390/s20102923 - 21 May 2020
Cited by 2 | Viewed by 1140
Abstract
The smart classrooms of the future will use different software, devices and wearables as an integral part of the learning process. These educational applications generate a large amount of data from different sources. The area of Multimodal Learning Analytics (MMLA) explores the affordances [...] Read more.
The smart classrooms of the future will use different software, devices and wearables as an integral part of the learning process. These educational applications generate a large amount of data from different sources. The area of Multimodal Learning Analytics (MMLA) explores the affordances of processing these heterogeneous data to understand and improve both learning and the context where it occurs. However, a review of different MMLA studies highlighted that ad-hoc and rigid architectures cannot be scaled up to real contexts. In this work, we propose a novel MMLA architecture that builds on software-defined networks and network function virtualization principles. We exemplify how this architecture can solve some of the detected challenges to deploy, dismantle and reconfigure the MMLA applications in a scalable way. Additionally, through some experiments, we demonstrate the feasibility and performance of our architecture when different classroom devices are reconfigured with diverse learning tools. These 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. Future work should aim to deploy this architecture in real educational scenarios with MMLA applications. Full article
(This article belongs to the Special Issue Teaching and Learning Advances on Sensors for IoT)
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Open AccessArticle
Fostering Environmental Awareness with Smart IoT Planters in Campuses
Sensors 2020, 20(8), 2227; https://doi.org/10.3390/s20082227 - 15 Apr 2020
Viewed by 1304
Abstract
The decrease in the cost of sensors during the last years, and the arrival of the 5th generation of mobile technology will greatly benefit Internet of Things (IoT) innovation. Accordingly, the use of IoT in new agronomic practices might be a vital part [...] Read more.
The decrease in the cost of sensors during the last years, and the arrival of the 5th generation of mobile technology will greatly benefit Internet of Things (IoT) innovation. Accordingly, the use of IoT in new agronomic practices might be a vital part for improving soil quality, optimising water usage, or improving the environment. Nonetheless, the implementation of IoT systems to foster environmental awareness in educational settings is still unexplored. This work 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 system they used. Three different types of planters were implemented, namely, hydroponic system, vertical garden, and rectangular planters. This work presents three key contributions that might help to improve teaching and learning processes. First, a holistic architecture describing how IoT ecosystems can be implemented in higher education settings is presented. Second, the results of an evaluation exploring teamwork performance in multidisciplinary groups is reported. Third, alternative initiatives to promote environmental awareness in educational contexts (based on the lessons learned) are suggested. The results of the evaluation show that multidisciplinary work including students from different expertise areas is highly beneficial for learning as well as on the perception of quality of the work obtained by the whole group. These conclusions rekindle the need to encourage work in multidisciplinary teams to train engineers for Industry 4.0 in Higher Education. Full article
(This article belongs to the Special Issue Teaching and Learning Advances on Sensors for IoT)
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Open AccessArticle
Block-Based Development of Mobile Learning Experiences for the Internet of Things
Sensors 2019, 19(24), 5467; https://doi.org/10.3390/s19245467 - 11 Dec 2019
Cited by 3 | Viewed by 1146
Abstract
The Internet of Things enables experts of given domains to create smart user experiences for interacting with the environment. However, development of such experiences requires strong programming skills, which are challenging to develop for non-technical users. This paper presents several extensions to the [...] Read more.
The Internet of Things enables experts of given domains to create smart user experiences for interacting with the environment. However, development of such experiences requires strong programming skills, which are challenging to develop for non-technical users. This paper 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. A workshop with students without previous experience with Internet of Things (IoT) and mobile app programming was conducted to evaluate the propositions. As a result, students were able to create small IoT apps that ingest, process and visually represent data in a simpler form as using App Inventor’s standard features. Besides, an experimental study was carried out in a mobile app development course with academics of diverse disciplines. Results showed it was faster and easier for novice programmers to develop the proposed app using new stream processing blocks. Full article
(This article belongs to the Special Issue Teaching and Learning Advances on Sensors for IoT)
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