Special Issue "Remote Sensing and IoT for Smart Learning Environments"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Priyan Malarvizhi Kumar

Guest Editor
Computer Science and Engineering Department, Kyung Hee University, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 17104, Korea
Interests: Internet of Things; Big Data Analytics; Machine Learning; Health Data Analytics
Special Issues and Collections in MDPI journals
Dr. Hari Mohan Pandey
Website
Guest Editor
Department of Computer Science, Edge Hill University, UK
Interests: artificial intelligence; soft computing techniques; natural language processing; language acquisition and machine learning algorithms

Special Issue Information

Dear Colleagues,

The educational industry has been continuously adapting to technological advancement since its inception. Access to digital space has increased huge opportunities for the learning world. Conservative models in learning have seen both success and failure over the years. Specialized approaches with personalized factors have become one of the important parts of the learning curve for students with the implementation of smart learning environment. There is a variety of smart learning cultures prevalent in the environment, as every institution or organization has their own approaches, procedures, techniques, and processes in the creation of their learning environment. Since all students have different abilities, inculcating smart learning methods may bring out hidden abilities. The failure in every learning system is the focus of human centered challenges. To overcome these challenges, more accurately advanced sensing technologies can be helpful. Identifying the various changes in the environment has become an easy way by using remote sensors

Advanced technologies like remote sensing and the Internet of Things (IoT) will be perfect for controlling various activities in a smart learning environment. Utilizing smart devices such as Smart Television, computing devices, mobile device apps, various tracking sensors, and many more in the learning environment can be used in the administering of smart learning. Also, integrating remote sensing and IoT can provide various opportunities for learners and teachers in conducting research and training to channel knowledge in schools, colleges, universities, or organizations. Moreover, enhancing educational innovations in physical spaces shall create a faster and more perfect learning curve. In addition, remote technologies can digitally connect academicians, teachers, counselors, and students in a single platform. Here, the consideration of implementing security and safety measures in these remote sensing technologies also acts as an added advantage of creating a quality learning environment. Currently, the smart learning environment is being equipped with the application of various sensing technologies like posture learning, emotion recognition, context-aware learning, augmented reality, ambient learning, etc. 

However, concentrating on the areas of planning, framework, protocol, and algorithm for smart sensing can increase the high performance of hardware platform or software framework. The main aim of this Special Issue is to invite scholars, academicians, and professionals to submit their research works, ideas, and implementations related to remote sensing and IoT platforms for Smart Learning Environments

The scope of this Special Issue includes but is not limited to the following:

  • Creating a ubiquitous learning environment using remote sensing and IoT
  • Sensor data processing and integration for Smart Learning Environments
  • Need of energy efficient protocols for remote sensing and IoT in smart environments
  • Efficient network protocols for smart objects based on communication platforms
  • A design-based thinking to adapt a framework for Smart Learning Environments
  • Best practices in implementation of smart learning environments
  • Need of smart sensing technologies for learning environments
  • Remote sensing and IoT based pedagogical tools for supporting smart learning
  • A study on policy-related issues for remote sensing and IoT based learning platforms
  • Architectural frameworks for IoT based Smart Learning Environments
  • Advanced sensor technologies used in learning environments: an overview
  • Autonomous sensor networks for smart learning environments

Dr. Priyan Malarvizhi Kumar
Dr. Hari Mohan Pandey
Dr. Gautam Srivastava
Guest Editors

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. Remote Sensing 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.

Published Papers (2 papers)

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Open AccessArticle
Comparative Study of IoT-Based Topology Maintenance Protocol in a Wireless Sensor Network for Structural Health Monitoring
Remote Sens. 2020, 12(15), 2358; https://doi.org/10.3390/rs12152358 - 23 Jul 2020
Cited by 2
Abstract
A structural health monitoring (SHM) system is an approach for identifying the damages caused to various kinds of structures using different system functions and providing the necessary feedback about structure’s conditions. As civil structures are the backbone of our society, to determine its [...] Read more.
A structural health monitoring (SHM) system is an approach for identifying the damages caused to various kinds of structures using different system functions and providing the necessary feedback about structure’s conditions. As civil structures are the backbone of our society, to determine its daily operations is a very important issue. The performance measurement of those structures is manual whereas a computer-based monitoring system could automatically assess the structural damages and identify its exact location. Recently, wireless sensor networks (WSNs) have attracted a great deal of attention for remote sensing applications due to flexibility to measure of various activity of large scale network. Since technology is advancing day by day, the overall cost of a monitoring system is also decreased. However, the major challenging fact of a WSNs is to provide scalability for covering a large area. The main question is arisen how much capable have of a monitoring system to turn off unnecessary nodes to save energy while there are no events detected. To support the scalability required of an existing network and save the node energy for future use, we propose a topology maintenance protocol integrated with construction to address the issue of a node’s energy consumption by placing it optimally and extending the monitoring system’s lifetime. As per the authors’ acknowledgement that, a little attention has been paid to developing such a hybrid approach. To mitigate node energy consumption issue with large scale support, an Internet of Things (IoT)-based maintenance approach is the best candidate for obtaining better system lifetime responses. Therefore, the main goal of this work is to develop an ‘on-the-fly’-based topology maintenance monitoring system, which can maintain a network’s infrastructure while gathering a node’s information to switch its state regularly when the present network is no longer optimal. Full article
(This article belongs to the Special Issue Remote Sensing and IoT for Smart Learning Environments)
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Review

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Open AccessReview
Remote Sensing in Agriculture—Accomplishments, Limitations, and Opportunities
Remote Sens. 2020, 12(22), 3783; https://doi.org/10.3390/rs12223783 - 19 Nov 2020
Abstract
Remote sensing (RS) technologies provide a diagnostic tool that can serve as an early warning system, allowing the agricultural community to intervene early on to counter potential problems before they spread widely and negatively impact crop productivity. With the recent advancements in sensor [...] Read more.
Remote sensing (RS) technologies provide a diagnostic tool that can serve as an early warning system, allowing the agricultural community to intervene early on to counter potential problems before they spread widely and negatively impact crop productivity. With the recent advancements in sensor technologies, data management and data analytics, currently, several RS options are available to the agricultural community. However, the agricultural sector is yet to implement RS technologies fully due to knowledge gaps on their sufficiency, appropriateness and techno-economic feasibilities. This study reviewed the literature between 2000 to 2019 that focused on the application of RS technologies in production agriculture, ranging from field preparation, planting, and in-season applications to harvesting, with the objective of contributing to the scientific understanding on the potential for RS technologies to support decision-making within different production stages. We found an increasing trend in the use of RS technologies in agricultural production over the past 20 years, with a sharp increase in applications of unmanned aerial systems (UASs) after 2015. The largest number of scientific papers related to UASs originated from Europe (34%), followed by the United States (20%) and China (11%). Most of the prior RS studies have focused on soil moisture and in-season crop health monitoring, and less in areas such as soil compaction, subsurface drainage, and crop grain quality monitoring. In summary, the literature highlighted that RS technologies can be used to support site-specific management decisions at various stages of crop production, helping to optimize crop production while addressing environmental quality, profitability, and sustainability. Full article
(This article belongs to the Special Issue Remote Sensing and IoT for Smart Learning Environments)
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