IoT-Based Remote Monitoring as a Distance (Online) Laboratory for Applied Learning

• Published in: SN computer science Vol. 6; no. 2; p. 114
• Main Authors: Kadham, Nithin Rao, Krishna, P. Gopi, Ravi, K. Srinivasa
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 28.01.2025; Springer Nature B.V
• Subjects: Access control; Accessibility; Algorithms; Authentication; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Distance learning; Education; Educational objectives; Educational technology; Engineering; Experiments; Information Systems and Communication Service; Interfaces; Internet of Things; Laboratories; Learning management systems; Network reliability; Online instruction; Original Research; Pattern Recognition and Graphics; Remote monitoring; School environment; Skills; Software; Software Engineering/Programming and Operating Systems; Students; Teachers; Teaching; Vision; Web 2.0; Cayenne myDevices; Multi-task cascaded convolutional networks; LMS; Cloud; IoT
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-024-03649-9

Currently, sharing information and online services is an integral part of our daily lives, particularly in education, and e-learning systems are becoming more popular. However, teaching technical disciplines necessitates a great deal of practical experience. As a result, remote labs are an appealing alternative, providing new capabilities for both students and teachers. Because lab practices are an important aspect of learning in engineering subjects, delivering the same practical knowledge that on-campus students get to remote learners has proven to be a problem. This problem stems from a shortage of laboratory equipment, as well as the instructor's limited accessibility for help and check-off. Permitted students can use the internet connection to log into the lab and utilize the prototype within the given time limit. In this research, we present an Internet of Things (IoT)- based solution for distant students to record their test results in the cloud using the Cayenne myDevices platform. As a result, teachers may check them off depending on the information, which is accessible at any time and from any location. To utilize this feature, the student must first log in. The student will gain access to the lab's experimental environment after sending an one-time-password (OTP) through a QR code and receiving feedback with the OTP. The lab experiment uses Multi-task Cascaded Convolutional Networks (MTCNN) to detect faces and extract facial embeddings for authentication. The system also uses a QR code generated by the system, which is validated by the server to ensure secure login. This dual authentication approach ensures that both biometric data (face) and the QR code match for secure access to the remote laboratory, enhancing security and preventing unauthorized access. The system is tested under various lighting and network conditions for robustness and reliability.