| Title |
IoT Based Virtual E-Learning System for Sustainable Development of Smart Cities |
| ID_Doc |
44226 |
| Authors |
Setiawan, R; Devadass, MMV; Rajan, R; Sharma, DK; Singh, NP; Amarendra, K; Ganga, RKR; Manoharan, RR; Subramaniyaswamy, V; Sengan, S |
| Title |
IoT Based Virtual E-Learning System for Sustainable Development of Smart Cities |
| Year |
2022 |
| Published |
Journal Of Grid Computing, 20, 3 |
| Abstract |
Globally, cities are emerging into Smart Cities (SC) as a result of sustainable cities and the adaption of recent Internet of Things (IoT) technology. It is becoming essential to involve students in sustainability as engineering and technology are crucial elements in fixing the past adverse effects on our globe. Engineering e-learners are being educated on the sustainable development of SC in many Smart e-learning Tools (SeT) and infrastructure faculties around the world, especially in developing Asian countries such as India. This research paper presents an advanced solution for interactive Smart Learning Environment (SLE) systems based on new IoT technologies in the Virtual Reality (VR) and Augmented Reality (AR) found in Smart Learning Environments (SLE) for SC people. The proposed IoT-Ve-LS system provides an optimized solution for online classes to attend classes using VR/AR glasses to feel the interactions between Smart Digital Devices (SDD) as practically as in practice. The new Virtual e-Learning System (Ve-LS) is experimental, allows automatic Information and Communications Technology (ICT) development, and offers an extraordinary SLE for increased global recognition. This paper focuses on IoT-Ve-LS, a tool for SLE. The IoT-Ve-LS domain has been fast-growing through the emerging technological trends of the IoT. The IoT-Ve-LS method used in the design and implementation allows flexible usage and integration of the online courses by SLE. The impacts of empirical E-learning evaluation on implementing IoT techniques in online tutoring have been analysed to find out its research hypothesis. Our IoT-sensor-based Reservoir Computing allows the classification of short-term learning language sentences relatively quickly, highlighting the minimal training time and optimized solution of real-time cases for controlling temporal and sequential signals at the cloud computing level. The triangulation analysis in information gathering endorses the theoretical models that use computable and personalized approaches. |
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