A Context-aware Architecture for Energy Saving in Smart Classroom Environments

Paudel, Prabesh; Kim, Sang-Kyoon; Park, Soonyoung; Choi, Kyoung-Ho

doi:10.1109/icce.2019.8661842

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…Adaptive and context-aware features of SLEs are constructed to "render support to learners in such a way that learning is possible anywhere, anytime and at the learner's convenience (Agbo andOyelere 2019, p.1061). " A novel context-aware architecture for a smart classroom (Paudel et al 2019) with the use of the convolutional three-dimensional network model and long short term memory network is demonstrated effective in saving energy in classroom environments. Context-aware automation of classrooms (Miraoui 2018) improved education quality by "automating tasks that are not directly related to the content of the courses and consume time and effort that would affect the smooth running of the educational process (p.1). "…”

Section: Learning Strategy-related Topicsmentioning

confidence: 99%

Past, present, and future of smart learning: a topic-based bibliometric analysis

Chen

Zou

Xie³

et al. 2021

Int J Educ Technol High Educ

111

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Innovative information and communication technologies have reformed higher education from the traditional way to smart learning. Smart learning applies technological and social developments and facilitates effective personalized learning with innovative technologies, especially smart devices and online technologies. Smart learning has attracted increasing research interest from the academia. This study aims to comprehensively review the research field of smart learning by conducting a topic modeling analysis of 555 smart learning publications collected from the Scopus database. In particular, it seeks answers to (1) what the major research topics concerning smart learning were, and (2) how these topics evolved. Results demonstrate several major research issues, for example, Interactive and multimedia learning, STEM (science, technology, engineering, and mathematics) education, Attendance and attention recognition, Blended learning for smart learning, and Affective and biometric computing. Furthermore, several emerging topics were identified, for example, Smart learning analytics, Software engineering for e-learning systems, IoT (Internet of things) and cloud computing, and STEM education. Additionally, potential inter-topic directions were highlighted, for instance, Attendance and attention recognition and IoT and cloud computing, Semantics and ontology and Mobile learning, Feedback and assessment and MOOCs (massive open online courses) and course content management, as well as Blended learning for smart learning and Ecosystem and ambient intelligence.

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Section: Learning Strategy-related Topicsmentioning

confidence: 99%

Past, present, and future of smart learning: a topic-based bibliometric analysis

Chen

Zou

Xie³

et al. 2021

Int J Educ Technol High Educ

111

View full text Add to dashboard Cite

show abstract

“…In addition, the author proposes two different control approaches including switching and dimming, and two control modes manual and auto for light switching and dimming. Paudel et al [13] present a contextaware architecture for energy conservation in a classroom environment. In particular, authors propose to use a Long Short-Term Memory (LSTM) to predict the classroom temperature and humidity based on classified activities of students to figure out when to turn on or off devices (i.e., lights, air conditioners, heaters, etc.…”

Section: Related Workmentioning

confidence: 99%

An IoT-Based Energy Conservation Smart Classroom System

Noor¹,

Atlam²,

Almars³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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With the increase of energy consumption worldwide in several domains such as industry, education, and transportation, several technologies played an influential role in energy conservation such as the Internet of Things (IoT). In this article, we describe the design and implementation of an IoT-based energy conservation smart classroom system that contributes to energy conservation in the education domain. The proposed system not only allows the user to access and control IoT devices (e.g., lights, projectors, and air conditions) in real-time, it also has the capability to aggregate the estimated energy consumption of an IoT device, the smart classroom, and the building based on the energy consumption and cost model that we propose. Moreover, the proposed model aggregates the estimated energy cost according to the Saudi Electricity Company (SEC) rates. Furthermore, the model aggregates in real-time the estimated energy conservation percentage and estimated money-saving percentage compared to data collected when the system wasn't used. The feasibility and benefits of our system have been validated on a real-world scenario which is a classroom in the college of computer science and engineering, Taibah University, Yanbu branch. The results of the experimental studies are promising in energy conservation and cost-saving when using our proposed system.

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“…One particular growing area of interest is in the area of smart classroom airconditioning using automated tools and techniques (Aghniaey et al, 2019). This in turn have given rise to a number of key research studies in thermal conditioning such as in (Alberti et al, 2018a, Alberti et al, 2018b where Non-linear Autoregressive Neural Networks (NARX-NETs) were used for precise air temperature prediction utilizing a combination of real and synthetic sequential temperature datasets obtained from the existing building structures of classrooms and in Paudel et al (2019) where the Long Short-Term Memory (LSTM) neural technique was used for the predicting some conditioning variables within a classroom such as temperature, humidity and luminance and in a contextaware energy saving system. Also in (Osegi et al, 2021) an Auditory Machine Intelligence (AMI) technique has been applied in the smart bed context for classroom conditioning automation Proposed LR Method LR models are simple, and straight-forward technique, but highly efficient in solving a large number of linearly separable problems.…”

Section: Related Workmentioning

confidence: 99%

Linear Regressor Model for Internet of Things Automated Thermal Conditioning of a Smart Classroom Environment Using Limited dataset

Amiefamonyo,

Anireh,

Emmah

2023

EJCSIT

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This research paper presents a Linear Regressor (LR) modeling approach for automated thermal conditioning of smart spaces in a classroom environment using limited data constraints. Sensitive studies were performed in order to identify the breaking point of the LR model of polynomial-order-of-1 below which it will not be possible to obtain meaningful estimates and controllability actions, considering a range between 5% and 30% limited training data points. Based on several simulation experiments, it was found that the LR breaks at the 10% training data level. This result is valuable for recommender systems that may experience difficulty in obtaining enough primary dataset. Applications that provide early warning signals will also find this study useful.

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A Context-aware Architecture for Energy Saving in Smart Classroom Environments

Cited by 5 publications

References 5 publications

Past, present, and future of smart learning: a topic-based bibliometric analysis

Past, present, and future of smart learning: a topic-based bibliometric analysis

An IoT-Based Energy Conservation Smart Classroom System

Linear Regressor Model for Internet of Things Automated Thermal Conditioning of a Smart Classroom Environment Using Limited dataset

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