Improving Educational Outcomes and Economic Efficiency with Wireless Sensor Networks in Smart Classrooms
The application of Wireless Sensor Networks (WSNs) in smart classrooms can transform the learning experience, achieving educational goals and economic efficiency simultaneously. This research looks into the application of WSNs for monitoring and controlling environmental and operational factors like temperature, humidity, illumination, noise levels, and student activity within learning environments. With WSNs, real time monitoring and analysis is possible which significantly enhances personalized learning by improving student comfort, concentration, academic performance, and overall achievement. Moreover, automated energy resource management facilitated by WSNs optimizes resource efficiency and operational cost containment while enabling predictive maintenance of classroom infrastructure. The technology allows educators and decision makers to make better informed choices using data, thereby enhancing teaching approaches and improving learning outcomes. In this research, simulations and other case studies illustrate the potential of WSNs to transform education systems, demonstrating smart classroom solutions that are scalable, affordable, and sustainable. These conclusions emphasize that installing WSNs in educational institutions enhances the academic experience while advancing the framework for technological growth and sustainable development. This paper supports the greater implementation of WSNs as a means to modernize educational systems and enhance economic productivity within the scholarly realm.
Authors
- Fazliddin Jumaniyazov (ORCID: https://orcid.org/0000-0001-6069-8031)
- T A Sabitova
- Dadaxon Abdullayev
- Sayfiddin Juraev
- Elza Salakhova
- Dilnavoz Shavkidinova
- Kamoliddin Saidov
- Oltinoy Masalieva
Publication Details
- Journal
- Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications
- Published
- 2025-06-30
- DOI
- https://doi.org/10.58346/jowua.2025.i2.045
- Citations
- 1
- Primary Topic
- IoT-based Smart Home Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.73