Page 506 - Kaleidoscope Academic Conference Proceedings 2024

Session 6: Enabling technologies
S6.1 Technologies for Quality and Sustainable Online Education in Rural India: A Comprehensive
Review*
Aashish Jain (Department of Education, GNCT of Delhi, New Delhi, India); Rohit Khokher (Chief
Technology Officer, Vidya Prakashan Mandir (P) Ltd., Meerut, Uttar Pradesh, India)

This review article critically examines the landscape of technologies to provide sustainable,
equitable, inclusive, and quality online education in rural India. The focus is on understanding the
challenges, opportunities, and impact of technology adoption in enhancing education outcomes in
rural areas. The review encompasses a comprehensive analysis of various aspects, including access
to e-learning tools, infrastructure development, teacher training, student engagement, and the
transformative role of technology in reshaping traditional teaching methods. The review begins by
providing an overview of the current state of education in rural India, highlighting the disparities
in educational access and quality between urban and rural regions. It then delves into an in-depth
exploration of the potential of technology to bridge these gaps, examining initiatives such as digital
classrooms, online platforms, mobile learning apps, and digital content repositories that are being
leveraged to improve educational access and outcomes in rural communities.
S6.2 Dempster-Shafer Theory-Based Indoor Region Prediction With Single Wi-Fi Access Point for
Static Object Localization

Ritesh Kumar (Indian Institute of Information Technology, Allahabad, Prayagraj, India)

Indoor localization has gained significant attention in recent years due to its applications in various
domains, including robotics, asset tracking, and location-based services. Traditional indoor
localization systems rely on several access points or infrastructure, which can sometimes be costly
and difficult to establish. The purpose of this paper is to investigate the viability of using a single
access point for indoor localisation designed exclusively for static items. We offer a unique method
that delivers accurate and economical indoor localization by utilising received signal strength
(RSS) measurements and machine learning methods.The localization algorithm used in this system
maps the RSSI values to appropriate locations inside the interior environment using machine
learning techniques. In the beginning, a training phase is carried out to gather a dataset of RSSI
readings and associated ground truth locations. After then, the dataset is utilised to train a machine
learning model that can infer the user's location from current RSSI readings.

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