Design and Analysis of an Automated IoT System for Data Flow Optimization in Higher Education Institutions Aderonke A. Adegbenjo 1 , Ernest E. Onuiri 1* , Olamide B. Kalesanwo 1 , Michael O. Agbaje 1 , Samuel B. Abel 1 , Oluwayemisi B. Fatade 1 , Afolarin I. Amusa 1 , Kelechi C. Umeaka 1 , Eseosa Ehioghae 2 , Korede O. Onamade 3 1 Department of Computer Science, School of Computing and Engineering Sciences , Babcock University, Ilishan-Remo 121103 Nigeria 2 Verisk, Norwich NR6 7RL, UK 3 Climedo HQ, Munich 80336, Germany Corresponding Author Email: onuirie@babcock.edu.ng https://doi.org/10.18280/jesa.560520 ABSTRACT Received: 3 October 2023 Revised: 20 October 2023 Accepted: 26 October 2023 Available online: 31 October 2023 The transformative capacity of the Internet of Things (IoT) has become evident across various sectors. Despite its potential, a discernible hesitancy exists in its adoption within higher education institutions. This research explores the specific advantages and challenges of implementing IoT in the context of higher education, particularly in optimizing data-driven decision-making processes. The approach focuses on creating a comprehensive IoT framework tailored for higher education, encompassing a foundational data warehouse layer, an intermediary application layer for streamlining data, a message broker layer for data orchestration, a granular message consumer layer for data refinement, and a central data lake that consolidates both real-time and structured data. This system adeptly manages a continuous stream of structured and real-time data. With the integration of Kafka and TensorFlow, real-time video streams are processed, providing enhanced security measures for campuses. Biometric devices, strategically positioned, offer detailed data on institutional dynamics, all converging into a central data reservoir. This vast data collection presents profound insights, shaping administrative strategies and improving institutional efficiency. The adoption of IoT in higher education holds vast potential, yet challenges persist. Striking a balance between surveillance and privacy, ensuring data integrity, and navigating the complexities of scalability are vital considerations. However, with careful and strategic implementation, IoT integration can usher in a revolutionary era of data-driven academic operations, enhancing both security and institutional efficiency. Keywords: Internet of Things (IoT), IoT framework, Kafka stream, data lake, advanced analytics, data visualization, automated systems, higher education institutions, real-time data processing 1. INTRODUCTION In an era marked by rapid technological evolution, the Internet of Things (IoT) stands out as a beacon of transformative potential. It promises a paradigm shift in how devices communicate, interact, and ultimately serve human needs [1]. This interconnected web of devices, spanning from everyday household items to sophisticated industrial machinery, offers unparalleled opportunities across industries. Notably, higher education stands at the cusp of this transformative wave, poised to reap the multifaceted benefits of IoT. Higher education institutions, traditionally viewed as bastions of knowledge and learning, are increasingly being recognized for their role as hubs of innovation and technological advancement. Within this context, the IoT serves as more than just a network of connected devices. It represents a confluence of opportunities to enhance operational efficiency, foster research, improve student experiences, and even redefine pedagogical methodologies. Consider the possibilities: environmental sensors that dynamically adjust classroom conditions based on occupancy and weather, smart ID scanners that not only register student attendance but also provide insights into space utilization and student engagement patterns, and connected laboratory equipment that ensures optimal usage while providing real- time data to researchers [2, 3]. Yet, the journey to fully harness the potential of IoT in higher education is not devoid of challenges. A significant concern arises from the observed disconnect between the vast amounts of data generated by IoT devices and their actual utilization in decision-making processes within institutions. In many academic settings, there is an overwhelming wealth of data being generated, but only a fraction of it is being strategically analyzed and employed. For instance, while a smart device might meticulously track and record student attendance or lab equipment usage, this data often remains siloed, failing to be integrated into a broader analytical framework that could offer insights about student performance, equipment efficiency, or even predictive analytics about future trends. Such a glaring disparity between data availability and its utilization is not just an operational inefficiency; it is a lost opportunity. Institutions that do not leverage this data risk missing out on insights that could drive strategic decisions, optimize resources, and ultimately enhance the overall educational experience [4, 5]. With these considerations in mind, this study delves into the intricacies of designing and Journal Européen des Systèmes Automatisés Vol. 56, No. 5, October, 2023, pp. 889-897 Journal homepage: http://iieta.org/journals/jesa 889