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Enhancing IoT-Based Smart Indoor Air Quality Monitoring in University Hostels: A Case Study in Kano Municipal LGA, Kano State

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Background of the Study
University hostels are dynamic living environments where indoor air quality plays a critical role in occupant health and overall well-being. In Kano Municipal LGA, Kano State, issues such as poor ventilation, high occupancy, and inadequate maintenance have led to compromised indoor air quality in hostel facilities. IoT-based smart indoor air quality monitoring systems have emerged as innovative solutions to these challenges by providing continuous real-time data on pollutants, particulate matter, humidity, and temperature (Salihu, 2023). These systems employ an array of sensors that transmit data to centralized platforms, allowing for prompt adjustments and maintenance actions. The integration of such technology not only enhances occupant comfort but also contributes to energy efficiency by optimizing the operation of ventilation systems (Kano, 2024). The adoption of IoT-driven monitoring is particularly pertinent in university hostels, where high-density living conditions can exacerbate the spread of airborne diseases and diminish academic performance due to poor environmental conditions. By leveraging real-time analytics and automated feedback mechanisms, smart air quality systems facilitate proactive interventions that reduce exposure to harmful pollutants. Moreover, the historical reliance on manual inspection and periodic maintenance in hostels has proven insufficient in addressing rapid changes in air quality. The IoT approach represents a paradigm shift toward continuous monitoring and adaptive management of indoor environments (Jibril, 2025). Although the benefits are compelling, challenges such as initial setup costs, sensor calibration, and data interpretation remain. These obstacles are compounded by the need for technical expertise to maintain and operate the systems effectively. In light of these factors, this study intends to examine the effectiveness of IoT-based smart indoor air quality monitoring systems in university hostels, highlighting their potential to improve living conditions while also identifying the technical and operational challenges that must be overcome for widespread adoption.

Statement of the Problem
University hostels in Kano Municipal LGA face significant challenges related to indoor air quality due to overcrowding, inadequate ventilation, and poor maintenance practices. Traditional methods of air quality management in these hostels have proven ineffective, leading to a buildup of pollutants that can adversely affect the health of residents. Despite the promising capabilities of IoT-based smart indoor air quality monitoring systems, their implementation in hostel settings is hampered by several issues. High initial costs for sensor networks, the need for continuous calibration, and technical difficulties in integrating these systems with existing infrastructure have all contributed to slow adoption (Ahmed, 2023). Furthermore, the reliability of data collected from sensors can be compromised by environmental factors and improper maintenance, raising concerns about the accuracy of the monitoring systems (Bello, 2024). In many cases, a lack of trained personnel to manage and interpret the data further exacerbates these challenges, resulting in delayed or inadequate responses to deteriorating air conditions. Additionally, there is a scarcity of localized studies that assess the real-world performance of these systems within university hostels, making it difficult for administrators to gauge their effectiveness and justify the investment. These issues, combined with concerns about data security and the potential for technical malfunctions, create significant barriers to the successful implementation of smart indoor air quality monitoring systems. Without addressing these multifaceted problems, hostel residents may continue to experience suboptimal living conditions that can negatively impact their academic performance and overall health. This study aims to investigate these issues in depth, providing a comprehensive analysis of the operational challenges and potential solutions to optimize the use of IoT-based air quality monitoring in university hostels.

Objectives of the Study

  • To assess the performance of IoT-based smart indoor air quality monitoring systems in university hostels.

  • To identify the key factors influencing indoor air quality in hostel environments.

  • To propose recommendations for improving air quality monitoring and management in university hostels.

Research Questions

  • How effective are IoT-based indoor air quality monitoring systems in improving air conditions in university hostels?

  • What are the main factors affecting indoor air quality in hostel environments?

  • What strategies can be recommended to optimize the performance of these monitoring systems?

Significance of the Study
This study is significant in its focus on improving indoor air quality in university hostels through IoT-based monitoring systems. The findings will provide valuable insights for university administrators and policymakers on enhancing living conditions and promoting health and well-being in student accommodations. Additionally, the research will contribute to the broader discourse on sustainable building management practices in educational institutions (Ahmed, 2024).

Scope and Limitations of the Study
The study is limited to evaluating IoT-based smart indoor air quality monitoring systems in university hostels within Kano Municipal LGA, Kano State. It focuses on system performance, influencing factors, and recommendations for improvement. The results may not be applicable to other building types or regions.

Definitions of Terms

  • IoT (Internet of Things): A network of connected devices that communicate and share data.

  • Indoor Air Quality Monitoring System: A system that measures and analyzes the quality of indoor air using sensors.

  • University Hostel: Residential facilities provided by universities for student accommodation.





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