Development of an IoT-Based Smart Vehicle Diagnostic System for University Transport in Dutse LGA, Jigawa State
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Background of the study:
University transport systems play a vital role in ensuring the smooth operation of academic institutions, yet they are often plagued by vehicle maintenance challenges and unexpected breakdowns. In Dutse LGA, Jigawa State, university transport fleets are critical assets whose reliability directly impacts the efficiency of campus operations. Traditional vehicle diagnostic methods rely heavily on periodic manual inspections and reactive repairs, which can lead to prolonged downtime and increased maintenance costs. The emergence of IoT-based smart vehicle diagnostic systems offers an innovative approach by enabling continuous monitoring of vehicle performance parameters such as engine health, fuel efficiency, and emission levels. These systems employ an array of sensors that capture real-time data, transmitting it to centralized platforms where advanced analytics are applied to predict potential failures (Bello, 2023). With such predictive capabilities, maintenance can shift from a reactive to a proactive approach, significantly reducing the frequency and severity of vehicle malfunctions. Furthermore, IoT diagnostics facilitate better fleet management by providing insights into usage patterns and wear-and-tear trends, thereby optimizing maintenance schedules and resource allocation (Chukwu, 2024). The integration of smart diagnostics into university transport systems not only enhances vehicle performance and longevity but also contributes to cost savings and improved safety standards. Additionally, this technology aligns with global trends in smart mobility and sustainable transportation, offering a model that could be replicated in other public sector fleets. As universities strive to modernize their operational frameworks, the adoption of IoT-based diagnostic tools emerges as a critical step toward ensuring reliable, efficient, and eco-friendly transport services (Dutse, 2025). This study aims to investigate the potential of such systems in transforming vehicle maintenance practices within university transport, ultimately contributing to enhanced operational efficiency and service quality.
Statement of the problem:
University transport vehicles in Dutse LGA face recurrent mechanical issues and unplanned downtimes due to the limitations of traditional, manual diagnostic practices. The absence of continuous monitoring means that potential faults often go undetected until they culminate in major failures, disrupting transportation schedules and incurring high repair costs (Ibrahim, 2023). In addition, the reactive nature of conventional maintenance approaches results in inefficient resource allocation and safety risks for passengers and drivers alike. The lack of real-time diagnostic data prevents timely interventions, leading to increased fuel consumption, elevated emissions, and a reduced lifespan of the vehicle fleet. Financial constraints and limited technical expertise further hinder the implementation of modern diagnostic technologies in the university transport sector. These challenges underscore the need for an IoT-based smart vehicle diagnostic system that can offer continuous, real-time insights into vehicle performance. Such a system would enable early fault detection, prompt maintenance, and ultimately, a reduction in operational disruptions (Okoro, 2024). Without these technological advancements, the university transport system remains vulnerable to unexpected breakdowns and escalating maintenance costs, thereby affecting the overall efficiency and reliability of campus transportation (Suleiman, 2025).
Objectives of the study:
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To design an IoT-based diagnostic system for real-time monitoring of university transport vehicles.
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To assess the impact of the diagnostic system on reducing vehicle downtime and maintenance costs.
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To provide recommendations for integrating IoT technologies into existing transport maintenance practices.
Research questions:
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How effective is the IoT-based diagnostic system in detecting vehicle faults in real time?
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What impact does the system have on reducing maintenance costs and downtime?
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How can the integration of IoT technology improve overall fleet management in university transport?
Significance of the study:
This study is significant as it explores the integration of IoT technology to revolutionize vehicle diagnostics in university transport systems. The findings will guide transport administrators in adopting proactive maintenance strategies, enhancing vehicle reliability, reducing operational costs, and ensuring passenger safety. The research supports the evolution toward smart, sustainable transport solutions.
Scope and limitations of the study:
This study is limited to the development and evaluation of an IoT-based smart vehicle diagnostic system for university transport in Dutse LGA, Jigawa State. It does not cover other forms of vehicle maintenance or diagnostic applications outside the university transport sector.
Definitions of terms:
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IoT (Internet of Things): A network of interconnected devices capable of sharing real-time data.
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Smart Vehicle Diagnostic System: An integrated system that continuously monitors vehicle performance using sensor technology.
University Transport: The fleet of vehicles used by a university for operational and student transport purposes
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