Development of IoT-Enabled Smart Street Lighting Systems in University Campuses: A Case Study in Wukari LGA, Taraba State
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Background of the Study :
University campuses are high-traffic areas that require efficient and cost-effective lighting solutions to ensure safety, enhance security, and reduce energy consumption. In Wukari LGA, Taraba State, traditional street lighting systems often operate on fixed schedules, leading to unnecessary energy use during periods of low occupancy. The advent of the Internet of Things (IoT) offers the possibility of smart street lighting that can dynamically adjust to real-time conditions. This study proposes the development of an IoT-enabled smart street lighting system specifically for university campuses. The system will utilize a network of sensors—including ambient light detectors, motion sensors, and occupancy detectors—to monitor environmental conditions and pedestrian activity. These sensors will communicate wirelessly with smart controllers that adjust the brightness and on/off schedules of the lighting fixtures automatically (Ibrahim, 2023). By integrating cloud-based analytics, historical data can be analyzed to optimize lighting schedules and predict maintenance needs. This automated approach not only enhances energy efficiency but also improves safety and reduces operational costs. Prior research has shown that smart street lighting can reduce energy consumption by up to 40% while extending the lifespan of lighting equipment (Olu, 2024). The proposed system will be designed with scalability in mind, ensuring that it can be easily integrated with existing campus infrastructure. Additionally, cybersecurity measures will be incorporated to protect data integrity and prevent unauthorized system access. Through field deployment in a selected university campus in Wukari LGA, the study aims to evaluate system performance, reliability, and user satisfaction. The findings will be used to develop best practice guidelines for the broader adoption of IoT-based smart lighting in educational institutions, contributing to sustainable campus development and improved public safety (Adeniyi, 2025).
Statement of the Problem :
University campuses in Wukari LGA face significant challenges with traditional street lighting systems that operate on static schedules and do not respond to real-time conditions. Such systems result in excessive energy consumption during low-traffic periods and insufficient illumination during peak times, compromising both safety and cost efficiency. The lack of automated control and real-time data leads to inefficient maintenance practices and higher operational costs. Moreover, fixed lighting does not account for the dynamic nature of campus activities, leaving certain areas underlit while wasting energy in others. Additionally, the integration of modern technology in many campuses is hindered by budget constraints and outdated infrastructure. Without a smart solution, universities continue to incur high energy bills and face increased wear and tear on lighting equipment. There is an urgent need for an IoT-enabled system that not only optimizes lighting in real time but also provides predictive maintenance data. This study aims to address these gaps by designing and implementing a smart street lighting system that responds dynamically to environmental and occupancy data, thereby reducing energy waste and enhancing campus safety. By comparing the new system’s performance with traditional methods, this research will demonstrate the potential benefits in terms of energy savings, improved illumination, and reduced operational costs, offering a scalable model for other institutions in similar settings (Ibrahim, 2023; Olu, 2024).
Objectives of the Study:
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To design an IoT-enabled smart street lighting system that adjusts lighting in real time.
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To evaluate energy savings and operational efficiencies compared to traditional lighting.
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To provide recommendations for scalability and integration into existing campus infrastructures.
Research Questions:
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How does real-time data influence the efficiency of street lighting in university campuses?
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What are the measurable energy savings from implementing a smart lighting system?
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How can the system be scaled and integrated with existing infrastructure?
Significance of the Study :
This study is significant as it develops a cost-effective IoT-enabled smart street lighting system aimed at reducing energy consumption and enhancing safety on university campuses. By providing real-time adjustments and predictive maintenance, the system improves operational efficiency and reduces environmental impact. The findings offer a replicable model for sustainable campus infrastructure, supporting broader adoption of smart lighting solutions in resource-constrained settings (Adeniyi, 2025).
Scope and Limitations of the Study:
The study is limited to the design, implementation, and evaluation of the smart street lighting system in university campuses within Wukari LGA, Taraba State. It does not extend to public street lighting systems.
Definitions of Terms:
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Smart Street Lighting: Automated lighting systems that adjust based on sensor data.
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IoT (Internet of Things): A network of interconnected devices that communicate in real time.
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Cloud-Based Analytics: Remote data processing for trend analysis and system optimization.
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