Commentary on “Development of Microcontroller-Based Smart Device for Substance Detection in University Environment”

Substance abuse remains a significant challenge in many academic institutions, affecting student health, academic performance and campus security. According to the National Drug Law Enforcement Agency (NDLEA) 2023 report, it is estimated that over 14.3 million Nigerians are involved in drug abuse, with youths aged between 18 and 35 accounting for 40% of this figure [1]. Substance abuse among university students can be attributed to various factors, including stress, peer pressure, curiosity, family challenges and undue exposure to drugs and alcohol [2]. Academic pressures and the transition to independent living away from home can also contribute to increased experimentation with substances. Also lack of awareness about the risks associated with drug and alcohol misuse, coupled with limited access to right counselling and support services from the university, can further exacerbate the problem among students [3]. In response to this pressing concern, technologically innovative approaches are needed to effectively monitor and combat substance abuse activities in most universities’ environments. Traditional methods of substance detection, such as manual inspections and periodic drug testing, often fall short due to inefficiency, human error, and the inability to provide real-time monitoring [4,5]. The development of a microcontroller-based smart device for substance detection, as presented in this research, is a commendable step towards leveraging technology to address this issue effectively. This microcontroller- based smart device designed specifically for detecting smoking and drinking activities on university campuses. Upon detecting suspicious activities associated with smoking or drinking activities, the device will immediately notify university law enforcement in the university and provide them with precise GPS coordinates of the location where the activity was detected. By integrating sensor-based detection with microcontroller technology, the study introduces an automated, real-time monitoring system capable of enhancing security and promoting a safer academic environment.

The application of microcontroller-based systems in security and surveillance has gained traction in recent years due to their affordability, scalability, and efficiency. This study’s approach to using microcontrollers in substance detection is particularly significant because it shifts the focus from reactive measures to proactive monitoring. By providing real-time alerts to campus security personnel, the system helps in preventing substance-related incidents before they escalate. Furthermore, the study underscores the importance of Internet of Things (IoT) integration, enabling remote access and data collection. This is crucial in a university environment where multiple locations need to be monitored simultaneously. The real-time data transmission feature enhances rapid decision-making and intervention, reducing response time in the event of a substance-related emergency.

In our study we present a smart detection system aimed at addressing substance abuse in university environments by using chemical sensors to detect illegal or harmful substances. The MQ 4 sensors can accurately identify substances commonly abused by students, helping to detect addiction-related behaviours early. With a microcontroller at the core, the system processes data efficiently and communicates real-time alerts to security personnel or university staff. Its modular design ensures that it can be applied across different areas of the campus, aiding in the identification of students involved in substance abuse, who could then be subjected to counselling, rehabilitation, or disciplinary action.

While the study introduces an innovative approach to substance detection, several challenges and limitations need to be addressed in future research. Detection accuracy remains a concern, as false positives may occur due to environmental factors, sensor drift, or interference from other substances, leading to unnecessary alerts. Privacy issues also arise, as implementing substance detection systems in universities must carefully balance security with students' rights, ensuring compliance with ethical and legal standards. Additionally, the system's maintenance, calibration, and initial deployment costs could pose challenges, and further exploration of automated calibration methods and cost-reduction strategies would make the system more accessible and efficient.

To enhance the system's effectiveness and expand its application, future research should explore several key areas. Integrating machine learning algorithms could boost detection accuracy by analysing patterns in the data, minimizing false positives, and distinguishing between harmless and illicit substances more effectively. Multi-sensor fusion, which combines various sensor types like infrared, gas, and spectroscopy-based detectors, could strengthen the system’s reliability by cross-validating detected substances. Additionally, incorporating blockchain technology could ensure data integrity and prevent tampering with detection records, while the development of a mobile app would make real-time monitoring and alerts more accessible to security personnel. Finally, future research should address legal and ethical considerations to ensure the system complies with privacy laws and institutional policies.

The implementation of microcontroller-based smart detection systems has far-reaching implications for academic institutions. By preventing substance abuse on campus, universities can create a safer and more conducive learning environment. Additionally, this technology can serve as a deterrent, discouraging students from engaging in substance abuse due to the increased likelihood of detection. Moreover, the research opens up opportunities for interdisciplinary collaboration between computer engineers, law enforcement agencies and policymakers. By working together, stakeholders can develop comprehensive strategies to integrate technology-driven solutions into campus security frameworks.

The study on the development of a microcontroller-based smart device for substance detection in university environments represents a significant advancement in campus security. By leveraging sensor technology, IoT integration, and wireless communication, the system offers a proactive and efficient approach to substance monitoring. While challenges such as detection accuracy, privacy concerns, and maintenance requirements must be addressed, the potential benefits of this technology far outweigh its limitations. Future enhancements, particularly through AI integration, blockchain security, and mobile applications, could further improve its efficiency and effectiveness. Ultimately, this research serves as a foundation for future technological innovations in campus safety, demonstrating how engineering solutions can be applied to real-world problems. By refining and expanding upon this work, researchers and policymakers can contribute to safer academic environments, fostering a culture of security and well-being in educational institutions worldwide.

1. Odili I (2023) Nigerians involved in drug abuse – NDLEA. Premium Times, Wuse, Abuja, Nigeria.
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5. Gowrishankar J, PushpaKarthick P, Balasundaram G, Kaliappan E, Prabaharan N (2021) Arduino-based alcohol sensing alert with engine locking system. International Conference on Mobile Computing and Sustainable Informatics: ICMCSI 2020, Springer International Publishing: 293-305.