Artificial Intelligence-Powered Four-Wall Intrusion Prevention System using PIC Microcontroller: Suggestions for Further Development and Growth

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Authors: I. A. Kadawa, U. S. Musa, A. Y. Abdullahi, Y. Y. Sulaiman, A. M. Gambale, M. A. Baballe
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Abstract

Today's society relies heavily on digital electronics, particularly in the security sector, and is highly integrated. In order to protect the north wall, south wall, east wall, and west wall, a security system was created for this study. A light-emitting diode (LED) and a light-dependent resistor (LDR) are used in the sensor that is installed on each wall. The light-dependent resistor (LDR) is prevented from receiving light from the light-emitting diode (LED) whenever someone or something passes the wall. This means that the peripheral interface controller, which serves as the brain of this study, receives a 0-volt output from the light-dependent resistor (LDR). The system instantly rotates the DC motor that holds the closed-circuit television (CCTV) camera to stop at the crossed wall in order to record a real-time video and send it to the buzzer whenever the peripheral interface controller detects 0 volts. This signals the liquid crystal display (LCD) to indicate that the specific wall has been crossed. The purpose of the buzzer is to alert others to an infringement. All of the walls are not being crossed if the peripheral interface controller (PIC) senses 5 volts from each light-dependent resistor (LDR) on the wall. The peripheral interface controller (PIC) will show "all walls ok" on the liquid crystal display (LCD) if every wall's light-dependent resistor (LDR) produces 5 volts. Because life depends on it, the system also shows a calendar (time and date) on a liquid crystal display (LCD). Approximately 96% of the system is predicted to be dependable based on calculations. To further develop and grow an Artificial Intelligence (AI)-powered four-wall intrusion prevention system using a PIC microcontroller, focus should be on integrating advanced sensor technology, incorporating more sophisticated AI/ML models, enabling Internet of Things (IoT) connectivity, and enhancing the system's ability to reduce false alarms.

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
6 January 2026