This paper presents the design and simulation of an adaptive energy-efficient Internet of Things (IoT)-based security surveillance system developed for resource-constrained environments characterized by unstable power supply and unreliable network connectivity. The proposed system integrates passive infrared (PIR) and ultrasonic sensors with intelligent operational mode switching to optimize energy consumption while maintaining reliable surveillance performance. Hardware simulation was implemented using Proteus 8.17 Professional, while software functionalities including image processing and real-time monitoring were developed using Python, OpenCV, and Node-RED. The developed framework dynamically switches between continuous monitoring and trigger-based monitoring according to environmental activity conditions and time-of-day analysis. Experimental simulation results demonstrated that the adaptive surveillance architecture achieved an average energy savings of 43.90% compared with conventional surveillance systems. Statistical validation using the Wilcoxon signed-rank test confirmed the significance of the observed energy reduction (p < 0.00001). Economic evaluation revealed projected annual cost savings of approximately ₦110,856 with an estimated return on investment period of about 5.5 months. Environmental assessment further showed an annual CO₂ emission reduction of 428.31 kg. The results demonstrate that the proposed adaptive IoT surveillance system successfully balances energy efficiency, security effectiveness, affordability, and operational reliability. The developed architecture provides a scalable and sustainable surveillance solution suitable for deployment in developing regions and other energy-constrained environments.
Mokwenye Nwachukwu Godson (2026) studied this question.