Queen Honey Bee Migration-Based Optimization for Battery Management of Internet of Things Devices in High-Risk Emergency Scenarios - Widiatmoko, Dekki; Aripriharta, Aripriharta; Sujito, Sujito (Universitas Muhammadiyah Yogyakarta, 2025)
Efficient energy management in Internet of Things (IoT) devices is critical in dynamic, resource-constrained operational environments. This study proposes the Queen Honey Bee Migration (QHBM) optimization algorithm for managing Li-ion battery performance in IoT systems, employing the Shepherd battery model to simulate the nonlinear discharge behavior under varying load conditions. Three simulation scenarios of increasing complexity (5, 10, and 20 monitoring points) are used to represent urban operational dynamics. The performance of QHBM is quantitatively compared with four conventional optimization algorithms seperti Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm (GA), and Firefly Algorithm (FA). Results show that QHBM maintains a current range of 3.80–5.20 A and a voltage range of 3.65–3.95 V, with State of Charge (SoC) predictions between 75–98%. It also achieves the fastest computation time (0.42–1.20 seconds) and demonstrates more stable performance under high-load dynamic scenarios compared to the other methods. This approach provides an adaptive and efficient optimization framework to support energy-aware decision-making in IoT systems operating in energy-constrained urban environments.