NONE 20260727111126253 wseas/wseas content-registration-form@crossref.org content-registration-form International Journal of Computational and Applied Mathematics & Computer Science 2769-2477 Babangida Zubairu Department of Computer Science, Faculty of Natural and Applied Sciences, Umaru Musa Yar’Adua University, Katsina, NIGERIA Amina Nura Department of Computer Science, Faculty of Natural and Applied Sciences, Umaru Musa Yar’Adua University, Katsina, NIGERIA Zigbee is a wireless communication protocol intended for short-range, low-power, and low-data-rate applications. It is frequently utilized in Internet of Things (IoT) devices, industrial controls, and home automation systems. Due to their low power consumption and use of a strong communication protocol to guarantee dependable data transfer, Zigbee devices are popular in Internet of Things applications and are appropriate for battery-operated devices. But even on these networks, energy usage can be very high, particularly when there is a lot of network traffic or when the deployment is vast. In IoT situations, this might result in shorter network lifetimes and more frequent battery replacements for battery-operated devices, which is both impractical and expensive. These issues show that in order to increase device lifespans and decrease energy waste, Zigbee-based IoT networks require energy-efficient solutions. There is a lack of comparative, device-level energy optimization studies in Zigbee-based IoT networks that jointly evaluate Rainfall Optimization and Salp sSwarm metaheuristic algorithms using multiple energy consumption metrics within a unified network framework. Reducing energy usage in Zigbee-based networks without sacrificing dependability and performance is the aim of this study. In order to reduce the average energy consumption, energy consumption per device, and energy consumption per connection, two optimization algorithms—Rainfall Optimization Algorithm (ROA) and Salp Swarm Algorithm (SSA) were optimized in this work. The findings indicate that both optimized algorithms were successful in reducing overall energy usage. 07 27 2026 07 27 2026 76 7 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/232028.2026.6.7 https://wseas.com/journals/camcs/2026/a14camcs-007(2026).pdf M. N. Alatawi, “Optimization of home energy management systems in smart cities using bacterial foraging algorithm and deep reinforcement learning for enhanced renewable energy integration,” International Transactions on Electrical Energy Systems, Vol. 2024, 2024, Article 2194986. N. H. J. Alhamdane and M. Nickray, “Enhancing the efficiency of routing strategies in WSNs using live streaming algorithms,” Journal of Techniques, Vol. 6, No. 4, 2024, pp. 27–39. M. Ayedi, E. 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