Dynamic NB-IoT Configuration: A Machine Learning-Driven Optimization Framework

The deployment of Cellular Internet of Things (CIoT) is expected to reach over six billion devices by 2030. Many of these devices will be located in remote areas where replacing or recharging their batteries would be difficult and expensive. Therefore, it is crucial to configure these devices for efficient energy use to avoid frequent battery replacements or recharging. However, optimizing the energy consumption of CIoT devices, considering their applications and operating environmental conditions, presents a complex challenge. In response to this challenge, we propose the Gradient-Boosted Learning Optimization for Battery Efficiency (GLOBE) framework for dynamic configuration of Narrowband Internet of Things (NB-IoT) devices. GLOBE adjusts the radio layer of NB-IoT devices based on data transmission patterns and network conditions, enabling swift and automated reconfiguration. Our results demonstrate that GLOBE reduces energy consumption by 30% to 75% compared to baseline configurations, offering significant benefits for both network operators and end devices by improving energy efficiency.