A supervised active learning method for identifying critical nodes in IoT networks

The energy efficiency of wireless sensor networks (WSNs) as a key feature of the Internet of Things (IoT) and fifth-generation (5G) mobile networks is determined by several key characteristics, such as hop count, user's location, allocated power, and relay. Identifying important nodes, known as critical nodes, in IoT networks that involve a massive number of interconnected devices and sensors significantly affects these characteristics. However, it also requires a significant computational overhead and energy consumption. To address this issue, we introduce a novel supervised active learning method for identifying critical nodes in IoT networks aimed at enhancing the energy efficiency of WSNs in 5G environments. Our experimental results, designed to closely replicate varied and complex IoT network scenarios focusing on mission-critical multi-hop IoT applications, demonstrate the proposed method's capability to improve adaptability and computational efficiency. These results suggest a strong potential for mission-critical applications in real-world large-scale multi-hop WSN environments in 5G, as well as massively distributed IoT.