The Fresnel zone has been critical in advancing Integrated Sensing and Communication technologies, significantly enhancing the accuracy of observing changes in Non-Line-of-Sight paths. With the widespread deployment of Internet of Things (IoT) devices, leveraging IoT signals in conjunction with the Fresnel zone for sensing research has become increasingly feasible. As a result, this paper proposes an Enhanced channel estimation and Channel impulse response-based Fresnel boundary crossing frequency Estimation (ECFE) method based on IoT signals. This approach focuses on improving sensing performance in IoT scenarios. The repetitive transmission characteristics of IoT signals are leveraged to enhance the accuracy of Channel State Information estimation. The narrow bandwidth of IoT signals allows for more precise monitoring of channel dynamics from the Channel Impulse Response perspective, and integrating information across all subcarriers further improves the accuracy of dynamic path change detection. Simulation results demonstrate that the ECFE method performs robustly under various Signal-to-Noise Ratio conditions, providing reliable Fresnel Boundary Crossing Frequency estimation. This method shows significant potential for IoT applications, providing more reliable prior information for tasks such as path prediction and object localization.