For rapid infrastructure assessment following natural and man-made emergencies, the utilization of minimally invasive and cost-effective drone deployable sensor packages has gained significant attention. While compact sensors with wireless data transfer capabilities have demonstrated potential for monitoring structural dynamics of critical infrastructure, such systems typically require data to be processed off-device and often off-site. These additional steps hinder the rapid assessment aspect. A challenge arises when transmission is not feasible due to degraded communication links during natural or man-made emergencies. Moreover, off-site data processing may add unnecessary delays to actions that can be taken by emergency personnel following infrastructure damage. To maximize the usefulness of sensor packages for rapid infrastructure assessment, the integration of edge computing techniques into the sensors themselves to analyze data in real time presents a promising solution. The objective of this work is to demonstrate edge computing for frequency-based structural health monitoring techniques to showcase the effectiveness of on-device data processing for the rapid assessment of infrastructure. The proposed approach continuously computes the power spectral density of windowed vibration measurements taken from a structure of interest that has the potential to experience further damage, for example, the monitoring of a bridge immediately after a flooding event. This work presents contributions in terms of a methodology, focusing on the hardware implementation of edge computing algorithms. Additionally, a study of the performance and resource utilization of a windowed power spectral density processing algorithm on-device is provided.