Embedded devices are increasingly connected to the Internet to provide new and innovative applications in many areas. These devices (Edge devices or "Things" in IoT) are heterogeneous sensors, cameras and even robots performing sometimes certain tasks locally. Fog Computing (or Fog robotics) optimizes the management of these tasks, offering data management mechanisms (computation and storage) closer to the data source. Nevertheless, many aspects remain closed to Fog computing environments like resource needs estimation in such environments. Indeed, such a topic remains a critical challenge, as it falls under either solving very complex optimization problems or comparing hypothetical scenarios very time consuming and/or expensive for deployment in a real environment. To help on this challenge we built SERFRI, an approach to estimate the resource needs in Fog robotics environments based on simulation. This approach optimizes simultaneously the duration and the Fog resources utilization cost in order to determine the minimum resource requirements compromising both metrics. We validated this approach on an existing robotics use case. This one aims at deploying a human face detection service on streaming images.