## 3 FaaS-FR Model for Cognitive Robots  
In the cloud robot paradigm, there are various deployment models, including private, community, public, and hybrid clouds. [PERSON] et al. [PERSON] et al. (2018) introduced these models into the robot clouds. This study  
Figure 1: Fog robotics schematic [PERSON] et al. (2018). proposes and evaluates various connection methods that occur in the implementation of cloud robots. Public cloud models exchange large amounts of data and information across networks and clouds. The cloud can be used to share data in a computing environment that everyone can share. However, the exclusive materials of an individual should be served in a proper separate manner. In a personal robot cloud, a server or cloud is privately connected to a home or company. Personal robot clouds can form an external and independent cloud and distribute the robot's computing power through the servers. In the view of fog robotics, the function of personal cloud servers can be matched with fog robot servers which personally support edges (local robot system) [5]. Therefore, we adopt the term \"fog robotic server\" instead of \"cloud robotic server\" because the server works not for other's robots but for specific robots privately. However, fog robotics models generally have a hierarchical model consisting of clouds, fog servers, and edges. With these models, it is difficult for edges directly to access to a cloud and get the result of services which have specific functionality with enough computing power. In this paper, to overcome these matters, an advanced model, FaaS-FR model, is proposed as shown in Figure 2. In the model, all the functional modules of the cognitive robot are classified according to privacy, security, and computing power, and have their own networking with concept of fog robotics. The functions of the robot are suitably divided for being worked on edges, fog robot servers, and public robot clouds, respectively. In the case of information possibly being a violation of privacy, it is computed and stored in an edge or a fog robot server. If the edge needs to use the public robot cloud to utilize an advanced computing service, it can access the cloud directly to reduce latency or go through a fog robot server to the cloud. The reason that a new term is coined, FaaS, is that the classified functions of the robot can be offloaded on the cloud or fog servers according to the security, latency, privacy, and computing power. Figure 3 shows a schema of modular cognitive functions of a general structure of cognitive robots. It has perception modules comprising sensing, object recognition, and speech recognition. Therefore, the robot can talk with humans regarding the visual situation that the robot recognizes. The robot also has behavior modules such as utterance and motion. In the higher part, there are interpretation and generation modules which can produce descriptive cognitive information from the perceptional. Memory modules are used to store the descripted cognitive information and can be retrieved for the future by a reasoning procedure with a virtual imager and cognitive grammar. In the view of functionality including privacy, security, safety, latency, and computing power, the functions can be  
Figure 2: The schematic of function as a service based fog robotic (FaaS-FR) model. divided with three parts: sensing and actuation part (SAP), privacy and security part (PSP), and high performance part (HPP). The SAP which is covered by a solid line and marked with a circle, is the essential part including operating system (OS), sensing, and actuation which are indispensable for the robot. This part should have OS, perception functions including sensors, cameras and microphones, and behavior functions including speakers and actuators. These functions are dependent on the hardware of the robot and cannot be taken over by others. The PSP is covered with a dashed single-dotted line and marked with a rectangle; it should be installed on edges or fog servers.