It is recommended that older adults maintain an independent lifestyle; however, they become more prone to accidents. Constant monitoring of the health state of the elderly is essential. There is a significant amount of research on monitoring systems for the elderly who live alone. The idea is to assist before any unforeseen situation occurs [14; 15; 16; 17; 18]. For that, small data capture devices are not enough. Robust computational infrastructures are needed to process requests in realtime. Thus, IoT allows the creation of more precise treatments. The data are collected by wireless sensor networks (WSNs) or Body Sensor Networks (BSNs). WSNs/BSNs integrate autonomously spatially distributed sensors, cooperatively transmitting their data to remote servers. Sensors collect patients' data and forward them to another piece of equipment (gateway) responsible for receiving and retransmitting the data. In a second moment, a gateway can route data to many distributed servers, local servers (e.g., edge computing), or to the cloud [19; 20; 21]. BSNs are responsible for monitoring the elderly and notifying relatives and doctors in an emergency [22]. A large amount of generated data must have powerful computing resources to process data as fast as possible. The cloud and fog computing resources can be used in this scenario [23]. Cloud computing is a centralized architecture and consists of large data centers located around the world. Furthermore, the cloud is more powerful than the fog regarding processing resources and storage capacity. However, the cloud is highly dependent on internet connections [23; 24; 25; 26]. The fog extends cloud computing and its services to the network's edge, thus, bringing together its advantages and benefits; it has other advantages such as low latency and less dependence on internet connections for data transmission. In addition to fog computing comes edge computing operative directly in the device layer, performing some kind of processing, even if of little computational complexity, to further reduce the volume of communication, in addition to collaborating to provide autonomy in decision making in the layer of things. Evaluating the performance of the hybrid fog-cloud-edge architecture with real experiments sometimes is impracticable. Evaluating prototypes in real environments is financially costly and often not viable. Analytical models can be useful in this context, making predictions based on probabilities. A queuing model or system can be briefly described as follows: form: users (or customers or customers) arrive to receive a certain service and, due to the impossibility of immediate assistance, they form a waiting list. The terms user and service are used here with a broad sense. We may refer to cars arriving at gas station toll booths, machines waiting to be fixed, parts following an assembly line, or messages transmitted through communication channels. The objective of queuing theory is to optimize the performance of a system, reducing its operating costs. For this, the system must meet some minimum performance criteria. This paper proposes to evaluate elderly monitoring systems using a queueing network model. The evaluated architecture includes a sensor network attached to a person generating data and using fog and a cloud as processing resources. Therefore, the main contributions of this paper are the following:  
* A performance queuing model as a useful mechanism for health monitoring systems users focused on fog/cloud to evaluate the performance even in the initial stages of development. About 21 parameters can be calibrated. * Load balancing analysis was performed considering the distribution of jobs between the cloud and the fog. Six strategies were tested, including random probabilities, round-robin, least utilization, JSQ (Join the Shortest Queue), and shortest response time. The routing strategy probabilities were more efficient with different arrival rates.