*2.2.1 Cloud-based solutions*

A network, cloud servers, and a mobile device make up cloud-only medical architectures. The issue of high latency is exacerbated by these components' potentially large distances between them (**Figure 1**). A comparison of distributed, or fog, cloud architectures, and traditional cloud architectures has recently been included in a number of medical monitoring solutions. For real-time emergency situations such as fall detection and stroke mitigation, which both require immediate medical response times, cloud-only solutions have retrieval times that are too high. Frequently sending

### **Figure 1.**

*General fog/edge architecture for healthcare systems [5].*

*A Simulation Model of a Blockchain-Based Decentralized Patient Information Exchange… DOI: http://dx.doi.org/10.5772/intechopen.109591*

data to the cloud for computation results in increased power consumption and costs, which is especially true in today's world when sensors generate a lot of data. When compared with a distributed computing architecture with multiple computing nodes in various locations, the typical cloud service demonstrated high latency and low sustained performance. Additionally, cloud-based solutions lack a low-cost mobile environment, which is essential for many patient monitoring scenarios [5].

#### *2.2.2 Edge and fog-based solutions*

Data processing is moved closer to the network edge with edge and fog-based solutions, resulting in faster response times and improved energy efficiency. Rather than continually moving information to the cloud for figuring activities, which represents the energy costs, information can be mined and handled by on edge devices and servers nearer to the client. In situations involving health monitoring, low latency of edge and fog solutions enables prompt arrival of emergency medical assistance. Privacy and security remain major issues due to a large amount of data that is typically sent to cloud services, particularly in situations where a patient's medical data could be hacked. Improved privacy can be achieved by disseminating data across a fog rather than concentrating it in a single area of the network. Device usability is also important because accurate data transmission depends on these sensors being easy enough for untrained personnel to use. The particular needs that are addressed are [5]:


The fog is typically referred to as a decentralized distributed computing system in which various entities own multiple fog devices and organizations can participate from a variety of locations, including hospitals, schools, airports, and smart hubs. According to the investigators, fog computing is a virtualized environment that is tasked with the delivery, storage, and computing of resources in cloud computing centers. It is not entirely outside the network space. These are a variety of fog nodes with limited computing and storage capabilities. Fog computing is widely regarded as an extension of the cloud that is close to devices that collect data for resourceconstrained IoT. These devices are referred to as fog nodes and have storage, a network connection, and computational power. They are situated in various geographical locations that have network connectivity. These fog nodes are located close to devices that collect data [4].

The main characteristics of fog computing are given below [4]:

• Adaptability: These are made up of a lot of network sensors and other fog devices that handle computing tasks and provide storage resources.

