# Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture
## 3 Proposed IoT-Enabled Healthcare Architecture
### How the Proposed Healthcare Architecture Works
In addition, each HM knows whether any device leaves the system due to technical or communication problems by waiting a reasonable amount of time after a predetermined time in which the off-device should transmit its regular control message if the status of its cluster is not critical. Moreover, the UM and HMs maintain the specifications (specs) of each healthcare device. The important fields in this specs file are the priority, current function, and current device parameters, such as power level. Additionally, the most important function of the HM and UM is determining a suitable computing center and its alternatives for each device (cloud, fog, mist, or edge). The computing strategy is selected for the device after considering three parameters: the device location, data prioritization, and data size. The device location is determined in order to select the nearest computing center. Data prioritization determines the sensitivity degree of the data required to be processed in the computing center. The data size is considered to determine which computing strategy can process this size of data. Most management processes are achieved between the HM and its devices. However, many management functions should be accomplished between the HM and UM. The UM selects an alternative for any failed HM. In addition, UM is considered a communication link between HMs. If any HM needs data about a specific device in such a cluster, it can query the UM. The UM can change HMs, assign new HMs, or delete HMs. Elected specialists in the healthcare organization determine the data classification rules. These rules may change from one healthcare organization to another. The data are prioritized depending on a group of rules, such as the type of patients, specialties, medicines, and location. Health data are gathered through sink nodes that are assigned in the clusters and direct this data to the prioritize. Then, the prioritize applies the prioritization rules to the data and sends them to cloud computing centers. The cloud center computes each data point according to its importance level and available quality of service (QoS). Afterward, the cloud center responds to the user. The output of the prioritize is a number of queues equal to the number of data importance levels. First, the data are distributed among the priority queues depending on their importance. However, if the available QoS in such a queue is less than that required to process the data in that queue, the data importance is changed to the next level and transferred to a less important queue after decreasing its required QoS. The process continues until all healthcare data are serviced (in efficient networks), or some data with low importance are delayed until their required QoS is available. The UM should synchronize between healthcare organizations in the IoT environment regarding many issues, such as changing importance levels for specific data when changing the organization, calculating data importance levels using fog-computing centers instead of cloud-computing centers, balancing between computing center loads, and fault tolerance issues. The mathematical analysis of the prioritization work is stated in [34; 35], in which the value of n equals five (see Figure 5).