# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
###### Abstract  
Population health monitoring based on the Internet of Medical Things (IoMT) is becoming an important application trend healthcare improvement. This work aims to develop an autonomous network architecture, collecting sensor data with a cluster topology, forwarding information through relay nodes, and applying edge computing and transmission scheduling for network scalability and operational efficiency. The proposed distributed network architecture incorporates data compression technologies and effective scheduling algorithms for handling the transmission scheduling of various physiological signals. Compared to existing scheduling mechanisms, the experimental results depict the network performance and show that in analyzing the delay and jitter, the proposed WPQ-based algorithms have reduced the delay and jitter ratio by about 40% and 19.47% compared to LLQ with priority queueing scheme, respectively. The experimental results also demonstrate that the proposed network topology is more effective than the direct path transmission approach in terms of energy consumption, which suggests that the proposed network architecture may improve the development of medical applications with body area networks such that the goal of self-organizing population health monitoring can be achieved. clustering; topology management; data compression; scheduling algorithm; network traffic control 2023238553[[https://doi.org/10](https://doi.org/10)]([https://doi.org/10](https://doi.org/10)). reception, and processing of the data. Currently, many effective data compression methods for physiological signals are widely used. Data compression methods are classified into three types [1]: (a) direct data compression method, (b) transformational data compression methods, and (c) parameter extraction-based data compression methods [2]. Among these three categories, transform based data compression is always preferred, because these techniques provide efficient results in terms of CR and signal reconstruction [3]. In these categories, the signal is first transformed from one domain (mostly time domain) to the other domain and then rejection of irrelevant coefficients is performed. The examples of transformed-based techniques are: sine transform, Fourier transform (FT), cosine transform, wavelet transform (WT), etc. Since the wavelet transform is useful to locate and analyze the non-stationary physiological signals (e.g., electrocardiography (ECG), electroencephalography (EEG), and electromyography (EMG)) in both time and frequency domains, it is applied for data compression of physiological signals in this work. Data gathering is a critical process, which includes protocols and scheduling strategies to fulfill the network requirements (e.g., increased lifetime, reliability, and self-configuration). Scheduling is a process by which the system determines which task should be executed at the given time, and thus achieves load balancing and efficient system resource allocation. However, improper scheduling algorithms may waste transmission resources or lead to performance degradation (e.g., packet collisions). To organize the time constraints of multiple data forwarding tasks, these tasks need to be carefully arranged to adjust the load well between sensors. Many queue scheduling algorithms have been proposed, where weighted fair queueing (WFQ) is one of the widely used scheduling algorithms due to its fair bandwidth allocation and traffic prioritization capabilities. Besides considering the scheduling schemes, to conserve limited energy resources, aggregate information from individual sensor nodes, and further provide network scalability and robustness, in this paper, sensor nodes are grouped into clusters, where network topology management is performed by setting weights and countdown timers between sensors, clusterhead (CH), inter-clusterhead (inter-CH), relay and base station through a hierarchical clustering approach.