Design of an Incremental Graph-Based Routing Model for Enhancing QoS of Dense IoMT Networks, using Truncated Side Chains
Abstract
The expansion of the IoMT domain is fast-paced, resulting in the development of a pressing demand for intelligent routing techniques capable of effectively addressing network congestion issues by providing high levels of QoS and safeguarding sensitive healthcare data transmission processes. Modern health facilities employ multiple medical devices that constantly produce and transfer important medical data, making communication between these devices indispensable in practice. Nevertheless, the currently available routing strategies frequently do not sufficiently account for the spatio-temporal characteristics of network nodes and ensure data routing security. Therefore, this study aims to design an incrementally improved graph-based routing scheme that improves the quality of service in densely populated IoMT environments based on the truncated sidechain technique. This scheme accounts for the spatial and temporal properties of network nodes and employs a hybrid optimization algorithm comprising a combination of the Teacher–Learner-based Firefly Optimization Algorithm and Graph-based Q-Learning for calculating optimal routes. Furthermore, the truncated sidechain technique is utilized to improve the security of transmitted medical data, and the Grey Wolf–Whale Optimization algorithm is used to find the best length of the sidechain by a Fuzzy Analytic Hierarchy Process approach. The results of experimentation are showing clearly that the suggested model gives better efficiency due to five different aspects which are reducing the communication time delay by 10.5%, enhancing the energy efficiency by 8.5%, improving the throughput by 3.9% and increasing the packet delivery rate by 1.5% and decreasing the jitter rate by 8.3%. The system has been proved to properly function to make the IoT routing process secure and efficient by enhancing the performance of blockchain through three distinctive reductions in energy consumption which include 5.9% reduction in the mining energy consumption, 4.9% reduction in the storage energy consumption, and 12.5% reduction in the mining time.