Design of an Iterative Method for Enhanced Routing in Blockchain-Powered IoMT Networks Featuring Patient-Condition-Aware and Predictive Time Series Techniques

Authors

  • Vounteru Srikanth Reddy, Kumar Debasis

Keywords:

IoMT, Blockchain, Federated Learning, Dynamic Routing, Machine Learning

Abstract

In the realm of the Internet of Medical Things (IoMT), the efficient routing of critical patient data stands as a paramount necessity, driven by the rapid evolution of healthcare technologies and the increasing demand for real-time, reliable medical data transmission. Traditional routing mechanisms in IoMT networks often fall short due to their static nature and inability to adapt to the dynamic requirements of medical applications, resulting in significant delays and congestion. This work introduces an advanced suite of routing methodologies tailored for blockchain-powered IoMT networks that address these limitations by incorporating machine learning algorithms to enhance routing decisions dynamically. Firstly, the Patient-Condition-Aware Dynamic Routing (PCADR) methodology leverages real-time patient data to modify network routes dynamically. This approach prioritizes data transmissions based on the severity and urgency of patient conditions, thereby ensuring that critical information is expedited. By integrating patient vital signs and medical histories into routing decisions, PCADR achieves a notable 20% reduction in data transmission latency for urgent cases, illustrating its effectiveness in personalized healthcare delivery. Secondly, Predictive Time Series Routing (PTSR) employs time series analysis to forecast future network traffic patterns. By analyzing historical traffic and environmental sensor data, PTSR proactively optimizes routing strategies to accommodate anticipated changes in network load. This method has demonstrated a 30% reduction in network congestion, significantly enhancing the timeliness and reliability of data delivery across the network. Thirdly, Privacy-Preserving Federated Routing (PPFR) utilizes federated learning to develop routing models collaboratively across distributed IoMT devices while maintaining strict data privacy. This decentralized approach not only complies with stringent privacy regulations but also refines routing accuracy by 15% compared to centralized models, without exposing sensitive patient information sets. Lastly, Context-Aware Environmental Routing (CAER) integrates environmental sensing with routing mechanisms to mitigate data transmission errors influenced by adverse environmental conditions. By adjusting routes based on real-time temperature and humidity data, CAER reduces data corruption risks, achieving a 25% decrease in transmission errors.

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Published

12.06.2024

How to Cite

Vounteru Srikanth Reddy. (2024). Design of an Iterative Method for Enhanced Routing in Blockchain-Powered IoMT Networks Featuring Patient-Condition-Aware and Predictive Time Series Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 2330–2342. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/6619

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

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