Paper Details

Name: Dinesh Anand, Avinash Kaur & Parminder Singh
Country: India

Year: 2026
Volume: Volume No: 13, January, Year: 2026 (Special Issue)
Page Number: 432-443
DOI: https://doi.org/10.5281/zenodo.19018234

ABSTRACT
The swift progress of IoMT and related technologies has transformed healthcare by enabling real-time data collection, monitoring, and analysis through medical sensors, wearable devices, and IoT-driven applications. However, these innovations have led to obstacles associated with data processing and transmission, particularly in traditional cloud computing models due to latency, bandwidth constraints, and security concerns. Fog computing extends cloud capabilities closer to the edge prototype, addresses these challenges by activating live data processing, reducing latency, and enhancing
security and scalability. This paper explores the Incorporation of IoMT with fog computing and evaluates various optimization techniques aimed at improving the Quality of Service (QoS) in healthcare applications. A comparative analysis of performance metrics, including reliability, latency, energy efficiency, and security, is conducted across different devices, communication protocols, and network configurations. The paper also highlights key contributions, such as the development of secure load balancing techniques, the use of Federated Learning for privacy-preserving data analysis, and the application of multipopulational genetic algorithms for adaptive QoS-aware service composition. While promising, several challenges remain in ensuring data privacy and real-time processing in critical healthcare environments. This
research provides recommendations for optimizing QoS in IoMT applications, ensuring better healthcare outcomes, and
proposes a framework for future IoMT deployment that incorporates emerging technologies like AI, edge computing, and
blockchain.
Keywords—IoMT, QoS, Fog Computing, Task Completion Time, Reliability, Energy Consumption, Response Time.