Year: 2025
Type: article
Abstract: In this paper the game theory procedures are applied for healthcare monitoring systems and it is analysed using two types of evolutionary algorithms that incorporate Artificial Intelligence (AI) based events. As most of the existing approaches face challenges in establishing real-time connectivity, optimizing decision-making processes, and minimizing latency in Internet of Things (IoT)-based healthcare applications the limitations needs to be addressed. Hence with analytical equivalences that are crucial in game theory, a unique system model is developed using a deterministic framework where four key performers are strategically connected to improve decision-making and security against potential data breaches. By incorporating two evolutionary algorithms, the proposed approach optimizes the state of action for each participant while reducing energy consumption and processing delay. The model is validated through four case studies, demonstrating an average improvement of 60% over existing methodologies. These findings highlight the effectiveness of integrating game theory with evolutionary optimization to enhance real-time healthcare monitoring.
Source: Scientific Reports
Authors: Shitharth Selvarajan, Hariprasath Manoharan, Taher Al-Shehari, Nasser A. Alsadhan, Subav Singh
Language: English
Topic: Internet of Things and AI
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Funder: King Saud University
OpenAlex ID: W4409964021