A Nonlinear Fuzzy–AHP Based Multi-Criteria Optimization Model for Dynamic Trust Evolution in Decentralized Healthcare Networks

Abstract

Fog computing has emerged as a transformative paradigm for latency sensitive healthcare applications such as real-time Electroencephalogram (EEG) monitoring. While fog-based processing significantly reduces communication delay, decentralization introduces complex trust management challenges[6]. Existing trust mechanisms are either static, reputation based, or lackmathematical rigor, making them vulnerable to adversarial  manipulation including bad-mouthing, on-off behavior, and distributed denial-of-service attacks.This paper presents a multi-criteria trust optimization framework integrating Analytic Hierarchy Process (AHP), time-aggregated weightedmodeling, fuzzy stabilization, and PROMETHEE-based ranking validation[12]. Trust is modeled as a bounded dynamic function over multi dimensional performance vectors. Convergence is established through stability analysis, and statistical validation confirms performance superiority over baseline approaches[13]. Simulation and real-time containerized implementation demonstrate improved detection accuracy, reduced latency, and enhanced robustness in decentralized healthcare environments