Data Aggregation Mechanisms With Dynamic Integrity Trustworthiness Evaluation Framework for Datacenters

This research proposes a data aggregation framework (a system for combining data from multiple sources) that evaluates how trustworthy different data sources are using dynamic Bayesian networks (a model that updates trust scores based on changing network behavior over time). The framework combines trust measurement with the minimum spanning tree protocol (an algorithm for efficient data routing) to improve how data centers process large amounts of information, achieving significant reductions in computational, communication, and storage costs.