Certain investigations on brain fog consortium images using IOT

Abstract

This paper aims to develop an efficient fog computing-based brain fog consortium framework for early detection and continuous monitoring of cognitive impairment using IoT-enabled physiological sensing. The proposed methodology integrates EEG, ECG, blood pressure, temperature, and behavioural sensors with fog-layer analytics to distinguish persistent brain fog patterns from temporary cognitive fluctuations. Data are collected from a consortium of over 200 individuals and processed using signal preprocessing, clustering-based behavioural analysis, and lightweight CNN-based cognitive-state classification deployed at the fog layer, while long-term validation is supported by cloud resources. Experimental results demonstrate improved detection accuracy, reduced latency, and reliable identification of risk-prone individuals through real-time fog-level processing. The study highlights the practical implications of enabling remote, low-latency cognitive health monitoring and decision support for clinicians and caregivers, reducing dependence on centralized cloud systems. The original contribution of this research lies in proposing a unified brain fog consortium model that combines physiological sensing, fog computing, and intelligent analytics to support scalable, real-time cognitive assessment and intervention.