Optimized Resource Allocation in Monitoring of Biological Systems Using Fog Computing and Big Data Analytics for Enhanced Health Management

The speed of biometric data generated by health monitoring devices requires real-time processing that conventional cloud systems cannot provide due to the high latency and poor resource allocation. To overcome these dilemmas, the present paper proposes a Crayfish Optimization-based Hadoop MapReduce (CO-HMR) framework. This new fog-big-data architecture optimizes resource allocation for monitoring biological systems. It preprocesses vital-system data at the edge and dynamically allocates fog-layer resources using the Crayfish Optimization Algorithm (COA) to minimize processing delays. Hadoop MapReduce is also used to run large-scale data to deliver reliability and scalability. Experimental findings indicate that CO-HMR achieves 98.21% accuracy, 2.27 s of execution time, 19.1 s of latency, and 2.12 J of energy consumption, surpassing those of existing fog-based healthcare models. The results show the possibilities of CO-HMR to empower efficient, low-latency, and energy-conscious smart healthcare monitoring.