Criticality and Utility-aware Fog Computing System for Remote Health Monitoring

TL;DR

This paper presents a criticality and utility-aware fog computing system for remote health monitoring, introducing a heuristic algorithm to efficiently allocate resources, maximizing system utility while considering patient criticality and medical center profit.

Contribution

It proposes a novel resource allocation heuristic for fog computing in healthcare, balancing patient criticality and profit, with near-optimal performance demonstrated through simulations.

Findings

Heuristic achieves 96% of optimal utility

System utility is maximized considering criticality and profit

Algorithm operates in polynomial time

Abstract

Growing remote health monitoring system allows constant monitoring of the patient's condition and performance of preventive and control check-ups outside medical facilities. However, the real-time smart-healthcare application poses a delay constraint that has to be solved efficiently. Fog computing is emerging as an efficient solution for such real-time applications. Moreover, different medical centers are getting attracted to the growing IoT-based remote healthcare system in order to make a profit by hiring Fog computing resources. However, there is a need for an efficient algorithmic model for allocation of limited fog computing resources in the criticality-aware smart-healthcare system considering the profit of medical centers. Thus, the objective of this work is to maximize the system utility calculated as a linear combination of the profit of the medical center and the loss of patients. To measure profit, we propose a flat-pricing-based model. Further, we propose a swapping-based heuristic to maximize the system utility. The proposed heuristic is tested on various parameters and shown to perform close to the optimal with criticality-awareness in its core. Through extensive simulations, we show that the proposed heuristic achieves an average utility of $96\%$ of the optimal, in polynomial time complexity.