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.
@article{arxiv.2105.11097,
title = {Criticality and Utility-aware Fog Computing System for Remote Health Monitoring},
author = {Moirangthem Biken Singh and Navneet Taunk and Naveen Kumar Mall and Ajay Pratap},
journal= {arXiv preprint arXiv:2105.11097},
year = {2022}
}