Multi-Objective Load-balancing Strategy for Fog-driven Patient-Centric Smart Healthcare System in a Smart City

Abstract

The spatially concentrated architecture of the cloud environment causes excessive latency and network congestion in traditional smart healthcare systems designed for smart cities. Fog computing underpins IoT-enabled smart city solutions for latency sensitivity by putting computing power closer to the network boundary. However, resource management issues degrade service quality and accelerate energy depletion in real-time smart healthcare systems, as the fog node workload has increased exponentially. This paper offers a fog-driven patient-centric smart healthcare system for an e-healthcare environment to maintain Quality of Service (QoS) during severe traffic load on a fog platform. The multi-objective EQLS (Energy-efficient QoS-aware Load balancing Strategy), is proposed to stabilize workload among processing nodes to increase real-time sensitivity of critical tasks within optimal response time and energy usage. Using the iFogSim simulator to present the significance of research work, the proposed technique is compared to existing load-balancing policies (Round Robin (RR) and Fog Node Placement Algorithm (FNPA)) regarding energy usage, response time, and cost. The simulation results reveal that EQLS saves 8.7% and 14.9% more energy and 6.2% and 13.4% greater response time over FNPA and RR, respectively. The results signify that the proposed approach can efficiently support real-time applications of smart cities.