Cloud-Fog Interoperability in IoT-enabled Healthcare Solutions

TLDR

IoT-enabled healthcare faces latency, load, and heterogeneity challenges, and while cloud computing is foundational, its centralized architecture limits performance, prompting the adoption of fog computing to bring resources closer to data sources. This paper proposes a fog‑based IoT‑healthcare architecture that integrates cloud and fog services for interoperable solutions. The authors evaluate the architecture via iFogSim simulations, analyzing distributed computing, latency reduction, data communication optimization, and power consumption. Simulation results show reduced instance cost, network delay, and energy usage.

Abstract

The issue of utilizing Internet of Things (IoT) in Healthcare solutions relates to the problems of latency sensitivity, uneven data load, diverse user expectations and heterogeneity of the applications. Current explorations consider Cloud Computing as the base stone to create IoT-Enable solution. Nonetheless, this environment entails limitations in terms of multi-hop distance from the data source, geographical centralized architecture, economical aspects, etc. To address these limitations, there is a surge of solutions that apply Fog Computing as an approach to bring computing resources closer to the data sources. This approach is being fomented by the growing availability of powerful edge computing at lower cost and commercial developments in the area. Nonetheless, the implementation of Cloud-Fog interoperability and integration implies in complex coordination of applications and services and the demand for intelligent service orchestrations so that solutions can make the best use of distributed resources without compromising stability, quality of services, and security. In this paper, we introduce a Fog-based IoT-Healthcare solution structure and explore the integration of Cloud-Fog services in interoperable Healthcare solutions extended upon the traditional Cloud-based structure. The scenarios are evaluated through simulations using the iFogSim simulator and the results analyzed in relation to distributed computing, reduction of latency, optimization of data communication, and power consumption. The experimental results point towards improvement in instance cost, network delay and energy usage.