Abstract
Recent advances in mobile technologies and cloud computing services have inspired the development of cloud-based real-time health monitoring systems. However, the transfer of health-related data to the cloud contributes to the burden on the networking infrastructures, leading to high latency and increased power consumption. Fog computing is introduced to relieve this burden by bringing services to the users' proximity. This study proposes a new fog computing architecture for health monitoring applications based on a Gigabit Passive Optical Network (GPON) access network. An Energy-Efficient Fog Computing (EEFC) model is developed using Mixed Integer Linear Programming (MILP) to optimize the number and location of fog devices at the network edge to process and analyze the health data for energy-efficient fog computing. The performance of the EEFC model at low data rates and high data rates health applications is studied. The outcome of the study reveals that a total energy saving of 36% and 52% are attained via processing and analysis the health data at the fog in comparison to conventional processing and analysis at the central cloud for low data rate application and high data rate application, respectively. We also developed a real-time heuristic; Energy Optimized Fog Computing (EOFC) heuristic, with energy consumption performance approaching the EEFC model. Furthermore, we examined the energy efficiency improvements under different scenarios of devices idle power consumption and traffic volume.
Highlights
The recent increase in chronic diseases, the ageing population and the increasing costs of healthcare have led to the revolution of remote health monitoring in developed countries [1]
This work has investigated the energy efficiency of an integrated healthcare approach that uses fog computing with the central cloud to serve low data rate and high data rate health monitoring applications
The Efficient Fog Computing (EEFC) model achieved 36% total energy savings compared to the EECC model where the processing is performed at the central cloud
Summary
The recent increase in chronic diseases, the ageing population and the increasing costs of healthcare have led to the revolution of remote health monitoring in developed countries [1]. We develop a framework for energy efficient fog based real-time health monitoring systems. Fog computing has been identified as a potential paradigm that can contribute to reducing the energy consumption of networking infrastructure and processing while providing the same health monitoring services as cloud computing. This framework is based on our previous research efforts on developing energy efficient architectures for cloud data centres and core networks [12], [17], [23], [29], [30].
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