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Recent advances in mobile technologies and cloud computing services haveinspired the development of cloud-based real-time health monitoring systems.However, the transfer of health-related data to the cloud contributes to theburden on the networking infrastructures, leading to high latency and increasedpower consumption. Fog computing is introduced to relieve this burden bybringing services to the users proximity. This study proposes a new fogcomputing architecture for health monitoring applications based on a GigabitPassive Optical Network (GPON) access network. An Energy-Efficient FogComputing (EEFC) model is developed using Mixed Integer Linear Programming(MILP) to optimize the number and location of fog devices at the network edgeto process and analyze the health data for energy-efficient fog computing. Theperformance of the EEFC model at low data rates and high data rates healthapplications is studied. The outcome of the study reveals that a total energysaving of 36% and 52% are attained via processing and analysis the health dataat the fog in comparison to conventional processing and analysis at the centralcloud for low data rate application and high data rate application,respectively. We also developed a real-time heuristic; Energy Optimized FogComputing (EOFC) heuristic, with energy consumption performance approaching theEEFC model. Furthermore, we examined the energy efficiency improvements underdifferent scenarios of devices idle power consumption and traffic volume.

Energy Efficient Fog based Healthcare Monitoring Infrastructure