Energy Efficient Fog-Based Healthcare Monitoring Infrastructure

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.

Smart healthcare IoT applications based on fog computing: architecture, applications and challenges

“…Some typical results are as follows: in [ 42 ], authors reduced the patient data retrieval time by 28.5% compared to existing solutions. Moreover, in [ 43 ], authors improved energy consumption up to 52% compared to Cloud-based systems in high data speed scenarios.…”

“…The robust increase of Cloud-based healthcare IoT applications leads to the consumption of amount huge energy. According to energy efficiency direction, Isa et al [ 43 ] proposed a fog computing-based architecture for healthcare IoT applications to saving and optimise energy consumption. Specifically, they proposed an efficient energy fog-based computing model, called EEFC to optimize the location and number of fog servers at the edge layer.…”

“…Authors used a multi-objective cuckoo search algorithm (MOCSA) to solve the multi‑objective optimization problem of both energy and latency. The simulation results show that the proposed solution improved average latency the amount [ 5 – 42 ]% and reduce energy consumption about [ 28 – 43 ]% compared to several existing solutions.…”

Energy Minimized Federated Fog Computing over Passive Optical Networks

“…We then optimize and evaluate the energy efficiency of this architecture compared to a Non-Federated architecture by utilizing a Mixed Integer Linear Programming (MILP) model that optimizes the placement of Virtual Machines (VMs) requested by end-users while aiming to reduce the total networking and processing power consumption. We benefit from our previous work in energy efficiency that tackled areas such as distributed processing in the IoT/Fog [18]- [21], green core and data centre (DC) networks [22]- [31] , [32]- [37], network virtualization and service embedding in core and IoT networks [38]- [41] and machine learning and network optimization for healthcare systems [42]- [45] and network coding in the core network [46], [47].…”

Energy Efficient Resource Allocation in Federated Fog Computing Networks

“…In this paper, we improve the energy efficiency of distributed federated fog units by optimizing VMs placement whilst taking into consideration the inter-VMs traffic. We benefit from our previous work in energy efficiency that tackled areas such as distributed processing in the IoT/Fog layer [21]- [24], green core and data centre (DC) networks [25]- [34], [35]- [40], network virtualization and service embedding in core and IoT networks [41]- [44] and machine learning and network optimization for healthcare systems [45]- [48] and network coding in the core network [49], [50]. Our previous work in [1] optimized connectivity among distributed fog units to facilitate the communication between different servers.…”