Table 1.
QoS requirements for medical and healthcare data transfer rates [15, 16].
Table 2.
Fig 1.
Data transmission between healthcare IoTs, end-users, and cloud servers using FC.
Table 3.
The various techniques used by different authors in their proposed research works.
Table 4.
The comparative analysis for minimization of communication latency (CL), computation latency (CPL), and network latency (NL).
The table also lists the authors’ names along with the techniques used.
Table 5.
Data dictionary for the dataset used in our simulation [38].
Fig 2.
The healthcare IoT data transmission model consists of fog nodes, master fog controller, end-users (u) and cloud server.
Fig 3.
The healthcare IoT system model consists of healthcare IoT devices, classified PHD, fog gateways, fog servers, and virtual machines (VM’s).
Fig 4.
The NN states, an input layer, a hidden layer, and softmax layer.
Fig 5.
Algorithm flow chart for real-time data packet communication using RL, NN, and FIS in the FC environment.
Fig 6.
Schematic diagram of the FIS.
Fig 7.
PHD classified as low risk, normal and high-risk using FIS and membership functions in the fuzzy logic system.
Fig 8.
PHD classification using linear SVM.
Table 6.
The description of fog device.
Table 7.
The description of the edge module.
The CPU length (processing capacity) is in million instruction per second (MIPS).
Table 8.
ECG sensor configuration in iFogSim simulator.
Table 9.
The network links description.
Fig 9.
A graphical user interface (GUI) to build physical topology arrangements.
Fig 10.
Communication latency comparison between FC and cloud computing.
Fig 11.
Network latency comparison between FC and cloud computing in IoT infrastructure.
Fig 12.
Computation latency comparison between FC and cloud computing.
Fig 13.
RAM consumption in FC and cloud computing.
Fig 14.
Network usage in FC and cloud computing.