This is the MATLAB based simulation of an optimized Vehicular Fog Computing framework that minimizes the latency during the computation tasks offloading in the whole VFC-based scenario... %% updates on 10 January 2024
%%%% Steps to execute the simulation and achieve the results as depicted in the article (https://doi.org/10.1016/j.iot.2023.100912)....
Step 1: Generation and displaying the curve of the 'TSM' approach and the first step of the Monte Carlo Simulation, that is placing random samples above and below the curve of 'TSM' approach...
The Matlab data code and Matlab file used for the are:
***MATLAB data source file-1: Covered_Areas_TSM_RR_BCQI_PF11January2024 and ***
MATLAB code/simulation file-1: RunningTSMSchedueler10january2023
Step 2: Generate the covered areas of 'TSM' approach and all other approaches using the MATLAB data (.mat format) and source file (.m format). These MATLAB files are:
MATLAB data source file-1: Covered_Areas_TSM_RR_BCQI_PF11January2024 and
MATLAB source file : Plotting_Covered_Areas_all_methods_09July2024.
Kindly note that the MATLAB source data file (.mat format) here in this case is MATLAB data source file-1: Covered_Areas_TSM_RR_BCQI_PF13January2024 as mentioned already in step-1. And, we assume here that you have loaded this MATLAB source data file (.mat format) before executing the m files mentioned in this step and step-1.
Step 3: Plot the run time of the 'TSM' approach and other benchmark methods using and loading MATLAB data source file (.mat format) and MATLAB code file (.m format). These MATLAB files are:
the MATLAB source data file (.mat format): RUNTime_TSM_BestCQI_PF_RR_19May2023*** and
executable the MATLAB source/code file (.m format) : Runtime_BCQI_RR_PF_TSM_19May2023***