This repository contains a *light-weight simulator used to illustrate the use of Delay Tolerant Networks as a supplement for Cloud Connectivity for Rural Remote Patient Monitoring.
classification.csv: A csv file containing the daily acivities and their classifications.activity_tracker.ipynb: A jupyter notebook containing the analysis of the Mobility Sample Data.simulation.ipynb: A jupyter notebook for running a simulation and analyzing results of a simulation
For more information about the simulator, please refer to our paper. If you use this simulator or code in your own work, we are happy to receive a citation.
Esther Max-Onakpoya, Oluwashina Madamori, Faren Grant, Robin Vander-pool, Ming-Yuan Chih, David K. Ahern, Eliah Aronoff-Spencer, and Corey E.Baker. 2019. Augmenting Cloud Connectivity with Opportunistic Networksfor Rural Remote Patient Monitoring.
Based on the IPUMS ATUS terms and conditions, redistribution of ATUS data is not permitted https://www.atusdata.org/atus/terms.shtml. However, you may obtain an extract from their website: https://www.atusdata.org/atus-action/variables/group
To replicate our work, you would need to obtain the 2017 sample and use the following variables:
| Type | Variable | Label |
|---|---|---|
| H | RECTYPE | Record Type |
| H | CASEID | ATUS Case ID |
| H | METRO | Metropolitan/central city status |
| P | FULLPART | Full time/part time employment status |
| A | ACTLINE | Activity line number |
| A | ACTIVITY | Activity |
| A | START | Activity start time |
| A | STOP | Activity stop time |
We converted the data from dat to the csv format ('b.csv' file), which had the following headings: RECTYPE, CASEID, METRO, FULLPART, ACTLINE, ACTIVITY, START, STOP. A viable way to do the conversion is by using the syntax file to extract and read the data via SAS, SPSS, or STATA to convert the data to csv.