In ndnSIm we can obtain different metrics by default such as rateTracer, appDelayTracer and csTracer. You can even write your own tracer in ndnSIM. Following are some steps which i followed to obtain graphs in R studio using the results generated by ndnSIM. 1. Copy the tracer file (in my case it is rate-trace.txt) from ns-3 folder and paste it into the working directory of R Studio (recommended). Note : type getwd() in R studio console and it will show you the path to the working directory. In my case the path is ("C:/Users/Atif/Documents"). 2. Install ggplot2 package in R studio if you don't have it already. 3. Run following code for and get all graphs library (ggplot2) data = read.table("C:/Users/Atif/Documents/rate-trace.txt", header=TRUE) ggplot(data, aes(x=Time, y=Kilobytes, color=Type)) + geom_line () + facet_wrap(~ FaceDescr) Conclusion Above mentioned steps are very basic and easy to use. if you have any questi
ndnSIM is a modular open source NS-3-based Named Data Networking (NDN) simulator which implements all basic operations of NDN such as Forwarding Information Base (FIB), Pending Interest Table (PIT), Content Store (CS). Following are some steps which i followed for installation of ndnSIM on Ubuntu 16.04. Prerequisites Following modules / libraries are necessary in order to run ndnSIM. 1. sudo apt-get update 2. sudo apt-get install python-software-properties 3. sudo add-apt-repository ppa:boost-latest/ppa 4. sudo apt-get update 5. sudo apt-get install libboost-all-dev 6. sudo apt-get install libssl-dev 7. sudo apt-get install build-essential 8. sudo apt-get install libsqlite3-dev libcrypto++-dev 9. sudo apt-get install python-dev python-pygraphviz python-kiwi 10. sudo apt-get install python-pygoocanvas python-gnome2 11. sudo apt-get install python-rsvg ipython 12. sudo apt-get install doxygen graphviz python-sphinx python-pip 13.