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Showing posts from November, 2020

PyTorch Code for Simple Neural Networks for MNIST Dataset

Flying Adhoc Network Simulation (FANET) using NS3

In this post, we will see how to simulation a Flying Adhoc Network (FANET) simulation using NS3.  Its actually MANET with 3D mobility Model called Gauss Markov Mobility Model.  See the following video for more details and explanation: So all the nodes are flying in a 3D Fashion with X axis, Y Axis and Z Axis  The default values of the three axes are  X axis can be (-100m, 100m) Y axis can be (-100m, 100m) Z axis can be (0m, 100m) We will take the following example for experimenting the Flying Adhoc Networks. To begin with, We use the following parameters for Simulation: The name of the File is fanetex.cc #include "ns3/point-to-point-module.h" #include "ns3/ipv4-global-routing-helper.h" #include <fstream> #include <string> #include "ns3/core-module.h" #include "ns3/network-module.h" #include "ns3/applications-module.h" #include "ns3/mobility-module.h" #include "ns3/config-store-module.h" #include &quo

Installing NS-3.31 in Ubuntu 20.04

Installing ns-3.31 in Ubuntu 20.04 - 64 bit OS. Follow the full video for more details: Fresh installation of Ubuntu OS  $] sudo apt update $] sudo apt install build-essential autoconf automake libxmu-dev $] sudo apt install build-essential autoconf automake libxmu-dev python-pygraphviz cvs mercurial bzr git cmake p7zip-full python-matplotlib python-tk python-dev python-kiwi python-gnome2 python-gnome2-desktop qt4-dev-tools qt4-qmake qt4-qmake qt4-default gnuplot-x11 wireshark extract to /home/pradeepkumar $] echo $HOME $] cd ns-allinone-3.31/ $] ./build.py --enable-examples --enable-tests you will get the following screen after the installation $] cd ns-allinone-3.31/ns-3.31/ $] ./waf --run hello-simulator  This will display  "Hello Simulator"  Which indicates that ns3 is installed successfully. $] ./waf --run first