The implementation of an analytical model for the assessment of multi-hop networks using the IEEE 802.15.4 standard. The focus is on the Media Access Control (MAC) layer. It supports CSMA/CA as well as TDMA techniques such as TSCH and DSME.
Florian Kauer and Volker Turau, An Analytical Model for Wireless Mesh Networks with Collision-Free TDMA and Finite Queues, EURASIP Journal on Wireless Communications and Networking 2018:149, June 2018. https://doi.org/10.1186/s13638-018-1146-x
Florian Meier and Volker Turau, Analytical Model for IEEE 802.15.4 Multi-Hop Networks with Improved Handling of Acknowledgements and Retransmissions. arXiv:1501.07594 [cs.NI]. https://arxiv.org/abs/1501.07594
Florian Meier and Volker Turau, An Analytical Model for Fast and Verifiable Assessment of Large Scale Wireless Mesh Networks in Proceedings of the 11th International Conference on the Design of Reliable Communication Networks (DRCN), Kansas City, MO, USA, March 2015. https://doi.org/10.1109/DRCN.2015.7149011
The program is based on the Portable, Extensible Toolkit for Scientific Computation (PETSc) and requires version 3.8.4. So the first step is to install it according to this documentation. For Linux with APT and Bash it basically boils down to:
sudo apt-get update
sudo apt-get install git build-essential gcc g++ gfortran python
cd ~/
git clone https://github.com/koalo/petsc
cd petsc
echo "export PETSC_DIR=`pwd`" >> ~/.bashrc
echo "export PETSC_ARCH=linux-gnu-c-debug" >> ~/.bashrc
source ~/.bashrc
./configure --with-cc=gcc --with-cxx=g++ --with-fc=gfortran --download-mpich --download-fblaslapack
make all test
Now to the software in this repository:
sudo apt-get install libboost-graph-dev libboost-program-options-dev libboost-system-dev libboost-filesystem-dev
cd ~/
git clone https://github.com/koalo/AnalyticalMultiHop analyticalmultihop
cd analyticalmultihop
make
The program is now ready to use. However, if you want to use the Python interface (see below), the following steps have to be executed, too:
sudo apt-get install python3 python3-pip graphviz libgraphviz-dev pkg-config libfreetype6-dev
sudo -H pip3 install virtualenv
cd ~/analyticalmultihop
virtualenv -p python3 ./amenv
source ./amenv/bin/activate
pip install -r ./requirements.txt
The main executables are csma_model and tdma_model. They are started with several files that define the model parameters, the topology and the schedule. Besides manual creation there are two possibilities to automatically generate these files: A Python interface and a C++ based preprocessor. The first one is more easy to use and to extend. The second one is faster and more suitable if you want to analyze networks with hundreds or thousands of nodes. In the following the Python interface is used.
Activate the virtualenv. You have to do this every time you start a new terminal.
source ~/analyticalmultihop/amenv/bin/activate
Start the quick start script with
cd ~/analyticalmultihop
./quickstart.py
After some seconds, two figures should be shown that look similar to the ones above. In this example, every node in the network sends Poisson distributed traffic with a given rate to node 0. The plot shows the probabilities that a packet sent out by node 11 is successfully forwarded to node 0. When using the CSMA MAC layer, many packets are lost due to collisions. For TDMA, all packets arrive for lower rates, but as soon as the network is saturated, the queues overflow and the PDR drops sharply.
Have a look into the quickstart.py file to learn how to generate new topologies or schedules. If you have any questions or a scenario you want to evaluate, do not hesitate to contact me at [email protected].