robust lora

robust-lora

This repo contains the implementation for paper

Automating Reliable and Fault-Tolerant Design of LoRa-based IoT Networks

Xiaofan Yu, Weihong Xu, Ludmila Cherkasova, Tajana Šimunić Rosing.

CNSM 2021. Best Paper Award.

Environments

Python 3.7

ns3-3.31 + lorawan module

MATLAB 2020a + SNOPT 7.7

File Structure

.
├── LICENSE
├── README.md    // This file
├── alg          // Algorithms for LoRa gateway placement and device configuration
├── data         // End device location data and how to generate path loss matrix 
├── ns3-exp      // Scripts to test in ns3
└── relaxOpt     // MATLAB scripts to call SNOPT to optimally solve the relaxed problem

Data

In ./data folder, we include the tutorial on how to generate the datasets.

Each dataset includes (i) end devices locations, (ii) candidate gateway locations and (iii) path loss matrix with (i, j) entry representing the path loss in dB between the ith end device and jth gateway.

Detailed instructions are listed in data/README.md.

Algorithms

In ./alg folder, we implement the following algorithms:

• Proposed greedy gateway placement and device configuration heuristic (./alg/RGreedy.py).
• The above proposed heuristic with clustering acceleration (./alg/clustering.py). Need the scikit-learn package.
• Genetic algorithm (./alg/RGenetic.py). Need the geneticalgorithm package.

The following baselines are included:

• Greedy heuristic in Ousat 2019 (./alg/ICIOT.py)

./alg/main.py sets which algorithm to run and the parameters of the problem.

To run the algorithms:

python3 ./alg/main.py

ns-3 Experiments

To run the ns-3 simulations, first install ns3-3.31.

Then, clone our modified lorawan module and copy the test script:

cd root-of-ns3/ns-3.31/src
git clone https://github.com/Orienfish/lorawan.git
cp path-to-this-repo/ns3-exp/adr.cc root-of-ns3/ns-3.31/scratch

To run the ns-3 simulation:

cd root-of-ns3/ns-3.31
./waf --run adr

Multiple parameters can be set with the command:

./waf --run "adr --MType=Confirmed --intfrPowerdBm=-126 --nPeriods=72"

For more details, check the help function and the source code.

Multiple bash scripts are included in ./ns3-exp folder for running experiments.

Relax Optimization

We develop a relaxed problem and solve it optimally with SNOPT. The implementation is done in MATLAB using interfaces to SNOPT (in ./relaxOpt folder).

To compare the relaxed bound with the greedy solution under randomly placed end devices, first run algorithms with python3 ./alg/main.py, then the generated device locations, candidate gateway locations and necessary communication matrices are directly saved to ./relaxOpt.

Open MATLAB and run ./relaxOpt/relaxedOpt.m.

License

MIT