Using distributed self-fault diagnosis (DSFD) method, faults in wireless sensor networks (WSNs) can be easily detected. In this method, each sensor node collects its neighbourhood sensor node data and uses the statistical-based method for detecting its own fault status. Discussed various statistical-based method such as standard deviation, interquartile range, median absolute deviation (MAD), Sn and Qn scale estimator for detection of the fault in WSNs. The result of the experiment shows that standard deviation and interquartile range fails to detect the fault, if multiple nodes are faulty, while MAD, Sn and Qn scale estimator detects the fault even 20-30% of the nodes are faulty.

OS X , Windows & Linux:

• Clone the repository
• Install dependencies
  • pip3 install -r requirements.txt

modify the config.yaml file with your testing parameters then run python main.py. Go to resources folder to see the different file for more analysis. currently four different statistical method is supported. this repo is under development, you may see many updates in future.

from src.config import Config
from src.dsfd import DSFD

def main():
    config = Config("config.yaml")
    dsfd = DSFD(config)
    print(dsfd.run())

if __name__ == "__main__":
    main()

Read the paper. Read More.

1. Fork it (https://github.com/deepak7376/wsnFault/fork)
2. Create your feature branch (git checkout -b feature/fooBar)
3. Commit your changes (git commit -am 'Add some fooBar')
4. Push to the branch (git push origin feature/fooBar)
5. Create a new Pull Request