This repository contains the python code for randomly generating a wireless sensor network on a 2D plane, inserting a wormhole into the network, and a detection algorithm for identifying the affected sensors.
The detection approach was described in the following paper: Károly Harsányi, Attila Kiss, Tamás Szirányi: Wormhole detection in wireless sensor networks using spanning trees, 2018 IEEE International Conference on Future IoT Technologies (Future IoT).
The code was tested with:
- python 3.5 and 3.6
- matplotlib 2.2.3
- numpy 1.15.4
- networkx 2.2
- scipy 1.2.0
The following command deploys a wireless sensor network with random deployment and quasi-unit-disk-graph communication model into a 10x10 area. The number of sensors is 400 and the communication radius of the nodes is 1.2. The adversary's radio receivers have a radius of 0.6 and they are placed with at least 6 hop-distance between them. k and th (lambda) are the parameters of the detection algorithm (see the aforementioned article). Make_plot=True visualizes the results. If make_plot is set to False, the output is a simple confusion matrix.
python3 detect_wormhole.py --deployment_type=random --communication_model=QUDG --num_nodes=400 --comm_radius=1.2 --side_len=10 --wormhole_type=3 --wormhole_radius=0.6 --wormhole_min_dist=6 --k=7 --th=5 --make_plot=True
See
python3 detect_wormhole.py -h
for more information about the arguments
Figure 1 shows the classification results from the different root nodes, and Figure 2 shows the network layout with the wormhole nodes and the predicted wormhole nodes colored blue and pink respectively.
