In this project, we've applied the subjective logic as a trust model in a VANet. We've used Mininet-wifi to simulate the subjective logic method we've created.
This project is created as part of a bachelor’s degree thesis at the University of Geneva in information system!
- Download Mininet-wifi : https://github.com/intrig-unicamp/mininet-wifi
- Go into Mininet-wifi directory
cd Mininet-wifi - Clone this repository
git clone https://gitlab.unige.ch/Ismet.Turhan/research_project - Go into the folder
cd research_project - Launch the topology
sudo python3 mytopo.py - Start your simulations !
The subjective logic is a trust model based on the Bayesian probabilities to ensure data accuracy through a network. It is based on a human interactions comparison and uses opinions as the main trust relation values. An opinion is based on four values the belief, disbelief, uncertainty and base rate. It is also possible to consider an opinion based on 3 values the count of good interaction, bad interaction and the base rate. From opinions, it is possible to derive a trust value which will be between 0 and 1, 0 being total distrust and 1 total trust.
To add new opinion simply use the library as following :
trust = sub_logic.SubLog(False, {'a':0.5, 'r':0, 's':1})
trust = sub_logic.SubLog(True, {'a':0.5, 'b':0.1, 'd':0.5, 'u': 0.4})
The Graph class represents a node's opinion storage. It provides methods for adding direct and indirect opinions, retrieving opinions, computing trust, and printing opinions.
Graph(my_node_id): Initializes a graph with the specified node ID.
add_direct_nodes(node_id, opinion): Adds direct opinions to the graph for the specified node ID.
add_indirect_nodes(opinion_of, opinion_on, opinion): Adds indirect opinions to the graph.
get_my_opinion(node_id): Retrieves the opinion of the current node about a specified node.
get_opinion_of(node_id, on_node): Retrieves the opinion about a node from another node.
compute_trust(node_id): Computes trustworthiness towards a specified node.
print_graph(): Prints the stored opinions in the graph.
get_my_nodes(): Retrieves a list of nodes for which the current node has opinions.
The SubLog class represents an opinion and provides methods for calculating trust, transitivity, and printing opinions.
SubLog(is_opinion, data): Initializes an opinion. If is_opinion is True, provide data for a, b, d, and u. If is_opinion is False, provide data for r, s, and a.
trust(): Calculates and returns the trust value based on the opinion.
transitivity(opinion1): Computes the transitivity of two opinions.
opinion_print_sr(): Returns a dictionary with opinion values in terms of good and bad interactions.
opinion_print_bdu(): Returns a dictionary with opinion values in terms of belief, disbelief, and uncertainty.
cumulative_fusion(opinion_1, opinion_2) The cumulative_fusion function combines two opinions using the cumulative fusion method.
To simulate the subjective logic, we provided 2 scripts with the same topology with 4 static nodes.
One topology is constituted with 4 good nodes
sudo python3 sim4n4g.py
The second topology contains 3 good nodes and 1 bad node
sudo python3 sim4n3g.py
By using the -s flag you can save the simulations result
Feel free to modify or create new topology by referring to the Mininet documentation : https://github.com/intrig-unicamp/mininet-wifi
#Author : Ismet Turhan