Reputation and Trust Approach for Security and Safety Assurance in Intersection Management System
Abstract
:1. Introduction
2. Related Work
2.1. VANETs
- Dynamic topology of the system. As described above, the VANETs should support communication between nodes in the conditions of their high mobility. For this reason, traditional authentication methods cannot be applied. Even though scientific papers propose solutions to these challenges, several issues remain unsolved [22].
- Time limits. The system safety depends on the time of data delivery and decision making based on the received data. To ensure safe operation, it is necessary to adhere to strict limits on the time of delivery, processing, and sending data, since the high dynamics of the system leaves critically little time for data processing.
- Network scale. Prospects for the development of VANETs imply a large number of nodes interacting with each other, and the number of nodes constantly changes. Safe and stable operation of the entire system requires a scalable network infrastructure that can operate in an ever-changing network topology.
- Ability to counter malicious network attacks. As in any network, VANETs are prone to malicious attacks. Since the functioning of VANETs is directly related to the participation of people, the implementation of an attack on VANETs can lead to critical consequences. Necessary mechanisms to counter attacks should include authentication procedures, non-repudiation, access control, privacy protection, confidentiality, integrity, and accessibility assurance. Classification of attacks to which AVs in VANETs are exposed is given in [5].
- Fault-tolerance. ITS collects and processes data obtained from vehicle sensors and uses it to optimize traffic and broadcast emergency and informational messages. The transmission of incorrect data due to a malfunctioning vehicle sensor, for example data on the speed or current location, can lead to traffic accidents and endanger other road users.
2.2. Trust and Reputation Models and Approaches in VANETs
3. Trust and Reputation Approach Description
3.1. Truth, Trust and Reputation Models
- Passive knowledge represents the current knowledge about the environment that is not the result of collaborative actions.
- Active knowledge is the knowledge gained in the process of collaborative actions.
- , where is the truth of data, received from agent ;
- , where is the reputation of the agent , calculated by agent ; and
- , where is the trust value to agent of agent .
3.1.1. Truth
3.1.2. Reputation
3.1.3. Trust
4. Empirical Study
4.1. Software Simulation
- The software testing ground is represented as matrix and is divided into square elementary sectors.
- The road map is known to all the AVs.
- There are only straight roads and their coordinates coincide with the coordinates of the elementary sectors located in the same row or column.
- Each road must belong to either vertical or horizontal type.
- m is the set of elementary sectors, based on a road map, and the planned start and final positions of the MAV.
- s is the speed; speed is understood as the amount of elementary sectors crossed by a MAV per one conditional discretized time segment.
- is the sequence of steps for the MAV to go through the planned path (calculated on the basis of m and s; one step is passed in one conditional discretized time segment).
- There is an array of reputation values of other MAVs for each discretized time segment t from the beginning of the interaction to the discretized time segment preceding the current interval : .
4.1.1. Experiments Setup
Movement
- Testing ground: elementary sectors; 4 lanes for AVs driving: 2 vertical (oncoming and passing), 2 horizontal (oncoming and passing); an example of the software testing ground is represented in the Figure 1.
- MAVs can drive in any direction within the roadway, according to the direction of the roads.
- On the testing ground, the probability of the appearance of new MAVs is given in advance, while the number of appearing MAVs is determined randomly.
- MAVs speed is constant and equal to 1.
- The testing ground is spatially limited.
- In the case more than one MAVs pretend to be in the same elementary section contemporaneously, the MAVs give way to each other, taking into account the maximization of the intersection capacity, as in Equation (13):
Simulation Organization
- The experiment was divided into four groups with the probability of a new vehicle appearance with the values equal to 0.25, 0.5, 0.75, and 1, respectively.
- Each experiment group had 1000 tests with a duration of 1000 discretized time segments.
Reputation Calculation
- The initial value of MAV reputation was set as 0.5.
- The probability that new MAV was a saboteur (could transmit bogus data) was 0.5.
- A saboteur, depending on the situation, could transmit either correct or incorrect data.
- The legitimate MAV also could transmit incorrect data due to technical failures; the probability of MAV technical fail occurrence was set as 10%.
- If the vehicle transmitted incorrect data, then its value was equal to 0, otherwise .
- The MAV was detected as saboteur if its reputation was equal to or less than 0.25; such a strict threshold meant that neither saboteurs nor legitimate MAVs with technical problems should not leave the road, because they can quickly cause traffic collapses and provoke fatal consequences.
Limitations
- The simulation was conducted in the traffic area without pedestrians.
- No vehicle had the priority except maximum intersection capacity value.
- No external obstacles were situated on the roads.
Results Validation
- To assess the results, four parameters were calculated: true positive (TP), false positive (FP), true negative (TN), and false negative (FN):
- -
- TP is the case when data transmitted by a MAV were bogus, and its MAV was classified by another group as a saboteur.
- -
- FP is the case when data transmitted by a MAV were correct and its MAV was classified by another group as a saboteur.
- -
- TN is the case when data transmitted by a MAV were correct and its MAV was classified by another group as legitimate.
- -
- FN is the case when data transmitted by a MAV were bogus and its MAV was classified by another group as legitimate.
4.1.2. Results
4.2. Physical Simulation
4.2.1. Description of the Physical Testing Ground
- single on-board computer;
- servos’ control module;
- two servos;
- video-camera;
- ultrasonic range finder (URF);
- wireless module; and
- power supply unit.
4.2.2. Physical Simulation Setup
4.3. Results
5. Discussion
6. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
References
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Number of the Iteration | Reputation Value | Time (s) |
---|---|---|
1 | 0.5 | 0 |
2 | 0.367 | 30 |
3 | 0.11 | 41 |
4 | - | - |
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Chuprov, S.; Viksnin, I.; Kim, I.; Marinenkov, E.; Usova, M.; Lazarev, E.; Melnikov, T.; Zakoldaev, D. Reputation and Trust Approach for Security and Safety Assurance in Intersection Management System. Energies 2019, 12, 4527. https://doi.org/10.3390/en12234527
Chuprov S, Viksnin I, Kim I, Marinenkov E, Usova M, Lazarev E, Melnikov T, Zakoldaev D. Reputation and Trust Approach for Security and Safety Assurance in Intersection Management System. Energies. 2019; 12(23):4527. https://doi.org/10.3390/en12234527
Chicago/Turabian StyleChuprov, Sergey, Ilya Viksnin, Iuliia Kim, Egor Marinenkov, Maria Usova, Eduard Lazarev, Timofey Melnikov, and Danil Zakoldaev. 2019. "Reputation and Trust Approach for Security and Safety Assurance in Intersection Management System" Energies 12, no. 23: 4527. https://doi.org/10.3390/en12234527