Bring Your Own Reputation: A Feasible Trust System for Vehicular Ad Hoc Networks
Abstract
:1. Introduction
2. Related Work
2.1. Decentralized Systems
2.2. Centralized Systems
2.3. Shortcomings in Current Proposals
2.4. Comparative Analysis of Schemes
3. Proposed System
3.1. System Assumptions
3.2. Reputation System
- Simple Summation: obtained by adding the number of positive and negative reviews separately. Reputation is the difference between the total number of positives and negatives.
- Average of ratings: consists in calculating the average of all ratings.
- Average weighted by factors: based on the calculation of a weighted average of all ratings, considering factors such as the time of the evaluation, distance, context, role of the node in the network, reliability of nodes, etc.
- Bayesian system: consists in the calculation of the reputation by updating of statistical functions such as binomial beta or multinomial probability density of Dirichlet. The updated value is obtained by the combination of the previous value with the new evaluation.
3.3. Roles of the Authorities GTA, CA, and CCO
3.4. Vehicular Decision Mechanism
4. Implementation of a Model
4.1. System Model
- Sybil attack: a single node attempts to create multiple identities or false pseudonyms to gain greater influence for their messages on the network.
- Newcomer attack: in case a new node can easily sign up to join the network, a malicious node can erase its past by registering as a new participant.
- Betrayal attack: before initiating its attack, the node behaves honestly in the network to achieve a reputation of high level.
- Inconsistency attack: the attacker tries to degrade the efficiency of the mechanism, by repeatedly switching its behavior between honest and dishonest.
- Bad-mouthing/Ballot Stuffing attack: nodes can provide intentionally incorrect ratings on other nodes in a positive or negative way, trying to influence their reputation on the network.
- Collusion attack: a group of nodes act cooperatively to create fake messages or influence the reputation of other nodes.
4.2. Description of the Restricted Scenario
4.3. Description of the Manhattan-Grid Scenario
4.4. Reputation and Decision Mechanism
- Always Negative Decisions: the decision value will always be the lowest.
- Majority Voting: the decision value consists of the average of all recorded events that have been reported by other vehicles during the period of decision.
- Highest Reputation Level: the decision corresponds to the event reported by the vehicle with the highest reputation. If there is more than one vehicle with the highest reputation, the mechanism considers the average of them.
- Weighted Voting by Reputation: the decision value is calculated as the weighted average of the reputation and the event reported by each node during the period of decision.
- Always Positive decisions: the decision value will always be the highest.
4.5. Simulation Environment
4.6. Metrics Definition
5. Performance Analysis
5.1. Results of the Restricted Scenario
- R0D0: without reputation and always negative decisions;
- R1D1: without reputation and majority voting;
- R2D2: Simple Summation and highest reputation level;
- R2D3: Simple Summation and weighted voting;
- R3D2: Bayesian Inference with longevity and highest reputation level;
- R3D3: Bayesian Inference with longevity and weighted voting;
- R4D3: Bayesian Inference without longevity and weighted voting;
- R0D4: without reputation and always positive decisions.
5.2. Results of the Manhattan-Grid Scenario
- D1M1: low density and 10% of malicious nodes;
- D1M2: low density and 30% of malicious nodes;
- D1M3: low density and 50% of malicious nodes;
- D2M1: medium density and 10% of malicious nodes;
- D2M2: medium density and 30% of malicious nodes;
- D2M3: medium density and 50% of malicious nodes;
- D3M1: high density and 10% of malicious nodes;
- D3M2: high density and 30% of malicious nodes;
- D3M3: high density and 50% of malicious nodes.
5.3. Response of the Reputation Mechanisms
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Author | Administration | Reputation Mechanism | Privacy | Robustness | Network Impact | Vehicular Decision |
---|---|---|---|---|---|---|
Ostermaier et al. (2007) | Decentralized | Not Addressed | Not Addressed | Not Addressed | Not Addressed | Voting |
Raya et al. (2008) | Decentralized | Dempster-Shafer | Pseudonym | Not Addressed | Not Addressed | Data Centered |
Dötzer (2008) | Decentralized | Summation | Fixed Pseudonym | Not Addressed | Not Addressed | Voting |
Lo et al. (2009) | Decentralized | Summation | Not Addressed | Not Addressed | Not Addressed | Confidence Threshold |
De Paula (2010) | Decentralized | Discrete | Fixed Identity | Partially Addressed | Addressed | Voting |
Ding et al. (2010) | Decentralized | Fuzzy | Fixed Identity | Not Addressed | Not Addressed | Fuzzy |
Huang (2011) | Decentralized | Not Addressed | Pseudonym | Partially Addressed | Not Addressed | Data Centered |
Marmol et al. (2011) | Decentralized | Summation | Not Addressed | Partially Addressed | Not Addressed | Confidence Threshold |
Jalali (2011) | Decentralized | Fuzzy | Not Addressed | Not Addressed | Not Addressed | Confidence Threshold |
Fernandes (2013) | Decentralized | Bayesian Beta | Fixed Identity | Not Addressed | Addressed | Voting |
Yang (2013) | Decentralized | SummationSimilarity | Fixed Identity | Not Addressed | Not Addressed | Confidence Threshold |
Park et al. (2011) | Centralized | Summation | Fixed Identity | Partially Addressed | Not Addressed | Not Addressed |
Li et al. (2012) | Centralized | Summation | Pseudonym | Addressed | Addressed | Reputation Level |
Liao et al. (2013) | Centralized | Probabilistic Bayesian | Fixed Identity | Not Addressed | Not Addressed | Confidence Threshold |
Mühlbauer (2018) | Centralized | Summation or Probabilistic | Pseudonym | Addressed | Addressed | High Reputation or Voting |
Factor/Level | 0 | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|
Reputation Mechanism | Without Reputation | Without Reputation | Simple Summation | Bayesian Inference with Longevity | Bayesian Inference without Longevity |
Decision Mechanism | Always Negative Decisions | Majority Voting | Highest Reputation Level | Weighted Voting by Reputation | Always Positive Decisions |
Attack Types | ------ | Newcomer | Betrayal | Inconsistency | ------ |
Factor/Level | 1 | 2 | 3 |
---|---|---|---|
Malicious Nodes | 10% | 30% | 50% |
Vehicular Density | 20 vehicles | 60 vehicles | 100 vehicles |
Attack Types | Newcomer | Betrayal | Inconsistency |
Parameter | Value |
---|---|
Vehicle Size—Cars | 5 m |
Vehicle Size—Bus, Trucks | 10 m |
Minimum Distance Between Vehicles | 2 m |
Maximum Acceleration | 3 m/s2 |
Maximum Deceleration | 6 m/s2 |
Velocity of Vehicles | 12 m/s |
Distribution Variation of Velocity | Normal |
Sumo Speed Factor | 1 |
Sumo Speed Deviation | 0.5 |
Entry Probability of Cars | 90% |
Entry Probability of Bus, Trucks | 10% |
Results | Event Occurrence | ||
---|---|---|---|
Absence | Presence | ||
Decision | Negative | True Negative (TN) | False Negative (FN) |
Positive | False Positive (FP) | True Positive (TP) |
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Mühlbauer, R.; Kleinschmidt, J.H. Bring Your Own Reputation: A Feasible Trust System for Vehicular Ad Hoc Networks. J. Sens. Actuator Netw. 2018, 7, 37. https://doi.org/10.3390/jsan7030037
Mühlbauer R, Kleinschmidt JH. Bring Your Own Reputation: A Feasible Trust System for Vehicular Ad Hoc Networks. Journal of Sensor and Actuator Networks. 2018; 7(3):37. https://doi.org/10.3390/jsan7030037
Chicago/Turabian StyleMühlbauer, Ricardo, and João Henrique Kleinschmidt. 2018. "Bring Your Own Reputation: A Feasible Trust System for Vehicular Ad Hoc Networks" Journal of Sensor and Actuator Networks 7, no. 3: 37. https://doi.org/10.3390/jsan7030037