# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles

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## Abstract

**:**

## 1. Introduction

#### 1.1. Clustering Algorithms

#### 1.2. Path Similarity

## 2. Related Works and Problems

#### 2.1. DSRC/WAVE

#### 2.2. Data Dissemination Scheme Based on Clustering and Probabilistic Broadcasting

_{int}interval which is a preset data transmission interval. Then, when the cluster member receives a disseminated message while the timer is running, it increments the counter t

_{c}. The transmission probability P is determined according to the numerical value of the counter t

_{c}as shown in Equation (5).

## 3. Proposed Method

#### 3.1. Term Definitions

#### 3.2. Path-Based Clustering Model

#### 3.2.1. Concept Definitions

#### 3.2.2. Use-Cases of PCDP

^{2}. In other words, there is a high possibility of deviation from the route in the city area. In such a scenario, for example, the following may occur:

#### 3.3. Clustering Algorithm for Applying the Path-based Clustering Model

#### 3.3.1. Path Distance Algorithm

_{n}, the i-th road ID on p

_{n}is r

_{ni}and the length of p

_{n}is l

_{n}, the algorithm for calculating the distance of the path is shown in Algorithm 1. Also, Algorithm 1 has a time complexity of O (n) because it computes the path distance using the finite n road information that each neighbor has.

Algorithm 1 Path distance algorithm | |

1. | Set i = 1, j = 1, d = 0 |

2. | For each r_{mi} and r_{nj} of p_{m}, p_{n} |

3. | If r_{mi} is the same as r_{nj}, then |

4. | Add 1 to i and j |

5. | Else then |

6. | Add 1 to i and d |

7. | Add (l_{m} − j) to d |

8. | Set d as d over max(l_{m}, l_{n}) |

Algorithm 2 Clustering algorithm with path distance | |

1. | Set b = 0, flg = false, parameter th, assign p |

2. | If there is a cluster head among neighbors, then |

3. | While not found proper cluster head and flg not set |

4. | For each $m\in \left\{neighbors\right\}$ |

5. | Calculate path distance d_{m} |

6. | If d_{m} < b, then |

7. | Set b = d_{m} |

8. | Set p = m |

9. | If b < th, then |

10. | Send cluster assignment request to p |

11. | Else then |

12. | Set timer t |

13. | Else then |

14. | Broadcast cluster head announcement |

1. | Sub procedure t: |

2. | If there is no d_{m} lower than th, then |

3. | Send cluster head announcement |

#### 3.3.2. Clustering Process

#### 3.3.3. Message Format for a Clustering Process

#### 3.4. Data Dissemination Protocol

#### 3.4.1. A Data Dissemination Algorithm

Algorithm 3 Data dissemination algorithm | |

Parameter m: received message, n: current node | |

1. | If n is in a cluster, then |

2. | If the source’s cluster ID $\ne $ n’s cluster ID, then |

3. | Set m’s source and cluster ID field to n’s and broadcast m |

4. | Else if n is the cluster head, then |

5. | Set m’s source to n’s and broadcast m |

6. | Else then |

7. | Drop m |

8. | Else then |

9. | Set m’s source to n’s and broadcast m |

_{m}is calculated as Equation (7).

_{l}is the length of the normal block. In other words, this means that the message is stored and transmitted while passing through two blocks. Therefore, more messages are propagated to other clusters, and the information propagation rate of the entire network can be increased.

#### 3.4.2. Message Format for a Data Dissemination Process

## 4. Results

#### 4.1. Simulation Environment

#### 4.2. Cluster Robustness Evaluation

#### 4.3. Overhead Evaluation

#### 4.4. Data Dissemination Rate Evaluation

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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Term | Definition |
---|---|

Unclustered node | Node not joined to any cluster |

Cluster head | A node that manages the cluster in a cluster |

Cluster member | A node belonging to a cluster but not a cluster head |

Path distance | Similarity distance between path information of each node |

Data dissemination rate | The percentage of messages disseminated across the network |

Survivability | The degree to which a cluster member can be maintained as a member of a cluster for a unit of time |

Data Dissemination | Delivering messages to the entire network so that as many nodes as possible receive |

Variable | Value | Unit |
---|---|---|

Area Size | 36,000 | m^{2} |

Grid Size | 1000 | m^{2} |

Size of Grid | 6 × 6 | |

Maximum Speed | 80 | km/h |

Number of Nodes Per Route | 5 | |

Number of Routes | 924 | |

Simulation Time | 18,000 | S |

Bitrate | 6 | Mbps |

Signal Strength | −20 | dBm |

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**MDPI and ACS Style**

Seo, M.; Lee, S.; Lee, S.
Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles. *Symmetry* **2019**, *11*, 260.
https://doi.org/10.3390/sym11020260

**AMA Style**

Seo M, Lee S, Lee S.
Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles. *Symmetry*. 2019; 11(2):260.
https://doi.org/10.3390/sym11020260

**Chicago/Turabian Style**

Seo, MinSeok, SeungGwan Lee, and Sungwon Lee.
2019. "Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles" *Symmetry* 11, no. 2: 260.
https://doi.org/10.3390/sym11020260