# Joint Task Offloading, Resource Allocation, and Security Assurance for Mobile Edge Computing-Enabled UAV-Assisted VANETs

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## Abstract

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## 1. Introduction

- We propose an MEC-enabled UAV-assisted VANET to provide vehicles with the low latency and reliable computing services through UAVs. Specifically, we adopt comprehensive task processing delay as the optimization objective by jointly considering the transmission model and the security assurance model from the vehicle to the MEC server on UAV, and the task computation model of the local vehicle and the edge UAV.
- We design a network optimization scheme by jointly considering the task offloading, the resource allocation, and the security assurance for VANETs. Moreover, to fully exploit the advantages of the MEC-enabled UAV-assisted VANET architecture, we use UAVs for boosting VANETs communications to improve VANETs’ computation ability. Furthermore, we propose an efficient iterative algorithm based the relax-and-rounding method and the Lagrangian method, which can effectively solve the joint optimization problem.
- We conduct simulations to evaluate the performance of our proposed scheme. The simulation results indicate that our designed MEC-enabled UAV-assisted VANET architecture is superior to the traditional ground-based VANETs. In addition, our proposed scheme can achieve significant performance superiority compared with other schemes in the successful task processing ratio and the task processing delay.

## 2. Related Works

#### 2.1. UAV-Assisted VANETs

#### 2.2. MEC-Enabled UAV-Assisted VANETs

#### 2.3. Synthesis

## 3. System Model and Problem Formulation

#### 3.1. Network Model

**Part 1:**The time ${T}_{i,j}^{1}$ that vehicle i moves from the starting point to the coverage of UAV j, i.e., ${T}_{i,j}^{1}={{\displaystyle \sum}}_{k=1}^{j-1}\frac{{L}_{k}}{{v}_{i}}$, ${L}_{k}\in \mathbf{L}$.

**Part 2:**The transmission time ${T}_{i,j}^{2}$ for vehicle i to offload the task ${\mathrm{\Xi}}_{i}$ to UAV j via a wireless channel.

**Part 3:**The time ${T}_{i,j}^{3}$ that for UAV j processes the task ${\mathrm{\Xi}}_{i}$.

#### 3.2. Transmission Model

#### 3.3. Task Computation Model

#### 3.4. Security Assurance Model

#### 3.5. Problem Formulation

## 4. Joint Task Offloading, Resource Allocation, and Security Assurance Algorithm

#### 4.1. Problem Transformation

**Lemma**

**1.**

#### 4.2. MEC Server Selection

**Lemma**

**2.**

#### 4.3. Joint Task Offloading and Resource Allocation

**Lemma**

**3.**

#### 4.3.1. Optimization of Resource Allocation

#### 4.3.2. Optimization of Task Offloading

#### 4.4. Overall Algorithm

Algorithm 1 The Proposed LBTO Algorithm. |

1: Initialization |

2: Set iterations $k=0$. |

3: Set ${\mathbf{x}}^{k}$, ${\mathbf{f}}^{k}$, and ${\lambda}^{k}$. |

4: repeat |

5: k = k+1. |

6: Based on ${\mathbf{f}}^{k-1}$ and ${\lambda}^{k-1}$, we can solve the optimization problem P3 by the constraint relaxation scheme and the relax-and-rounding method. |

7: until Obtain the feasible solution ${x}_{i,j}^{*}$. |

8: repeat |

9: According to the Lagrangian dual decomposition, we can solve the optimization problem P7 and P8. |

10: Calculate the optimal solution ${f}_{i,j}^{*}$ and ${\lambda}_{i,j}^{*}$. |

11: until Convergence. |

#### 4.5. Potential Applications

## 5. Simulation Results

#### 5.1. Simulation Parameters

#### 5.2. Simulation Results

## 6. Discussion

#### 6.1. Research Questions

#### 6.2. Research Limitations and Future Research Prospects

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

## Appendix B

## Appendix C

## References

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**Figure 1.**A mobile edge computing (MEC) architecture in unmanned aerial vehicle (UAV)-assisted vehicular ad hoc networks (VANETs).

**Figure 2.**Joint task offloading, resource allocation, and security assurance for MEC-enabled UAV-assisted VANETs.

**Figure 5.**Comparison of the task processing delay with respect to the local computation resources under different algorithms.

**Figure 6.**Comparison of the successful task processing ratio with respect to the local computation resources under different algorithms.

**Figure 7.**Comparison of the task processing delay with respect to the vehicle speed under different algorithms.

**Figure 8.**Comparison of the successful task processing ratio with respect to the vehicle speed under different algorithms.

**Figure 9.**Comparison of the successful task processing ratio with respect to the UAV flight altitude under different algorithms.

**Figure 10.**Comparison of the successful task processing ratio with respect to the number of vehicles under the number of MEC servers on UAVs.

**Figure 11.**Comparison of the successful task processing ratio with respect to the local computation resources under different task sizes.

**Figure 12.**Comparison of the successful task processing ratio with respect to the vehicle speed under different task intervals.

**Figure 13.**Comparison of the successful task processing ratio with respect to the number of vehicles under different task maximum allowed latencies.

Parameter | Value |
---|---|

Vehicle transmission power | 1 Watt |

UAV transmission power | 5 Watt |

Number of UAVs | 5 |

Number of vehicles | [0, 60] |

UAV computing resources | 20 GHz |

Vehicle computing resources | 1 GHz |

Task size | 10 MB |

Task interval | 30 s |

Task maximum allowed latency | 10 s |

Simulation times | 1000 |

No. | Research Questions. |
---|---|

1 | As the obstructions of vehicles can affect the service quality of vehicle-RSU links, how can we improve the connectivity of VANETs? |

2 | As the obstructions of vehicles can affect the service quality of vehicle-RSU links, how can we improve the connectivity of VANETs? |

3 | How do we minimize the task processing delay for MEC-enabled UAV-assisted VANETs by jointly considering the task offloading, the resource allocation, and the security assurance? |

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## Share and Cite

**MDPI and ACS Style**

He, Y.; Zhai, D.; Huang, F.; Wang, D.; Tang, X.; Zhang, R.
Joint Task Offloading, Resource Allocation, and Security Assurance for Mobile Edge Computing-Enabled UAV-Assisted VANETs. *Remote Sens.* **2021**, *13*, 1547.
https://doi.org/10.3390/rs13081547

**AMA Style**

He Y, Zhai D, Huang F, Wang D, Tang X, Zhang R.
Joint Task Offloading, Resource Allocation, and Security Assurance for Mobile Edge Computing-Enabled UAV-Assisted VANETs. *Remote Sensing*. 2021; 13(8):1547.
https://doi.org/10.3390/rs13081547

**Chicago/Turabian Style**

He, Yixin, Daosen Zhai, Fanghui Huang, Dawei Wang, Xiao Tang, and Ruonan Zhang.
2021. "Joint Task Offloading, Resource Allocation, and Security Assurance for Mobile Edge Computing-Enabled UAV-Assisted VANETs" *Remote Sensing* 13, no. 8: 1547.
https://doi.org/10.3390/rs13081547