Optimization of Computation Offloading for UAV-Assisted Intelligent Transportation Systems Considering Age of Information

ZHONG Weifeng; HUANG Xumin; KANG Jiawen; XIE Shengli

doi:10.11999/JEIT230459

Volume 46 Issue 3

Mar. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(3): 934-943

ZHONG Weifeng, HUANG Xumin, KANG Jiawen, XIE Shengli. Optimization of Computation Offloading for UAV-Assisted Intelligent Transportation Systems Considering Age of Information[J]. Journal of Electronics & Information Technology, 2024, 46(3): 934-943. doi: 10.11999/JEIT230459

Citation:

ZHONG Weifeng, HUANG Xumin, KANG Jiawen, XIE Shengli. Optimization of Computation Offloading for UAV-Assisted Intelligent Transportation Systems Considering Age of Information[J]. Journal of Electronics & Information Technology, 2024, 46(3): 934-943. doi: 10.11999/JEIT230459

Citation:

ZHONG Weifeng, HUANG Xumin, KANG Jiawen, XIE Shengli. Optimization of Computation Offloading for UAV-Assisted Intelligent Transportation Systems Considering Age of Information[J]. Journal of Electronics & Information Technology, 2024, 46(3): 934-943. doi: 10.11999/JEIT230459

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Optimization of Computation Offloading for UAV-Assisted Intelligent Transportation Systems Considering Age of Information

doi: 10.11999/JEIT230459 cstr: 32379.14.JEIT230459

School of Automation, Guangdong University of Technology, Guangzhou 510006, China

Funds: The National Natural Science Foundation of China (62003099, 62001125, 62102099), The Guangzhou Basic and Applied Basic Research Project (2023A04J1704, 2023A04J0340, 2023A04J1699)

Received Date: 2023-05-19
Rev Recd Date: 2023-09-27

Available Online: 2023-10-16

Publish Date: 2024-03-27

Abstract

Abstract

The intelligent transportation system that combines Unmanned Aerial Vehicle (UAV) based traffic monitoring and Mobile Edge Computing (MEC) technologies is considered. In order to ensure the timeliness of data and reduce energy consumption in the system, a UAV computation offloading optimization method considering Age of Information (AoI) is proposed. Firstly, the UAV-assisted MEC system model is established to allow the MEC server to cache commonly used applications and provide UAVs with computation offloading, which supports the UAVs to perform traffic monitoring tasks. By jointly optimizing UAV task offloading decisions, UAV uplink and downlink communication bandwidth allocation, and computing resource allocation of offloaded tasks, the total energy consumption of all UAVs and the MEC server is minimized while satisfying constraints of AoI and resource capacities. Secondly, the system energy consumption minimizing problem is a mixed-integer non-convex optimization problem. Discretization and linearization methods are adopted to quickly obtain an approximately optimal solution to the problem. A discrete point generation algorithm is designed to adjust the approximation error. Finally, simulation results show that even for large non-convex problems, the proposed method can quickly obtain approximately optimal solutions and can satisfy constraints of AoI in different task scenarios, minimizing the system energy consumption as much as possible. The simulation results verify the effectiveness of the proposed method.
- Mobile Edge Computing (MEC),
- Intelligent transportation system,
- Unmanned Aerial Vehicle (UAV),
- Age of Information (AoI),
- Energy consumption optimization

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References(25)

References

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