Mobile Edge Computing Task Offloading Strategy Based on Parking Cooperation in the Internet of Vehicles

Due to the limited computing capacity of onboard devices, they can no longer meet a large number of computing requirements. Therefore, mobile edge computing (MEC) provides more computing and storage capabilities for vehicles. Inspired by a large number of roadside parking vehicles, this paper takes...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-06, Vol.22 (13), p.4959
Main Authors: Shen, Xianhao, Chang, Zhaozhan, Niu, Shaohua
Format: Article
Language:English
Subjects:
Collaboration
Communication
Computation offloading
Cooperation
Energy consumption
genetic algorithm
Genetic algorithms
Internet
Internet of Vehicles
mountain climbing algorithm
Mountains
moving edge calculation
Optimization algorithms
Parking
Quality of service
Queuing theory
roadside parking
Servers
Strategy
task collaborative offloading
Vehicles
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Summary:Due to the limited computing capacity of onboard devices, they can no longer meet a large number of computing requirements. Therefore, mobile edge computing (MEC) provides more computing and storage capabilities for vehicles. Inspired by a large number of roadside parking vehicles, this paper takes the roadside parking vehicles with idle computing resources as the task offloading platform and proposes a mobile edge computing task offloading strategy based on roadside parking cooperation. The resource sharing and mutual utilization among roadside vehicles, roadside units (RSU), and cloud servers (cloud servers) were established, and the collaborative offloading problem of computing tasks was transformed into a constraint problem. The hybrid genetic algorithm (HHGA) with a mountain-climbing operator was used to solve the multi-constraint problem, to reduce the delay and energy consumption of computing tasks. The simulation results show that when the number of tasks is 25, the delay and energy consumption of the HHGA algorithm is improved by 24.1% and 11.9%, respectively, compared with Tradition. When the task size is 1.0 MB, the HHGA algorithm reduces the system overhead by 7.9% compared with Tradition. Therefore, the proposed scheme can effectively reduce the total system cost during task offloading.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22134959