Multi-Leader Single-Follower Stackelberg Game Task Offloading and Resource Allocation Based on Selection Optimization in Internet of Vehicles
Mobile edge computing (MEC) provides a new solution to meet the latency-sensitive and computation-intensive application requirements of vehicle networks. Different from existing work, a novel multi-leader single-follower game model is designed, in which each leader can both compete and cooperate wit...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10136643/ |
| _version_ | 1797788507834417152 |
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| author | Yanqiang Li Lijuan Li Yang Xia Daifeng Zhang Yong Wang |
| author_facet | Yanqiang Li Lijuan Li Yang Xia Daifeng Zhang Yong Wang |
| author_sort | Yanqiang Li |
| collection | DOAJ |
| description | Mobile edge computing (MEC) provides a new solution to meet the latency-sensitive and computation-intensive application requirements of vehicle networks. Different from existing work, a novel multi-leader single-follower game model is designed, in which each leader can both compete and cooperate with each other. In the case of the leader, a profit maximization optimization problem is proposed. Since the problem is a convex function, there is a Nash equilibrium of the game. Each leader sets the optimal unit price by the amount of computational resources required by the follower. For the followers, a multi-objective optimization problem is formulated with the objective of minimizing the task processing delay and cost. Then, the task offloading and resource allocation based on selection optimization(TRSO) algorithm is proposed to achieve a tradeoff between latency and cost. Specifically, the computational resources are fixed and the Karush-Kuhn-Tucker (KKT) algorithm is used to jointly optimize the task division ratio and the bandwidth allocation ratio to minimize the task processing delay. In addition, the dichotomous method is used to optimize the computational resources of edge servers (ESs) under the task latency constraint so that the total cost is minimized. Simulation results confirm that the proposed TRSO is superior to the task offloading and resource allocation optimization (TRO) algorithm. Under the same delay, compared with TRO, the task processing cost of TRSO is reduced by approximately 83.3%, significantly reducing the overall task processing cost. |
| first_indexed | 2024-03-13T01:37:33Z |
| format | Article |
| id | doaj.art-22aca01ddd464fbf8fbc6dfaf5c610c7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T01:37:33Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-22aca01ddd464fbf8fbc6dfaf5c610c72023-07-03T23:00:18ZengIEEEIEEE Access2169-35362023-01-0111644306444110.1109/ACCESS.2023.328041210136643Multi-Leader Single-Follower Stackelberg Game Task Offloading and Resource Allocation Based on Selection Optimization in Internet of VehiclesYanqiang Li0Lijuan Li1https://orcid.org/0009-0002-3451-1151Yang Xia2Daifeng Zhang3Yong Wang4https://orcid.org/0009-0006-8236-7712Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaInstitute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaShandong University, Jinan, ChinaInstitute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaInstitute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaMobile edge computing (MEC) provides a new solution to meet the latency-sensitive and computation-intensive application requirements of vehicle networks. Different from existing work, a novel multi-leader single-follower game model is designed, in which each leader can both compete and cooperate with each other. In the case of the leader, a profit maximization optimization problem is proposed. Since the problem is a convex function, there is a Nash equilibrium of the game. Each leader sets the optimal unit price by the amount of computational resources required by the follower. For the followers, a multi-objective optimization problem is formulated with the objective of minimizing the task processing delay and cost. Then, the task offloading and resource allocation based on selection optimization(TRSO) algorithm is proposed to achieve a tradeoff between latency and cost. Specifically, the computational resources are fixed and the Karush-Kuhn-Tucker (KKT) algorithm is used to jointly optimize the task division ratio and the bandwidth allocation ratio to minimize the task processing delay. In addition, the dichotomous method is used to optimize the computational resources of edge servers (ESs) under the task latency constraint so that the total cost is minimized. Simulation results confirm that the proposed TRSO is superior to the task offloading and resource allocation optimization (TRO) algorithm. Under the same delay, compared with TRO, the task processing cost of TRSO is reduced by approximately 83.3%, significantly reducing the overall task processing cost.https://ieeexplore.ieee.org/document/10136643/Vehicular networksedge computingtask offloadingStackelberg gamemulti-objective optimization |
| spellingShingle | Yanqiang Li Lijuan Li Yang Xia Daifeng Zhang Yong Wang Multi-Leader Single-Follower Stackelberg Game Task Offloading and Resource Allocation Based on Selection Optimization in Internet of Vehicles IEEE Access Vehicular networks edge computing task offloading Stackelberg game multi-objective optimization |
| title | Multi-Leader Single-Follower Stackelberg Game Task Offloading and Resource Allocation Based on Selection Optimization in Internet of Vehicles |
| title_full | Multi-Leader Single-Follower Stackelberg Game Task Offloading and Resource Allocation Based on Selection Optimization in Internet of Vehicles |
| title_fullStr | Multi-Leader Single-Follower Stackelberg Game Task Offloading and Resource Allocation Based on Selection Optimization in Internet of Vehicles |
| title_full_unstemmed | Multi-Leader Single-Follower Stackelberg Game Task Offloading and Resource Allocation Based on Selection Optimization in Internet of Vehicles |
| title_short | Multi-Leader Single-Follower Stackelberg Game Task Offloading and Resource Allocation Based on Selection Optimization in Internet of Vehicles |
| title_sort | multi leader single follower stackelberg game task offloading and resource allocation based on selection optimization in internet of vehicles |
| topic | Vehicular networks edge computing task offloading Stackelberg game multi-objective optimization |
| url | https://ieeexplore.ieee.org/document/10136643/ |
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