Cooperative Task Allocation in Edge Computing Assisted Vehicular Crowdsensing

被引:0
|
作者
Jiang, Yili [1 ,3 ]
Mang, Kuan [1 ]
Qian, Yi [1 ]
Hu, Rose Qingyang [2 ]
机构
[1] Univ Nebraska, Dept Elect & Comp Engn, Lincoln, NE 68588 USA
[2] Utah State Univ, Dept Elect & Comp Engn, Logan, UT 84322 USA
[3] Nanjing Univ Posts & Telecommun, Coll Commun & Informat Engn, Nanjing, Peoples R China
基金
美国国家科学基金会;
关键词
task allocation; cooperation; edge computing; vehicular crowdsening; PRIVACY;
D O I
10.1109/GLOBECOM46510.2021.9685136
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As a popular scenario of mobile crowdsensing, edge computing assisted vehicular crowdsensing (EVCS) encourages vehicles to participate in sensing data with the equipped devices. Due to the vehicular mobility, vehicles may dynamically enter and leave the coverage area of an edge node, leading to recurrent task allocations that consume excessive communication and computational resources. How to avoid recurring recruitment in task allocation is challenging. In this paper, we propose an optimization framework to facilitate task allocation by utilizing the cooperation between edge nodes. The proposed framework avoids complicated recruitment procedures while maximizing the connection time between the recruited vehicles and the edge node. Due to the NP-hardness of the formulated optimization problem, we design a reinforcement learning based algorithm to solve the problem with high accuracy and efficiency. Simulation results show the effectiveness of our proposed framework.
引用
收藏
页数:6
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