文献类型： 外文期刊

作者： Li, Changming ¹ ; Yu, Baojun ² ; Su, Qianfu ³ ; Zhang, Hongchen ¹ ;

作者机构： 1.Changchun Guanghua Univ, Engn Technol R&D Ctr, Changchun 130033, Peoples R China

2.Jilin Univ, Sch Management, Changchun 130033, Peoples R China

3.Jilin Acad Agr Sci, Inst Plant Protect, Changchun 130033, Peoples R China

关键词： Allocation strategy; edge computing; unmanned aerial vehicle; wireless charging

期刊名称：IEEE ACCESS （ 影响因子：3.476； 五年影响因子：3.758 ）

ISSN： 2169-3536

年卷期： 2022 年 10 卷

页码：

收录情况： SCI

摘要： In recent years, the number of electronic devices and users has increased sharply with the rapid development and progress of electronic information technology. The motivation of this paper is to optimize the organization's resource allocation strategy in the Internet of Things (IoT) environment. The optimal path planning and information processing efficiency are improved through Unmanned Aerial Vehicle (UAV) technology and migration optimization algorithm. The research method is migration optimization algorithm and UAV dynamic network. The information processing capability of traditional wireless communication systems has gradually been unable to meet the actual information processing needs. Therefore, a static network migration algorithm is constructed based on multiple-user multilateral edge computing servers. It migrates each user's information processing task to the neighboring edge confidence processing server and uses the information processing server to perform auxiliary calculations. The simulation model adds a utility function that simulates energy consumption, delay weighting, and maximum extreme value, combined with the allocation strategy of optimizing each user's information processing task to achieve the optimization goal. The static network migration algorithm established in this simulation has better results than other benchmark algorithms. Both scenario 1 and scenario 2 in the simulation show very close performance to the optimal solution. Meanwhile, a migration algorithm that can provide wireless charging for UAVs is built by a dynamic edge computing model based on the time associated with the UAV base station and multiple end users. Combined with completing the information processing tasks in each time slot, the energy arrival is also non-directional. The dynamic network migration algorithm can optimize the number of tasks absorbed by the end-user based on the current online status of the system without knowing the global information. The optimized target equation is related to the queue stability, and the parameter V has a linear relationship with the queue backlog length. Here, the problem of computing migration is studied in Mobile Edge Computing (MEC). The results show that the utility function of the weighted sum has an approximately linear relationship with the weights. As the value of the utility function increases, so does the weight function. The optimal data throughput of the proposed model is 70,000 bits, while the optimal data throughput of the state-of-the-art model is 68,000 bits. Therefore, the data transmission performance of the model presented here is better than that of other models. MEC can be significantly improved the efficiency of organizational resource allocation. Combining UAV and wireless charging technology, the computing and communication resource allocation issues of the UAV's edge computing system are comprehensively discussed to improve the performance and efficiency of the network.