{"title":"UAV-assisted wireless charging and data processing of power IoT devices","authors":"","doi":"10.1007/s00607-023-01245-y","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>To ensure the reliability and operational efficiency of the grid system, this paper proposes an unmanned aerial vehicle (UAV)-assisted Power Internet of Things (PIoT), which obtains real-time grid data through PIoT devices to support the management optimization of the grid system. Compared with traditional UAV-assisted communication networks, this paper enables data collection and energy transmission services for PIoT devices through UAVs. Firstly, the flight-hover-communication protocol is used. When the UAVs approach the target devices, they stop flying and remain hovering to provide services. The UAV selects full duplex mode in the hovering state, i.e., within the coverage area of the UAV, it can collect data from the target device while providing charging for other devices. Secondly, the UAVs can provide services to the required devices in sequence. Considering the priorities of the devices, both the data queue state and the energy pair state of network devices are considered comprehensively. Therefore, the optimization problem is constructed as a multi-objective optimization problem. First, the multi-objective optimization problem is transformed into a Markov process. Then, a multi-objective dynamic resource allocation algorithm based on reinforcement learning is proposed for solving the multi-objective optimization problem. The simulation results show that the proposed resource allocation scheme can effectively achieve a reasonable allocation of UAV resources, joint multi-objective optimization, and improved system performance.</p>","PeriodicalId":10718,"journal":{"name":"Computing","volume":"7 1","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s00607-023-01245-y","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
To ensure the reliability and operational efficiency of the grid system, this paper proposes an unmanned aerial vehicle (UAV)-assisted Power Internet of Things (PIoT), which obtains real-time grid data through PIoT devices to support the management optimization of the grid system. Compared with traditional UAV-assisted communication networks, this paper enables data collection and energy transmission services for PIoT devices through UAVs. Firstly, the flight-hover-communication protocol is used. When the UAVs approach the target devices, they stop flying and remain hovering to provide services. The UAV selects full duplex mode in the hovering state, i.e., within the coverage area of the UAV, it can collect data from the target device while providing charging for other devices. Secondly, the UAVs can provide services to the required devices in sequence. Considering the priorities of the devices, both the data queue state and the energy pair state of network devices are considered comprehensively. Therefore, the optimization problem is constructed as a multi-objective optimization problem. First, the multi-objective optimization problem is transformed into a Markov process. Then, a multi-objective dynamic resource allocation algorithm based on reinforcement learning is proposed for solving the multi-objective optimization problem. The simulation results show that the proposed resource allocation scheme can effectively achieve a reasonable allocation of UAV resources, joint multi-objective optimization, and improved system performance.
期刊介绍：
Computing publishes original papers, short communications and surveys on all fields of computing. The contributions should be written in English and may be of theoretical or applied nature, the essential criteria are computational relevance and systematic foundation of results.
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