Xiaoyang Zhou;Jiaao Xu;Tingting Guo;Shouyang Wang;Benjamin Lev
{"title":"3-D Hover Location and Drone Routing Optimization for One-to-Many Continuous Wireless Charging Problem","authors":"Xiaoyang Zhou;Jiaao Xu;Tingting Guo;Shouyang Wang;Benjamin Lev","doi":"10.1109/TEM.2024.3487654","DOIUrl":null,"url":null,"abstract":"The Internet of Things presents a significant economic value potential, where sensors play a crucial role in its infrastructure. However, the restricted electricity of sensors limits the lifespan of the entire network. The development of modern charging technology has made it possible to realize simultaneous one-to-many charging using drones. To make better use of this charging technology, this article investigates the problem of charging sensors by drones in three-dimensional (3-D) space. Differing from the existing literature, the 3-D hovering location of the drone affects the number of sensors being charged simultaneously and consequently the service time of charging. Additionally, we consider the scenario where multiple drones serve sensors in the same area, necessitating the assurance of continuous charging services. We propose a mixed integer formulation model to minimize the total cost of completing the recharging task. To solve this problem, an improved genetic algorithm is developed. Extensive experiments are conducted to show the superiority of our proposed method, the effect of one-to-many wireless charging mode, and the sensitivities of the results to the parameters including charging radius and wind scale. This article contributes to further insights into the optimization of wireless charging strategies for sensor networks and other similar problems.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"29-46"},"PeriodicalIF":4.6000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Engineering Management","FirstCategoryId":"91","ListUrlMain":"https://ieeexplore.ieee.org/document/10737672/","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
The Internet of Things presents a significant economic value potential, where sensors play a crucial role in its infrastructure. However, the restricted electricity of sensors limits the lifespan of the entire network. The development of modern charging technology has made it possible to realize simultaneous one-to-many charging using drones. To make better use of this charging technology, this article investigates the problem of charging sensors by drones in three-dimensional (3-D) space. Differing from the existing literature, the 3-D hovering location of the drone affects the number of sensors being charged simultaneously and consequently the service time of charging. Additionally, we consider the scenario where multiple drones serve sensors in the same area, necessitating the assurance of continuous charging services. We propose a mixed integer formulation model to minimize the total cost of completing the recharging task. To solve this problem, an improved genetic algorithm is developed. Extensive experiments are conducted to show the superiority of our proposed method, the effect of one-to-many wireless charging mode, and the sensitivities of the results to the parameters including charging radius and wind scale. This article contributes to further insights into the optimization of wireless charging strategies for sensor networks and other similar problems.
期刊介绍:
Management of technical functions such as research, development, and engineering in industry, government, university, and other settings. Emphasis is on studies carried on within an organization to help in decision making or policy formation for RD&E.