{"title":"Genetic Algorithm Optimization of Sensor Placement for CO2 Concentration Observation","authors":"Tomoaki Matsuda;Shusuke Narieda","doi":"10.23919/comex.2024XBL0103","DOIUrl":null,"url":null,"abstract":"In our previous studies, we introduced a method for determining the optimal sensor placement of wireless sensor networks for monitoring indoor carbon dioxide (CO2) concentrations. This method, based on brute force, has proven to be accurate and reliable. However, the computational complexity increases exponentially with an increase in the number of sensors. Therefore, this study proposes a novel approach for optimal sensor node placement based on a genetic algorithm (GA) that offers a more efficient alternative to the brute force method. By utilizing the GA, we achieved optimal sensor placement with reduced computational complexity. To validate the effectiveness of our GA based method, we conducted numerical experiments using observed CO2 concentration. The results demonstrate that our proposed approach not only achieves optimal sensor placement but also maintains the accuracy of the observations.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":null,"pages":null},"PeriodicalIF":0.3000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10591722","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEICE Communications Express","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10591722/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In our previous studies, we introduced a method for determining the optimal sensor placement of wireless sensor networks for monitoring indoor carbon dioxide (CO2) concentrations. This method, based on brute force, has proven to be accurate and reliable. However, the computational complexity increases exponentially with an increase in the number of sensors. Therefore, this study proposes a novel approach for optimal sensor node placement based on a genetic algorithm (GA) that offers a more efficient alternative to the brute force method. By utilizing the GA, we achieved optimal sensor placement with reduced computational complexity. To validate the effectiveness of our GA based method, we conducted numerical experiments using observed CO2 concentration. The results demonstrate that our proposed approach not only achieves optimal sensor placement but also maintains the accuracy of the observations.
在之前的研究中,我们介绍了一种确定无线传感器网络最佳传感器位置的方法,用于监测室内二氧化碳(CO2)浓度。事实证明,这种基于蛮力的方法准确可靠。然而,随着传感器数量的增加,计算复杂度也呈指数增长。因此,本研究提出了一种基于遗传算法(GA)的优化传感器节点布置的新方法,为蛮力法提供了更有效的替代方案。通过利用遗传算法,我们在降低计算复杂度的同时实现了传感器的最佳布置。为了验证基于 GA 的方法的有效性,我们使用观测到的二氧化碳浓度进行了数值实验。结果表明,我们提出的方法不仅实现了传感器的最佳布置,还保持了观测结果的准确性。