Xingzhen Bai, Maoyong Cao, Lu Liu, John Panneerselvam, Qiao‐Xi Sun
{"title":"Efficient Estimation and Control of WSANs for the Greenhouse Environment","authors":"Xingzhen Bai, Maoyong Cao, Lu Liu, John Panneerselvam, Qiao‐Xi Sun","doi":"10.1145/2996890.3007853","DOIUrl":null,"url":null,"abstract":"This paper investigates the collaborative estimation and control problem of wireless sensor and actuator networks (WSANs) in the greenhouse environments. In order to reduce the energy consumption of the sensor nodes, each node is designated to transmit data to the actuator nodes under the non-uniform transmission rate mode. Considering the mutual effect of related clusters, a collaborative control scheme of the actuator nodes is proposed to enhance the estimation accuracy and convergence speed. Combining the fuzzy neural network with the PID control algorithm, the actuators conduct reliable control over the greenhouse environmental parameters. Performance evaluation analysis on the greenhouse temperature behaviors are presented to demonstrate the effectiveness of our proposed scheme in controlling the greenhouse environmental changes.","PeriodicalId":350701,"journal":{"name":"2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2996890.3007853","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
This paper investigates the collaborative estimation and control problem of wireless sensor and actuator networks (WSANs) in the greenhouse environments. In order to reduce the energy consumption of the sensor nodes, each node is designated to transmit data to the actuator nodes under the non-uniform transmission rate mode. Considering the mutual effect of related clusters, a collaborative control scheme of the actuator nodes is proposed to enhance the estimation accuracy and convergence speed. Combining the fuzzy neural network with the PID control algorithm, the actuators conduct reliable control over the greenhouse environmental parameters. Performance evaluation analysis on the greenhouse temperature behaviors are presented to demonstrate the effectiveness of our proposed scheme in controlling the greenhouse environmental changes.