{"title":"面向精准农业的无线传感器网络终端节点定位研究","authors":"Liying Zhang, Lina Zhao, Huixiu Li","doi":"10.1117/12.2673388","DOIUrl":null,"url":null,"abstract":"This paper proposes an improved wireless sensor network node location algorithm to locate terminal nodes of ZigBee wireless network in soybean farmland. The Gaussian data screening model was used to modify the measured distance of the received signal intensity, and the strategy of introducing variogram was adopted on the basis of standard particle swarm optimization. The advantage of each variogram was applied to the population in the process of algorithm search, so that the particles could jump out of the local optimal, ensure the global search traversal ability, and obtain the precise location of sensor nodes. The comparison between the standard particle swarm positioning algorithm without correction distance and the improved positioning algorithm in this paper shows that the improved positioning method has higher accuracy.","PeriodicalId":176918,"journal":{"name":"2nd International Conference on Digital Society and Intelligent Systems (DSInS 2022)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on terminal node localization in wireless sensor networks for precision agriculture\",\"authors\":\"Liying Zhang, Lina Zhao, Huixiu Li\",\"doi\":\"10.1117/12.2673388\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes an improved wireless sensor network node location algorithm to locate terminal nodes of ZigBee wireless network in soybean farmland. The Gaussian data screening model was used to modify the measured distance of the received signal intensity, and the strategy of introducing variogram was adopted on the basis of standard particle swarm optimization. The advantage of each variogram was applied to the population in the process of algorithm search, so that the particles could jump out of the local optimal, ensure the global search traversal ability, and obtain the precise location of sensor nodes. The comparison between the standard particle swarm positioning algorithm without correction distance and the improved positioning algorithm in this paper shows that the improved positioning method has higher accuracy.\",\"PeriodicalId\":176918,\"journal\":{\"name\":\"2nd International Conference on Digital Society and Intelligent Systems (DSInS 2022)\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2nd International Conference on Digital Society and Intelligent Systems (DSInS 2022)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2673388\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2nd International Conference on Digital Society and Intelligent Systems (DSInS 2022)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2673388","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on terminal node localization in wireless sensor networks for precision agriculture
This paper proposes an improved wireless sensor network node location algorithm to locate terminal nodes of ZigBee wireless network in soybean farmland. The Gaussian data screening model was used to modify the measured distance of the received signal intensity, and the strategy of introducing variogram was adopted on the basis of standard particle swarm optimization. The advantage of each variogram was applied to the population in the process of algorithm search, so that the particles could jump out of the local optimal, ensure the global search traversal ability, and obtain the precise location of sensor nodes. The comparison between the standard particle swarm positioning algorithm without correction distance and the improved positioning algorithm in this paper shows that the improved positioning method has higher accuracy.