Link quality prediction based on random forest
Link quality prediction is vital to the upper layer protocol design of wireless sensor networks.Selecting high quality links with the help of link quality prediction mechanisms can improve data transmission reliability and network communication efficiency.The Gaussian mixture model algorithm based o...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2019-04-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019025/ |
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| _version_ | 1841539378127568896 |
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| author | Linlan LIU Shengrong GAO Jian SHU |
| author_facet | Linlan LIU Shengrong GAO Jian SHU |
| author_sort | Linlan LIU |
| collection | DOAJ |
| description | Link quality prediction is vital to the upper layer protocol design of wireless sensor networks.Selecting high quality links with the help of link quality prediction mechanisms can improve data transmission reliability and network communication efficiency.The Gaussian mixture model algorithm based on unsupervised clustering was employed to divide the link quality level.Zero-phase component analysis (ZCA) whitening was applied to remove the correlation between samples.The mean and variance of signal to noise ratio,link quality indicator,and received signal strength indicator were taken as the estimation parameters of link quality,and a link quality estimation model was constructed by using a random forest classification algorithm.The random forest regression algorithm was used to build a link quality prediction model,which predicted the link quality level at the next moment.In different scenarios,comparing with exponentially weighted moving average,triangle metric,support vector regression and linear regression prediction models,the proposed prediction model has higher prediction accuracy. |
| format | Article |
| id | doaj-art-b677866a64f44465a014bfddc18d9537 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2019-04-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-b677866a64f44465a014bfddc18d95372025-01-14T07:16:49ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2019-04-014020221159726677Link quality prediction based on random forestLinlan LIUShengrong GAOJian SHULink quality prediction is vital to the upper layer protocol design of wireless sensor networks.Selecting high quality links with the help of link quality prediction mechanisms can improve data transmission reliability and network communication efficiency.The Gaussian mixture model algorithm based on unsupervised clustering was employed to divide the link quality level.Zero-phase component analysis (ZCA) whitening was applied to remove the correlation between samples.The mean and variance of signal to noise ratio,link quality indicator,and received signal strength indicator were taken as the estimation parameters of link quality,and a link quality estimation model was constructed by using a random forest classification algorithm.The random forest regression algorithm was used to build a link quality prediction model,which predicted the link quality level at the next moment.In different scenarios,comparing with exponentially weighted moving average,triangle metric,support vector regression and linear regression prediction models,the proposed prediction model has higher prediction accuracy.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019025/wireless sensor networklink quality predictionrandom forestlink quality level |
| spellingShingle | Linlan LIU Shengrong GAO Jian SHU Link quality prediction based on random forest Tongxin xuebao wireless sensor network link quality prediction random forest link quality level |
| title | Link quality prediction based on random forest |
| title_full | Link quality prediction based on random forest |
| title_fullStr | Link quality prediction based on random forest |
| title_full_unstemmed | Link quality prediction based on random forest |
| title_short | Link quality prediction based on random forest |
| title_sort | link quality prediction based on random forest |
| topic | wireless sensor network link quality prediction random forest link quality level |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019025/ |
| work_keys_str_mv | AT linlanliu linkqualitypredictionbasedonrandomforest AT shengronggao linkqualitypredictionbasedonrandomforest AT jianshu linkqualitypredictionbasedonrandomforest |