SHU Jian, LIU Song, LIU Linlan, et al., “Research on Link Quality Estimation Mechanism for Wireless Sensor Networks Based on Support Vector Machine,” Chinese Journal of Electronics, vol. 26, no. 2, pp. 377-384, 2017, doi: 10.1049/cje.2017.01.013
Citation: SHU Jian, LIU Song, LIU Linlan, et al., “Research on Link Quality Estimation Mechanism for Wireless Sensor Networks Based on Support Vector Machine,” Chinese Journal of Electronics, vol. 26, no. 2, pp. 377-384, 2017, doi: 10.1049/cje.2017.01.013

Research on Link Quality Estimation Mechanism for Wireless Sensor Networks Based on Support Vector Machine

doi: 10.1049/cje.2017.01.013
Funds:  This work is supported by the National Natural Science Foundation of China (No.61363015, No.61262020, No.61501218, No.61501217), and the Key Research Foundation of Education Bureau of Jiangxi Province of China (No.GJJ150702).
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  • Corresponding author: LIU Linlan (corresponding author) was born in 1968. She is a professor and M.S. supervisor at the School of Information Engineering in Nanchang Hangkong University. Her main research interests include wireless sensor network and distributed system. (Email:liulinlan@nchu.edu.cn)
  • Received Date: 2014-07-22
  • Rev Recd Date: 2016-07-28
  • Publish Date: 2017-03-10
  • In the application of Wireless sensor networks (WSNs), effective estimation for link quality is a basic issue in guarantying reliable data transmission and upper network protocol performance. A link quality estimation mechanism is proposed, which is based on Support vector machine (SVM) with multi-class classification. Under the analysis of the wireless link characteristics, two physical parameters of communication, Receive signal strength indicator (RSSI) and Link quality indicator (LQI), are chosen as estimation parameters. The link quality is divided into five levels according to Packet reception rate (PRR). A link quality estimation model based on SVM with decision tree is established. The model is built on kernel functions of radial basis and polynomial respectively, in which RSSI, LQI are the input parameters. The experimental results show that the model is reasonable. Compared with the recent published link quality estimation models, our model can estimate the current link quality accurately with a relative small number of probe packets, so that it costs less energy consumption than the one caused by sending a large number of probe packets. So this model which is high efficiency and energy saving can prolong the network life.
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