ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (12): 2662-2672.doi: 10.7544/issn1000-1239.2020.20190592

Previous Articles     Next Articles

Link Quality Estimator Based on Adaptive General Regression Neural Network

Shu Jian, Gao Su, Chen Yubin   

  1. (School of Software, Nanchang Hangkong University, Nanchang 330063)
  • Online:2020-12-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61962037, 61762065), the key program of the Natural Science Foundation of Jiangxi Province (20202BABL202039,20181BAB202015), and the Innovation Foundation for Postgraduate Student of Jiangxi Province (YC2018-S384).

Abstract: How to appropriately select link quality metrics and build a link quality estimation model with better performance, generalization capability, and lower time complexity is one of the key challenges in wireless sensor networks. We select M\-{CS}={μ,r,σ\+2}, where μ={μ\-{lqi}, μ\-{rssi},μ\-{snr}},r={r\-{lqi},r\-{rssi},r\-{snr}},σ\+2={σ\+2\-{lqi},σ\+2\-{rssi},σ\+2\-{snr}}, as link quality metric candidate set. A link quality metric selection algorithm based on wrapper is proposed, which employs adaptive general regression neural network (AGRNN) to evaluate the importance of link quality metric candidate subsets so as to select link quality metrics. Taking advantages of general regression neural network (GRNN) in classification and time complexity, this paper proposes a link quality estimation model based on AGRNN which assigns different smoothing factors to each link quality metric and adaptively modifies them by using back propagation. Indexes, such as accuracy, recall, generalization error and computing time, are used to evaluate the link quality estimation models. In scenarios of the indoor scenario, the park scenario and the motorway scenario, the experimental results show that the proposed link quality estimation model can achieve better performance, generalization capability, and lower time complexity, compared with link quality estimation models based on polynomial, support vector classifier and random forest respectively.

Key words: wireless sensor networks, link quality estimation, metric selection algorithm based on wrapper, adaptive general regression neural network

CLC Number: