ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2015, Vol. 52 ›› Issue (8): 1842-1851.doi: 10.7544/issn1000-1239.2015.20140670

• 网络技术 • 上一篇    下一篇



  1. (南昌航空大学软件学院 南昌 330063)(
  • 出版日期: 2015-08-01
  • 基金资助: 

Fuzzy Support Vector Regression-Based Link Quality Prediction Model for Wireless Sensor Networks

Shu Jian,Tang Jin,Liu Linlan,Hu Gang,Liu Song   

  1. (School of Software, Nanchang Hangkong University, Nanchang 330063)
  • Online: 2015-08-01

摘要: 在无线传感器网络中,链路是实现节点互连和多跳通信的基本元素,链路质量是拓扑控制、路由协议和移动管理的基础,准确的链路质量预测不仅可以提高整个网络的数据吞吐率,降低节点能耗,还可延长整个网络的工作时间.在分析现有链路质量预测方法的基础上,提出一种基于模糊支持向量回归机(fuzzy support vector regression, FSVR)的链路质量预测模型,以降低噪声与孤立点对预测性能的影响.通过收集不同场景下的链路质量样本,考虑不稳定链路中数据分布的特点,该模型采用无监督模糊核聚类算法(kernel fuzzy c-means, KFCM)自动划分样本集,并获得样本隶属度;采用混沌粒子群优化算法(chaos particle swam optimization, CPSO)选择子模型参数.实验结果表明,与基于经验风险的BP神经网络相比,基于模糊支持向量回归机的链路质量预测模型具有更好的预测精度和泛化能力.

关键词: 无线传感器网络, 链路质量预测, 支持向量回归机, 模糊核聚类, 混沌粒子群

Abstract: In wireless sensor networks (WSNs), link is a key element to achieve interconnects and multi-hop communication. Link quality is the fundamental of upper protocols, such as topology control, routing, and mobile management. The effective link quality prediction (LQP) can not only improve networks throughput and decrease node energy consumption, but also prolong network life time. In this paper, we give a concrete analysis about the related works on WSNs link quality prediction. A novel model, fuzzy support vector regression (FSVR), is proposed to predict link quality, which makes the impact of noise and outliers get high accuracy. The link quality samples are collected from three different scenarios. Taking the character of data distribution in unstable links into consideration, a kernel fuzzy c-means (KFCM) algorithm as an unsupervised learning algorithm, is applied to cluster the training set automatically in terms of partition coefficient and exponential separation (PCAES). The membership degree of samples is obtained to get fuzzy set for FSVR. The chaos particle swarm optimization (CPSO) algorithm is employed on each cluster in order to choose the suitable parameter combination for the model. The experimental results show that compared with the empirical risk-based BP neural network prediction methods, the proposed prediction model achieves higher accuracy and better generalization ability.

Key words: wireless sensor networks (WSNs), link quality prediction (LQP), support vector regression (SVR), kernel fuzzy c-means (KFCM), chaos particle swarm optimization (CPSO)