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| 基于云模型的无线传感器网络链路质量的预测 |
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刘琳岚1,2, 谷小乐1,2, 刘松1,3, 舒坚1,3
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1.南昌航空大学物联网技术研究所, 江西 南昌 330063;2.南昌航空大学信息工程学院, 江西 南昌 3300633;3.南昌航空大学软件学院, 江西 南昌 330063
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| 摘要: |
| 无线传感器网络中,节点通过单跳或多跳传递信息.如能提前获知链路质量信息,为上层路由选择链路提供参考,是感知信息实时、准确地送达监控中心的基础.在分析现有基于智能学习链路质量预测方法的基础上,提出一种基于云模型的链路质量预测机制.通过收集不同场景下的链路质量样本,采用自适应高斯云变换对训练样本中的 RSSI(received signal strength indication),LQI(link quality indicator),SNR(received signal strength indication)及PRR(packet reception rate)进行链路划分;考虑到传感器节点的资源受限问题,采用 Apriori 算法对划分后的链路质量参数 RSSI,LQI,SNR 及 PRR 进行关联规则挖掘;在此基础上,基于三维云正向发生器预测链路质量.仿真结果表明,与基于 BP 神经网络的预测方法相比,提出的链路质量预测机制具有较高的预测精度. |
| 关键词: 无线传感器网络 链路质量预测 链路质量划分 高斯云变换 三维正向云 |
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| 基金项目:国家自然科学基金(61363015,61501217);江西省高等学校科技落地计划(KJLD14054) |
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| Cloud Model-Based Link Quality Prediction Model for Wireless Sensor Networks |
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LIU Lin-Lan1,2, GU Xiao-Le1,2, LIU Song1,3, SHU Jian1,3
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1.Internet of Things Technology Institute, Nanchang Hangkong University, Nanchang 330063, China;2.School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China;3.School of Software, Nanchang Hangkong University, Nanchang 330063, China
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| Abstract: |
| Message is delivered between nodes through single hop or multiple hops in wireless sensor networks(WSNs). Obtaining link quality information in advance which provides reference for the upper routing protocol to select link is the basis of delivering to the monitoring center accurately and in time. In this paper, a concrete analysis about the related works on WSNs link prediction based on intelligent learning is presented. A novel model, Cloud Model, is proposed to predict link quality. The large amounts of link quality samples are collected from different scenarios, and then adaptive gauss cloud transformation is applied to clustering training samples, such as RSSI, LQI, SNR and PRR. Taking the limit of node's resources into consideration, an Apriori algorithm is applied to mining the association rules from the RSSI, LQI, SNR and PRR which had been clustered. At last, three dimensional cloud model is employed in WSNs link prediction. Comparing with BP neural network prediction method, the proposed prediction model achieves higher accuracy as simulation experiment results show. |
| Key words: wireless sensor network link quality prediction link quality cluster adaptive Gauss cloud transformation three dimensional cloud model |
