基于随机森林的链路质量预测

doi:10.11959/j.issn.1000-436x.2019025

Abstract

Abstract:

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.

Key words: wireless sensor network, link quality prediction, random forest, link quality level

CLC Number:

TP393

Linlan LIU,Shengrong GAO,Jian SHU. Link quality prediction based on random forest[J]. Journal on Communications, 2019, 40(4): 202-211.

Figures/Tables 19

References 17

[1]	蒋婵, 李陶深, 梁俊斌 . 移动低占空比无线传感器网络中低时延的数据持续性提高算法[J]. 通信学报, 2018,39(3): 53-62.
	JIANG C , LI T S , LIANG J B . Low-latency algorithm for improving data persistence in mobile low-duty-cycle wireless sensor network[J]. Journal on Communications, 2018,39(3): 53-62.
[2]	张策, 张霞, 李鸥 ,等. 不可靠链路下基于压缩感知的WSN数据收集算法[J]. 通信学报, 2016,37(9): 131-141.
	ZHANG C , ZHANG X , LI O ,et al. Compressive sensing based data gathering algorithm over unreliable links in WSN[J]. Journal on Communications, 2016,37(9): 131-141.
[3]	NOUHA B , LUCA M , HABIB Y ,et al. Radio link quality estimation in wireless sensor networks:a survey[J]. ACM Transactions on Sensor Networks, 2012,8(4): 688-722.
[4]	TANG Q , WEN H , ZHAO J ,et al. Load balanced and efficient data collection protocol for wireless sensor networks[J]. International Journal of High Performance Computing ＆ Networking, 2017,10(6): 463-473.
[5]	LIU D , CAO Z , ZHANG Y ,et al. Achieving accurate and real-time link estimation for low power wireless sensor networks[J]. IEEE/ACM Transactions on Networking, 2017,25(4): 2096-2109.
[6]	LU J , ZHU Y P , XU Z Q ,et al. A reliable wireless sensor network routing method for power transmission line monitoring[J]. Power System Technology, 2017,41(2): 644-650.
[7]	JAYASRI T , HEMALATHA M . Link quality estimation for adaptive data streaming in WSNs[J]. Wireless Personal Communications, 2017,94(3): 1543-1562.
[8]	BOANO C A , Zú?IGA M A , VOIGT T ,et al. The triangle metric:fast link quality estimation for mobile wireless sensor net-works[C]// International Conference on Computer Communication and Networks. 2010: 1-7.
[9]	SUN W , LU W , LI Q ,et al. WNN-LQE:wavelet-neural-network-based link quality estimation for smart grid WSNs[J]. IEEE Access, 2017,5: 12788-12797.
[10]	MI X , ZHAO H , ZHU J . Research on EWMA based link quality evaluation algorithm for WSN[C]// Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC). 2011: 757-759.
[11]	FONSECA R , GNAWALI O , JAMIESON K ,et al. Four bit wireless link estimation[C]// ACM HotNets. 2007: 1-6.
[12]	GOMES R D , QUEIROZ D V , LIMAFILHO A C ,et al. Real-time link quality estimation for industrial wireless sensor networks using dedicated nodes[J]. Ad Hoc Networks, 2017,59: 116-133.
[13]	SHU J , LIU S , LIU L ,et al. Research on link quality estimation mechanism for wireless sensor networks based on support vector machine[J]. Chinese Journal of Electronics, 2017,26(2): 377-384.
[14]	FARKAS K , HOSSMANN T , LEGENDRE F ,et al. Link quality prediction in mesh networks[J]. Computer Communications, 2008,31(8): 1497-1512.
[15]	SRINIVASAN K , DUTTA P , TAVAKOLI A ,et al. Understanding the causes of packet delivery success and failure in dense wireless sensornetworks[C]// International Conference on Embedded Networked Sensor Systems. 2006: 419-433.
[16]	BACCOUR N , PUCCINELLI D , VOIGT T ,et al. Overview of link quality estimation[M]. Berlin: SpringerPress, 2013.
[17]	赵腾, 王林童, 张焰 ,等. 采用互信息与随机森林算法的用户用电关联因素辨识及用电量预测方法[J]. 中国电机工程学报, 2016,36(3): 604-614.
	ZHAO T , WANG L T , ZHANG Y ,et al. Relation factor identification of electricity consumption behavior of users and electricity demand forecasting based on mutual information and random forests[J]. Proceedings of the CSEE, 2016,36(3): 604-614.

Metrics

Recommended 0

No Suggested Reading articles found!

参数	参数值
发送功率/dBm	31
信道	26
探测分组数量/个	30
分组发送速率/（个·秒^-1）	5
发送周期/ms	200
测试周期/s	10

实验场景	EWMA	Triangle	SVR	LR	RFR
校园广场	0.193 9	0.155 8	0.089 3	0.104 9	0.019 6
校园室内走廊	0.160 1	0.125 7	0.117 0	0.099 2	0.027 0
校园小树林	0.925 3	0.656 2	0.281 1	0.428 5	0.255 2
校园停车场	0.502 6	0.404 7	0.358 0	0.375 0	0.143 8

模型	平均预测时间/s
EWMA	0.002
Triangle	0.004
SVR	0.015
LR	0.005
RFR	0.023

Link quality prediction based on random forest

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 19

References 17

Related Articles 15

Metrics

Recommended 0

[1]	Yue ZHAO, Zan LI, Bing LI, Benjian HAO. TDoA localization-refused area analysis and node placement strategy research [J]. Journal on Communications, 2023, 44(1): 1-13.
[2]	Cuiran LI, Xuejie WANG, Jianli XIE, Anqi LYU. Routing algorithm for railway monitoring linear WSN based on improved PSO [J]. Journal on Communications, 2022, 43(5): 155-165.
[3]	Xiujuan DU, Lijuan WANG, Jingping LIU, Long JIN. Ranging localization method for nodes in underwater wireless sensor network based on zeroing neural dynamics [J]. Journal on Communications, 2022, 43(10): 177-185.
[4]	Aijing SUN, Shichang LI, Yicai ZHANG. WSN clustering routing algorithm based on PSO optimized fuzzy C-means [J]. Journal on Communications, 2021, 42(3): 91-99.
[5]	Zhijun WU,Shengyan ZHOU,Jin LEI. Proactive migration model of SWIM service based on situation awareness [J]. Journal on Communications, 2019, 40(8): 123-132.
[6]	Tianjing WANG,Xiuqin LI,Guangwei BAI,Hang SHEN. Multi-target localization algorithm based on adaptive grid in wireless sensor network [J]. Journal on Communications, 2019, 40(7): 197-207.
[7]	Xiaochen HAO,Ning YAO,Lixia XIE,Jiaojiao WANG,Liyuan WANG. Research on WSN lifetime optimization game algorithm combined power and channel [J]. Journal on Communications, 2019, 40(4): 62-70.
[8]	Xiaonian WU,Chuyun ZHANG,Runlian ZHANG,Yaping SUN. Clustering routing protocol based on improved PSO algorithm in WSN [J]. Journal on Communications, 2019, 40(12): 114-123.
[9]	Haoran LIU,Heyao ZHAO,Yujing DENG,Xingqi WANG,Rongrong YIN. Coverage control algorithm for wireless sensor networks based on non-cooperative game [J]. Journal on Communications, 2019, 40(1): 71-78.
[10]	Jian SHU,Manlan LIU,Yaqing SHANG,Yubin CHEN,Linlan LIU. Link quality prediction model based on Gaussian process regression [J]. Journal on Communications, 2018, 39(7): 148-156.
[11]	Shengli ZHOU,Canghong JIN,Lifa WU,Zheng HONG. Research on cloud computing users’ public safety trust model based on scorecard-random forest [J]. Journal on Communications, 2018, 39(5): 143-152.
[12]	Junbin LIANG,Xiang ZHOU,Taoshen LI. Energy saving proactive neighbor discovery algorithm in mobile low-duty-cycle wireless sensor network [J]. Journal on Communications, 2018, 39(4): 45-55.
[13]	Xiaochen HAO,Liyuan WANG,Jinshuo LIU,Lixia XIE,Wenhuan ZHANG. Resource allocation optimization algorithm based on double populations differential evolution in WSN [J]. Journal on Communications, 2018, 39(4): 68-75.
[14]	Tian WANG,Yang LI,Weijia JIA,Guojun WANG,Shaoliang PENG. Research progress of sensor-cloud security [J]. Journal on Communications, 2018, 39(3): 35-52.
[15]	Chan JIANG,Taoshen LI,Junbin LIANG. Low-latency algorithm for improving data persistence in mobile low-duty-cycle wireless sensor network [J]. Journal on Communications, 2018, 39(3): 53-62.