基于多维尺度的矿山无线传感器网络定位算法
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摘要
无线传感器网络(WSN)应用于具有复杂性,高度动态性以及无线信号的传播不确定性的矿井较为困难。为了获取地下矿井中大量矿工的位置,部署了无线传感器网络用于获取佩戴于矿工身上的无线移动节点的位置信息。引入了基于卡尔曼滤波估计以及弹性粒子的定位算法,用于节点间的距离测量,提高了动态环境下测距模型所获取的距离信息的精度;基于分簇机制减小网络计算复杂度,并使用了Ao A和余弦定理得到网络中无法直接连接的节点间的距离信息,通过簇之间的距离矩阵的整合得到最终网络的距离矩阵;使用多维尺度(MDS)定位算法重构出网络中所有移动节点的最终位置。实验结果表明,算法有效的减小了定位误差,适用于具有高度动态性与复杂结构的矿井无线传感器网络。
Wireless sensor networks( WSNs) are difficult to apply to mines with complex,high dynamic,and propagation uncertainties of wireless signals. In order to obtain the position of a large number of miners underground,this paper deployed a wireless sensor network to obtain the position information of wireless mobile nodes that are worn by miners. First,Kalman Filter estimation and localization algorithm based on Elastic Particles are introduced to measure the distance between nodes,which improves the accuracy of the distance information obtained by the ranging model under dynamic environment. Second,the clustering mechanism is used to reduce the computational complexity of the network,both of the AoA and the cosine theorem are used to obtain the distance information between nodes that cannot be directly connected in the network. The distance matrix of the final network is obtained through the integration of distance matrix between clusters. Last,a multidimensional scaling( MDS) localization algorithm is introduced to reconstruct the final position of all mobile nodes in the network. The experimental results show that the proposed algorithm can effectively reduce the localization errors and is suitable for wireless sensor networks in sub-surface mines with highly dynamic and complex structures.
Wireless sensor networks( WSNs) are difficult to apply to mines with complex,high dynamic,and propagation uncertainties of wireless signals. In order to obtain the position of a large number of miners underground,this paper deployed a wireless sensor network to obtain the position information of wireless mobile nodes that are worn by miners. First,Kalman Filter estimation and localization algorithm based on Elastic Particles are introduced to measure the distance between nodes,which improves the accuracy of the distance information obtained by the ranging model under dynamic environment. Second,the clustering mechanism is used to reduce the computational complexity of the network,both of the AoA and the cosine theorem are used to obtain the distance information between nodes that cannot be directly connected in the network. The distance matrix of the final network is obtained through the integration of distance matrix between clusters. Last,a multidimensional scaling( MDS) localization algorithm is introduced to reconstruct the final position of all mobile nodes in the network. The experimental results show that the proposed algorithm can effectively reduce the localization errors and is suitable for wireless sensor networks in sub-surface mines with highly dynamic and complex structures.
引文
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[23]王建平,徐恒,李奇越.基于卡尔曼滤波的矿井移动节点定位算法研究[J].电子测量与仪器学报,2013,27(2):120-126.WANG J P,XU H,LI Q Y.Research of algorithm for mobile node localization in mine based on Kalman filtering[J].Journal of Electronic Measurement&Instrument,2013,27(2):120-126.
[24]YIGITLER H,JANTTI R,PATWARI N.On lognormality of RSSI in narrowband receivers under static conditions[J].IEEE Signal Processing Letters,2017,24(4):367-371.
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[27]LIU Z Z,ZHANG Y J.MDS-MAP localization algorithm based on pseudo random code correlation detection[J].Journal of Electronic Measurement&Instrumentation,2017,31(2):245-250.
[2]薛光辉,王斌,沈博闻,等.无线传感器网络在煤矿设备状态监测中的应用研究[J].机电产品开发与创新,2017,30(2):87-88.XUE G H,WANG B,SHEN B W,et al.Application study of WSN in the condition monitorting of coal equipments[J].Development&Innovation of Machinery&Electrical Products,2017,30(2):87-88.
[3]乐志伟,王浩,谢小军.基于RSSI障碍势能矫正的定位算法[J].电子测量与仪器学报,2017,31(2):200-207.LE ZH W,WANG H,XIE X J.Localization algorithm based on RSSI obstacles potential energy correction[J].Journal of Electronic Measurement&Instrumentation,2017,31(2):200-207.
[4]吴中华.距离均衡的自组织无线传感器网络分簇算法[J].重庆师范大学学报(自然科学版),2017(1):73-77.WU ZH H.Equilibrium self-organizing wireless sensor network clustering algorithm[J].Journal of Chongqing Normal University(Natural Science Edition),2017(1):73-77.
[5]刘统玉,王纪强,孟祥军,等.面向矿山安全物联网的光纤传感器[J].工矿自动化,2018(3):1-7.LIU T Y,WANG J Q,MENG X J,et al.Optical fiber sensors for mine safety Io T[J].Industrial and Mining Automation,2018(3):1-7.
[6]方如举,王建平,孙伟.智能配电网无线传感器通信网络的跨层协作控制[J].电子测量与仪器学报,2018,32(2):128-136.FANG R J,WANG J P,SUN W.Cross layer cooperation control of wireless sensor communication network applied in smart distribution grid[J].Journal of Electronic Measurement and Instrumentation,2018,32(2):128-136.
[7]KIZIROGLOU M E,BOYLE D E,YEATMAN E M,et al.Opportunities for sensing systems in mining[J].IEEETransactions on Industrial Informatics,2016,13(1):278-286.
[8]ABRUDAN T E,KYPRIS O,TRIGONI N,et al.Impact of rocks and minerals on underground magneto-inductive communication and localization[J].IEEE Access,2016(4):3999-4010.
[9]AKKAS,M A.Using wireless underground sensor networks for mine and miner safety[J].Wireless Networks,2018,24(1):17-26.
[10]SONG M Z,QIAN J S.Improved sequence-based localization applied in coal mine[J].International Journal of Distributed Sensor Networks,2016,DOI:10.1177/1550147716669615.
[11]Abdelkader T,Naik K,Nayak A,et al.A performance comparison of delay-tolerant network routing protocols[J].IEEE Network,2016,30(2):46-53.
[12]DOHERTY L,PISTER K S J,GHAOUI L E.Convex position estimation in wireless sensor networks[C].20th Joint Conference of the IEEE Computer and Communications Societies.Proceedings,IEEE,2001:1655-1663.
[13]FENG X F,QI H B.Improvement and simulation for a localization based on APIT[C].International Conference on Computer Technology and Development,IEEE,2009:68-72.
[14]COSTA J A,PATWARI N,HERO A O.Distributed weighted-multidimensional scaling for node localization in sensor networks[J].Acm Transactions on Sensor Networks,2006,2(1):39-64.
[15]PEI Z,DENG Z,XU S,et al.Anchor-free localization method for mobile targets in coal mine wireless sensor networks[J].Sensors,2009,9(4):2836-2850.
[16]YI S,RUML W.Improved MDS-based localization[C].Joint Conference of the IEEE Computer and Communications Societies,2004:2640-2651.
[17]HU J F,CAO J,ZHAO Y,et al.A MDS-based localization algorithm for large-scale wireless sensor network[C].International Conference on Computer Design and Applications IEEE,2010:V2-566-V2-570.
[18]RAO W B,ZHU H,LU Z.An advanced distributed MDS-MAP algorithm for WSNs[C].International Conference on E-Business and Information System Security,IEEE,2009:1-5.
[19]ZUO X F,SHEN W J.Improved algorithm about mutishortest path problem based on floyd algorithm[J].Computer Science,2017,44(5):232-234.
[20]YU W J,WANG S C.A hierarchical MDS-based localization algorithm for wireless sensor networks[C].16th IST Mobile&Wireless Communications Summit,2007:1-5.
[21]SHON M,CHOI W,CHOO H.A cluster-based MDSscheme for range-free localization in wireless sensor networks[C].International Conference on Cyber-enabled Distrib,IEEE,2010.
[22]SHON M,JO M,CHOO H.An interactive cluster-based MDS localization scheme for multimedia information in wireless sensor networks[J].Computer Communications,2012,35(15):1921-1929.
[23]王建平,徐恒,李奇越.基于卡尔曼滤波的矿井移动节点定位算法研究[J].电子测量与仪器学报,2013,27(2):120-126.WANG J P,XU H,LI Q Y.Research of algorithm for mobile node localization in mine based on Kalman filtering[J].Journal of Electronic Measurement&Instrument,2013,27(2):120-126.
[24]YIGITLER H,JANTTI R,PATWARI N.On lognormality of RSSI in narrowband receivers under static conditions[J].IEEE Signal Processing Letters,2017,24(4):367-371.
[25]STEENDAM H,WANG T Q,ARMSTRONG J.CramerRao bound for AOA-based VLP with an aperture-based receiver[C].International Conference on Communications,IEEE,2017:1-6.
[26]FENG Q,LI B.Convolution theorem for fractional cosine:Sine transform and its application[J].Mathematical Methods in the Applied Sciences,2017,DOI:10.1002/mma.4251.
[27]LIU Z Z,ZHANG Y J.MDS-MAP localization algorithm based on pseudo random code correlation detection[J].Journal of Electronic Measurement&Instrumentation,2017,31(2):245-250.