基于半定规划的WSAN分布式定位技术研究
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摘要
无线传感与激励网络(Wireless Sensor and Actuator Network, WSAN)是一种在WSN(Wireless Sensor Network)中引入激励器节点(actuators)而形成的功能更加完善的网络。这种网络通过激励节点之间以及激励节点与传感节点之间的互相协同来完成对网络环境中的信息的感知、处理,并做出相应的反馈行为。在某种意义上,WSAN不仅能够对物理世界的信息进行感知,同时能够做出改变物理世界的行为。由于微电子技术、嵌入式技术及无线通信技术的发展,WSAN中节点的成本、功耗都有所降低,感知和传输能力大大提高,因此应用网络规模也越来越大。与此同时,WSAN的重要支撑技术——节点定位技术也面临基于新的网络特点的新挑战。结合WSAN网络的定位需求,基于对传统定位算法的分析研究,本文重点研究了在静态WSAN中基于半定规划的大规模网络分布式定位算法。
首先,简要介绍了WSAN网络架构、节点组成、网络特点以及WSAN定位技术面临的挑战,概述了基于到达时间等几种基础测距技术及三边定位算法等几种经典定位方法。其次,分析研究了传统定位分类方法中集中式和分布式的定位分类标准以及在此两种分类标准下的六种定位经典定位算法,通过原理分析和仿真实验验证了两类算法各自存在的优缺点。然后,讨论了在WSAN网络中有噪和无噪的情况下节点定位的半定规划(Semi-definite Programming, SDP)建模问题,并指出了半定规划的定位结果可能存在高秩性以及中心汇聚问题,据此分析了经典的半定规划求精算法——梯度搜索算法。接着,概括和总结了三类现有的大规模定位算法,并选取了三种分类中的典型算法进行理论分析及仿真比较,讨论了现有的大规模网络定位算法在定位精度、定位时间上受噪声、通信半径以及锚节点分布的影响及变化情况。通过三种算法的比较给出每种算法适用的定位环境。
最后,本文研究了一种新的边松弛方法,从不同角度对其在FSDP及SSDP算法中的性能进行了仿真验证。针对大规模WSAN定位问题中,基于SDP的分簇算法中部分簇会出现定位复杂度过高的问题,提出了一种新的基于边松弛的分簇定位算法—EES-Cluster。该算法通过对每一个网络簇子图进行边的松弛预处理,减少了边的数目,在网络分簇数目较少时,能有效降低定位过程的计算复杂度,同时较好地保持较高的定位精度,并减少簇头节点信息融合的功耗。通过仿真验证,EES-Cluster算法能有效降低分簇算法的复杂度,提高大规模WSAN的定位效率。
As the augment of actuators, wireless sensor and actuator network (WSAN) becomes a more powerful network compared with wireless sensor network (WSN). Through the coordination between the actuators, actuators and sensor nodes in these networks, the sensor nodes can detect the physical information in the around environment and send it to the right actuator nodes, then the actuator nodes will do corresponding action to the environment. In some sense, WSAN can not only perceive the physical world, but also change the physical world at the same time. As the development of microelectronics, embedded technology and wireless communication technology, the cost and power consumption of nodes have been greatly reduced, the perceiving and transmission capacity of nodes have been greatly improved, so the network size is becoming larger and larger. Meanwhile, as one of the key supporting technologies in WSAN, more challenges are faced for the positioning technology because of the new network features. Considering the localization requirements in WSAN and based on the study of the traditional location algorithm, this paper focuses on the research of distributed localization algorithms based on semi-definite programming (SDP) in the large-scaled static WSAN.
First of all, the network architecture and the characteristics of WSAN are briefly described, the challenges of WSAN localization techniques are introduced, an overview of several basic ranging technologies and positioning algorithms is provided. Secondly the detailed study of the centralized and distributed positioning classification criteria is performed. Six classical positioning algorithms under these two classification criteria are discussed and simulated to evaluate the performance of the two kinds of algorithms. Thirdly the SDP positioning models are established both in the noise and noiseless environment, a kind of classic SDP refinement algorithm called the gradient search method is analyzed, the high rank problem and the congregations toward the center problem of the SDP solution are discussed. Then three kinds of SDP based location methods are summarized for large-scaled network, three typical algorithms are discussed and tested to evaluate their performance under different impact factors such as different radio ranges or noise factors, corresponding application environments for these algorithms are discussed based on the simulation and analysis.
At last, a new edge sparsification method is studied, the performance evaluation of FSDP and SSDP algorithms based on this method is executed from different aspects. Aiming at the large-scaled net localization problem that the computational complexity of the cluster based SDP distributed localization algorithm in some clusters is high due to the non-uniformly clustering, a new distributed localization algorithm named EES-Cluster (Equivalent Edge Sparsification Cluster) is proposed. Based on the sparsification processing to each cluster diagram of the whole network, the number of edges is reduced. When the number of cluster is limited, this algorithm can effectively reduce the computation complexity in the localization process, at the same time it can keep the location accuracy, and decrease the power consumption in cluster header nodes. Simulation results and analysis show that EES-Cluster can effectively decrease the computation complexity, and improve the location efficiency of large-scaled WSAN.
首先,简要介绍了WSAN网络架构、节点组成、网络特点以及WSAN定位技术面临的挑战,概述了基于到达时间等几种基础测距技术及三边定位算法等几种经典定位方法。其次,分析研究了传统定位分类方法中集中式和分布式的定位分类标准以及在此两种分类标准下的六种定位经典定位算法,通过原理分析和仿真实验验证了两类算法各自存在的优缺点。然后,讨论了在WSAN网络中有噪和无噪的情况下节点定位的半定规划(Semi-definite Programming, SDP)建模问题,并指出了半定规划的定位结果可能存在高秩性以及中心汇聚问题,据此分析了经典的半定规划求精算法——梯度搜索算法。接着,概括和总结了三类现有的大规模定位算法,并选取了三种分类中的典型算法进行理论分析及仿真比较,讨论了现有的大规模网络定位算法在定位精度、定位时间上受噪声、通信半径以及锚节点分布的影响及变化情况。通过三种算法的比较给出每种算法适用的定位环境。
最后,本文研究了一种新的边松弛方法,从不同角度对其在FSDP及SSDP算法中的性能进行了仿真验证。针对大规模WSAN定位问题中,基于SDP的分簇算法中部分簇会出现定位复杂度过高的问题,提出了一种新的基于边松弛的分簇定位算法—EES-Cluster。该算法通过对每一个网络簇子图进行边的松弛预处理,减少了边的数目,在网络分簇数目较少时,能有效降低定位过程的计算复杂度,同时较好地保持较高的定位精度,并减少簇头节点信息融合的功耗。通过仿真验证,EES-Cluster算法能有效降低分簇算法的复杂度,提高大规模WSAN的定位效率。
As the augment of actuators, wireless sensor and actuator network (WSAN) becomes a more powerful network compared with wireless sensor network (WSN). Through the coordination between the actuators, actuators and sensor nodes in these networks, the sensor nodes can detect the physical information in the around environment and send it to the right actuator nodes, then the actuator nodes will do corresponding action to the environment. In some sense, WSAN can not only perceive the physical world, but also change the physical world at the same time. As the development of microelectronics, embedded technology and wireless communication technology, the cost and power consumption of nodes have been greatly reduced, the perceiving and transmission capacity of nodes have been greatly improved, so the network size is becoming larger and larger. Meanwhile, as one of the key supporting technologies in WSAN, more challenges are faced for the positioning technology because of the new network features. Considering the localization requirements in WSAN and based on the study of the traditional location algorithm, this paper focuses on the research of distributed localization algorithms based on semi-definite programming (SDP) in the large-scaled static WSAN.
First of all, the network architecture and the characteristics of WSAN are briefly described, the challenges of WSAN localization techniques are introduced, an overview of several basic ranging technologies and positioning algorithms is provided. Secondly the detailed study of the centralized and distributed positioning classification criteria is performed. Six classical positioning algorithms under these two classification criteria are discussed and simulated to evaluate the performance of the two kinds of algorithms. Thirdly the SDP positioning models are established both in the noise and noiseless environment, a kind of classic SDP refinement algorithm called the gradient search method is analyzed, the high rank problem and the congregations toward the center problem of the SDP solution are discussed. Then three kinds of SDP based location methods are summarized for large-scaled network, three typical algorithms are discussed and tested to evaluate their performance under different impact factors such as different radio ranges or noise factors, corresponding application environments for these algorithms are discussed based on the simulation and analysis.
At last, a new edge sparsification method is studied, the performance evaluation of FSDP and SSDP algorithms based on this method is executed from different aspects. Aiming at the large-scaled net localization problem that the computational complexity of the cluster based SDP distributed localization algorithm in some clusters is high due to the non-uniformly clustering, a new distributed localization algorithm named EES-Cluster (Equivalent Edge Sparsification Cluster) is proposed. Based on the sparsification processing to each cluster diagram of the whole network, the number of edges is reduced. When the number of cluster is limited, this algorithm can effectively reduce the computation complexity in the localization process, at the same time it can keep the location accuracy, and decrease the power consumption in cluster header nodes. Simulation results and analysis show that EES-Cluster can effectively decrease the computation complexity, and improve the location efficiency of large-scaled WSAN.
引文
[1]曹向辉.无线传感器/执行器网络的体系结构与算法研究.浙江大学博士学位论文,2011.
[2]Wark, T., Crossman, C., Hu, et al. The design and evaluation of a mobile sensor/actuator network for autonomous animal control. Information Processing in Sensor Networks, Cambridge,2007:206-215.
[3]C. J. Rozell and D. H. Johnson. Power scheduling for wireless sensor and actuator networks. Information Processing in Sensor Networks,2007:470-478.
[4]S. Kim, M. Kojima, M. Mevissen, et al. Exploiting sparsity in linear and nonlinear matrix inequalities via positive semidefinate matrix completion. Tokyo Institute of Technology, Tokyo,2009.
[5]P. Biswas, Y. Ye. Semidefinite programming for ad hoc wireless sensor network localization. Information Processing in Sensor Networks, Berkeley,2004:46-54.
[6]Zizhuo Wang, Song Zheng, et al. Further relaxations of the SDP approach to sensor network localization. Working paper, Dept of Management Science and Engineering, Stanford University, SIAM J. Opt,2008,19(2):655-673.
[7]Q. Shi and C. He. A SDP approach for range-free localization in wireless sensor networks. IEEE ICC'08, Beijing,2008:4214-4218.
[8]K. W. K. Lui, W. K. Ma, et al. Semi-definite programming algorithms for sensor network node localization with uncertainties in anchor positions and/or propagation speed. IEEE Transactions on Signal Processing,2009,57(2):752-763.
[9]周旭,李善仓,王新衍,大规模传感器网络局部半定规划的节点定位算法.西安交通大学学报,2009,43(8)：38-42.
[10]Hongyang Chen, Gang Wang, et al. Non-line-of-sight node localization based on semi-definite programming in wireless sensor networks. Transactions on Wireless Communications,2012,1(11):108-116.
[11]Haitao Bao, Saunders Ma, et al. Cluster based localization algorithm in wireless networks. Communication Systems,2012:458-462.
[12]郎为民.无线传感器与执行器网络(WSAN)研究.数据通信,2011：9-13.
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[18]Gang Wang. A semidefinite relaxation method for energy-based source localization in sensor networks. IEEE Transactions on Vehicular Technology,2011,60(5):2293-2296.
[19]Ferruccio Barsi, et al. Efficient location training protocols for heterogeneous sensor and actor networks. IEEE Transactions on Mobile Computing,2011,3(10):377-391.
[20]Hussain, M. Dynamic node placement for multi-hop localization in cluttered environments Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON),2011,8:167-169.
[21]Ian F., Akyildiz, et al. Wireless sensor and actor networks:Research challenges. Ad Hoc Networks Journal.2004,2(4):351-367.
[22]Martin Haenggi. Mobile sensor-actuator networks:Opportunities and challenges. Cellular neural networks and their applications,2002,7:283-290.
[23]赵文栋,彭来献,田畅.无线传感网络协作机制综述.计算机应用,2009,29(8)：2183-2187.
[24]朗为民,杨德鹏，等.无线传感器与执行器网络层次结构研究.技术交流,2011,8.
[25]朗为民,杨德鹏,等.无线传感器与执行器网络骨干网研究.技术交流,2011,7.
[26]Michael S. Pukish, et al. Recent developments in wireless hardware design, modeling, and analysis for industrial applications. IECON Annual Conference on IEEE Industrial Electronics Society,2012,38:6315-6319.
[27]符琦WSANs中的Sensor与Actor协调机制的研究.国防科学技术大学硕士学位论文,2006.
[28]Rezgui, A. Eltoweissy, et al. Service oriented sensor-actuator networks. IEEE Communications Magazine,2007,45(12):92-100.
[29]C.Wenjie, C.Lifeng, et al. A real time dynamic traffic control system based on wireless sensor network. International Conference Workshops in Parallel Processing (ICPP), 2005:258-264.
[30]A. R. Kulaib, R. M. Shubair. An overview of localization techniques for wireless sensor networks. International Conference on Innovations in Information Technology, 2011:166-173.
[31]汪炀.无线传感器网络定位技术研究.中国科学技术大学博士学位论文,2007.
[32]I. Stojmenovic. Handbook of sensor networks:algorithms and architectures. John Wiley and Sons, Oct.2005.
[33]Doherty L, Pister KSJ, Ghaoui LE. Convex position estimation in wireless sensor networks. IEEE Computer and Communications Societies,2001,3:1655-1663.
[34]赵朝丽.无线传感器网络分布式非测距算法研究.山东大学硕士学位论文,2008.
[35]Binwei Deng, Guangming Huang. Improved centroid localization algorithm in WSNs. International Conference on Intelligent System and Knowledge Engineering,2008,3: 1260-1265.
[36]Hongbin Tan, Feng Liu. Research and implementation of APIT positioning algorithm in WSN. International Conference on Fuzzy Systems and Knowledge Discovery,2012,9: 2212-2216.
[37]Carl Ellis and Mike Hazas. A comparison of MDS-MAP and non-linear regression. International Conference on Indoor Position and Indoor Navigation,2010.
[38]Lnaee Doheyrt, Luarent et al. Convex position estimation in Wireless Sensor Networks. IEEE Computer and Communications Societies,2001,4(3):1655-1663
[39]Peng Aiping, Guo Xiaosong, et al. A distributed localization scheme for wireless sensor networks based on Bounding-Box algorithm. International Conference on Electronic Measurement & Instruments,2009,9:2984-2989.
[40]Weihua Pan, Xiaodan Liu. Wireless sensor networks based on the DV-Hop localization algorithm. International Conference on Computational and Information Sciences,2012, 4:1073-1076.
[41]Dragos Niculescu, Badri Nath. DV based positioning in ad hoc networks. Journal of Telecommunication Systems,2003,22(NO.1/4):267-280.
[42]王建刚.无线传感器网络分布式节点定位算法研究.西北工业大学硕士学位论文,2006.
[43]Lnaee Doheyrt, Luarent, et al. Convex position estimation in wireless sensor networks. IEEE Computer and Communications Societies,2001,4(3):1655-1663.
[44]Tzu-Chen Liang, Ta-Chung Wang, Yinyu Ye. A gradient search method to round the semidefinite programming relaxation solution for ad hoc wireless sensor network localization. Technical report, Dept of Management Science and Engineering. Stanford university, April 2004.
[45]Zhi Li, Hui Chen, Jing Qian, Shouyin Liu. A distributed multi-coordinate localization algorithm for wireless sensor networks. International Symposiums on Information Processing,2008:573-577.
[46]何国钢.无线传感器网络分布式定位技术研究.西南交通大学硕士学位论文,2012.
[47]Biswas P, Ye Y. A distributed method for solving semidefinite programming arising from ad hoc wireless sensor network localization. Management Science and Engineering, Stanford University,2003.
[48]张品,徐智福,孙岩.一种新的基于簇头优化的WSN路由协议.传感技术学报,2009,22(7)：1013-1017.
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[51]Man-Cho So A, Ye Yinyu. Theory of semidefinite programming for sensor network localization. Math. Prog. Ser. B,2007,109(2):367-384.
[2]Wark, T., Crossman, C., Hu, et al. The design and evaluation of a mobile sensor/actuator network for autonomous animal control. Information Processing in Sensor Networks, Cambridge,2007:206-215.
[3]C. J. Rozell and D. H. Johnson. Power scheduling for wireless sensor and actuator networks. Information Processing in Sensor Networks,2007:470-478.
[4]S. Kim, M. Kojima, M. Mevissen, et al. Exploiting sparsity in linear and nonlinear matrix inequalities via positive semidefinate matrix completion. Tokyo Institute of Technology, Tokyo,2009.
[5]P. Biswas, Y. Ye. Semidefinite programming for ad hoc wireless sensor network localization. Information Processing in Sensor Networks, Berkeley,2004:46-54.
[6]Zizhuo Wang, Song Zheng, et al. Further relaxations of the SDP approach to sensor network localization. Working paper, Dept of Management Science and Engineering, Stanford University, SIAM J. Opt,2008,19(2):655-673.
[7]Q. Shi and C. He. A SDP approach for range-free localization in wireless sensor networks. IEEE ICC'08, Beijing,2008:4214-4218.
[8]K. W. K. Lui, W. K. Ma, et al. Semi-definite programming algorithms for sensor network node localization with uncertainties in anchor positions and/or propagation speed. IEEE Transactions on Signal Processing,2009,57(2):752-763.
[9]周旭,李善仓,王新衍,大规模传感器网络局部半定规划的节点定位算法.西安交通大学学报,2009,43(8)：38-42.
[10]Hongyang Chen, Gang Wang, et al. Non-line-of-sight node localization based on semi-definite programming in wireless sensor networks. Transactions on Wireless Communications,2012,1(11):108-116.
[11]Haitao Bao, Saunders Ma, et al. Cluster based localization algorithm in wireless networks. Communication Systems,2012:458-462.
[12]郎为民.无线传感器与执行器网络(WSAN)研究.数据通信,2011：9-13.
[13]P. Sikka, P. Corke, P. Valencia, et al. Wireless ad hoc sensor and actuator networks on the farm. Information Processing in Sensor Networks,2006:492-499.
[14]马建庆,钟亦平,张世永.ServLoc:无线传感反应网络的安全位置服务机制. Journal of Software,2008,19(10):2628-2637.
[15]Ismail Guneydas, Kemal Akaya, Ali Bicak. Actor positioning in wireless sensor and actor networks using matching theory. IEEE International Conference on Distributed Computing Systems Workshops,2009:1545-1550.
[16]Muazzam Ali Khan, Ghalib A., et al. An efficient and reliable clustering algorithm for wireless sensor actor networks. IEEE International Circuits and Systems (MWSCAS). 2010,53:332-338.
[17]Giacomo Ghidinit, Cristina M.Pinotti, et al. A semi-distributed localization protocol for wireless sensor and actor networks. IEEE International Conference on Pervasive Computing and Communications Workshops,2010,8:438-443.
[18]Gang Wang. A semidefinite relaxation method for energy-based source localization in sensor networks. IEEE Transactions on Vehicular Technology,2011,60(5):2293-2296.
[19]Ferruccio Barsi, et al. Efficient location training protocols for heterogeneous sensor and actor networks. IEEE Transactions on Mobile Computing,2011,3(10):377-391.
[20]Hussain, M. Dynamic node placement for multi-hop localization in cluttered environments Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON),2011,8:167-169.
[21]Ian F., Akyildiz, et al. Wireless sensor and actor networks:Research challenges. Ad Hoc Networks Journal.2004,2(4):351-367.
[22]Martin Haenggi. Mobile sensor-actuator networks:Opportunities and challenges. Cellular neural networks and their applications,2002,7:283-290.
[23]赵文栋,彭来献,田畅.无线传感网络协作机制综述.计算机应用,2009,29(8)：2183-2187.
[24]朗为民,杨德鹏，等.无线传感器与执行器网络层次结构研究.技术交流,2011,8.
[25]朗为民,杨德鹏,等.无线传感器与执行器网络骨干网研究.技术交流,2011,7.
[26]Michael S. Pukish, et al. Recent developments in wireless hardware design, modeling, and analysis for industrial applications. IECON Annual Conference on IEEE Industrial Electronics Society,2012,38:6315-6319.
[27]符琦WSANs中的Sensor与Actor协调机制的研究.国防科学技术大学硕士学位论文,2006.
[28]Rezgui, A. Eltoweissy, et al. Service oriented sensor-actuator networks. IEEE Communications Magazine,2007,45(12):92-100.
[29]C.Wenjie, C.Lifeng, et al. A real time dynamic traffic control system based on wireless sensor network. International Conference Workshops in Parallel Processing (ICPP), 2005:258-264.
[30]A. R. Kulaib, R. M. Shubair. An overview of localization techniques for wireless sensor networks. International Conference on Innovations in Information Technology, 2011:166-173.
[31]汪炀.无线传感器网络定位技术研究.中国科学技术大学博士学位论文,2007.
[32]I. Stojmenovic. Handbook of sensor networks:algorithms and architectures. John Wiley and Sons, Oct.2005.
[33]Doherty L, Pister KSJ, Ghaoui LE. Convex position estimation in wireless sensor networks. IEEE Computer and Communications Societies,2001,3:1655-1663.
[34]赵朝丽.无线传感器网络分布式非测距算法研究.山东大学硕士学位论文,2008.
[35]Binwei Deng, Guangming Huang. Improved centroid localization algorithm in WSNs. International Conference on Intelligent System and Knowledge Engineering,2008,3: 1260-1265.
[36]Hongbin Tan, Feng Liu. Research and implementation of APIT positioning algorithm in WSN. International Conference on Fuzzy Systems and Knowledge Discovery,2012,9: 2212-2216.
[37]Carl Ellis and Mike Hazas. A comparison of MDS-MAP and non-linear regression. International Conference on Indoor Position and Indoor Navigation,2010.
[38]Lnaee Doheyrt, Luarent et al. Convex position estimation in Wireless Sensor Networks. IEEE Computer and Communications Societies,2001,4(3):1655-1663
[39]Peng Aiping, Guo Xiaosong, et al. A distributed localization scheme for wireless sensor networks based on Bounding-Box algorithm. International Conference on Electronic Measurement & Instruments,2009,9:2984-2989.
[40]Weihua Pan, Xiaodan Liu. Wireless sensor networks based on the DV-Hop localization algorithm. International Conference on Computational and Information Sciences,2012, 4:1073-1076.
[41]Dragos Niculescu, Badri Nath. DV based positioning in ad hoc networks. Journal of Telecommunication Systems,2003,22(NO.1/4):267-280.
[42]王建刚.无线传感器网络分布式节点定位算法研究.西北工业大学硕士学位论文,2006.
[43]Lnaee Doheyrt, Luarent, et al. Convex position estimation in wireless sensor networks. IEEE Computer and Communications Societies,2001,4(3):1655-1663.
[44]Tzu-Chen Liang, Ta-Chung Wang, Yinyu Ye. A gradient search method to round the semidefinite programming relaxation solution for ad hoc wireless sensor network localization. Technical report, Dept of Management Science and Engineering. Stanford university, April 2004.
[45]Zhi Li, Hui Chen, Jing Qian, Shouyin Liu. A distributed multi-coordinate localization algorithm for wireless sensor networks. International Symposiums on Information Processing,2008:573-577.
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