基于WSN的定位跟踪关键技术研究
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摘要
无线传感器网络(Wireless Sensor Networks, WSN)节点定位技术是无线传感器网络的重要支撑技术之一,传感器节点的位置信息在无线传感器网络的诸多应用领域中扮演着重要的角色。本论文面向大型建筑室内外定位、导航服务的实际应用需求,针对无线传感器网络节点的自身定位问题进行了研究。在调研和分析已有的各种定位算法的基础上,从非测距定位方法、测距定位方法和移动节点的跟踪算法三个方面分别提出了基于自适应粒子群优化(Adaptive Particle Swarm Optimization, APSO)的WSN定位算法、基于差分似然估计(Difference Maximum Likelihood Estimation, DMLE)的WSN节点定位算法和基于粗糙神经网络的自适应多模交互式(Rough Set and Neural Network Adaptive Interacting Multiple Model, RSAIMM)的WSN跟踪算法。为了验证本论文提出算法的有效性,开发了大型建筑室内及周边定位原型系统,对本论文提出的算法进行了验证。
(1)基于APSO的WSN定位算法
针对无线传感器网络要求低成本、低功耗的要求,为了克服现有非测距WSN节点定位方法计算量大、定位精度受节点密度影响较大的缺点,本论文将粒子群理论引入非测距定位算法中,提出了一种基于自适应粒子群优化的WSN定位算法。仿真结果表明,该算法在不需要增加任何额外硬件设备和通信负荷的情况下,比DV-Hop算法定位精度提高了20%以上,定位精度受节点密度的影响与DV-Hop算法相比明显减小。
(2)基于DMLE的WSN节点定位算法
为了克服接收信号强度测量误差对无线传感器网络节点自身定位精度的影响,在对极大似然估计定位算法和接收信号强度指数(Received Signal Strength Indication, RSSI)模型分析的基础上,定义了个体差异差分系数、距离差分系数和距离差分定位方程,将测距差分修正和极大似然估计相结合提出了一种RSSI测距差分修正极大似然估计定位算法。仿真结果表明,该算法有效抑制了由于环境变化所引起的RSSI测量误差,定位精度可达2.5m以下。
(3)基于RSAIMM的WSN跟踪算法
为了克服复杂环境和运动模式对节点跟踪带来的影响,进一步提高跟踪精度,采用基于粗糙神经网络的双滤波器并行结构,提出了基于粗糙神经网络的自适应跟踪算法。当目标在机动和非机动之间变化时,粗糙神经网络在线自动输出匹配特征值,以足够准确的系统方差适应目标的运动变化并保持对目标状态的高精度跟踪。仿真结果表明,该算法与传统的多模交互式跟踪算法相比,跟踪精度提高了23.15%。
(4)大型建筑室内及周边定位原型系统
为了验证提出的定位、跟踪算法的有效性,给出了系统结构设计、功能设计和软件架构的整体方案,在此基础上,开发了面向定位跟踪应用的WSN验证平台,设计了客户服务终端、无线传感器网络节点以及系统服务器软件。原型系统定位、跟踪性能达到了预期的实验效果,证明了本论文提出定位算法的有效性,为下一步的研究奠定了基础。
Wireless sensor networks (WSN) nodes localization technology is one of the important supporting technology of WSN, and location information of sensor nodes plays an important role in many WSN application areas. Intended for location and navigation application on requirements of indoor and peripheral areas of large buildings, the issues of WSN node's own localization are presented in this paper. After research and analysis of a variety of existing localization algorithms, a WSN localization algorithm based on adaptive particle swarm optimization (APSO), WSN node localization algorithm based on difference maximum likelihood estimation(DMLE), and WSN tracking algorithm based on rough set and neural network adaptive interacting multiple model(RSAIMM) are put forward respectively, according to free ranging localization method, RSSI ranging localization method and tracking algorithm of moving node. Localization demonstration systems of indoor and peripheral areas of large building are developed to verify the validity of algorithms in this paper.
(l)WSN location algorithm based on APSO
In response to the requirement of WSN for low cost and low power consumption, and in order to overcome existing large scales of computing and location accuracy easily affected by the node density of existing non-ranging WSN node positioning method, the algorithm of particle swarm theory into the non-locating position is induced coming up with WSN localization algorithm based on APSO. Without additional hardware device and communication load, positioning accuracy of the new algorithm is improved more than20%than DV-Hop algorithm, and positioning accuracy affected by the node density is significantly reduced compared with the DV-Hop algorithm.
(2) Differential likelihood estimation localization algorithm based on DMLE
To overcome the effect of received signal strength measurement errors on WSN node itself positioning precision, and based on the foundation of maximum likelihood estimation positioning algorithm and RSSI model analysis, a RSSI ranging difference amendment Maximum likelihood estimation positioning algorithm is presented, by defined individual difference coefficient, distance difference coefficient and distance difference positioning equation, the combination difference amendment, and maximum likelihood estimation. The RSSI measurement error due to environmental changes and positioning accuracy, effectively inhibited in the algorithm, can reach within2.5m.
(3)WSN tracking algorithm based on RSAIMM
In order to overcome the effect of the complex environment and movement patterns on node tracking and further to increase tracking accuracy, adaptive tracking algorithm based on rough neural network introduced by adapting dual-filter parallel structures based on rough neural network. When the goals change between the motor and non-motor, rough neural network on-line can automatically output matching eigenvalues with the change of sufficient and accurate system variance matching target motion and high accuracy tracking of the target state maintained. The tracking accuracy of the algorithm can be improved by23.15%, compared with the traditional multi-mode interactive tracking algorithms.
(4) Indoor and peripheral areas of large building location prototype system
In order to verify the effectiveness of the proposed localization and tracking algorithm, the overall program of the system structure design, functional design and software architecture are developed. On this basis, a WSN verification platform for location and tracking applications is developed, by designing customer service terminals, nodes in wireless sensor networks, and server software. Prototype system's location and tracking performance has achieved the desired effect, proved the validity of the thesis proposed location algorithm, and laid the foundations for future study.
(1)基于APSO的WSN定位算法
针对无线传感器网络要求低成本、低功耗的要求,为了克服现有非测距WSN节点定位方法计算量大、定位精度受节点密度影响较大的缺点,本论文将粒子群理论引入非测距定位算法中,提出了一种基于自适应粒子群优化的WSN定位算法。仿真结果表明,该算法在不需要增加任何额外硬件设备和通信负荷的情况下,比DV-Hop算法定位精度提高了20%以上,定位精度受节点密度的影响与DV-Hop算法相比明显减小。
(2)基于DMLE的WSN节点定位算法
为了克服接收信号强度测量误差对无线传感器网络节点自身定位精度的影响,在对极大似然估计定位算法和接收信号强度指数(Received Signal Strength Indication, RSSI)模型分析的基础上,定义了个体差异差分系数、距离差分系数和距离差分定位方程,将测距差分修正和极大似然估计相结合提出了一种RSSI测距差分修正极大似然估计定位算法。仿真结果表明,该算法有效抑制了由于环境变化所引起的RSSI测量误差,定位精度可达2.5m以下。
(3)基于RSAIMM的WSN跟踪算法
为了克服复杂环境和运动模式对节点跟踪带来的影响,进一步提高跟踪精度,采用基于粗糙神经网络的双滤波器并行结构,提出了基于粗糙神经网络的自适应跟踪算法。当目标在机动和非机动之间变化时,粗糙神经网络在线自动输出匹配特征值,以足够准确的系统方差适应目标的运动变化并保持对目标状态的高精度跟踪。仿真结果表明,该算法与传统的多模交互式跟踪算法相比,跟踪精度提高了23.15%。
(4)大型建筑室内及周边定位原型系统
为了验证提出的定位、跟踪算法的有效性,给出了系统结构设计、功能设计和软件架构的整体方案,在此基础上,开发了面向定位跟踪应用的WSN验证平台,设计了客户服务终端、无线传感器网络节点以及系统服务器软件。原型系统定位、跟踪性能达到了预期的实验效果,证明了本论文提出定位算法的有效性,为下一步的研究奠定了基础。
Wireless sensor networks (WSN) nodes localization technology is one of the important supporting technology of WSN, and location information of sensor nodes plays an important role in many WSN application areas. Intended for location and navigation application on requirements of indoor and peripheral areas of large buildings, the issues of WSN node's own localization are presented in this paper. After research and analysis of a variety of existing localization algorithms, a WSN localization algorithm based on adaptive particle swarm optimization (APSO), WSN node localization algorithm based on difference maximum likelihood estimation(DMLE), and WSN tracking algorithm based on rough set and neural network adaptive interacting multiple model(RSAIMM) are put forward respectively, according to free ranging localization method, RSSI ranging localization method and tracking algorithm of moving node. Localization demonstration systems of indoor and peripheral areas of large building are developed to verify the validity of algorithms in this paper.
(l)WSN location algorithm based on APSO
In response to the requirement of WSN for low cost and low power consumption, and in order to overcome existing large scales of computing and location accuracy easily affected by the node density of existing non-ranging WSN node positioning method, the algorithm of particle swarm theory into the non-locating position is induced coming up with WSN localization algorithm based on APSO. Without additional hardware device and communication load, positioning accuracy of the new algorithm is improved more than20%than DV-Hop algorithm, and positioning accuracy affected by the node density is significantly reduced compared with the DV-Hop algorithm.
(2) Differential likelihood estimation localization algorithm based on DMLE
To overcome the effect of received signal strength measurement errors on WSN node itself positioning precision, and based on the foundation of maximum likelihood estimation positioning algorithm and RSSI model analysis, a RSSI ranging difference amendment Maximum likelihood estimation positioning algorithm is presented, by defined individual difference coefficient, distance difference coefficient and distance difference positioning equation, the combination difference amendment, and maximum likelihood estimation. The RSSI measurement error due to environmental changes and positioning accuracy, effectively inhibited in the algorithm, can reach within2.5m.
(3)WSN tracking algorithm based on RSAIMM
In order to overcome the effect of the complex environment and movement patterns on node tracking and further to increase tracking accuracy, adaptive tracking algorithm based on rough neural network introduced by adapting dual-filter parallel structures based on rough neural network. When the goals change between the motor and non-motor, rough neural network on-line can automatically output matching eigenvalues with the change of sufficient and accurate system variance matching target motion and high accuracy tracking of the target state maintained. The tracking accuracy of the algorithm can be improved by23.15%, compared with the traditional multi-mode interactive tracking algorithms.
(4) Indoor and peripheral areas of large building location prototype system
In order to verify the effectiveness of the proposed localization and tracking algorithm, the overall program of the system structure design, functional design and software architecture are developed. On this basis, a WSN verification platform for location and tracking applications is developed, by designing customer service terminals, nodes in wireless sensor networks, and server software. Prototype system's location and tracking performance has achieved the desired effect, proved the validity of the thesis proposed location algorithm, and laid the foundations for future study.
引文
[1]Ye W, H.J., Estrin D, Medium access control with coordinated adaptive sleeping for wireless sensor networks. IEEE/ACM Transactions on Networking,2004 .6. 12(3): p. 493-506.
[2]Dam T Van, L.K., An adaptive energy-efficient MAC protocol for wireless sensor networks, in The 1st International Conference on Embedded Networked Sensor Systems. 2003: California, USA. p. 171-180.
[3]曾胜,马晓茜,城市地下商业建筑中人员疏散模型的研究[J].建筑科学,2008.24(5)：p.27-32.
[4]崔喜红,李强等,基于多智能体技术的公共场所人员疏散模型研究[J].系统仿真学报,2008.20(4)：p.1006-1010.
[5]毕振颇,刘志勤,基于桥梁监控系统的无线传感器网络路由协议在NS2下的仿真[J].兵工自动化,2008.27(3)：p.241-247.
[6]赵展春,基于无线传感器网络的机房环境监控系统实现[J].计算机工程与设计,2008.29：p.454-458.
[7]黄进宏,左菲,曾明,一种基于能量优化的无线传感网络自适应组织结构和协议[J].电讯技术,2002(6)：p.118-121.
[8]王春雷,黄玉等,基于无线传感器网络的火灾监控系统设计与实现[J].计算机工程与设计,2007.28(10)：p.28-29.
[9]钱春丽,张兴敢,用于矿井环境监测的无线传感器网络[J].电子技术应用,2006(9)：p.123-126.
[10]陈平,网格化城市管理新模式[M].北京：北京大学出版社,2006.
[11]REEDJH, KRIZMANKJ, WOERNERBD, etal., An overiew of the challenges and progress in meeting the E-911 requriement for loeation sevrice. IEEE Cornrnunieations Magazine, 1998.36(4):p. 30-37.
[12]Eschenauer.L, G.V., A key-management scheme for distributed sensor networks, in The 9th ACM Conference on Computer and Communication Security. 2002: Washington, USA. p. 41-47.
[13]Perrig A, S.R., Wen V, et al, Security protocols for sensor networks, in The 7th Annual International Conference on Mobile Computing and Networks. 2001:Rome, Italy. p. 189-199.
[14]Madden S, F.M.J., Hellerstein J M, et al, The design of an acquisitional query processor for sensor networks, in ACM SIGMOD International Conference on Management of Data. 2003: San Diego, California. p. 491-502.
[15]Niculescu D, N.B., Trajectory based forwarding and its applications, in Annual International Conference on Mobile Computing and Networking(MOBICOM 2003). 2003,: San Diego, p. 260-272
[16]Deb B, B.S., Nath B, Information assurance in sensor networks, in International Workshop on Sensor Networks an Applications(WSNA). 2003: San Diego, USA. p. 160-168.
[17]Chachulski Szymon, J.M., Katti Sachin, et al, Trading structure for randomness in wireless opportunistic routing. ACM SIGCOMM Computer Communication Review, 2007. 37(4): p. 169-180.
[18]任维政,徐连明等,基于RSSI的测距差分修正定位算法[J].传感技术学报,2008.21(7)：p.1247-250.
[19]徐连明,任维政等,基于LBS终端GPS信息的准无损压缩算法[J].大连海事大学学报,2008.34(4)：p.11-15.
[20]任维政等,网格环境下基于免疫克隆策略的资源分析和任务调度[J].解放军理工大学学报,2008.9(5)：p.479-482.
[21]Weizheng Ren, Z.D., Lianming Xu,Yujia Zhu, Research of architecture for digital campus LBS in Pervasive Computing Environment[C].2008 3rd International Conference on Pervasive Computing and Applications(ICPCA08) (IEEE),2008: p. 473-478.
[22]刘美生,全球定位系统及其应用综述(三)----GPS的应用.中国测试技术,2007.33(1)：p.5-11.
[23]田金鹏,无线传感器网络节点定位技术研究[D].上海大学博士论文,2008.12.
[24]C, C, An energy efficient and delay sensitive centralized MAC protocol for wireless sensor networks. Computer Standards & Interfaces, 2008.1.30(12): p. 20-31.
[25]25. Carley T W, B.M.A., Barua R, et al, Contention-free periodic message scheduler medium access control in wireless sensor/actuator networks, in The 24th IEEE International Real-Time Systems Symp(RTSS 2003).2003: Cancun, USA. p. 298-307.
[26]TianHe.ChengduHuang, B.B., JohnA.Stankovie, TarekAbdelzaher, Range-Free Localization Schemes in Large Scale Sensor Networks, in the 9th annual international conference on Mobile Computing and Networking, I.P.o.t.t.a,i.co.M.C.a. Networking(MobiCom), Editor. 2003, ACMPress: SanDiego, California, USA. p. 81-95.
[27]B, N.D.N., DV based positioning in Ad Hoc networks, in Telecommunication System's. 2003, Springe: Berlin, p. 267-280.
[28]SPec: Smartdust chip with integrated RF communications. http://www.jlnlabs.com/jhill_cs/spec/. 2001.
[29]Bulusu N, E.D., Heidemann J., Tradeoffs in location support systems:The Case for quality-expressive location models for applications, in theUbieomp 2001 Workshop on Location Modeling for Applications 2001,ttp://lecs,cs.ucla.edu/-bulus/papers/ BulusuOld. pdf: Atlanta.
[30]L.Girod.V.Bvehovskiy, l.Elson.and D.Estrm.Locating tiny sensors in lime and space:a case study, in IEEE International Conference on Computer Design. VLSI in Computers and Processors(ICCD'0.). 2002, Germany IEEE Computer Society: Freiburg,. p. 214-218.
[31]A.H.P.S.A.W.P.W., The anatomy of aContext-aware application. Mobile Composing and Networking, 1999: p. 59-68.
[32]PinPoint, Corporation. Website, 2001. http://www.pinpointco.com.
[33]Estrin, L.G.a.D., Robust Range Estimation using Acoustic and Multimodal Sensing, in IEEE RSJ International Conference on Intelligent Robots and Systems, U.-I.C. Society, Editor. 2001:Mani,Hawaii. p. 1312-1320.
[34]Andrews Savvides, C.-C.H., Man, B.Srivastava, Dynamic Fine-Grained Localization in.Ad-Hoc Networks of Sensors. Mobile Computing and Networking, 2001. 7:p. 166-179.
[35]Nath, D.N.a.B., Ad Hoc Positioning System(APS)using AoA, in The 22nd Annual Joint Conference of the IEEE Computer And Communications Societies. 2003, CA,USA IEEE Computer and Communications Societies: San Francisco, p.1734-1743.
[36]Li., A.N.a.K., Adirectionality based location discovery scheme for wireless sensor networks, in the First ACM International Workshop on Wireless Sensor Networks and APPlication. 2002: Atlanta,Georgia. p.105-111.
[37]Seapahn Meguerdiehian, Sesa Slijepcevic, V.K.M.P., Localized Algorlthms in Wireless Ad-Hoc Networks:Location Discovery And Sensor Exposure, in the 2001 ACM International Symposium on Mobile Ad Hoc Networking & Computing. 2001, USAACMPress: Long Beach, p. 106-116.
[38]P.Bergamo.O.Maznni, Localization in Sensor Networks with Fading and Mobility, in Thel3th IEEE.International SymPosium On Personal, I.C. Soeiety, Editor. 2002: Lisbon, Portugal. p. 750-754.
[39]Savarese C, R.J.L.K., Robust Positioning algorithms for Distributed ad-hoc wireless sensor networks, in the USENIX Technical Annual Conf. 2002: Monterey, p. 317-327.
[40]NiniPamaBulusu, J.H.a.D.F., GPS-less Low Cost Outdoor Localization for Very Small Devices. IEEE Personal Communications, 2000 7(5):p. 28-34.
[41]D Nicolescu and B, N., DV based Positioning in ad-hoc networks. Telecommunication Systems, 2003. 22: p. 267-280.
[42]Rmnl, Y.S.W., Ying Zhang fourth ACM international symposium on ACM Press. 2003.6: p. 201-212.
[43]Karlof Chris, S.N., Wagner David, A link layer security architecture for wireless sensor networks, in ACM Conference on Embedded Networked Sensor Systems(SENSYS'O4). 2004: Maryland, USA. p. 162-175.
[44]Wood A, S.J., Denial of service in sensor networks. IEEE Computer, 2002. 35(10): p. 54-62.
[45]Xu Wenyuan, T.W., Zhang Yanyong, Channel surfing: Defending wireless sensor networks from interference, in International Conference on Information Processing in Sensor Networks(IPSN). 2007: Massachusetts, USA. p. 499-508.
[46]Li Mingyan, K.I., Poovendran Radha. Optimal jamming attacks and network defense policies in wireless sensor networks, in IEEE International Conference on Computer Communications(INFOCOM 2007). 2007: Alaska, USA. p.1307-1315.
[47]汪炀,无线传感器网络定位技术研究[D].中国科学技术大学博士论文,2007.
[48]Hu Y-C, P.A., Johnson D, A defense against wormhole attacks in wireless ad hoc networks, in IEEE International Conference on Computer Communications(INFOCOM2003). 2003: San Francisco, USA. p. 1976-1986.
[49]史龙,无线传感器网络自身定位算法研究[D].西北工业大学硕士论文,2005.6.
[50]Jian Ying, C.S., Zhang Zhan, et al, Protecting receiver-location privacy in wireless sensor network, in IEEE International Conference on Computer Communications(INFOCOM 2007).2007: Alaska, USA. p. 1955-1963.
[51]Kamat Pandurang, Z.Y., Trappe Wade, et al, Enhancing source-location privacy in sensor network routing, in The 25th IEEE International Conference on Distributed Computing Systems(ICDCS). 2005: Columbus, OH, USA. p. 599-608.
[52]52. Scott C-H Huang, D.D.-Z., New constructions on broadcast encryption and key pre-distribution schemes, in IEEE International Conference on Computer Communications(INFOCOM 2005). 2005:New York, USA. p. 515-523.
[53]马斌,一种基于Euclidean的无线传感器网络自身定位算法-Hop-Euclidean[D]电子科技大学硕士论文,2006.04.
[54]McCune Jonathan M, S.E., Perrig Adrian, et al., Detection of denial-of-message attacks on sensor network broadcasts, in IEEE Symposium on Security and Privacy(S&P). 2006: California, USA. p. 64-78.
[55]卫大鹏,无线传感器网络节点定位算法的研究[D].太原理工大学硕士论文,2008.05.
[56]宫召杰,无线传感器网络中的自身定位算法研究[D].中国海洋大学硕士论文,2006.04.
[57]孙庭波,无线传感器网络定位算法研究[D].中国科学技术大学博士论文,2008.04.
[58]郭明洁,无线传感网络自身定位算法研究[D].北京邮电大学硕士论文,2009.02.
[59]冯贤文,基于多维定标的无线传感器网络定位算法研究[D].北京邮电大学硕士论文,2008.03.
[60]石琴琴,无线传感器网络节点自定位系统及其算法研究[D].上海交通大学博士论文,2009.05.01.
[61]马万兴,无线传感器网络定位算法优化及定位精度评估[D],.北京邮电大学硕士论文,2009.02.
[62]高森,无线感应网-分布式定位算法研究[D].江南大学硕士论文,2008.03.
[63]白波,无线传感器网络节点自身定位算法的研究[D].东北大学硕士论文,2008.06.
[64]罗茜,无线传感器网络节点自定位算法研究[D].西南交通大学硕士论文,2008.05.
[65]王博,无线传感器网络节点定位及自定位研究[D].中北大学硕士论文,2009.05.
[66]郭昀,无线传感器网络中节点自定位系统和算法[D].华中科技大学硕士论文,2007.01.
[67]李春蓉,无线传感器网络自定位技术研究[D].西南交通大学硕士论文,2006.06.
[68]马兴传,无线传感器网络节点定位研究[D].江西理工大学硕士论文,2008.12.
[69]魏欣,电气设备在线监测的无线传感器网络定位研究[D].重庆大学硕士论文,2009.05.
[70]杨石磊,无线传感器网络中自定位算法的研究[D].中南大学硕士论文,2008.05.
[71]Kamat Pandurang, X.W., Trappe Wade, et al, Temporal privacy in wireless sensor networks, in International Conference onDistributed Computing Systems(ICDCS). 2007: Toronto, Canada, p. 426-428.
[72]张军,詹志辉等,计算智能[M].清华大学出版社,2009.
[73]陈兆乾,周志华等,神经计算研究现状及发展趋势,in http://cs.nju.edu.cn/-gchen/teaching/phdcourse/chenzq-paper.doc. 2009.
[74]王裔丹,制造链协同调度方法研究[D].沈阳工业大学硕士论文,2008.12.
[75]初红霞,智能决策支持系统中模型自动选择的研究[D],.哈尔滨工程大学硕士论文,2006.02.01.
[76]潘泽文,基于改进APSO-GRNN的区域物流需求预测研究[D].中南大学硕士论文,2009.06.
[77]刘志立,基于粒子群算法的宽带天线匹配网络研究[D].哈尔滨工程大学硕士论文,2009.01.01.
[78]朱泉同,人脑MR图像分割方法研究[D].南京信息工程大学硕士论文,2008.05.
[79]吕振肃,侯志荣,自适应变异的粒子群优化算法[J].电子学报,2004.33(3)：p.416-419.
[80]徐雅香,粒子群算法及在神经网络分类器中的应用[D].西安电子科技大学硕士论文,2008.05.01.
[81]刘侠,初.,王科俊,基于粒子群算法的证券组合投资模型的研究.商业研究,2006.08.25.
[82]Trelea I, The particle swarm optimization algorithm. Convergence analysis and parameter selection, 2003. 6(85):p. 317-325.
[83]Eberhart R, S.Y., Comparing inertia weights and construction factors in particle swarm optimization. IEEE Congress on Evolutionary Computation, 2000: p. 84-88.
[84]张丽平,粒子群算法的理论与实践[D].浙江大学博士学位论文,2005.6.
[85]田雨波,混合神经网络技术.科学出版社,2009.
[86]雷开友,粒子群算法及其应用研究[D].西南大学博士论文,2006.04.01.
[87]赵志刚,苏一丹,带自变异算子的粒子群优化算法[J].计算机工程与应用,2006.05.13(1)：p.45-47.
[88]M, H.T.H.C.D.B.B., Range free localization schemes in large scale sensor networks. 2003, Proceedings of the 9th Annual International Conference on Mobile Computing and Network.
[89]D, B.L.S.H.J.E.I., Gpsless low cost outdoor localization for very small devices. IEEE Personal Communications. 7(5): p. 28-34.
[90]E, D.T.L.P.K.S.G.I.L., Convex position estimation in wireless sensor networks. http://robotics, eecs. berkeley. edu/-elghaoui/pdffiles/In focom.pdf, 2009.
[91]T, D.C., A genetic algorithm for simultaneous localization and maping, in the 2003 IEEE International Conference on Robotics and Automation, P. of, Editor. 2003, IEEE: NewYork. p. 434-439.
[92]B, K.A.A.M.GV., Smiulated annealing based localization in wireless sensor network, in the 30th IEEE Conference on Local Computer Network's, P. of, Editor. 2005, IEEE: NewYork. p. 513-514.
[93]陈星舟,廖明宏,林建华,基于粒子群优化的无线传感器网络节点定位改进[J].计算机应用,2010.30(7)：p.1736-1738.
[94]赵仕俊,孙美玲,唐懿芳,基于遗传模拟退火算法的无线传感器网络定位算法[J].计算机应用与软件,2009.26(10)：p.189-192.
[95]王驭风,王岩,基于矢量和粒子群优化的传感器网络节点定[J]
[96]计算机应用,2009.29(1)：p.309-311.
[97]Ye F, L. H., Cheng J, et al, A two-tier data dissemination model for large-scale wireless sensor networks, in Annual International Conference on Mobile Computing and Networking(MOBICOM2002). 2002: Atlanta. p. 148-159.
[98]Liu X, H.Q.F., Zhang Y, Haystacks:Balancing push and pull for discovery in large-scale sensor networks, in ACM Conference on Embedded Networked Sensor Systems(SENSYS'O4).2004: Baltimore, Maryland. p. 122-133.
[99]Rajendran V, O.K., Garcia-Luna-Aceves J, Energy-efficient, Collision-free medium access control for wireless sensor networks, in The 1st International Conference on Embedded Networked Sensor Systems(SENSYS 2003). 2003:Los Angeles, USA. p. 181-192.
[100]Lu G, K.B., Raghavendra C, An adaptive energy-efficient and low-latency MAC for tree-based data gathering in wireless sensor networks. Wireless Communications and Mobile Computing, 2007.9. 7(7): p. 863-875.
[101]Muruganathan S D, M.D.C.F., Bhasin, R I, et al., A centralized energy-efficient routing protocol for wireless sensor networks. IEEE Communications Magazine, 2005.43((3)): p. 8-13.
[102]Lindsey S, R.C., Sivalingam K, Data gathering algorithms in sensor networks using energy metrics. IEEE Transactions on Parallel and Distributed Systems, 2002.9. 13(9): p. 924-935.
[103]Brack Jehoshua, G.J., Jiang Andrew, Medial axis based geometric routing in sensor networks, in Annual International Conference on Mobile Computing and Networking(MOBICOM2005).2005: Germany. p. 88-102.
[104]K. Pahlavan, P.K.a.J.B., Wideband radio propagation modeling for indoor geolocation applications. IEEE Comn. Mag,1998:p. 60-65.
[105]Ganesan, G.R., Shenker S, et al, Highly-resilient, energy-efficient multipath routing in wireless sensor networks. Mobile Computing and Communications Review, 2002. 1(2): p. 11-25.
[106]Deb B, B.S., Nath B, Reliable information forwarding using multiple paths in sensor networks, in The 28th Annual IEEE Conference on Local Computer Networks(LCN). 2003: Bonn, Germany, p. 406-415.
[107]Chen, K., Balakrishnan H, et al, An energy-efficient coordination algorithm for topology maintenance in Ad Hoc wireless networks. ACM Wireless Networks Journal, 2002. 8(5): p. 481-494.
[108]Mann, R.P., Namuduri, et al, Energy-aware routing protocol for ad hoc wireless sensor networks. Journal on Wireless Communications and Networking, 2005.5: p. 635-644.
[109]Cerpa, D.E., Adaptive self-configuring sensor networks topologies. IEEE Transactions on Mobile Computing, 2004.3(3): p. 272-285.
[110]陈维克等,基于RSSI的无线传感器网络加权质心定位算法[J].武汉理工大学学报,2006.30(2)：p.265-268.
[111]C, S.S.W.M.R., Power-Aware Routing in Mobile Ad hocNetworks[M]. New York: ACM Press, 1998: p. 181-190.
[112]Heinzelman W, C.A., Balakfishnan H, An Application Specific ProtocolArchitecture for Wireless Microsensor Networks[J]. IEEE Transactions on Wire Communications, 2002. 1(4): p. 660-670.
[113]Z, P., Rough set[J]. International Journal of Computer and Information Science, 1982. 11:p. 341-356.
[114]F, Z.Q.H.Z.W., A new approach for fault diagnosis in power systems based on rough set theory[A]. APSCOM, 1997: p. 597-602.
[115]Chessa S, S.P., Crash faults identification in wireless sensor networks[J]. Computer Communications, 2002. 25:p.1273-1282.
[116]Ta F E H, S.L., Fault diagnosis based on rough set theory[J]. Engineering Applications of Artificial Intelligence, 2003. 16(1): p. 39-43.
[117]Houseman L A, S.J.H., Hart J R, etal, PlantStar 2000: a plant-wide control platform for mineral sprocessing[J]. Minerals Engineering, 2001. 14(6): p. 593-600.
[118]戴晓强,刘维亭,基于模糊交互多模型的机动目标跟踪方法[J].弹箭与制导学报,2007.02.27(1)：p.34-37.
[119]朱志宇,基于模糊推理的自适应交互多模型目标跟踪算法[J].弹箭与制导学报,2008.28(1)：p.29-32.
[120]戴晓强,刘维亭,朱志宇,基于模糊自适应IMM算法的机动目标跟踪方法[J].船舶工程,2007.06.29(3)：p.1-4.
[121]敬忠良,徐宏,周雪琴,戴冠中,基于神经网络的机动目标信息融合与并行自适应跟踪[J].航空学报,1995.11.25.16(6)：p.716-711.
[122]张永胜,嵇成新,一种基于当前统计模型的模糊交互多模型算法[J].火力与指挥控 制,2003.28(1)：p.51-56.
[123]许浒,姜长生,辅小荣,基于粗神经网络的并行自适应滤波算法[J].盐城工学院学报(自然科学版),2005.18(1)：p.27-31.
[124]李德仁,朱欣焰,龚健雅,从数字地图到空间信息网格空间信息多级网格理论思考[J].武汉大学学报·信息科学版,2003.28(6)：p.642-649.
[125]董晓鲁,基于位置的服务在3G系统中的应用[J].电信网技术,2004.9(1)：p.62-68.
[126]学斌,程朋根,徐云,基于位置服务的关键技术与应用[J]江西科学,200523(1)：p.432-481.
[127]孙庆辉,骆剑承,周成虎,网格GIS数据信息发布的关键技术[J].地球信息科学,2004.6(1)：p.22-26.
[128]方金云,何建邦,网格GIS体系结构及其实现技术[J].地球信息科学,2010.11(4)：p.36-42.
[129]刘雪梅,柳永坡,顾国昌,0GSA2DAI框架模型的应用[J].大庆石油学院学报,2005.2(6)：p.112-114.
[2]Dam T Van, L.K., An adaptive energy-efficient MAC protocol for wireless sensor networks, in The 1st International Conference on Embedded Networked Sensor Systems. 2003: California, USA. p. 171-180.
[3]曾胜,马晓茜,城市地下商业建筑中人员疏散模型的研究[J].建筑科学,2008.24(5)：p.27-32.
[4]崔喜红,李强等,基于多智能体技术的公共场所人员疏散模型研究[J].系统仿真学报,2008.20(4)：p.1006-1010.
[5]毕振颇,刘志勤,基于桥梁监控系统的无线传感器网络路由协议在NS2下的仿真[J].兵工自动化,2008.27(3)：p.241-247.
[6]赵展春,基于无线传感器网络的机房环境监控系统实现[J].计算机工程与设计,2008.29：p.454-458.
[7]黄进宏,左菲,曾明,一种基于能量优化的无线传感网络自适应组织结构和协议[J].电讯技术,2002(6)：p.118-121.
[8]王春雷,黄玉等,基于无线传感器网络的火灾监控系统设计与实现[J].计算机工程与设计,2007.28(10)：p.28-29.
[9]钱春丽,张兴敢,用于矿井环境监测的无线传感器网络[J].电子技术应用,2006(9)：p.123-126.
[10]陈平,网格化城市管理新模式[M].北京：北京大学出版社,2006.
[11]REEDJH, KRIZMANKJ, WOERNERBD, etal., An overiew of the challenges and progress in meeting the E-911 requriement for loeation sevrice. IEEE Cornrnunieations Magazine, 1998.36(4):p. 30-37.
[12]Eschenauer.L, G.V., A key-management scheme for distributed sensor networks, in The 9th ACM Conference on Computer and Communication Security. 2002: Washington, USA. p. 41-47.
[13]Perrig A, S.R., Wen V, et al, Security protocols for sensor networks, in The 7th Annual International Conference on Mobile Computing and Networks. 2001:Rome, Italy. p. 189-199.
[14]Madden S, F.M.J., Hellerstein J M, et al, The design of an acquisitional query processor for sensor networks, in ACM SIGMOD International Conference on Management of Data. 2003: San Diego, California. p. 491-502.
[15]Niculescu D, N.B., Trajectory based forwarding and its applications, in Annual International Conference on Mobile Computing and Networking(MOBICOM 2003). 2003,: San Diego, p. 260-272
[16]Deb B, B.S., Nath B, Information assurance in sensor networks, in International Workshop on Sensor Networks an Applications(WSNA). 2003: San Diego, USA. p. 160-168.
[17]Chachulski Szymon, J.M., Katti Sachin, et al, Trading structure for randomness in wireless opportunistic routing. ACM SIGCOMM Computer Communication Review, 2007. 37(4): p. 169-180.
[18]任维政,徐连明等,基于RSSI的测距差分修正定位算法[J].传感技术学报,2008.21(7)：p.1247-250.
[19]徐连明,任维政等,基于LBS终端GPS信息的准无损压缩算法[J].大连海事大学学报,2008.34(4)：p.11-15.
[20]任维政等,网格环境下基于免疫克隆策略的资源分析和任务调度[J].解放军理工大学学报,2008.9(5)：p.479-482.
[21]Weizheng Ren, Z.D., Lianming Xu,Yujia Zhu, Research of architecture for digital campus LBS in Pervasive Computing Environment[C].2008 3rd International Conference on Pervasive Computing and Applications(ICPCA08) (IEEE),2008: p. 473-478.
[22]刘美生,全球定位系统及其应用综述(三)----GPS的应用.中国测试技术,2007.33(1)：p.5-11.
[23]田金鹏,无线传感器网络节点定位技术研究[D].上海大学博士论文,2008.12.
[24]C, C, An energy efficient and delay sensitive centralized MAC protocol for wireless sensor networks. Computer Standards & Interfaces, 2008.1.30(12): p. 20-31.
[25]25. Carley T W, B.M.A., Barua R, et al, Contention-free periodic message scheduler medium access control in wireless sensor/actuator networks, in The 24th IEEE International Real-Time Systems Symp(RTSS 2003).2003: Cancun, USA. p. 298-307.
[26]TianHe.ChengduHuang, B.B., JohnA.Stankovie, TarekAbdelzaher, Range-Free Localization Schemes in Large Scale Sensor Networks, in the 9th annual international conference on Mobile Computing and Networking, I.P.o.t.t.a,i.co.M.C.a. Networking(MobiCom), Editor. 2003, ACMPress: SanDiego, California, USA. p. 81-95.
[27]B, N.D.N., DV based positioning in Ad Hoc networks, in Telecommunication System's. 2003, Springe: Berlin, p. 267-280.
[28]SPec: Smartdust chip with integrated RF communications. http://www.jlnlabs.com/jhill_cs/spec/. 2001.
[29]Bulusu N, E.D., Heidemann J., Tradeoffs in location support systems:The Case for quality-expressive location models for applications, in theUbieomp 2001 Workshop on Location Modeling for Applications 2001,ttp://lecs,cs.ucla.edu/-bulus/papers/ BulusuOld. pdf: Atlanta.
[30]L.Girod.V.Bvehovskiy, l.Elson.and D.Estrm.Locating tiny sensors in lime and space:a case study, in IEEE International Conference on Computer Design. VLSI in Computers and Processors(ICCD'0.). 2002, Germany IEEE Computer Society: Freiburg,. p. 214-218.
[31]A.H.P.S.A.W.P.W., The anatomy of aContext-aware application. Mobile Composing and Networking, 1999: p. 59-68.
[32]PinPoint, Corporation. Website, 2001. http://www.pinpointco.com.
[33]Estrin, L.G.a.D., Robust Range Estimation using Acoustic and Multimodal Sensing, in IEEE RSJ International Conference on Intelligent Robots and Systems, U.-I.C. Society, Editor. 2001:Mani,Hawaii. p. 1312-1320.
[34]Andrews Savvides, C.-C.H., Man, B.Srivastava, Dynamic Fine-Grained Localization in.Ad-Hoc Networks of Sensors. Mobile Computing and Networking, 2001. 7:p. 166-179.
[35]Nath, D.N.a.B., Ad Hoc Positioning System(APS)using AoA, in The 22nd Annual Joint Conference of the IEEE Computer And Communications Societies. 2003, CA,USA IEEE Computer and Communications Societies: San Francisco, p.1734-1743.
[36]Li., A.N.a.K., Adirectionality based location discovery scheme for wireless sensor networks, in the First ACM International Workshop on Wireless Sensor Networks and APPlication. 2002: Atlanta,Georgia. p.105-111.
[37]Seapahn Meguerdiehian, Sesa Slijepcevic, V.K.M.P., Localized Algorlthms in Wireless Ad-Hoc Networks:Location Discovery And Sensor Exposure, in the 2001 ACM International Symposium on Mobile Ad Hoc Networking & Computing. 2001, USAACMPress: Long Beach, p. 106-116.
[38]P.Bergamo.O.Maznni, Localization in Sensor Networks with Fading and Mobility, in Thel3th IEEE.International SymPosium On Personal, I.C. Soeiety, Editor. 2002: Lisbon, Portugal. p. 750-754.
[39]Savarese C, R.J.L.K., Robust Positioning algorithms for Distributed ad-hoc wireless sensor networks, in the USENIX Technical Annual Conf. 2002: Monterey, p. 317-327.
[40]NiniPamaBulusu, J.H.a.D.F., GPS-less Low Cost Outdoor Localization for Very Small Devices. IEEE Personal Communications, 2000 7(5):p. 28-34.
[41]D Nicolescu and B, N., DV based Positioning in ad-hoc networks. Telecommunication Systems, 2003. 22: p. 267-280.
[42]Rmnl, Y.S.W., Ying Zhang fourth ACM international symposium on ACM Press. 2003.6: p. 201-212.
[43]Karlof Chris, S.N., Wagner David, A link layer security architecture for wireless sensor networks, in ACM Conference on Embedded Networked Sensor Systems(SENSYS'O4). 2004: Maryland, USA. p. 162-175.
[44]Wood A, S.J., Denial of service in sensor networks. IEEE Computer, 2002. 35(10): p. 54-62.
[45]Xu Wenyuan, T.W., Zhang Yanyong, Channel surfing: Defending wireless sensor networks from interference, in International Conference on Information Processing in Sensor Networks(IPSN). 2007: Massachusetts, USA. p. 499-508.
[46]Li Mingyan, K.I., Poovendran Radha. Optimal jamming attacks and network defense policies in wireless sensor networks, in IEEE International Conference on Computer Communications(INFOCOM 2007). 2007: Alaska, USA. p.1307-1315.
[47]汪炀,无线传感器网络定位技术研究[D].中国科学技术大学博士论文,2007.
[48]Hu Y-C, P.A., Johnson D, A defense against wormhole attacks in wireless ad hoc networks, in IEEE International Conference on Computer Communications(INFOCOM2003). 2003: San Francisco, USA. p. 1976-1986.
[49]史龙,无线传感器网络自身定位算法研究[D].西北工业大学硕士论文,2005.6.
[50]Jian Ying, C.S., Zhang Zhan, et al, Protecting receiver-location privacy in wireless sensor network, in IEEE International Conference on Computer Communications(INFOCOM 2007).2007: Alaska, USA. p. 1955-1963.
[51]Kamat Pandurang, Z.Y., Trappe Wade, et al, Enhancing source-location privacy in sensor network routing, in The 25th IEEE International Conference on Distributed Computing Systems(ICDCS). 2005: Columbus, OH, USA. p. 599-608.
[52]52. Scott C-H Huang, D.D.-Z., New constructions on broadcast encryption and key pre-distribution schemes, in IEEE International Conference on Computer Communications(INFOCOM 2005). 2005:New York, USA. p. 515-523.
[53]马斌,一种基于Euclidean的无线传感器网络自身定位算法-Hop-Euclidean[D]电子科技大学硕士论文,2006.04.
[54]McCune Jonathan M, S.E., Perrig Adrian, et al., Detection of denial-of-message attacks on sensor network broadcasts, in IEEE Symposium on Security and Privacy(S&P). 2006: California, USA. p. 64-78.
[55]卫大鹏,无线传感器网络节点定位算法的研究[D].太原理工大学硕士论文,2008.05.
[56]宫召杰,无线传感器网络中的自身定位算法研究[D].中国海洋大学硕士论文,2006.04.
[57]孙庭波,无线传感器网络定位算法研究[D].中国科学技术大学博士论文,2008.04.
[58]郭明洁,无线传感网络自身定位算法研究[D].北京邮电大学硕士论文,2009.02.
[59]冯贤文,基于多维定标的无线传感器网络定位算法研究[D].北京邮电大学硕士论文,2008.03.
[60]石琴琴,无线传感器网络节点自定位系统及其算法研究[D].上海交通大学博士论文,2009.05.01.
[61]马万兴,无线传感器网络定位算法优化及定位精度评估[D],.北京邮电大学硕士论文,2009.02.
[62]高森,无线感应网-分布式定位算法研究[D].江南大学硕士论文,2008.03.
[63]白波,无线传感器网络节点自身定位算法的研究[D].东北大学硕士论文,2008.06.
[64]罗茜,无线传感器网络节点自定位算法研究[D].西南交通大学硕士论文,2008.05.
[65]王博,无线传感器网络节点定位及自定位研究[D].中北大学硕士论文,2009.05.
[66]郭昀,无线传感器网络中节点自定位系统和算法[D].华中科技大学硕士论文,2007.01.
[67]李春蓉,无线传感器网络自定位技术研究[D].西南交通大学硕士论文,2006.06.
[68]马兴传,无线传感器网络节点定位研究[D].江西理工大学硕士论文,2008.12.
[69]魏欣,电气设备在线监测的无线传感器网络定位研究[D].重庆大学硕士论文,2009.05.
[70]杨石磊,无线传感器网络中自定位算法的研究[D].中南大学硕士论文,2008.05.
[71]Kamat Pandurang, X.W., Trappe Wade, et al, Temporal privacy in wireless sensor networks, in International Conference onDistributed Computing Systems(ICDCS). 2007: Toronto, Canada, p. 426-428.
[72]张军,詹志辉等,计算智能[M].清华大学出版社,2009.
[73]陈兆乾,周志华等,神经计算研究现状及发展趋势,in http://cs.nju.edu.cn/-gchen/teaching/phdcourse/chenzq-paper.doc. 2009.
[74]王裔丹,制造链协同调度方法研究[D].沈阳工业大学硕士论文,2008.12.
[75]初红霞,智能决策支持系统中模型自动选择的研究[D],.哈尔滨工程大学硕士论文,2006.02.01.
[76]潘泽文,基于改进APSO-GRNN的区域物流需求预测研究[D].中南大学硕士论文,2009.06.
[77]刘志立,基于粒子群算法的宽带天线匹配网络研究[D].哈尔滨工程大学硕士论文,2009.01.01.
[78]朱泉同,人脑MR图像分割方法研究[D].南京信息工程大学硕士论文,2008.05.
[79]吕振肃,侯志荣,自适应变异的粒子群优化算法[J].电子学报,2004.33(3)：p.416-419.
[80]徐雅香,粒子群算法及在神经网络分类器中的应用[D].西安电子科技大学硕士论文,2008.05.01.
[81]刘侠,初.,王科俊,基于粒子群算法的证券组合投资模型的研究.商业研究,2006.08.25.
[82]Trelea I, The particle swarm optimization algorithm. Convergence analysis and parameter selection, 2003. 6(85):p. 317-325.
[83]Eberhart R, S.Y., Comparing inertia weights and construction factors in particle swarm optimization. IEEE Congress on Evolutionary Computation, 2000: p. 84-88.
[84]张丽平,粒子群算法的理论与实践[D].浙江大学博士学位论文,2005.6.
[85]田雨波,混合神经网络技术.科学出版社,2009.
[86]雷开友,粒子群算法及其应用研究[D].西南大学博士论文,2006.04.01.
[87]赵志刚,苏一丹,带自变异算子的粒子群优化算法[J].计算机工程与应用,2006.05.13(1)：p.45-47.
[88]M, H.T.H.C.D.B.B., Range free localization schemes in large scale sensor networks. 2003, Proceedings of the 9th Annual International Conference on Mobile Computing and Network.
[89]D, B.L.S.H.J.E.I., Gpsless low cost outdoor localization for very small devices. IEEE Personal Communications. 7(5): p. 28-34.
[90]E, D.T.L.P.K.S.G.I.L., Convex position estimation in wireless sensor networks. http://robotics, eecs. berkeley. edu/-elghaoui/pdffiles/In focom.pdf, 2009.
[91]T, D.C., A genetic algorithm for simultaneous localization and maping, in the 2003 IEEE International Conference on Robotics and Automation, P. of, Editor. 2003, IEEE: NewYork. p. 434-439.
[92]B, K.A.A.M.GV., Smiulated annealing based localization in wireless sensor network, in the 30th IEEE Conference on Local Computer Network's, P. of, Editor. 2005, IEEE: NewYork. p. 513-514.
[93]陈星舟,廖明宏,林建华,基于粒子群优化的无线传感器网络节点定位改进[J].计算机应用,2010.30(7)：p.1736-1738.
[94]赵仕俊,孙美玲,唐懿芳,基于遗传模拟退火算法的无线传感器网络定位算法[J].计算机应用与软件,2009.26(10)：p.189-192.
[95]王驭风,王岩,基于矢量和粒子群优化的传感器网络节点定[J]
[96]计算机应用,2009.29(1)：p.309-311.
[97]Ye F, L. H., Cheng J, et al, A two-tier data dissemination model for large-scale wireless sensor networks, in Annual International Conference on Mobile Computing and Networking(MOBICOM2002). 2002: Atlanta. p. 148-159.
[98]Liu X, H.Q.F., Zhang Y, Haystacks:Balancing push and pull for discovery in large-scale sensor networks, in ACM Conference on Embedded Networked Sensor Systems(SENSYS'O4).2004: Baltimore, Maryland. p. 122-133.
[99]Rajendran V, O.K., Garcia-Luna-Aceves J, Energy-efficient, Collision-free medium access control for wireless sensor networks, in The 1st International Conference on Embedded Networked Sensor Systems(SENSYS 2003). 2003:Los Angeles, USA. p. 181-192.
[100]Lu G, K.B., Raghavendra C, An adaptive energy-efficient and low-latency MAC for tree-based data gathering in wireless sensor networks. Wireless Communications and Mobile Computing, 2007.9. 7(7): p. 863-875.
[101]Muruganathan S D, M.D.C.F., Bhasin, R I, et al., A centralized energy-efficient routing protocol for wireless sensor networks. IEEE Communications Magazine, 2005.43((3)): p. 8-13.
[102]Lindsey S, R.C., Sivalingam K, Data gathering algorithms in sensor networks using energy metrics. IEEE Transactions on Parallel and Distributed Systems, 2002.9. 13(9): p. 924-935.
[103]Brack Jehoshua, G.J., Jiang Andrew, Medial axis based geometric routing in sensor networks, in Annual International Conference on Mobile Computing and Networking(MOBICOM2005).2005: Germany. p. 88-102.
[104]K. Pahlavan, P.K.a.J.B., Wideband radio propagation modeling for indoor geolocation applications. IEEE Comn. Mag,1998:p. 60-65.
[105]Ganesan, G.R., Shenker S, et al, Highly-resilient, energy-efficient multipath routing in wireless sensor networks. Mobile Computing and Communications Review, 2002. 1(2): p. 11-25.
[106]Deb B, B.S., Nath B, Reliable information forwarding using multiple paths in sensor networks, in The 28th Annual IEEE Conference on Local Computer Networks(LCN). 2003: Bonn, Germany, p. 406-415.
[107]Chen, K., Balakrishnan H, et al, An energy-efficient coordination algorithm for topology maintenance in Ad Hoc wireless networks. ACM Wireless Networks Journal, 2002. 8(5): p. 481-494.
[108]Mann, R.P., Namuduri, et al, Energy-aware routing protocol for ad hoc wireless sensor networks. Journal on Wireless Communications and Networking, 2005.5: p. 635-644.
[109]Cerpa, D.E., Adaptive self-configuring sensor networks topologies. IEEE Transactions on Mobile Computing, 2004.3(3): p. 272-285.
[110]陈维克等,基于RSSI的无线传感器网络加权质心定位算法[J].武汉理工大学学报,2006.30(2)：p.265-268.
[111]C, S.S.W.M.R., Power-Aware Routing in Mobile Ad hocNetworks[M]. New York: ACM Press, 1998: p. 181-190.
[112]Heinzelman W, C.A., Balakfishnan H, An Application Specific ProtocolArchitecture for Wireless Microsensor Networks[J]. IEEE Transactions on Wire Communications, 2002. 1(4): p. 660-670.
[113]Z, P., Rough set[J]. International Journal of Computer and Information Science, 1982. 11:p. 341-356.
[114]F, Z.Q.H.Z.W., A new approach for fault diagnosis in power systems based on rough set theory[A]. APSCOM, 1997: p. 597-602.
[115]Chessa S, S.P., Crash faults identification in wireless sensor networks[J]. Computer Communications, 2002. 25:p.1273-1282.
[116]Ta F E H, S.L., Fault diagnosis based on rough set theory[J]. Engineering Applications of Artificial Intelligence, 2003. 16(1): p. 39-43.
[117]Houseman L A, S.J.H., Hart J R, etal, PlantStar 2000: a plant-wide control platform for mineral sprocessing[J]. Minerals Engineering, 2001. 14(6): p. 593-600.
[118]戴晓强,刘维亭,基于模糊交互多模型的机动目标跟踪方法[J].弹箭与制导学报,2007.02.27(1)：p.34-37.
[119]朱志宇,基于模糊推理的自适应交互多模型目标跟踪算法[J].弹箭与制导学报,2008.28(1)：p.29-32.
[120]戴晓强,刘维亭,朱志宇,基于模糊自适应IMM算法的机动目标跟踪方法[J].船舶工程,2007.06.29(3)：p.1-4.
[121]敬忠良,徐宏,周雪琴,戴冠中,基于神经网络的机动目标信息融合与并行自适应跟踪[J].航空学报,1995.11.25.16(6)：p.716-711.
[122]张永胜,嵇成新,一种基于当前统计模型的模糊交互多模型算法[J].火力与指挥控 制,2003.28(1)：p.51-56.
[123]许浒,姜长生,辅小荣,基于粗神经网络的并行自适应滤波算法[J].盐城工学院学报(自然科学版),2005.18(1)：p.27-31.
[124]李德仁,朱欣焰,龚健雅,从数字地图到空间信息网格空间信息多级网格理论思考[J].武汉大学学报·信息科学版,2003.28(6)：p.642-649.
[125]董晓鲁,基于位置的服务在3G系统中的应用[J].电信网技术,2004.9(1)：p.62-68.
[126]学斌,程朋根,徐云,基于位置服务的关键技术与应用[J]江西科学,200523(1)：p.432-481.
[127]孙庆辉,骆剑承,周成虎,网格GIS数据信息发布的关键技术[J].地球信息科学,2004.6(1)：p.22-26.
[128]方金云,何建邦,网格GIS体系结构及其实现技术[J].地球信息科学,2010.11(4)：p.36-42.
[129]刘雪梅,柳永坡,顾国昌,0GSA2DAI框架模型的应用[J].大庆石油学院学报,2005.2(6)：p.112-114.
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