基于距离估计的无线传感网络移动节点定位研究
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摘要
针对传统MCL算法定位精度低的不足,提出了一种基于距离估计的改进蒙特卡罗定位算法—DEMCL.首先根据网络连通度、锚节点信息和节点间的相邻关系估计目标节点与锚节点间的距离;然后利用该距离构建新的过滤条件加入算法的过滤阶段,以优化样本集和减小定位误差;最后以Matlab为工具对算法的定位性能进行仿真和分析.仿真结果表明:在同一环境下,与MCL算法和MCB算法相比,DEMCL算法能保证更高的定位精度,同时减少了无效定位的节点数目,网络覆盖率可达到98.83%.
Aiming at the disadvantage of low positioning accuracy of traditional Monte Carlo Localization method(MCL) algorithm, this paper proposes an improved Monte Carlo Localization algorithm-DEMCL, which is based on distance estimation. First, the estimation distance between the unknown nodes and anchor nodes is calculated based on the network connectivity, anchor node information and adjacent relationship of nodes. Then, a new filter condition is added to the filtering phase of the algorithm to optimize the sample set and reduce positioning errors. And the end of the article, Matlab is used to simulate and analyze the performance of the algorithm. The simulation results show that in the same environment, compared with the MCL algorithm and the MCB algorithm, the DEMCL algorithm can ensure higher positioning accuracy, while reducing the number of invalidly located nodes, the network coverage can reach 98.83%.
Aiming at the disadvantage of low positioning accuracy of traditional Monte Carlo Localization method(MCL) algorithm, this paper proposes an improved Monte Carlo Localization algorithm-DEMCL, which is based on distance estimation. First, the estimation distance between the unknown nodes and anchor nodes is calculated based on the network connectivity, anchor node information and adjacent relationship of nodes. Then, a new filter condition is added to the filtering phase of the algorithm to optimize the sample set and reduce positioning errors. And the end of the article, Matlab is used to simulate and analyze the performance of the algorithm. The simulation results show that in the same environment, compared with the MCL algorithm and the MCB algorithm, the DEMCL algorithm can ensure higher positioning accuracy, while reducing the number of invalidly located nodes, the network coverage can reach 98.83%.
引文
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[2]赵海军,夏金红,贺春林.基于多维标度和局部成本函数最小化的传感器节点定位算法[J].云南大学学报:自然科学版,2018,40(4):645-651.Zhao H J,Xia J H,He C L.The sensor node localization algorithm based on multidimensional scaling and minimizing local cost function[J].Journal of Yunnan University:Natural Sciences Edition,2018,40(4):645-651.
[3]赵小敏,陈庆章,张海洋,等.信标节点漂移情况下的无线传感器网络节点定位机制[J].通信学报,2015,36(2):38-47.Zhao X M,Chen Q Z,Zhang H Y,et al.Node localization scheme in wireless sensor networks under beacon drifting scenes[J].Journal on Communications,2015,36(2):38-47.
[4]任克强,庄放望.移动锚节点凸规划定位算法研究及改进[J].传感技术学报,2014,27(10):1 406-1 411.DOI:10.3969/j.issn.1004-1699.2014.10.019.Ren K Q,Zhuang F W.Research and Improvement of mobile anchor node localization algorithm based on convex programming[J].Chinese Journal of Sensors and Actuators,2014,27(10):1 406-1 411.
[5]刘三阳,胡亚静,张朝辉.基于RSSI测距的DV-Hop算法[J].控制与决策,2017,32(10):1 900-1 904.Liu S Y,Hu Y J,Zhang C H.Improved DV-Hop localization method based on RSSI[J].Control and Decision,2017,32(10):1 900-1 904.
[6]田洪亮,钱志鸿,王义君,等.能量分簇传感器网络距离误差校正MDS-MAP定位算法[J].电子与信息学报,2017,39(7):1 735-1 740.Tian H L,Qian Z H,Wang Y J,et al.Modified MDS-MAP localization algorithm with distance error correction in energy clustering wireless sensor networks[J].Journal of Electronics&Information Technology,2017,39(7):1 735-1 740.
[7]Shen S,Yang B,Qian K,et al.An improved amorphous localization algorithm for wireless sensor networks[C].International Conference on Networking&Network Applications,Hakodate,Japan,2016:69-72.DOI:10.1109/NaNA.2016.76.
[8]Hu L,Evans D.Localization for mobile sensor networks[C].International Conference on Mobile Computing&Networking,Philadelphia,PA,USA,2004.DOI:10.1145/1023720.1023726.
[9]Baggio A,Langendoen K.Monte-Carlo localization for mobile wireless sensor networks[J].Ad Hoc Networks,2008,6(5):718-733.DOI:10.1016/j.adhoc.2007.06.004.
[10]Hartung S,Kellner A,Rieck K,et al.Monte Carlo localization for path-based mobility in mobile wireless sensor networks[C].IEEE Wireless Communications&Networking Conference,Doha,Qatar,2016.DOI:10.1109/WCNC.2016.7564906.
[11]Lu J Y,Wang C.A new Monte Carlo mobile node localization algorithm based on Newton interpolation[J].EURASIP Journal on Wireless Communications and Networking,2018(1):161.
[12]Xia L,Tan Z,Liang L W.Optimization of Monte Carlo mobile node localization algorithm based on least squares fitting[C].Control and Decision Conference,Yinchuan,China,2016:3 630-3 634.
[13]李建坡,时明,钟鑫鑫,等.自适应蒙特卡罗无线传感器网络移动节点定位算法[J].吉林大学学报:工学版,2014,44(4):1 191-1 196.Li J P,Shi M,Zhong X X,et al.Self-adaptive Monte Carlo location algorithm of mobile nodes in WSN[J].Journal of Jilin University:Engineering and Technology Edition,2014,44(4):1 191-1 196.
[14]孟颍辉,闻英友,陈剑,等.基于估计距离的无需测距定位算法[J].东北大学学报:自然科学版,2014,35(7):921-925.Meng Y H,Weng Y Y,Chen J,et al.Range-free location algorithm based on estimated distances[J].Journal of Northeastern University:Natural Science Edition,2014,35(7):921-925.
[15]Huang W,Zhang Y,Shang Z,et al.To meet or not to meet:Finding the shortest paths in road networks[J].IEEE Transactions on Knowledge and Data Engineering,2018,30(4):772-785.
[16]康文雄,许耀钊.节点约束型最短路径的分层Dijkstra算法[J].华南理工大学学报:自然科学版,2017,45(1):66-73.Kang W X,Xu Y Z.A hierarchical Dijkstra algorithm for solving shortest path from constrained nodes[J].Journal of South China University of Technology:Natural Science Edition,2017,45(1):66-73.
[17]Bielecki W,Kraska K,Klimek T.Using basis dependence distance vectors in the modified Floyd-Warshall algorithm[J].Journal of Combinatorial Optimization,2015,30(2):253-275.DOI:10.1007/s10878-014-9740-2.
[18]Aini A,Salehipour A.Speeding up the Floyd-Warshall algorithm for the cycled shortest path problem[J].Applied Mathematics Letters,2012,25(1):1-5.DOI:10.1016/j.aml.2011.06.008.