基于自适应蒙特卡罗的动态无线传感器网络节点定位算法

静态无线网络传感器在设计过程中并未考虑到节点的移动性问题,导致目前的节点定位算法不能满足动态无线传感器网络定位的需求。针对传统动态传感器网络节点定位策略定位精度低、算法复杂、响应时间长等缺点, 在MCL的基础上引入了航位推算法, 提出了一种改进的自适应蒙特卡罗, 将Voronoi图和权值融合在MCL算法的粒子过滤阶段中以提高算法的定位精度。仿真分析说明, 在相同条件下, 与 MCL 算法相比, 改进后的MCL算法平均定位精度提高约50%, 即使采样粒子数目较少时也能保证较高的定位精度。

Abstract

The design process of static wireless sensor networks does not take into account the node's mobility problems, which leads to the current node location algorithm cannot meet the needs of dynamic positioning in wireless sensor networks. An Improved Adaptive Monte Carlo is proposed, aimed at improving the shortcomings of traditional dynamic sensor network node positioning strategy such as, low accuracy, complex algorithms, slow response, based on the basis of the MCL introduced the dead reckoning. Voronoi map and weights are fused in the particle filter of MCL algorithm to improve the positioning accuracy of the algorithm. Simulation analysis shows under the same conditions,the average localization accuracy is improved byabout 50%. By comparing with the MCL algorithm, even when the number of samples is small, the positioning accuracy can be guaranteed.

参考文献

[1] LOWELL J R.Military applications of localization, tracking and targeting［J］.IEEE Mreless Communications, 2011, 18(2): 60-65.

[2] ALEMDAR H, ERSOY C.Wireless sensor networks for healthcare: A survey ［J］.Computer Networks, 2010, 54(15): 2688-2710.

[3] LARIOS D F, BARBANCHO J, MOLINA F J, et al.LIS: Localization based on an intelligent distributed fuzzy system applied to a WSN［J］.Ad Hoc Networks, 2012, 10(3): 604-622.

[4] 朱海平, 于红丞, 钟小勇, 等.动态无线传感器网络的改进蒙特卡罗定位算法［J］.传感技术学报, 2012, 25(9): 1284-1288.

[5] DELEN D, SHARDA R, HARDGRAVE B C.The promise of RPID-based sensors in the perishables supply chain［J］.IEEE Wireless Communications, 2011, 18(2): 82-88.

[6] AKYILDIZ I F, STUNTEBECK E P.Wireless underground sensor networks: research challenges［J］.Ad-Hoc Networks, 2006(4): 669-686.

[7] WANG H, QI W, WANG K, et al.Mobile-assisted localization by stitching in wireless sensor networks: In Proc. IEEE Int. Conf. Commun, 2011, 1, ［C］.［S.I.］: ［s.n.］, 2011: 1-5.

[8] 李建坡, 时明, 钟鑫鑫. 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(4): 1191-1196.

[9] XU L X, EVANS D.Localization for mobile sensor networks: In Proceeding of the 10th anual International Conference on Mobile Computing and Networking［C］.［S.I.］: ［s.n.］,2004: 45-57.

[10] 刘志华, 李改燕, 刘晓爽.基于最小二乘法的蒙特卡洛移动节点定位算法［J］.传感技术学报, 2012, 25(4): 541-544.

[11] 朱海平, 于红丞, 钟小勇, 等.动态无线传感器网络的改进蒙特卡罗定位算法［J］.传感技术学报, 2012, 25(9): 1284-1288.

[12] 黄梅根, 常新峰.一种基于蒙特卡罗法的无线传感器网络移动节点定位算法研究［J］.传感技术学报, 2010, 23(4): 562-566.

张具琴, 蔡艳艳, 司小平, 郭学军. 基于自适应蒙特卡罗的动态无线传感器网络节点定位算法[J]. 应用激光, 2016, 36(4): 446. Zhang Juqin, Cai Yangyan, Si Xiaoping, Guo Xunjung. Research on Node Localization Algorithm based on Adaptive Monte Carlo Algorithm for Dynamic Sensor Networks[J]. APPLIED LASER, 2016, 36(4): 446.

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