Adaptive Cuckoo Optimization DV-HOP Localization Algorithm Based on Multi-Communication Radius and Error Correction
-
摘要: 针对WSN节点定位中非测距DV-HOP算法存在较大定位误差，提出了一种多通信半径误差修正自适应布谷鸟优化DV-HOP定位算法（MEACS-D）. 采用锚节点多通信半径广播消息，在跳数计数时将跳数小数化，以减小跳数长短不一造成的误差；再用虚拟相交圆几何方法计算1跳内节点与锚节的距离；通过在未知节点选择平均跳距时，加入各个锚节点权重进行计算来减少平均跳距误差；另引入可自适应搜索步长的布谷鸟算法代替极大似然估计法来定位节点坐标，以提高定位精度. 通过仿真，在不同锚节点、通信半径、总节点条件下MEACS-D算法较DV-HOP算法和原布谷鸟DV-HOP算法（CS-D）算法定位误差平均下降39.7%、10.6%，证明MEACS-D算法能有效减少定位误差.Abstract: An adaptive cuckoo optimization DV-HOP localization algorithm based on multi-communication radius and error correction （MEACS-D）is proposed, aiming at addressing the problem of lager error in wireless sensor networks of range-free DV-HOP algorithms. Using the anchor nodes, which have the ability to broadcast multi-communication radius messages, and to decimalize a counted number of hops （to reduce error caused by different jump lengths）, the virtual intersecting circle geometric method was used to calculate the distance between unknown nodes and anchor nodes in one hop. When the unknown node was used to select the average jump distance, consideration was given to adding each anchor node weight to reduce error. To calculate the node coordinates in order to improve the localization accuracy, the cuckoo algorithm with adaptive search step was introduced instead of the maximum likelihood estimation method. In simulation, by varying the values for the anchor nodes, communication radius, and total node, the localization error of the MEACS-D algorithm was reduced by 39.7% and 10.6%, compared to the DV-HOP algorithm and the original cuckoo DV-HOP （CS-D） algorithm, respectively. These results confirm that the MEACS-D algorithm can reduce the localization error effectively.
-
图 4 MEACS-D与DV-HOP定位误差对比
Figure 4. Comparison of localization error between MEACS-D and DV-HOP
-
钱志鸿,孙大洋,LENG V. 无线网络定位综述[J]. 计算机学报,2016,39(6): 1238-1256QIAN Zhihong, SUN Dayang, LENG V. A survey on localization model in wireless networks[J]. Chinese Journal of Computers, 2016, 39(6): 1238-1256 邓平,张红江. 一种无线传感器网络抗虫洞攻击DV-HOP定位算法[J]. 西南交通大学学报,2015,50(1): 51-57DENG Ping, ZHANG Hongjiang. A DV-HOP localization algorithm against wormhole attacks in WSN[J]. Journal of Southwest Jiaotong University, 2015, 50(1): 51-57 邓平,伍小梅. 一种基于粒子滤波的WSN自适应定位算法[J]. 西南交通大学学报,2014,49(2): 323-329DENG Ping, WU Xiaomei. An adaptive localization algorithm based on particle filter for Wireless Sensor Networks[J]. Journal of Southwest Jiaotong University, 2014, 49(2): 323-329 GUI L, VAL T, WEI A. Improvement of range-free localization technology by a novel DV-hop protocol in wireless sensor networks[J]. Ad Hoc Networks, 2015, 24(PB): 55-73 SANGWOO L, DONGYUL L, CHAEWOO L. Enhanced DV-Hop algorithm with reduced hop-size error in ad hoc networks[J]. Ieice Transactions on Communications, 2011, 94-B(7): 2130-2132 吴玉成,李江雯. 基于最优节点通信半径的改进DV-HOP定位算法[J]. 华南理工大学学报(自然科学版),2012,40(6): 36-42WU Yucheng, LI Jiangweng. Improved DV-HOP localization algorithm based on optimal communication radius of nodes[J]. Journal of South China University of Technology (Natural Science Edition), 2012, 40(6): 36-42 程超,钱志鸿,付彩欣,等. 一种基于误差距离加权与跳段算法选择的遗传优化DV-HOP定位算法[J]. 电子与信息学报,2015,37(10): 2418-2423CHENG Chao, QIAN Zhihong, FU Caixin, et al. Genetic optimization DV-HOP localization algorithm based on error distance weighted and HOP algorithm selection[J]. Journal of Electronics & Information Technology, 2015, 37(10): 2418-2423 李娟,刘禹,钱志鸿. 基于双通信半径的传感器网络DV-HOP定位算法[J]. 吉林大学学报(工学版),2013,44(2): 502-507LI Juan, LIU Yu, QIAN Zhihong. Improved DV-Hop localization algorithm based on two communication ranges for wireless sensor network[J]. Journal of Jilin University (Engineering and Technology Edition), 2013, 44(2): 502-507 GOYAL S, PATTERH M S. Wireless sensor network localization based on cuckoo search algorithm[J]. Wireless Personal Communications, 2014, 79(1): 223-234 doi: 10.1007/s11277-014-1850-8 刘登峰,章力,邴晓瑛,等. 基于布谷鸟差分算法优化的DV-Hop改进算法[J]. 系统仿真学报,2017,29(4): 791-797LIU Dengfeng, ZHANG Li, BING Xiaoying, et al. Localization method based on modified cuckoo difference optimization for wireless sensor networks[J]. Journal of System Simulation, 2017, 29(4): 791-797 王李进,尹义龙,钟一文. 逐维改进的布谷鸟搜索算法[J]. 软件学报,2013,24(11): 2687-2698WANG Lijing, YIN Yilong, ZHONG Yiwen. Cuckoo search algorithm with dimension by dimension Improvement[J]. Journal of Software, 2013, 24(11): 2687-2698 XUE Y G, DENG H W. The cuckoo search algorithm based on dynamic grouping to adjust flight scale[J]. Applied Mechanics & Materials, 2014, 543/544/545/546/547: 1822-1826 ZINEDDINE M. Vulnerabilities and mitigation techniques toning in the cloud:a cost and vulnerabilities coverage optimization approach using Cuckoo search algorithm with Levy flights[J]. Computers & Security, 2015, 48: 1-18 谭志,张卉. 基于节点间覆盖关系的改进DV-Hop算法[J]. 北京邮电大学学报,2014,37(1): 35-38TAN Zhi, ZHANG Hui. Improved DV-Hop localization algorithm based on coverage of nodes[J]. Journal of Beijing University of Posts and Telecommunications, 2014, 37(1): 35-38 SHAHZAD F, SHALTAMI T, SHAKSHUKHI E. DV-maxHop:a fast and accurate range-free localization algorithm for anisotropic wireless networks[J]. IEEE Transactions on Mobile Computing, 2017, 16: 2494-2505 doi: 10.1109/TMC.2016.2632715 CUI Z, SUN B, WANG G, et al. A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems[J]. Journal of Parallel & Distributed Computing, 2016, 103: 42-52 ZHANG Y, XIANG S, FU W, et al. Improved normalized collinearity DV-Hop algorithm for node localization in wireless sensor network[J]. International Journal of Distributed Sensor Networks, 2014, 2014(11): 1-14 -
下载:
点击查看大图
图(7)
计量
- 文章访问数: 509
- HTML全文浏览量: 222
- PDF下载量: 31
- 被引次数: 0