Computer Science ›› 2018, Vol. 45 ›› Issue (12): 77-80.doi: 10.11896/j.issn.1002-137X.2018.12.011

• Network & Communication • Previous Articles     Next Articles

Study on Monte Carlo Location Algorithm in Wireless Sensor Networks

ZHANG Qi-man, ZHANG Ying   

  1. (College of Information Engineering,Shanghai Maritime University,Shanghai 201306,China)
  • Received:2017-11-06 Online:2018-12-15 Published:2019-02-25

Abstract: In the field of node location of wireless sensor network,there exist problems of high location error and low sampling efficiency of the commonly used Monte Carlo-based location algorithm.In order to improve the sampling efficiency and location accuracy of mobile node in wireless sensor networks,this paper made use of Markov chain to sample,and proposed an improved location algorithm based on Monte Carlo.The new algorithm combines the Markov chain to complete the collection of node samples based on Monte Carlo algorithm,then filters them,and finally obtains the exact position of the node by weighting the obtained node position values.Simulation results indicate that the proposed algorithm has lower location error than other algorithms,and improves the sampling efficiency and location accuracy for moving nodes.

Key words: Markov chain, Mobile nodes locations, Monte Carlo algorithm, Wireless sensor networks

CLC Number: 

  • TP393
[1]MAO G,FIDAN B,ANDERSON B D O.Wireless sensor net-work localization techniques[J].Computer Networks,2007,51(10):2529-2553.
[2]SSU K,OU C,JIAU H C.Localization with mobile anchorpoints in wireless sensor networks[J].IEEE Transactions on Vehicular Technology,2005,54(3):1187-1197.
[3]ZENG Z,GAO J,WANG J.Corrected Range Weighted Centroid Localization Algorithm Based on RSSI for WSN[C]∥Procee-dings of the 2011 International Conference on Informatics,Cybernetics,and Computer Engineering (ICCE2011).Springer Berlin Heidelberg,2011:453-460.
[4]HU J,YU X,WANG B,et al.Localization Accuracy Improved Methods for Range-Free Localization Schemes in Wireless Sensor Network[J].Key Engineering Materials,2010,437:462-466.
[5]MICHAELIDES M P,LAOUDIAS C,PANAYIOTOU C G.Fault Tolerant Localization and Tracking of Multiple Sources in WSNs Using Binary Data[J].IEEE Transactions on Mobile Computing,2014,13(6):1213-1227.
[6]BAGGIO A,LANGENDOEN K.Monte-Carlo Localization for Mobile Wireless Sensor Networks[J].Journal of Ad Hoc Networks,2006,6(5):718-733.
[7]AYSEGUL A.An Efficient Monte Carlo-Based Localization Algorithm for Mobile Wireless Sensor Networks[J].Arabian Journal for Science & Engineering,2015,40(5):1375-1384.
[8]YI J,YANG S,CHA H.Multi-hop-based Monte Carlo Localization for Mobile Sensor Networks [C]∥IEEE Communications Society Conference on Sensor,Mesh and Ad Hoc Communications and Networks.2007:163-171.
[9]LI M,LUO T,XU H.Localization Algorithm Based on Anchor Node Select Model for Wireless Sensor Networks [J].Chinese Journal of Sensors and Actuators,2011,24(2):264-268.(in Chinese)
李敏,罗挺,徐华.一种基于参考节点选择模型的无线传感器网络定位算法[J].传感技术学报,201l,24(2):264-268.
[10]QU Q,XIA Y.Node Localization of Wireless Sensor Network Based on IMCB Algorithm [J].Computer Engineering,2014,40(7):42-46.(in Chinese)
曲强,夏勇.基于IMCB算法的无线传感器网络节点定位[J].计算机工程,2014,40(7):42-46.
[11]LIN S K,LI S Z,QIAO J Z,et al.Markov Location Prediction Based on User Mobile Behavior Similarity Clustering [J].Journal of Northeastern University,2016,37(3):323-326.(in Chinese)
林树宽,李昇智,乔建忠,等.基于用户移动行为相似性聚类的Markov位置预测[J].东北大学学报,2016,37(3):323-326.
[12]HABIB S J,MARIMUTHU P N.Empirical analysis of querybased data aggregation within WSN through Monte Carlo simulation[J].International Journal of Pervasive Computing and Communications,2012,8(4):329-343.
[13]RAYMOND R,MORIMURA T,OSOGAMI T,et al.Map matching with hidden Markov model on sampled road network[C]∥2012 21st International Conference on Pattern Recognition (ICPR).IEEE,2012:2242-2245.
[14]QIAN W,STANLEY K G,OSGOOD N D.The impact of spatial resolution and representation on human mobility predictability[OL].http://hdl.handle.net/10388/ETD-2012-11-835.
[15]LIAO L,FOX D,KAUTZ H.Location-based activity recognition using relational Markov networks[C]∥Proceedings of the 18th International Conference on Neural Information Processing Systems(NIPS’05).2005:787-794.
[16]BABU M V,RAMPRASAD A V.Discrete antithetic MarkovMonte Carlo based power mapping localization algorithm for WSN[C]∥2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT).2012:56-62.
[17]ZHENG J,WU C,CHEN Z.The Mobile Node Localization Algorithm Based on Monte Carlo[J].Advanced Materials Research,2013,712-715:1847-1850.
[18]HU L,EVANS D.Localization for Mobile Sensor Networks [C]∥Proceedings of the 10th Annual International Conference on Mobile Computing and Networking(MobiCom’04).2004:45-57.
[19]ZHANG S T.Research on Localization for Wireless Sensor Networks [D].Wuhan:Huazhong University of Science and Technology,2010.(in Chinese)
张松涛.无线传感器网络定位问题研究[D].武汉:华中科技大学,2010.
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