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Node Localization Algorithm For Wireless Sensor Network Based On Monte Carlo

Posted on:2019-01-25Degree:MasterType:Thesis
Country:ChinaCandidate:Z H DuanFull Text:PDF
GTID:2428330623962410Subject:Control Science and Engineering
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For the question as Wireless Sensor Network's localization technology,with the increasing demand of practical application,the continuous innovation,the development of application technology,the dynamic and complex evolution of application environment,the traditional static network localization algorithm no longer has the universality,and the localization algorithm based on dynamic wireless sensor network has become the mainstream of research and development.In this paper,a dynamic wireless sensor network localization algorithm based on Monte Carlo statistical method is described in detail,especially the Monte Carlo Box algorithm is studied systematically.Specific research work is listed as following:(I)On the basis of summarizing the limitations of the original MCB algorithm put forward the AABMCB algorithm.Here,we introduce the positioning strategy that locates the high-quality nodes at first,and then locates the remaining nodes together with the anchor nodes aiming at the problems of low localization efficiency,inaccurate sampling and large amount of computation,etc.Meanwhile the improved auxiliary anchor node selection function and the new sample area determination method considering primary positioning error are both given pertinently.Through the simulation analysis,we get that AABMCB algorithm does own a certain degree of improvement in aspects of localization accuracy,efficiency and coverage rate.(II)Because there is no solution to the problems like too many sampling times,poor timeliness,inaccurate sample estimation and so on for AABMCB algorithm,we do the two times optimization to put forward Optimized-AABMCB algorithm.While the original positioning order and the reference boundary coordinates are unchanged,the weighted centroid algorithm is introduced to determine the new anchor box.Meanwhile,use “supplementary anchor box” based on RSSI distance measuring to do the anchor box filtering and eventually,utilize the penalty function movement inertia weight to distinguish the advantages and disadvantages of sampling points during the period of coordinate's estimation.Through simulation analysis,we get that the sample times of the positioning process are greatly reduced and the positioning efficiency and accuracy are further improved at the cost of moderately increasing the calculation amount,also OAABMCB algorithm owns the better comprehensive positioning effect under different parameter conditions.
Keywords/Search Tags:Mobile Wireless Sensor Network, Node Localization, Monte Carlo Method, Auxiliary Anchor, Weighted Centroid Algorithm, Supplementary Anchor Box, Penalty Function Movement Inertia Weight
PDF Full Text Request
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