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Research On Distributed Node Localization Algorithm In Wireless Sensor Network

Posted on:2018-04-13Degree:MasterType:Thesis
Country:ChinaCandidate:F C LiFull Text:PDF
GTID:2348330518486554Subject:Control Science and Engineering
Abstract/Summary:PDF Full Text Request
The wireless sensor network has important research significance because of its great application value.Node location is one of the key support technologies.According to whether the calculation process of the localization algorithm is distributed in each node,it is divided into centralized positioning algorithm and distributed positioning algorithm.Compared with the centralized localization algorithm,the localization process of the distributed positioning algorithm is carried out in each node,which has advantages in the consumption of the positioning time,and because the unknown node itself is positioned,its adaptability and scalability are more centralized algorithm is superior.The intelligent bionic algorithm(including genetic algorithm,particle swarm algorithm,etc.)has a wide range of applications in the field of optimization.There are many successful examples of applying intelligent algorithms to wireless sensor network node location,but the time and energy consumption of intelligent algorithms have to be considered.In this paper,the localization problem of the current distributed positioning algorithm is proposed for different application scenarios.An improved Distance Vector-Hop(DV-Hop)algorithm is proposed,aiming at the error caused by the average hop distance and the distance between the unknown node and the anchor node.Firstly,the beacon nodes,which are selected to take part in the beacon average hop distance computing,are weighted to decrease the error.Secondly,the average hop distance of unknown node is selected according to the hops between the anchor node and unknown node.Lastly,the distance between beacon node and unknown node is calculated according to the hop-size action scope.The simulation results show that the improved algorithm effectively reduces the DV-Hop localization errors without additional hardware overhead.The particle swarm optimization can be imposed into DV-Hop to increase the accuracy.But larger location errors could occurs in the results given by the distance vector-hop algorithm improved with(PSO)because of the possible local optimization of the PSO.A DV-Hop algorithm combined with the genetic PSO(GAPSO-DV-Hop)is proposed for the problem.Firstly,the anchor nodes are selected according to the maximal ideal hops.Then the weighted average hop distance is calculated with the weights constructed from the distance/hops between anchor nodes and communication radius of anchor nodes.Secondly,the genetic-improved PSO is employed to replace the least square method.The improvements include shrinking the hunting area using proactive estimate,producing the particle queue according the crossover strategy,and making the worst individual dynamical mutating after each iteration.The simulation results show that the positioning accuracies given by the proposed GAPSO-DV-Hop algorithm are obviously better than those by the conventional DV-Hop and other referred algorithms.DV-Hop localization algorithm expends large time and communication consumption.In the case of high proportion of anchor nodes,centroid algorithm as a widely used distributed positioning algorithm with the advantages of simple parameters and small time consumption.The centroid genetic algorithm is p roposed by using the combination of the centroid algorithm,the genetic algorithm and the RSSI technique.Firstly,the RSSI technique is used to improve the centroid localization algorithm.According to the distance between the anchor node and the unknown node,unknown nodes locate itself using weight.For the unknown nodes which don't have enough anchor nodes to complete location,it can use the unknown nodes who have known their location to update to minor anchor node s.After improved centroid algorithm,unknown nodes have their exact position using improved genetic algorithm.The improvement of the genetic algorithm includes the initialization of the individual,the selection of the replication,the adaptive cross and mutation strategies.The simulation results show that the centroid genetic algorithm can improve the positioning accuracy compared with other improved algorithms when the proportion of anchor nodes is high.The three improved algorithms studied in this paper have improved the positioning accuracy under different application scenarios,and have wide application prospect.
Keywords/Search Tags:wireless sensor network, node localization, genetic particle swarm optimization algorithm, average hop distance, proactive estimation
PDF Full Text Request
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