scholarly journals Node Localization of Wireless Sensor Networks Based on Hybrid Bat-Quasi-Newton Algorithm

2015 ◽  
Vol 11 (6) ◽  
pp. 38 ◽  
Author(s):  
Shunyuan Sun ◽  
Baoguo Xu
Keyword(s):  
Least Square Method ◽  
Bat Algorithm ◽  
Least Square ◽  
Localization Algorithm ◽  
Positioning Precision ◽  
Newton Algorithm ◽  
The Third ◽  
Third Stage ◽  
Node Location ◽  
Quasi Newton

Concerning the problem that the least square method in the third stage of DV-Hop algorithm has low positioning accuracy, a localization algorithm was proposed which is the fusion of hybrid bat-quasi-Newton algorithm and DV-Hop algorithm. First of all, the Bat Algorithm ( BA) was improved from two aspects: firstly, the random vector β was adjusted adaptively according to bats fitness so that the pulse frequency had the adaptive ability. Secondly, bats were guided to move by the average position of all the best individuals before the current iteration so that the speed had variable performance; Then in the third stage of DV-Hop algorithm the improved bat algorithm was used to estimate node location and then quasi-Newton algorithm was used to continue searching for the node location from the estimated location as the initial searching point. The simulation results show that, compared with the traditional DV-Hop algorithm and the improved algorithm of DV-Hop based on bat algorithm( BADV-Hop) , positioning precision of the proposed algorithm increases about 16. 5% and 5. 18%, and the algorithm has better stability, it is suitable for high positioning precision and stability situation.

An Immune-Based Node Localization Algorithm for WSN

2012 ◽  
Vol 490-495 ◽  
pp. 1207-1211
Author(s):  
Nan Zhang ◽  
Jian Hua Zhang ◽  
Jian Ying Chen ◽  
Xiao Mei Qu
Keyword(s):  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Power Requirement ◽  
The Third ◽  
Third Stage ◽  
Additional Amount ◽  
Node Location ◽  
Anchor Nodes ◽  
Additional Hardware

Node localization technology is the premise and foundation of all applications in wireless sensor network. An improved DV-Hop algorithm was proposed aimed at the low-power requirement of wireless sensor networks. The distances between nodes and anchor nodes were used to calculate the node location in DV-Hop algorithm, and the immune algorithm was used to optimize the estimated location in the third stage of DV-Hop algorithm. The improved algorithm does not require additional hardware devices, and has smaller additional amount of communication and computation.

Video Nodes Based Collaborative Object Localization Framework in Wireless Multimedia Sensor Networks

2011 ◽  
Vol 317-319 ◽  
pp. 1078-1083 ◽  
Author(s):  
Qing Tao Lin ◽  
Xiang Bing Zeng ◽  
Xiao Feng Jiang ◽  
Xin Yu Jin
Keyword(s):  
Cluster Head ◽  
Computation Time ◽  
Least Square Method ◽  
Least Square ◽  
Object Localization ◽  
Wireless Multimedia ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Multimedia Sensor Networks ◽  
Localization Model

This paper establishes a 3-D localization model and based on this model, it proposes a collaborative localization framework. In this framework, node that observes the object sends its attitude information and the relative position of the object's projection in its camera to the cluster head. The cluster head adopts an algorithm proposed in this paper to select some nodes to participate localization. The localization algorithm is based on least square method. Because the localization framework is based on a 3-D model, the size of the object or other prerequisites is not necessary. At the end of this paper, a simulation is taken on the numbers of nodes selected to locate and the localization accuracy. The result implies that selecting 3~4 nodes is proper. The theoretical analysis and the simulation result also imply that a const computation time cost is paid in this framework with a high localization accuracy (in our simulation environment, a 0.01 meter error).

scholarly journals The measurements results adjustment by the Least Square Method

2021 ◽  
Vol 1 ◽  
pp. 1-8
Author(s):  
Oleksandr Samoilenko ◽  
Yurii Kuzmenko
Keyword(s):  
Reference Value ◽  
Least Square Method ◽  
Systematic Errors ◽  
Least Square ◽  
Key Comparisons ◽  
System Of Equations ◽  
The Third ◽  
Measurement Results ◽  
Free Case ◽  
Degrees Of Equivalence

The method for processing of the measurement results obtained from Comite International des Poids et Measures (CIPM) Key, Regional Metrology Organizations (RMO) or supplementary comparisons, from the proficiency testing by interlaboratory comparisons and the calibrations is proposed. It is named by authors as adjustment by least square method (LSM). Additive and multiplicative parameters for each measuring standard of every particular laboratory will be the results of this adjustment. As well as the parameters for each artifact. The parameters of the measurements standards are their additive and multiplicative degrees of equivalence from the comparison and the estimations of the systematic errors (biases) from calibrations. The parameters of the artifacts are the key comparisons reference value from the comparison and the assigned quantity values from the calibrations. The adjustment is considered as a way to solving a problem of processing the great amount of homogeneous measurements with many measuring standards at a different comparison levels (CIPM, RMO or supplementary), including connected problems. Four different cases of the adjustments are considered. The first one is a free case of adjustment. It was named so because of the fact that none of participants has any advantage except their uncertainties of measurements. The second one is a fixed case of adjustment. Measuring results of RMO and supplementary comparisons are rigidly linked to additive and multiplicative parameters of measuring standards of particular laboratories participated in CIPM key comparisons. The third one is a case of adjustment with dependent equations. This one is not so rigidly linked of the new comparisons results to previous or to some other comparisons as for fixed case. It means that the new results of comparisons are influenced by the known additive and multiplicative parameters and vice versa. The fourth one is a free case of adjustment with additional summary equations. In that case certain checking equations are added to the system of equations. So, the sum of parameters multiplied by their weights of all measurement standards for particular laboratories participated in comparisons should be equal to zero.

scholarly journals 3D Localization Algorithm Based on Linear Regression and Least Squares in NLOS Environments

2018 ◽  
Vol 11 (4) ◽  
pp. 1
Author(s):  
Jiang Li ◽  
Zhang Lei
Keyword(s):  
Linear Regression ◽  
Three Dimensional ◽  
Least Square Method ◽  
Mobile Terminal ◽  
Base Station ◽  
Least Square ◽  
Time Of Arrival ◽  
Localization Algorithm ◽  
Positional Accuracy ◽  
The Relationship

Based on the positive bias property of the time of arrival(TOA) measurement error caused by the non-line-of-sight(NLOS) propagation, a simple and effective three dimensional(3D) geometrical localization algorithm was proposed, the algorithm needs no prior knowledge of time delay distribution of TOA, and only linear regression was used to estimate the parameters of the relationship between the NLOS distance error and the true distance, thus, the approximate real distance between mobile terminal (MT) and base station (BS) was reduced, then, the 3D geometric localization of mobile terminal was carried out by the least square method. The experimental results shows the effectiveness of the algorithm, and the positional accuracy is far higher than the required accuracy by E-911 in NLOS environments.

scholarly journals A Method of Containment Leakage Rate Estimation Based on Convolution Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Hao Wang ◽  
Jingquan Liu ◽  
Guangyao Xie ◽  
Xianping Zhong ◽  
Xiangqi Fan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Nuclear Power ◽  
Power Plants ◽  
Least Square Method ◽  
Leakage Rate ◽  
Least Square ◽  
Nonlinear Fitting ◽  
The Third ◽  
Start Up

As the nuclear power plant containment is the third barrier to nuclear safety, real-time monitoring of containment leakage rate is very important in addition to the overall leakage test before an operation. At present, most of the containment leakage rate monitoring systems calculate the standard volume of moist air in the containment through monitoring parameters and calculate the daily leakage rate by the least square method. This method requires several days of data accumulation to accurately calculate. In this article, a new leakage rate modeling technique is proposed using a convolutional neural network based on data of the monitoring system. Use the daily monitoring parameters of nuclear power plants to construct inputs of the model and train the convolutional neural network with daily leakage rates as labels. This model makes use of the powerful nonlinear fitting ability of the convolutional neural network. It can use 1-day data to accurately calculate the containment leakage rate during the reactor start-up phase and can timely determine whether the containment leak has occurred during the start-up phase and deal with it in time, to ensure the integrity of the third barrier.

scholarly journals DV-Hop Node Location Algorithm Based on GSO in Wireless Sensor Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Ling Song ◽  
Liqin Zhao ◽  
Jin Ye
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Least Square Method ◽  
High Energy ◽  
Least Square ◽  
Wireless Sensor ◽  
Linear Solution ◽  
Node Location ◽  
Location Algorithm

Node location is one of the most important problems to be solved in practical application of WSN. As a typical location algorithm without ranging, DV-Hop is widely used in node localization of wireless sensor networks. However, in the third phase of DV-Hop, a least square method is used to solve the nonlinear equations. Using this method to locate the unknown nodes will produce large coordinate errors, poor stability of positioning accuracy, low location coverage, and high energy consumption. An improved localization algorithm based on hybrid chaotic strategy (MGDV-Hop) is proposed in this paper. Firstly, a glowworm swarm optimization of hybrid chaotic strategy based on chaotic mutation and chaotic inertial weight updating (MC-GSO) is proposed. The MC-GSO algorithm is used to control the moving distance of each firefly by chaos mutation and chaotic inertial weight when the firefly falls into a local optimum. The experimental results show that MC-GSO has better convergence and higher accuracy and avoids the premature convergence. Then, MC-GSO is used to replace the least square method in estimating node coordinates to solve the problem that the localization accuracy of the DV-Hop algorithm is not high. By establishing the error fitness function, the linear solution of coordinates is transformed into a two-dimensional combinatorial optimization problem. The simulation results and analysis confirm that the improved algorithm (MGDV-Hop) reduces the average location error, increases the location coverage, and decreases and balances the energy consumption as compared to DV-Hop and the location algorithm based on classical GSO (GSDV-Hop).

ZigBee Node Locating Technology Based on Genetic Algorithm

2011 ◽  
Vol 225-226 ◽  
pp. 70-74
Author(s):  
Tie Zhou Wu ◽  
Hui Jun Zhou ◽  
Biao Li ◽  
Qing Xiao
Keyword(s):  
Genetic Algorithm ◽  
Measurement Data ◽  
Least Square Method ◽  
Least Square ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Position Accuracy ◽  
The Common ◽  
Modified Algorithm ◽  
Step Localization

Location technology as Wireless Sensor Network’s support technology for most applications has been widely researched, ZigBee is one of representative technology of WSN. On the basis of the common two-step localization algorithm, this paper proposes a locating method that optimize the measurement data using genetic algorithm before locating calculation, then using multilateral measurement least-square method for calculating position. Experimental results show that the modified algorithm is significantly reduced the position error, effectively improve the position accuracy.

scholarly journals A Novel WSNs Localization Algorithm Based on Artificial Fish Swarm Algorithm

2016 ◽  
Vol 12 (1) ◽  
pp. 64 ◽  
Author(s):  
Xiaoying Yang ◽  
Wanli Zhang ◽  
Qixiang Song
Keyword(s):  
Optimal Solution ◽  
Least Square Method ◽  
Least Square ◽  
Localization Algorithm ◽  
Node Localization ◽  
Artificial Fish Swarm Algorithm ◽  
Network Cost ◽  
Artificial Fish Swarm ◽  
Unknown Node ◽  
The Cost

In node localization algorithm in Wireless Sensor Networks (WSNs), the least square method is affected by the measurement error, which leads to position error of the unknown node. In order to solve the problem that the error is too high, we propose a novel WSNs localization algorithm based on artificial fish swarm (AFSA). In the proposed algorithm, artificial fish swarm, which has some advantages such as requirements for the initial value and parameter setting is not high, the optimization speed is quick and so on, is introduced in position process. Firstly, the distances between nodes are obtained by using the TDOA algorithm. Then the geometrical position of the unknown nodes is estimated by the artificial fish swarm optimization algorithm. The simulation results show that compared with the least square method, the algorithm proposed in the paper can reduce the computation amount, get the optimal solution quickly and improve the accuracy of the node without increasing the cost and power consumption. Moreover, the number of beacon nodes is relatively small, so the network cost is reduced to a certain extent.

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