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.

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).

Artificial Fish Swarm Algorithm Based on Fast Image Matching

2011 ◽  
Vol 271-273 ◽  
pp. 297-302
Author(s):  
Miao Ma ◽  
Jiao He ◽  
Min Guo
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Fitness Function ◽  
Optimal Solution ◽  
Fast Method ◽  
Artificial Fish Swarm Algorithm ◽  
Template Image ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Exclusive Or

Due to the large amount of calculation and high time-consuming in traditional grayscale matching, this paper combines artificial fish algorithm of swarm intelligence with edge detection and the operation of bitwise exclusive or, and presents a fast method on feature matching. The method regards the problem of image matching as a process of searching the optimal solution. In order to provide artificial fish swarm algorithm with an appropriate fitness function, the operation of bitwise exclusive or and addition is employed to deal with the edge information extracted from the template image and the searching image. Then the best matching position is gradually approaching by swarming, following and other behaviors of artificial fish. Experimental results show that the proposed method not only significantly shortens the matching time and guarantees the matching accuracy, but also is robust to noise disturbance.

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 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 Algorithm and Design for RFID Velocity Measurement Based on Least Square Method

2011 ◽  
Vol 317-319 ◽  
pp. 1573-1576
Author(s):  
Lin Ying Jiang ◽  
Heng Zhang ◽  
Han Qing Tan
Keyword(s):  
Great Degree ◽  
Velocity Measurement ◽  
Least Square Method ◽  
Least Square ◽  
Measuring System ◽  
Rfid Technology ◽  
Velocity Measuring ◽  
The Cost

This paper comes up with an algorithm and application for velocity measurement based on RFID technology. This measurement can reduce the cost of velocity-measuring system to a great degree, and improve the accuracy of velocity measurement on the basis of the algorithm.

scholarly journals MNCE: Multi-Hop Node Localization Algorithm for Wireless Sensor Network Based on Error Correction

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 269
Author(s):  
Yinghui Meng ◽  
Yuewen Chen ◽  
Qiuwen Zhang ◽  
Weiwei Zhang
Keyword(s):  
Error Correction ◽  
Large Error ◽  
Least Square ◽  
Localization Algorithm ◽  
Target Node ◽  
Node Localization ◽  
Simulation Experiments ◽  
Connectivity Information ◽  
Localization Algorithms ◽  
Anchor Nodes

Considering the problems of large error and high localization costs of current range-free localization algorithms, a MNCE algorithm based on error correction is proposed in this study. This algorithm decomposes the multi-hop distance between nodes into several small hops. The distance of each small hop is estimated by using the connectivity information of adjacent nodes; small hops are accumulated to obtain the initial estimated distance. Then, the error-correction rate based on the error-correction concept is proposed to correct the initial estimated distance. Finally, the location of the target node is resolved by total least square methods, according to the information on the anchor nodes and estimated distances. Simulation experiments show that the MNCE algorithm is superior to the similar types of localization algorithms.

scholarly journals A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzyc-Means Clustering

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Li Ma ◽  
Yang Li ◽  
Suohai Fan ◽  
Runzhu Fan
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Clustering Algorithms ◽  
Optimal Solution ◽  
Medical Image Segmentation ◽  
Superior Result ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Fcm Clustering ◽  
Swarm Algorithm

Image segmentation plays an important role in medical image processing. Fuzzyc-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The proposed algorithm combines artificial fish swarm algorithm (AFSA) with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI) are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM).

scholarly journals Algorithmic Study on Position and Movement Method of Badminton Doubles

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Yu Xie ◽  
Xiaodong Xie ◽  
Huan Xia ◽  
Zhe Zhao
Keyword(s):  
Optimal Solution ◽  
Least Square Method ◽  
Standing Position ◽  
Plane Graph ◽  
Least Square ◽  
Optimal Position ◽  
Research Results ◽  
Parametric Functions ◽  
Save Energy ◽  
Left Front

The algorithms used by schedulers depend on the complexity of the schedule and constraints for each problem. The position and movement of badminton players in badminton doubles competition is one of the key factors to improve the athletes’ transition efficiency of offense and defense and the rate of winning matches and to save energy consumption. From the perspective of basic theory, the author conducts research on the position and movement of badminton doubles. Based on the numerical analysis method, the optimal model of standing position and direction composed of 7 nonlinear equations is established. In addition, the final of 10 matches of the super series of the world badminton federation in 2019 was selected as the sample of speed parameters. With the help of MATLAB mathematical analysis software, the numerical model established by the least square method was adopted to optimize the specific standing position and walking model. Ultimately, the optimal solution has been obtained, which can be represented on a plane graph. The optimal position of the attack station should be the blocking area (saddle-shaped area) and the hanging area (circular arc area in the middle). The optimal defensive positioning should be left defensive positioning area (left front triangle area) and right defensive positioning area (right front triangle area), which is consistent with our current experience and research results. The research results use mathematical tools to calculate the accurate optimal position in doubles matches, which has guiding significance to the choice of athletes’ position and walking position in actual combat and can also be used as a reference for training, providing a certain theoretical basis for the standing and walking of badminton doubles confrontation. The data collection and operation methods in this study can provide better calculation materials for artificial intelligence optimization and fuzzy operation of motion displacement, which is of great significance in the field of motion, simulation, and the call of parametric functions.

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