A Mobile Node Localization Algorithm Based on the Angle Self-Adjustment Model for Wireless Sensor Networks

2018 ◽  
Vol 33 (02) ◽  
pp. 1958004 ◽  
Author(s):  
Kangshun Li ◽  
Hui Wang ◽  
Fei Tang ◽  
Wei Li ◽  
Yunru Lu
Keyword(s):  
Prior Information ◽  
Gaussian Elimination ◽  
Mobile Node ◽  
Selection Mechanism ◽  
Localization Algorithm ◽  
Accuracy Control ◽  
Node Localization ◽  
Localization Accuracy ◽  
Moving Direction ◽  
Better Than

The goal of this study is to improve the accuracy of mobile node localization and to avoid the influence of moving direction-offsets introduced by the positioning system’s accuracy control. The proposed localization algorithm, which is based on an overlap self-adjustment method, and an anchor node selection mechanism which uses the Gaussian elimination method, is based on the error probability. The proposed algorithm has the advantages of requiring little prior information, and it reduces the power consumption. The simulation results show that the proposed algorithm is better than the self-adjustment localization (SAL) algorithm in terms of its localization accuracy.

A Sensor Node Localization Algorithm Based on Fuzzy RSSI Distance

2014 ◽  
Vol 543-547 ◽  
pp. 989-992
Author(s):  
Xiao Qin Li ◽  
Guang Rong Chen
Keyword(s):  
Sensor Node ◽  
Large Deviation ◽  
Sensor Nodes ◽  
Localization Algorithm ◽  
Node Localization ◽  
Localization Accuracy ◽  
Localization Algorithms ◽  
Traditional Algorithm ◽  
Intensity Changes ◽  
Better Than

The node self-localization is the basis of target localization for wireless sensor network (WSN), the WSN nodes localization algorithms have two types based on distance and non distance. The node localization based on RSSI is simple and widely used in application. According to the traditional WSN nodes localization algorithm, the RSSI signal intensity changes greatly and with nonlinearity. And it is converted into distance feature with a large deviation, which leads to inaccurate positioning and localization. In order to solve this problem, a sensor node localization algorithm is proposed based on fuzzy RSSI distance. The nodes information is collected based on RSSI ranging method. And the location information is processed with fuzzy operation. The disturbance from the environmental factors for the positioning is solved. The accuracy of the node localization is improved. Simulation result shows that this algorithm can locate the sensor nodes accurately. The localization accuracy is high, and the performance of nodes localization is better than the traditional algorithm. It has good application value in the WSN nodes distribution and localization design.

scholarly journals Salp Swarm Algorithm for Node Localization in Wireless Sensor Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Huthaifa M. Kanoosh ◽  
Essam Halim Houssein ◽  
Mazen M. Selim
Keyword(s):  
Computing Time ◽  
Wireless Sensor ◽  
Grey Wolf Optimizer ◽  
Localization Algorithm ◽  
Node Localization ◽  
Localization Accuracy ◽  
Salp Swarm Algorithm ◽  
Localization Scheme ◽  
Swarm Algorithm ◽  
Better Than

Nodes localization in a wireless sensor network (WSN) aims for calculating the coordinates of unknown nodes with the assist of known nodes. The performance of a WSN can be greatly affected by the localization accuracy. In this paper, a node localization scheme is proposed based on a recent bioinspired algorithm called Salp Swarm Algorithm (SSA). The proposed algorithm is compared to well-known optimization algorithms, namely, particle swarm optimization (PSO), Butterfly optimization algorithm (BOA), firefly algorithm (FA), and grey wolf optimizer (GWO) under different WSN deployments. The simulation results show that the proposed localization algorithm is better than the other algorithms in terms of mean localization error, computing time, and the number of localized nodes.

The Mobile Node Localization Algorithm Based on Monte Carlo

2013 ◽  
Vol 712-715 ◽  
pp. 1847-1850
Author(s):  
Jun Gang Zheng ◽  
Cheng Dong Wu ◽  
Zhong Tang Chen
Keyword(s):  
Monte Carlo ◽  
Mobile Node ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Localization Accuracy ◽  
Monte Carlo Localization ◽  
Simulation Results ◽  
Specialized Hardware ◽  
High Node

There exist some mobile node localization algoriths in wireless sensor netwok,which require high computation and specialized hardware and high node large density of beacon nodes.The Monte Carlo localization method has been studied and an improved Monte Carlo node localization has been proposed. Predicting the trajectory of the node by interpolation and combing sampling box to sampling. The method can improve the efficiency of sampling and accuracy. The simulation results show that the method has achieved good localization accuracy.

scholarly journals A Hybrid Mobile Node Localization Algorithm Based on Adaptive MCB-PSO Approach in Wireless Sensor Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Hua Wu ◽  
Ju Liu ◽  
Zheng Dong ◽  
Yang Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Three Dimensional ◽  
Mobile Node ◽  
Global Optimum ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Mobile Nodes ◽  
Node Localization ◽  
Localization Accuracy

In this paper, a hybrid adaptive MCB-PSO node localization algorithm is proposed for three-dimensional mobile wireless sensor networks (MWSNs), which considers the random mobility of both anchor and unknown nodes. An improved particle swarm optimization (PSO) approach is presented with Monte Carlo localization boxed (MCB) to locate mobile nodes. It solves the particle degeneracy problem that appeared in traditional MCB. In the proposed algorithm, a random waypoint model is incorporated to describe random movements of anchor and unknown nodes based on different time units. An adaptive anchor selection operator is designed to improve the performance of standard PSO for each particle based on time units and generations, to maintain the searching ability in the last few time units and particle generations. The objective function of standard PSO is then reformed to make it obtain a better rate of convergence and more accurate cost value for the global optimum position. Furthermore, the moving scope of each particle is constrained in a specified space to improve the searching efficiency as well as to save calculation time. Experiments are made in MATLAB software, and it is compared with DV-Hop, Centroid, MCL, and MCB. Three evaluation indexes are introduced, namely, normalized average localization error, average localization time, and localization rate. The simulation results show that the proposed algorithm works well in every situation with the highest localization accuracy, least time consumptions, and highest localization rates.

scholarly journals Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 343 ◽  
Author(s):  
Dezhi Han ◽  
Yunping Yu ◽  
Kuan-Ching Li ◽  
Rodrigo Fernandes de Mello
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Random Mutation ◽  
Localization Accuracy ◽  
De Algorithm ◽  
Distance Vector

The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements.

scholarly journals An Algorithm of Mobile Robot Node Location Based on Wireless Sensor Network

2017 ◽  
Vol 13 (05) ◽  
pp. 4 ◽  
Author(s):  
Peng An
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Simulation Experiments ◽  
Localization Accuracy ◽  
Node Location ◽  
Classical Localization ◽  
Localization Precision

In the wireless sensor network, there is a consistent one-to-one match between the information collected by the node and the location of the node. Therefore, it attempts to determine the location of unknown nodes for wireless sensor networks. At present, there are many kinds of node localization methods. Because of the distance error, hardware level, application environment and application costs and other factors, the positioning accuracy of various node positioning methods is not in complete accord. The objective function is established and algorithm simulation experiments are carried out to make a mobile ronot node localization.  The experimnettal results showed that  the proposed algorithm can achieve higher localization precision in fewer nodes. In addition, the localization algorithm was compared with the classical localization algorithm. In conclusion, it is verified that the localization algorithm proposed in this paper has higher localization accuracy than the traditional classical localization algorithm when the number of nodes is larger than a certain number

Improved DV-Hop Algorithm for Enhancing Localization Accuracy in WSN

2014 ◽  
Vol 543-547 ◽  
pp. 3256-3259 ◽  
Author(s):  
Da Peng Man ◽  
Guo Dong Qin ◽  
Wu Yang ◽  
Wei Wang ◽  
Shi Chang Xuan
Keyword(s):  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Error ◽  
Localization Algorithm ◽  
Node Localization ◽  
Localization Accuracy ◽  
Global Average ◽  
Key Technologies ◽  
Simulation Results ◽  
Range Free

Node Localization technology is one of key technologies in wireless sensor network. DV-Hop localization algorithm is a kind of range-free algorithm. In this paper, an improved DV-Hop algorithm aiming to enhance localization accuracy is proposed. To enhance localization accuracy, average per-hop distance is replaced by corrected value of global average per-hop distance and global average per-hop error. When calculating hop distance, unknown nodes use corresponding average per-hop distance expression according to different hop value. Comparison with DV-Hop algorithm, simulation results show that the improved DV-Hop algorithm can reduce the localization error and enhance the accuracy of sensor nodes localization more effectively.

Sign in / Sign up

Export Citation Format

Share Document