scholarly journals A Node Localization Algorithm for Wireless Sensor Networks Based on Virtual Partition and Distance Correction

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 330
Author(s):  
Yinghui Meng ◽  
Qianying Zhi ◽  
Minghao Dong ◽  
Weiwei Zhang
Keyword(s):  
Search Algorithm ◽  
Optimal Path ◽  
Distance Estimation ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Path Search ◽  
Communication Path ◽  
Range Free ◽  
Distance Correction

The coordinates of nodes are very important in the application of wireless sensor networks (WSN). The range-free localization algorithm is the best method to obtain the coordinates of sensor nodes at present. Range-free localization algorithm can be divided into two stages: distance estimation and coordinate calculation. For reduce the error in the distance estimation stage, a node localization algorithm for WSN based on virtual partition and distance correction (VP-DC) is proposed in this paper. In the distance estimation stage, firstly, the distance of each hop on the shortest communication path between the unknown node and the beacon node is calculated with the employment of virtual partition algorithm; then, the length of the shortest communication path is obtained by summing the distance of each hop; finally, the unknown distance between nodes is obtained according to the optimal path search algorithm and the distance correction formula. This paper innovative proposes the virtual partition algorithm and the optimal path search algorithm, which effectively avoids the distance estimation error caused by hop number and hop distance, and improves the localization accuracy of unknown nodes.

scholarly journals Efficient range-free localization using elliptical distance correction in heterogeneous wireless sensor networks

2018 ◽  
Vol 14 (1) ◽  
pp. 155014771875627 ◽  
Author(s):  
Wenlan Wu ◽  
Xianbin Wen ◽  
Haixia Xu ◽  
Liming Yuan ◽  
Qingxia Meng
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Likelihood Estimation ◽  
Cumulative Distribution ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Heterogeneous Wireless Sensor Networks ◽  
Heterogeneous Wireless Sensor Network ◽  
Range Free ◽  
Distance Correction

In this article, a novel range-free localization algorithm is proposed based on the modified expected hop progress for heterogeneous wireless sensor networks where all nodes’ communication ranges are different. First, we construct the new cumulative distribution function expression of expected hop progress to reduce the computational complexity. Then, the elliptical distance correction method is used to improve the accuracy of the estimation distance and simultaneously decrease overhead. Finally, using the modified distance, the coordinate of the unknown node can be obtained by maximum likelihood estimation. Compared with other algorithms for heterogeneous wireless sensor network, the proposed algorithm is superior in the localization accuracy and efficiency when used in random and uniform placement of nodes.

An Improved DV-HOP Localization Algorithm Based on Simulation Curve Fitting

2013 ◽  
Vol 684 ◽  
pp. 390-393 ◽  
Author(s):  
Lin Chen ◽  
Zhi Yi Fang ◽  
Wei Lv ◽  
Zhuang Liu
Keyword(s):  
Wireless Sensor Network ◽  
Curve Fitting ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Centroid Algorithm ◽  
Key Technologies ◽  
Accurate Position ◽  
Beacon Node ◽  
Range Free

Localization technology is one of the key technologies in Wireless Sensor Network (WSN). The Centroid algorithm, DV-HOP algorithm, APIT algorithm and Amorphous are the classic algorithms which are based on Range-free localization algorithm. This paper is improved on the basis of the DV-HOP and Weighted DV-HOP node localization algorithm, proposed an improved DV-HOP and weighted DV-HOP of WSN localization algorithm based on Simulation Curve Fitting (SCF). The SCF algorithm makes the process more refined during selecting the beacon node and the selected beacon node can be closer to the accurate position.

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.

scholarly journals EARP: Extended Area Multihop Node Localization Algorithm for Anisotropic Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yinghui Meng ◽  
Yuewen Chen ◽  
Qiuwen Zhang ◽  
Erlin Tian
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Sensor Applications ◽  
Localization Algorithms ◽  
Extended Area ◽  
The Impact ◽  
Communication Path

Localization is one of the essential problems in the Internet of Things (IoT) and other wireless sensor applications. Most traditional range-free localization algorithms ignore the anisotropy factors, which are frequently observed in wireless sensor networks (WSNs) and result in low positioning precision. To mitigate the impact of anisotropy on localization, this paper proposes an extended area multihop node localization method. The proposed method classifies and discusses the boundaries of the concave area within the communication range of the node and then uses the maximum split communication distance of the node to identify and mark the boundary of the concave area. When the shortest communication path between the nodes is affected by the concave area, the boundary of the concave area is expanded to obtain the new shortest communication path, and the node distance is obtained by comparing the changes in the communication path. After a large number of simulation tests, it is shown that the calculation accuracy of this scheme is better than that of similar modern mainstream localization algorithms.

scholarly journals Improved Two Hidden Layers Extreme Learning Machines for Node Localization in Range Free Wireless Sensor Networks

2021 ◽  
pp. 528-534
Author(s):  
Oumaima Liouane ◽  
◽  
Smain Femmam ◽  
Toufik Bakir ◽  
Abdessalem Ben Abdelali
Keyword(s):  
Extreme Learning Machine ◽  
Wireless Sensor ◽  
Isotropic Case ◽  
Localization Algorithm ◽  
Node Localization ◽  
Practical Applications ◽  
Learning Machines ◽  
Learning Machine ◽  
Hidden Layer ◽  
Range Free

Wireless Sensor Network (WSN) architectures are widely used in a variety of practical applications. In most cases of application, the event information transmitted by a sensor node via the network has no significance without the knowledge of its accurate geographical localization. In this paper, a method based on Machine Learning Technique (MLT) is proposed to improve node accuracy localization in WSN. We propose a Single Hidden Layer Extreme Learning Machine (SHL-ELM) and a Two Hidden Layer Extreme Learning Machine (THL-ELM) based methods for nodes localization in WSN. The suggested methods are applied in different Multi-hop WSN deployment cases. We focused on range-free localization algorithm in isotropic case and irregular environments. Simulation results demonstrate that the proposed THL-ELM algorithm greatly minimizes the average localization errors when compared to the Single Hidden Layer Extreme Learning Machine and the Distance Vector Hop (DV- Hop) algorithm.

Localization of WSN using Distributed Particle Swarm Optimization algorithm with precise references

2016 ◽  
Vol 7 ◽  
pp. 1
Author(s):  
Ravichander Janapati ◽  
Ch. Balaswamy ◽  
K. Soundararajan
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Research Area ◽  
Population Based ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Localization Algorithm ◽  
Swarm Optimization

Localization is the key research area in wireless sensor networks. Finding the exact position of the node is known as localization. Different algorithms have been proposed. Here we consider a cooperative localization algorithm with censoring schemes using Crammer Rao bound (CRB). This censoring scheme  can improve the positioning accuracy and reduces computation complexity, traffic and latency. Particle swarm optimization (PSO) is a population based search algorithm based on the swarm intelligence like social behavior of birds, bees or a school of fishes. To improve the algorithm efficiency and localization precision, this paper presents an objective function based on the normal distribution of ranging error and a method of obtaining the search space of particles. In this paper  Distributed localization of wireless sensor networksis proposed using PSO with best censoring technique using CRB. Proposed method shows better results in terms of position accuracy, latency and complexity.  

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.

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