scholarly journals A Kernel-Based Node Localization in Anisotropic Wireless Sensor Network

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wenxiu He ◽  
Fangfang Lu ◽  
Jingjing Chen ◽  
Yi Ruan ◽  
Tingjuan Lu ◽  
...  
Keyword(s):  
Wireless Sensors ◽  
Kernel Regression ◽  
Wireless Sensor ◽  
Polynomial Kernel ◽  
Node Localization ◽  
Free Node ◽  
Localization Accuracy ◽  
Simulation Results ◽  
Best Localization ◽  
Range Free

Wireless sensors localization is still the main problem concerning wireless sensor networks (WSN). Unfortunately, range-free node localization of WSN results in a fatal weakness–, low accuracy. In this paper, we introduce kernel regression to node localization of anisotropic WSN, which transfers the problem of localization to the problem of kernel regression. Radial basis kernel-based G-LSVR and polynomial-kernel-based P-LSVR proposed are compared with classical DV-Hop in both isotropic WSN and anisotropic WSN under different proportion beacons, network scales, and disturbances of communication range. G-LSVR presents the best localization accuracy and stability from the simulation results.

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.

Range-Free Node Localization Schemes for Wireless Sensor Networks

2012 ◽  
Vol 457-458 ◽  
pp. 825-833
Author(s):  
Qin Qin Shi ◽  
Jian Ping Zhang ◽  
Yun Xiang Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Node Localization ◽  
Free Node ◽  
Localization Accuracy ◽  
Network Connection ◽  
Original Scheme ◽  
Node Position ◽  
Range Free

Two range-free node localization schemes modified from the conventional DV-Hop scheme are presented in this work. Different node position derivation algorithms are used to enhance the localization accuracy of DV-Hop. The principle of the algorithms and the improvement approach are illustrated. Simulation shows that the modified schemes outperform the original scheme in terms of the localization accuracy as the network connection topology varies.

Range-Free Node Localization Schemes for Wireless Sensor Networks

2012 ◽  
Vol 457-458 ◽  
pp. 825-833
Author(s):  
Qin Qin Shi ◽  
Jian Ping Zhang ◽  
Yun Xiang Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Node Localization ◽  
Free Node ◽  
Range Free

A Novel Range-Free Node Localization Method for Wireless Sensor Networks

2022 ◽  
pp. 1-1
Author(s):  
Yong Jin ◽  
Lin Zhou ◽  
Lu Zhang ◽  
Zhentao Hu ◽  
Jing Han
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Node Localization ◽  
Free Node ◽  
Localization Method ◽  
Range Free

Range-free node localization in multi-hop wireless sensor networks

2016 ◽  
Author(s):  
Sim Zaidi ◽  
Ahmad El Assaf ◽  
Sofiene Affes ◽  
Nahi Kandil
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Node Localization ◽  
Free Node ◽  
Range Free

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 Performance Evaluation of Hop-Based Range-Free Localization Methods in Wireless Sensor Networks

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
M. Keshtgary ◽  
M. Fasihy ◽  
Z. Ronaghi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Hop Count ◽  
Localization Accuracy ◽  
Energy Consuming ◽  
Network Overhead ◽  
Entire Network ◽  
Evaluation Parameters ◽  
Range Free

Knowledge of nodes' locations is an important requirement for many applications in Wireless Sensor Networks. In the hop-based range-free localization methods, anchors broadcast the localization messages including a hop count value to the entire network. Each node receives this message and calculates its own distance with anchor in hops and then approximates its own location. In this paper, we review range-free localization methods and evaluate the performance of two methods: “DV-hop” and “amorphous” by simulation. We consider some parameters like localization accuracy, energy consumption, and network overhead. Recent papers that evaluate localization methods mostly concentrated on localization accuracy. But we have considered a group of evaluation parameters, energy consuming, and network overhead in addition to the location accuracy.

scholarly journals Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs

2021 ◽  
Author(s):  
Lismer Andres Caceres Najarro ◽  
Iickho Song ◽  
Kiseon Kim
Keyword(s):  
Sensor Node ◽  
State Of The Art ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Healthcare Industry ◽  
Target Node ◽  
Node Localization ◽  
Localization Accuracy ◽  
Fundamental Limitations ◽  
Points Of View

<p> </p><p>With the advances in new technological trends and the reduction in prices of sensor nodes, wireless sensor networks</p> <p>(WSNs) and their applications are proliferating in several areas of our society such as healthcare, industry, farming, and housing. Accordingly, in recent years attention on localization has increased significantly since it is one of the main facets in any WSN. In a nutshell, localization is the process in which the position of any sensor node is retrieved by exploiting measurements from and between sensor nodes. Several techniques of localization have been proposed in the literature with different localization accuracy, complexity, and hence different applicability. The localization accuracy is limited by fundamental limitations, theoretical and practical, that restrict the localization accuracy regardless of the technique employed in the localization process. In this paper, we pay special attention to such fundamental limitations from the theoretical and practical points of view and provide a comprehensive review of the state-of-the-art solutions that deal with such limitations. Additionally, discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives are presented.</p> <p><br></p>

Nelder Mead with Grasshopper Optimization Algorithm for Node Localization in Wireless Sensor Networks

2020 ◽  
Vol 17 (12) ◽  
pp. 5409-5421
Author(s):  
M. Santhosh ◽  
P. Sudhakar
Keyword(s):  
Optimization Algorithm ◽  
Wireless Sensor ◽  
Target Area ◽  
Node Localization ◽  
Localization Accuracy ◽  
Simplex Search ◽  
Grasshopper Optimization Algorithm ◽  
Anchor Nodes ◽  
Series Of Experiments ◽  
Grasshopper Optimization

Node localization in wireless sensor network (WSN) becomes essential to calculate the coordinate points of the unknown nodes with the use of known or anchor nodes. The efficiency of the WSN has significant impact on localization accuracy. Node localization can be considered as an optimization problem and bioinspired algorithms finds useful to solve it. This paper introduces a novel Nelder Mead with Grasshopper Optimization Algorithm (NMGOA) for node localization in WSN. The Nelder-Mead simplex search method is employed to improve the effectiveness of GOA because of its capability of faster convergence. At the beginning, the nodes in WSN are arbitrarily placed in the target area and then nodes are initialized. Afterwards, the node executes the NMGOA technique for estimating the location of the unknown nodes and become localized nodes. In the subsequent round, the localized nodes will be included to the collection of anchor nodes to perform the localization process. The effectiveness of the NMGOA model is validated using a series of experiments and results indicated that the NMGOA model has achieved superior results over the compared methods.

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