scholarly journals A New Distance Vector-Hop Localization Algorithm Based on Half-Measure Weighted Centroid

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Lu Jian Yin
Keyword(s):  
Network Connectivity ◽  
Average Error ◽  
Localization Algorithm ◽  
Simulation Experiments ◽  
Localization Accuracy ◽  
Jump Distance ◽  
Error Accumulation ◽  
Distance Vector ◽  
Beacon Node ◽  
Communication Radius

Considering the defects of the Distance Vector-Hop (DV-Hop) localization algorithm making errors and having error accumulation in wireless sensor network (WSN), we proposed a new DV-Hop localization algorithm based on half-measure weighted centroid. This algorithm followed the two-dimensional position distribution, designed the minimum communication radius, and formed a reasonable network connectivity firstly. Then, the algorithm corrected the distance between the beacon node and its neighbour node to form a more accurate jump distance so that the shortest path can be optimized. Finally, we theorized the proposed localization algorithm and verified it in simulation experiments, including same communication radius, different communication radii, and different node densities in same communication radius, and have compared the localization error and localization accuracy, respectively, between the proposed algorithm and the DV-Hop localization algorithm. The experiment’s result shows that the proposed localization algorithm have reduced the localization’s average error and improved the localization’s accuracy.

Sequence Localization Algorithm Based on 3D Voronoi Diagram in Wireless Sensor Network

2014 ◽  
Vol 644-650 ◽  
pp. 4422-4426 ◽  
Author(s):  
Xi Yang ◽  
Jun Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Voronoi Diagram ◽  
Wireless Sensor ◽  
Weighted Estimate ◽  
Localization Algorithm ◽  
Simulation Experiments ◽  
Localization Accuracy ◽  
3D Space ◽  
Unknown Node

For nodes’ self-localization in wireless sensor networks (WSN), a new localization algorithm called Sequence Localization algorithm based on 3D Voronoi diagram (SL3V) is proposed, which uses 3D Voronoi diagram to divide the localization space.It uses the polyhedron vertices as the virtual beacon nodes and constructs the rank sequence table of virtual beacon nodes. Then it computes Kendall coefficients of the ranks in the optimal rank sequence table and that of the unknown node. Finally, it realizes the weighted estimate of the unknown node by normalization processing Kendall coefficients. Simulation experiments prove that itcan obviously improve the localization accuracy compared with the traditional 2D sequence-based localization and can satisfy the need of localization for 3D space.

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

Research on Node Localization Algorithm in WSN Basing Machine Learning

2013 ◽  
Vol 756-759 ◽  
pp. 3568-3573
Author(s):  
Qing Zhang Chen ◽  
Yu Zheng Chen ◽  
Cong Ling Fan ◽  
Fan Yang ◽  
Peng Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Localization Algorithm ◽  
Node Localization ◽  
Localization Accuracy ◽  
Node Ratio ◽  
Location Algorithm ◽  
Beacon Node ◽  
Coverage Holes

Machine learning uses experience to improve its performance. Using Machine Learing, to locate the nodes in wireless sensor network. The basic idea is that: the network area is divided into several equal portions of small grids, each gird represents a certain class of Machine Learning algorithm. After Machine Learning algorithm has learnt the parameters using the known beacon nodes, it can classify the unknown nodes location classes, and further determine their coordinates. For the SVM OneAgainstOne Location Algorithm, the results of simulation show that it has a high localization accuracy and a better tolerance for the ranging error, while it doesnt require a high beacon node ratio. For the SVM Decision Tree Location Algorithm, the results show that this algorithm is not affected seriously by coverage holes, it is suitable for the network environment of nonuniformity distribution or existing coverage holes.

scholarly journals Regularized Least Square Multi-hops Localization Algorithm for wireless sensor networks

2021 ◽  
Author(s):  
Hend Liouane ◽  
Sana Messous ◽  
Omar Cheikhrouhou
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Connectivity ◽  
Least Square ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Location Awareness ◽  
Localization Algorithm ◽  
Multihop Wireless ◽  
Localization Accuracy

Abstract Multi-hop localization is a an important technique for Wireless Sensor Networks. Location awareness is very crucial for almost existing sensor network applications. However, using Global Positioning System (GPS) receivers to every node is very expensive. Therefore, the Distance Vector-Hop algorithm (DV-Hop) is proposed and very famous for its simplicity and localization accuracy for Wireless Sensor Networks. The cited algorithm uses a small number of anchor nodes, which are equipped with GPS, thus their locations are known, while other nodes estimate their location from the network connectivity information. However, DV-Hop presents some deficiencies and drawbacks in terms of localization accuracy. Therefore, we propose in this paper an improvement of DV-Hop algorithm, called Regularized Least Square DV-Hop Localization Algorithm for multihop wireless sensors networks. The proposed solution improves the location accuracy of sensor nodes within their sensing field in both isotropic and anisotropic networks. Simulation results prove that the proposed algorithm outperforms the original DV-Hop algorithm with up to 60%, as well as other related works, in terms of localization accuracy.

Accuracy analysis of BLE beacon-based localization in smart buildings

2021 ◽  
pp. 1-20
Author(s):  
Rosen Ivanov
Keyword(s):  
Use Case ◽  
Accuracy Analysis ◽  
Localization Algorithm ◽  
Computing Power ◽  
Smart Buildings ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Deployment Strategy ◽  
Accurate Localization ◽  
Localization Algorithms

The majority of services that deliver personalized content in smart buildings require accurate localization of their clients. This article presents an analysis of the localization accuracy using Bluetooth Low Energy (BLE) beacons. The aim is to present an approach to create accurate Indoor Positioning Systems (IPS) using algorithms that can be implemented in real time on platforms with low computing power. Parameters on which the localization accuracy mostly depends are analyzed: localization algorithm, beacons’ density, deployment strategy, and noise in the BLE channels. An adaptive algorithm for pre-processing the signals from the beacons is proposed, which aims to reduce noise in beacon’s data and to capture visitor’s dynamics. The accuracy of five range-based localization algorithms in different use case scenarios is analyzed. Three of these algorithms are specially designed to be less sensitive to noise in radio channels and require little computing power. Experiments conducted in a simulated and real environment show that using proposed algorithms the localization accuracy less than 1 m can be obtained.

scholarly journals Efficient Localization Algorithm for Near-Field Noncircular Sources via Dual-Polarization Sensor Array

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Jiaqi Song ◽  
Haihong Tao
Keyword(s):  
Source Localization ◽  
Near Field ◽  
Nuisance Parameter ◽  
Estimation Accuracy ◽  
Localization Algorithm ◽  
Dual Polarization ◽  
Rank Reduction ◽  
Localization Accuracy ◽  
Noncircular Signals ◽  
Polarization Sensor

Noncircular signals are widely used in the area of radar, sonar, and wireless communication array systems, which can offer more accurate estimates and detect more sources. In this paper, the noncircular signals are employed to improve source localization accuracy and identifiability. Firstly, an extended real-valued covariance matrix is constructed to transform complex-valued computation into real-valued computation. Based on the property of noncircular signals and symmetric uniform linear array (SULA) which consist of dual-polarization sensors, the array steering vectors can be separated into the source position parameters and the nuisance parameter. Therefore, the rank reduction (RARE) estimators are adopted to estimate the source localization parameters in sequence. By utilizing polarization information of sources and real-valued computation, the maximum number of resolvable sources, estimation accuracy, and resolution can be improved. Numerical simulations demonstrate that the proposed method outperforms the existing methods in both resolution and estimation accuracy.

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).

Low Cost Recursive Localization scheme for High Density Wireless Sensor Networks

2020 ◽  
pp. 263-285
Author(s):  
Badia Bouhdid ◽  
Wafa Akkari ◽  
Sofien Gannouni
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
High Density ◽  
Communication Overhead ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Localization Scheme ◽  
Implementation Costs ◽  
Cost Effective Alternative ◽  
Algorithm Implementation

While existing localization approaches mainly focus on enhancing the accuracy, particular attention has recently been given to reducing the localization algorithm implementation costs. To obtain a tradeoff between location accuracy and implementation cost, recursive localization approaches are being pursued as a cost-effective alternative to the more expensive localization approaches. In the recursive approach, localization information increases progressively as new nodes compute their positions and become themselves reference nodes. A strategy is then required to control and maintain the distribution of these new reference nodes. The lack of such a strategy leads, especially in high density networks, to wasted energy, important communication overhead and even impacts the localization accuracy. In this paper, the authors propose an efficient recursive localization approach that reduces the energy consumption, the execution time, and the communication overhead, yet it increases the localization accuracy through an adequate distribution of reference nodes within the network.

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