Range-free 3D node localization in anisotropic wireless sensor networks

Kumar, Anil; Khosla, Arun; Saini, J.S.; Sidhu, Satvir Singh

doi:10.1016/j.asoc.2015.05.025

Cited by 55 publications

(28 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Algorithms 2017, 10, 131 2 of 13 localization algorithms are the centroid algorithm [7], the Approximate Point-In-Triangulation APIT algorithm [8], the DV-Hop algorithm [9], the LACM algorithm [10], the LACMV algorithm [11], and the HPSO and BBO algorithm [12]. Among the above classical range-free location algorithms, the APIT localization scheme is widely used for localization estimation in wireless sensor networks, due to its simplicity and low power consumption.…”

Section: Apit Algorithmmentioning

confidence: 99%

“…According to Equation (1), λ is set to 0.2, and according to Equation (12), the impact of the mean and standard deviation weighting factors is given by α = 0.25, 0.5, 0.75; β = 0.25, 0.5, 0.75 respectively.…”

Section: Optimization Of the Mean And Standard Deviation Weighting Famentioning

confidence: 99%

“…The range-free location algorithms, on the other hand, can meet the localization precision in most applications without any additional hardware, such as a synchronous clock, which makes these algorithms suitable for complex communication conditions. Typical localization algorithms are the centroid algorithm [7], the Approximate Point-In-Triangulation APIT algorithm [8], the DV-Hop algorithm [9], the LACM algorithm [10], the LACMV algorithm [11], and the HPSO and BBO algorithm [12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Indoor Collaborative Coefficient-Triangle APIT Localization Algorithm

Chen

Zhang

et al. 2017

Algorithms

View full text Add to dashboard Cite

Abstract:The Approximate Point-In-Triangulation (APIT) localization algorithm is a widely used indoor positioning technology due to its simplicity and low power consumption. However, in practice, In-to-Out misjudgments exist regularly in APIT, and a considerable amount of nodes cannot be positioned due to the low node density. To tackle this issue, a Collaborative Coefficient-triangle APIT Localization (CCAL) algorithm is proposed. Firstly, an effective triangle criterion is put forward to reduce the probability of In-to-Out misjudgment and reduce the computational complexity. Then, a further Received Signal Strength Indicator (RSSI) location and weighted triangle coordinate calculation method is adopted to reduce the positioning error. Meanwhile, the idea of iterative collaborative positioning of the positioned unknown nodes is introduced to remarkably expand the localization coverage rate. Simulation results show that the proposed algorithm outperforms APIT, RSSI, and other improved algorithms in terms of both node location error and localization coverage rate.

show abstract

Section: Apit Algorithmmentioning

confidence: 99%

Section: Optimization Of the Mean And Standard Deviation Weighting Famentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Indoor Collaborative Coefficient-Triangle APIT Localization Algorithm

Chen

Zhang

et al. 2017

Algorithms

View full text Add to dashboard Cite

show abstract

“…It is important to obtain the sensor node's location, and the monitoring data will be meaningless without location information. For instance, in precision agriculture, the sensor nodes can gather data of light intensity, humidity, and temperature, which must be accompanied by the coordinates of the collectors [2]. Without such positional information, the observer cannot match the data with the region and make an appropriate decision.…”

Section: Introductionmentioning

confidence: 99%

A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks

Miao

Zhu

2018

J Wireless Com Network

View full text Add to dashboard Cite

The localization of sensor node is an essential problem for many economic forecasting applications in wireless sensor networks. Considering that the mobile sensors change their locations frequently over time, Monte Carlo localization algorithm utilizes the moving characteristics of nodes and employs the probability distribution function (PDF) in the previous time slot to estimate the current location by using a weighted particle filter. However, it also has the problem of insufficient number of valid samples, which further affects the node's localization accuracy. In this paper, differential evolution method is introduced into the Monte Carlo localization algorithm. The sample weight is taken as the objective function, and differential evolution algorithm is implemented in sample stage. Finally, the node position is estimated by making the sample close to the actual location of the node instead of being filtered out. The simulation results demonstrate that the proposed algorithmprovidesabetterpositionestimationwithlesslocalizationerror.

show abstract

“…Nodes localization in anisotropy network has recently received much attention [3][4][5][6][7][8]. DV-Hop algorithm [9] is widely used in large scale anisotropy network for nodes localization because of its simple implementation and low requirements for hardware.…”

Section: Introductionmentioning

confidence: 99%

NDSL: Node Density-Based Subregional Localization in Large Scale Anisotropy Wireless Sensor Networks

Tang

Zhang

Wang

et al. 2015

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Localization is emerging as a fundamental component in wireless sensor network and is widely used in the fields of environmental monitoring, national defense and military, transportation, and so on. Current positioning system, however, can only locate an object's position in isotropy wireless sensor network with high accuracy but cannot locate it accurately in anisotropy wireless sensor network. Besides, past proposals only mentioned anisotropy to show that connectivity of network is different in each direction. However, how to quantify the degree of anisotropy is not clearly pointed out. This paper introduces NDSL (node density-based subregional localization), a positioning system that is used in anisotropy wireless sensor network. The network is divided into many subregions where the nodes density is relatively uniform and then corrects the single-hop distance for each beacon node to locate unknown nodes. We also use nodes distribution and signals distribution to build a model to evaluate the degree of anisotropy for anisotropy network. Through the analysis of the degree of anisotropy for different topologies, the results show that the model is consistent with the facts. Results from actual deployments and simulation experiments show that the accuracy of NDSL algorithm obviously improves compared with DV-Hop algorithm.

show abstract

Range-free 3D node localization in anisotropic wireless sensor networks

Cited by 55 publications

References 37 publications

An Indoor Collaborative Coefficient-Triangle APIT Localization Algorithm

An Indoor Collaborative Coefficient-Triangle APIT Localization Algorithm

A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks

NDSL: Node Density-Based Subregional Localization in Large Scale Anisotropy Wireless Sensor Networks

Contact Info

Product

Resources

About