Difference DV_Distance Localization Algorithm Using Correction Coefficients of Unknown Nodes

Sun, Lijun; Chen, Tianfei

doi:10.3390/s18092860

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…The Dv-distance method [175] distributes each node that receives information from one or more neighbors directly connected to it. The iterative process continues until no information is exchanged between neighbors.…”

Section: ) Distance Vector (Dv-distance)mentioning

confidence: 99%

Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies

Ali¹,

Liu²,

He³

et al. 2021

IEEE Access

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Object identification and localization in indoor and outdoor environments are paramount issues in object-human interaction. Recent advancements in the data fusion capabilities of multi-sensor systems have paved the way for research on emerging object identification and positioning techniques. This review describes techniques and methods used in positioning technologies. State-of-the-art localization technologies are classified into range-based, range-free and AI-based categories. An in-depth analysis of localization approaches based on laser range finder, radio-frequency identification, ultra-wideband, inertial measurement unit, etc., are presented by providing a detailed comparison based on range, accuracy, measurement method, advantages, disadvantages, and their applications. Furthermore, we investigate stateof-the-art multimodal data fusion techniques that utilize probabilistic methods for the precise estimation of object identification in motion and its localization.INDEX TERMS Indoor and outdoor positioning systems; identification and localization; unstructured environment; multi-sensor system; multi-object; positioning and localization techniques.

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Section: ) Distance Vector (Dv-distance)mentioning

confidence: 99%

Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies

Ali¹,

Liu²,

He³

et al. 2021

IEEE Access

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“…Finally, the energy consumption of sensor nodes is also a main challenge with more GPS modules [ 8 , 9 ]. One solution is to use several sensor nodes equipped with localization modules, combined with known information in the network, to calculate the location of unknown nodes, which is node localization technology [ 10 , 11 , 12 , 13 ]. Nodes equipped with localization modules are called beacon nodes.…”

Section: Introductionmentioning

confidence: 99%

DV-Hop Algorithm Based on Multi-Objective Salp Swarm Algorithm Optimization

Liu

Zheng²,

Zheng

et al. 2023

Sensors

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The localization of sensor nodes is an important problem in wireless sensor networks. The DV-Hop algorithm is a typical range-free algorithm, but the localization accuracy is not high. To further improve the localization accuracy, this paper designs a DV-Hop algorithm based on multi-objective salp swarm optimization. Firstly, hop counts in the DV-Hop algorithm are subdivided, and the average hop distance is corrected based on the minimum mean-square error criterion and weighting. Secondly, the traditional single-objective optimization model is transformed into a multi-objective optimization model. Then, in the third stage of DV-Hop, the improved multi-objective salp swarm algorithm is used to estimate the node coordinates. Finally, the proposed algorithm is compared with three improved DV-Hop algorithms in two topologies. Compared with DV-Hop, The localization errors of the proposed algorithm are reduced by 50.79% and 56.79% in the two topology environments with different communication radii. The localization errors of different node numbers are decreased by 38.27% and 56.79%. The maximum reductions in localization errors are 38.44% and 56.79% for different anchor node numbers. Based on different regions, the maximum reductions in localization errors are 56.75% and 56.79%. The simulation results show that the accuracy of the proposed algorithm is better than that of DV-Hop, GWO-DV-Hop, SSA-DV-Hop, and ISSA-DV-Hop algorithms.

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“…Generally, the position information of SNs is indispensable, and monitoring without position information may not be useful. Hence, node localization is one of the most critical issues for WSN [4]. One of the simplest solutions to localize SNs is using the GPS or BeiDou, but it is not possible to equip with them on all SNs especially for the large-scale WSN, because of some constraints like cost, energy, and volume.…”

Section: Introductionmentioning

confidence: 99%

A Connectivity Weighting DV_Hop Localization Algorithm Using Modified Artificial Bee Colony Optimization

Chen

Sun

2019

Journal of Sensors

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Node localization is a fundamental issue in wireless sensor network (WSN), as many applications depend on the precise location of the sensor nodes (SNs). Among all localization algorithms, DV_Hop is a typical range-free localization algorithm characterized by such advantages as simple realization and low energy cost. From detailed analysis of localization error for the basic DV_Hop algorithm, we propose a connectivity weighting DV_Hop localization algorithm using modified artificial bee colony optimization. Firstly, the proposed algorithm calculates the average hop distance (AHD) of anchor nodes in terms of the minimum mean squared distance error between the estimated distances of anchor nodes and the corresponding actual distances from them. After that, a connectivity weighting method, considering the influence from both local network properties of anchor nodes and the distances from anchor nodes to unknown nodes, is designed to obtain the AHD of unknown nodes. In addition, we set up the weighting calculation proportion of anchor nodes at the same time. Finally, a modified artificial bee colony algorithm which enlarges searching space is used to optimize the execution of multilateral localization. The experimental results demonstrate that the connectivity weighting approach has better localization effect, and the AHD of unknown nodes close to true value can be obtained at a relatively large probability. Moreover, the modified artificial bee colony algorithm can reduce the probability of premature convergence, and thus the localization accuracy is further improved.

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Difference DV_Distance Localization Algorithm Using Correction Coefficients of Unknown Nodes

Cited by 5 publications

References 24 publications

Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies

Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies

DV-Hop Algorithm Based on Multi-Objective Salp Swarm Algorithm Optimization

A Connectivity Weighting DV_Hop Localization Algorithm Using Modified Artificial Bee Colony Optimization

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