A modified error-oriented weight positioning model based on DV-Hop

doi:10.3837/tiis.2022.02.003

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…According to the study, the non-ranging localization algorithm using communication hops can be roughly divided into two steps: distance estimation and node coordinates calculation. In the former step, the optimization of the hop count error plays a decisive role in the final positioning result of the algorithm [29]. In irregular regions, the shortest hop-count paths may produce detours due to obstacles, uneven distribution of nodes, etc.…”

Section: A Hop Count Constraints Between Nodes With Anchor Node Pair ...mentioning

confidence: 99%

Node Localization Algorithm for Irregular Regions Based on Particle Swarm Optimization Algorithm and Reliable Anchor Node Pairs

Li,

Liu,

Zou

et al. 2024

IEEE Access

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In wireless sensor networks (WSN), node localization is a key function, and only by knowing the coordinate positions of the nodes can correct decisions be made. In certain applications, such as smart cities, environmental monitoring, or industrial automation, irregular areas can be complex and affected by environmental complexity and inhomogeneity. Coverage gaps may exist between the nodes to be located and the anchor nodes, and communication paths between the nodes may deviate significantly from the ideal straight line, resulting in a large error between the final positioning result of the algorithm and the actual position. In order to solve this problem, this paper proposes a non-ranging node localization method (RANP-PSO) for irregular regions based on PSO algorithm and anchor node pair selection. The way firstly selects anchor node pairs with higher reliability parameters for the nodes to be located by introducing the hop count constraint mechanism for distance estimation; then uses the regularized least squares method for further constraints on the estimated distances; Finally, the PSO algorithm is utilized to optimize the coordinates of the target node, so as to solve to obtain the position of the node. When the proportion of anchor nodes is 20%, the communication radius of nodes is 30m, and the distribution density of nodes is 0.008, the proposed algorithm reduces the root mean square error by approximately 11.94% compared to AEML and LRAQS algorithms, 7.26% compared to the BDMCL algorithm, and 0.69% compared to the MSVR-DV-Hop algorithm. This demonstrates the advantage of the proposed algorithm in terms of localisation accuracy.

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Section: A Hop Count Constraints Between Nodes With Anchor Node Pair ...mentioning

confidence: 99%

Node Localization Algorithm for Irregular Regions Based on Particle Swarm Optimization Algorithm and Reliable Anchor Node Pairs

Li,

Liu,

Zou

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Chen et al [19] set the connectivity between beacon nodes and unknown nodes as weights for the weighted multilateral localization. Wang et al [24] created a model of the relation between error and hop count, set the error convergence relation, and used it in the weighted positioning equation solution. Cheng et al [25] based on the generalized probability association algorithm of time arrival and the unknown node estimation with the model probability and covariance matrix to improve the positioning accuracy.…”

Section: Related Workmentioning

confidence: 99%

Improved DV-Hop Model Based on theApplication of Variable Scenarios

Wang,

Shen,

Zhou

2023

Preprint

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This work proposes an improved DV-Hop model based on function analysisand simulation parameter determination (named FuncDV-Hop) to address theproblems of low positioning accuracy and strong scene dependence of the DV-Hop localization model. The undetermined coefficient optimization, step functionsegmentation experiment, weight function strategy with equivalent points, andmaximum likelihood estimation correction are introduced to reduce the positioning error by analyzing the error reasons in the average hop distance, distanceestimation, and least square calculation of the DV-Hop model. The experiment isdesigned using the control variable method. The total number of nodes, the ratioof beacon nodes, the communication radius, the number of beacon nodes, and thenumber of unknown nodes are designed as variables to control the experiment.Finally, the experiments of two stages of simulation parameter determinationand integration optimization are carried out. The simulation optimization rateof all scenes is between 23.70% and 75.76%, and the average optimization rateis 57.23%. Experimental results show that the FuncDV-Hop model has the highest optimization rate of more than 50% in all experimental scenarios comparedwith the other models, the localization error is reduced by more than 0.1, andthe optimization rate is increased by more than 10% in the record parameters ofthe existing wireless sensor network system.

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Wireless Sensor Network Localization Incorporating Gray Wolf Optimization and DV-Hop Algorithm

2024

IEEE Access

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A modified error-oriented weight positioning model based on DV-Hop

Cited by 3 publications

References 41 publications

Node Localization Algorithm for Irregular Regions Based on Particle Swarm Optimization Algorithm and Reliable Anchor Node Pairs

Node Localization Algorithm for Irregular Regions Based on Particle Swarm Optimization Algorithm and Reliable Anchor Node Pairs

Improved DV-Hop Model Based on theApplication of Variable Scenarios

Wireless Sensor Network Localization Incorporating Gray Wolf Optimization and DV-Hop Algorithm

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