An Anchor Node Selection Scheme for Improving RSS-Based Localization in Wireless Sensor Network

Cheng, Jing; Li, Ya; Xu, Qing‐Yuan

doi:10.1155/2022/2611329

Cited by 8 publications

(2 citation statements)

References 35 publications

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“…"Early stopping" method overcomes this drawback to enhance the localization efficiency and mitigates the average localization error. With suitable selection of ANs and a modified version of COA improves the accuracy of SNs location [20]. DECPSOHDV-Hop algorithm aims at ubiquitous positioning accuracy of TNs up to 90% using dynamic optimization method.…”

Section: Literature Reviewmentioning

confidence: 99%

Gaussian-newton Localization Through Multilateration Algorithm for Wireless Sensor Networks

Reddy

Chandra²

2023

Preprint

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With the continuous prevalence of wireless sensor network (WSN) applications in the recent days, localization of sensor nodes became an important aspect in research in terms of its accuracy, communication overhead and computational complexity. Localization plays an important role in location sensitive applications like object tracking, nuclear attacks, biological attacks, fire detection, traffic monitoring systems, intruder detections, and finding survivors in post-disasters, etc. The objective of localization is to identify the coordinates of target nodes using information provided by anchor nodes. Precision improvement of the sensor node positions is a key issue for an effective data transmission between sensor nodes and save the node’s energy as well as enhance the network lifetime. In this article, a cost-effective localization algorithm with minimal number of anchor nodes is proposed that uses nature inspired optimization techniques to enhance the localization accuracy compared to the state-of-the-art localization algorithms. The performance metrics considered for simulations and comparison with the existing algorithms include average localization accuracy, communication range, and the number of anchor nodes. The simulation results prove that the proposed gaussian-newton localization through multilateration algorithm (GNLMA) enhances the mean localization accuracy to 92.8% and the range measurement error is limited to 1.22meters. Depending on the communication range of sensor nodes, the average localization accuracy is achieved up to 94.4% using the proposed GNLMA.

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Section: Literature Reviewmentioning

confidence: 99%

Gaussian-newton Localization Through Multilateration Algorithm for Wireless Sensor Networks

Reddy

Chandra²

2023

Preprint

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“…Yan et al [14] used the weighted matrix and hyperbolic estimation to reduce the cumulative error of the localization solution. Cheng et al [28] selected the appropriate beacon nodes and assigned weights and used the improved cuckoo search algorithm to calculate the coordinates. Sharma et al [29] mixed the Eurasian Wolf optimizer and cuckoo search optimizer to design the algorithm 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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An Improved 3D-DV-Hop Localization Algorithm to Improve Accuracy for 3D Wireless Sensor Networks

Reddy,

Chandra

2024

SN COMPUT. SCI.

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An Anchor Node Selection Scheme for Improving RSS-Based Localization in Wireless Sensor Network

Cited by 8 publications

References 35 publications

Gaussian-newton Localization Through Multilateration Algorithm for Wireless Sensor Networks

Gaussian-newton Localization Through Multilateration Algorithm for Wireless Sensor Networks

Improved DV-Hop Model Based on theApplication of Variable Scenarios

An Improved 3D-DV-Hop Localization Algorithm to Improve Accuracy for 3D Wireless Sensor Networks

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