An Enhanced DV-Hop Positioning Scheme Based on Spring Model and Reliable Beacon Node Set

Chen, Tianfei; Hou, Shuaixin; Sun, Lijun

doi:10.1016/j.comnet.2022.108926

Cited by 11 publications

(2 citation statements)

References 37 publications

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“…This method requires additional ranging equipment, which inevitably increases the overall cost. Range-based algorithms are mainly based on angle of arrival (AOA), time of arrival (TOA), time difference of arrival (TDOA), and receive signal strength (RSSI) [ 17 , 18 , 19 , 20 ]. The range-free algorithm requires connectivity information between the unknown node and the beacon node.…”

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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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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show abstract

“…As a typical representative of nonranging algorithms, the DV-Hop algorithm has become a research hotspot in the engineering and academic fields. This algorithm is less affected by the environment and has low hardware requirements, which makes it for WSN with low cost, broadscale and simple node configuration [27]. Nevertheless, its localization accuracy is low.…”

Section: Introductionmentioning

confidence: 99%

A Novel Localization Technology Based on DV-Hop for Future Internet of Things

et al. 2023

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In recent years, localization has become a hot issue in many applications of the Internet of Things (IoT). The distance vector-hop (DV-Hop) algorithm is accepted for many fields due to its uncomplicated, low-budget, and common hardware, but it has the disadvantage of low positioning accuracy. To solve this issue, an improved DV-Hop algorithm—TWGDV-Hop—is put forward in this article. Firstly, the position is broadcast by using three communication radii, the hop is subdivided, and a hop difference correction coefficient is introduced to correct hops between nodes to make them more accurate. Then, the strategy of the square error fitness function is spent in calculating the average distance per hop (ADPH), and the distance weighting factor is added to jointly modify ADPH to make them more accurate. Finally, a good point set and Levy flight strategy both are introduced into gray wolf algorithm (GWO) to enhance ergodic property and capacity for unfettering the local optimum of it. Then, the improved GWO is used to evolve the place of each node to be located, further improving the location accuracy of the node to be located. The results of simulation make known that the presented positioning algorithm has improved positioning accuracy by 51.5%, 40.35%, and 66.8% compared to original DV-Hop in square, X-shaped, and O-shaped random distribution environments, respectively, with time complexity somewhat increased.

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Improved range-free localization algorithm based on reliable node optimization and enhanced sand cat optimization algorithm

Sun

Tian

2023

J Supercomput

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An Enhanced DV-Hop Positioning Scheme Based on Spring Model and Reliable Beacon Node Set

Cited by 11 publications

References 37 publications

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

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

A Novel Localization Technology Based on DV-Hop for Future Internet of Things

Improved range-free localization algorithm based on reliable node optimization and enhanced sand cat optimization algorithm

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