An improved DV-Hop algorithm based on PSO and Modified DE algorithm

Sun, Hai‐Wei; Wang, Dong; Li, Hongxing; Meng, Ziran

doi:10.1007/s11235-023-00991-w

Cited by 5 publications

(3 citation statements)

References 43 publications

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“…Therefore, weighted function is applied to enhance the precision of unknown nodes' location. The variance value of the error term starts from the wrong vector of the LSA approach and is derived according to the specification of the optimum weight function to generate the best predicted position of the unknown node [23][24][25]. The derivation equation is displayed in Equation ( 7).…”

Section: B Heteroscedasticity and Optimal Weight Function Correction ...mentioning

confidence: 99%

Application Research of DV-HOP Wireless Sensor Localization System Based on Geometric Constraint Optimization

Wu,

Dong

et al. 2024

IEEE Access

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The use of wireless sensors has expanded across various industries, highlighting the need for accurate positioning research. In irregular network environments, conventional distance vector jump algorithms result in poor localization performance. To solve this problem, an improved distance vector jump algorithm is proposed in this study. The algorithm introduces geometric constraint mechanism and optimized weighted DV-HOP localization algorithm. The proposed method applies an optimal weight function to determine the hop count from unknown nodes to anchor nodes and calculates the unknown node coordinates using the least squares method. Additionally, geometric constraint mechanisms are used to eliminate positioning errors caused by outliers. The experimental results demonstrate that the positioning error is 0.37% in C-type network environment, 0.38% in F-type network environment, 0.29% in L-type network environment and 0.30% in H-type network environment. These results verify the effectiveness of the proposed algorithm in reducing positioning errors, which can meet the accuracy requirements of practical applications in complex network environments.

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Section: B Heteroscedasticity and Optimal Weight Function Correction ...mentioning

confidence: 99%

Application Research of DV-HOP Wireless Sensor Localization System Based on Geometric Constraint Optimization

Wu,

Dong

et al. 2024

IEEE Access

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“…There are various aspects of IoT technology that are worth studying, and node localization is one of them. Accurate effective node positioning is the foundation for providing various monitoring location information services [7]. In addition, location information There are various aspects of IoT technology that are worth studying, and node localization is one of them.…”

Section: Introductionmentioning

confidence: 99%

“…How to estimate the coordinates of unknown nodes through efficient and low-cost positioning There are various aspects of IoT technology that are worth studying, and node localization is one of them. Accurate effective node positioning is the foundation for providing various monitoring location information services [7]. In addition, location information can also improve the efficiency of routing in the network and achieve network topology selfconfiguration [8,9].…”

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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