Researching | Distance Vect Hop Positioning Based on Double Communication Radius Improved Gray Wolf Algithm

Journals >Laser & Optoelectronics Progress >Volume 59 >Issue 7 >Page 0728003 > Article

Laser & Optoelectronics Progress
Vol. 59, Issue 7, 0728003 (2022)

Distance Vector Hop Positioning Based on Double Communication Radius and Improved Gray Wolf Algorithm

Pengchen Cai^1、2, Damin Zhang^1、2、*, Linna Zhang^1、2, Dexin Yin^1、2, and Weina Qin^1、2

Author Affiliations

¹College of Big Data and Information Engineering, Guizhou University, Guiyang , Guizhou 550025, China

²College of Mechanical Engineering, Guizhou University, Guiyang , Guizhou 550025China

show less

DOI: 10.3788/LOP202259.0728003 Cite this Article Set citation alerts

Pengchen Cai, Damin Zhang, Linna Zhang, Dexin Yin, Weina Qin. Distance Vector Hop Positioning Based on Double Communication Radius and Improved Gray Wolf Algorithm[J]. Laser & Optoelectronics Progress, 2022, 59(7): 0728003 Copy Citation Text

show less

Abstract

Aiming at the low positioning accuracy of distance vector hop (DV-Hop) algorithm in wireless sensor networks, a DV-Hop positioning algorithm based on double communication radius and improved gray wolf algorithm is proposed in this paper. First, the position of the beacon node is broadcast with the double communication radius, and the hop number between the beacon node and the unknown node is obtained, so as to get the distance between the beacon node and the unknown node. Then, an improved gray wolf algorithm is used to estimate the location of unknown nodes. Simulation results show that the improved DV-Hop algorithm has better positioning accuracy and stability than the traditional DV-Hop algorithm and similar algorithms.

Keywords

distance vector hop double communication radius grey wolf algorithm sensors wireless sensor network

D = |C \cdot X_{p} (t) - X (t)|

，（1）

X (t + 1) = X_{p} - A \cdot D

，（2）

A = 2 a \cdot r_{1} - a

，（3）

C = 2 r_{2}

，（4）

D_{α} = |C_{1} \cdot X_{α} - X (t)|

，（5）

D_{β} = |C_{2} \cdot X_{β} - X (t)|

，（6）

D_{δ} = |C_{3} \cdot X_{δ} - X (t)|

，（7）

X_{1} = X_{α} - A_{1} \cdot D_{α}

，（8）

X_{2} = X_{β} - A_{2} \cdot D_{β}

，（9）

X_{3} = X_{δ} - A_{3} \cdot D_{δ}

，（10）

X (t + 1) = \frac{X_{1} + X_{2} + X_{3}}{3}

，（11）

A_{1} = A_{2} = A_{3} = 2 \cdot a \cdot r_{1} - a

，（12）

C_{1} = C_{2} = C_{3} = 2 \cdot r_{2}

，（13）

|A_{1} \cdot C_{1}| = |A_{2} \cdot C_{2}| = |A_{3} \cdot C_{3}| = |2 \cdot a \cdot r_{1} - a| \cdot |2 \cdot r_{2}| = m

。（14）

\{\begin{array}{l} w_{α} = \frac{|A_{1} \cdot C_{1}|}{|A_{1} \cdot C_{1}| + |A_{2} \cdot C_{2}| + |A_{3} \cdot C_{3}|} = \frac{m}{m + m + m} = \frac{1}{3} \\ w_{β} = \frac{|A_{2} \cdot C_{2}|}{w α + |A_{2} \cdot C_{2}| + |A_{3} \cdot C_{3}|} = \frac{m}{\frac{1}{3} + m + m} = \frac{3 m}{1 + 6 m} \\ w_{δ} = \frac{|A_{3} \cdot C_{3}|}{w α + w β + |A_{3} \cdot C_{3}|} = \frac{m}{\frac{1}{3} + \frac{3 m}{1 + 6 m} + m} = \frac{18 m^{2} + 3 m}{18 m^{2} + 18 m + 1} \end{array}

，（15）

X (t + 1) = \frac{w_{α} \cdot X_{1} + w_{β} \cdot X_{2} + w_{δ} \cdot X_{3}}{3}

。（16）

X (t + 1) = \{\begin{matrix} \frac{\frac{1}{f_{α}} \cdot X_{1} + \frac{1}{f_{β}} \cdot X_{2} + \frac{1}{f_{δ}} \cdot X_{3}}{1 / f}, f_{i} \leq f_{a v g} \\ \frac{w_{α} \cdot X_{1} + w_{β} \cdot X_{2} + w_{δ} \cdot X_{3}}{3}, f_{i} > f_{a v g} \end{matrix}

，（17）

\frac{1}{f} = \frac{1}{f_{α}} + \frac{1}{f_{β}} + \frac{1}{f_{δ}}

，（18）

|s_{i} - d_{i}| < ε_{i}

。（19）

s_{i} = \sqrt[]{(x - x_{i})^{2} + (y - y_{i})^{2}}

，（20）

\{\begin{array}{l} d_{1} - ε_{1} \leq \sqrt[]{(x - x_{1})^{2} + (y - y_{1})^{2}} \leq d_{1} + ε_{1} \\ d_{2} - ε_{2} \leq \sqrt[]{(x - x_{2})^{2} + (y - y_{2})^{2}} \leq d_{2} + ε_{2} \\ . . . \\ d_{n} - ε_{n} \leq \sqrt[]{(x - x_{n})^{2} + (y - y_{n})^{2}} \leq d_{n} + ε_{n} \end{array}

。（21）

f (x, y) = \sum_{i = 1}^{n} |\sqrt[]{(x - x_{i})^{2} + (y - y_{i})^{2}} - d_{i}|

。（22）

X_{f i t n e s s} = \frac{1}{n} \times \sum_{i = 1}^{n} |\sqrt[]{(x_{i} - x_{j})^{2} + (y_{i} - y_{j})^{2}} - d_{j}|

。（23）

X_{e r r o r} = \sqrt[]{(x_{q} - x_{m})^{2} + (y_{q} - y_{m})^{2}}

，（24）

A_{e r r o r} = \frac{\sum_{n = 1}^{n} \sqrt[]{(x_{q}^{n} - x_{m}^{n})^{2} + (y_{q}^{n} - y_{m}^{n})^{2}}}{n}

。（25）

Get PDF(in Chinese)

Figures&Tables (15)

References (15)

Copy Citation Text

Pengchen Cai, Damin Zhang, Linna Zhang, Dexin Yin, Weina Qin. Distance Vector Hop Positioning Based on Double Communication Radius and Improved Gray Wolf Algorithm[J]. Laser & Optoelectronics Progress, 2022, 59(7): 0728003

Download Citation

Set citation alerts for the article

Set citation alerts for the article

Save the article for my favorites

Paper Information

Recommended Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology

Set citation alerts for the article

Please enter your email address