The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
3차원 공간에서 이종 방향성 무선 센서 네트워크의 자가 전개 문제를 해결하기 위해 본 논문에서는 3차원 공간에서 가중치 보로노이 다이어그램 기반 자가 전개 알고리즘(3DV-HDDA)을 제안한다. 3DV-HDDA 알고리즘은 모니터링 영역의 네트워크 커버리지 비율을 향상시키기 위해 가중 보로노이 다이어그램을 사용하여 센서 노드를 이동하고 가상 경계 토크를 도입하여 센서 노드를 회전시켜 센서 노드가 최적의 위치에 도달할 수 있도록 합니다. 이 작업에는 중앙 집중식 센서 노드의 위치를 기반으로 한 개선 알고리즘(3DV-HDDA-I)도 포함됩니다. 3DV-HDDA와 3DV-HDDA-I 알고리즘의 차이점은 후자의 경우 노드의 움직임이 가중 보로노이 그래프와 가상 힘에 의해 결정된다는 것입니다. 시뮬레이션 결과, 가상 힘 알고리즘과 비가중 보로노이 그래프 기반 알고리즘에 비해 3DV-HDDA 및 3DV-HDDA-I 알고리즘이 모니터링 영역의 네트워크 커버리지 비율을 효과적으로 향상시키는 것으로 나타났습니다. 가상 힘 알고리즘과 비교하여 3DV-HDDA 알고리즘은 커버리지를 75.93%에서 91.46%로 증가시키고, 3DV-HDDA-I 알고리즘은 커버리지를 76.27%에서 91.31%로 증가시킵니다. 비가중 보로노이 그래프 기반 알고리즘과 비교했을 때, 3DV-HDDA 알고리즘은 커버리지를 80.19%에서 91.46%로 향상시켰고, 3DV-HDDA-I 알고리즘은 커버리지를 72.25%에서 91.31%로 향상시켰습니다. 또한, 제안된 알고리즘의 60회 반복 후 에너지 소비는 가상 힘 알고리즘을 사용한 에너지 소비보다 적다. 실험 결과는 3DV-HDDA 및 3DV-HDDA-I 알고리즘의 정확성과 효율성을 보여줍니다.
Li TAN
Beijing Technology and Business University
Xiaojiang TANG
Beijing Technology and Business University
Anbar HUSSAIN
Beijing Technology and Business University
Haoyu WANG
Beijing Technology and Business University
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부
Li TAN, Xiaojiang TANG, Anbar HUSSAIN, Haoyu WANG, "A Weighted Voronoi Diagram-Based Self-Deployment Algorithm for Heterogeneous Directional Mobile Sensor Networks in Three-Dimensional Space" in IEICE TRANSACTIONS on Communications,
vol. E103-B, no. 5, pp. 545-558, May 2020, doi: 10.1587/transcom.2019EBP3111.
Abstract: To solve the problem of the self-deployment of heterogeneous directional wireless sensor networks in 3D space, this paper proposes a weighted Voronoi diagram-based self-deployment algorithm (3DV-HDDA) in 3D space. To improve the network coverage ratio of the monitoring area, the 3DV-HDDA algorithm uses the weighted Voronoi diagram to move the sensor nodes and introduces virtual boundary torque to rotate the sensor nodes, so that the sensor nodes can reach the optimal position. This work also includes an improvement algorithm (3DV-HDDA-I) based on the positions of the centralized sensor nodes. The difference between the 3DV-HDDA and the 3DV-HDDA-I algorithms is that in the latter the movement of the node is determined by both the weighted Voronoi graph and virtual force. Simulations show that compared to the virtual force algorithm and the unweighted Voronoi graph-based algorithm, the 3DV-HDDA and 3DV-HDDA-I algorithms effectively improve the network coverage ratio of the monitoring area. Compared to the virtual force algorithm, the 3DV-HDDA algorithm increases the coverage from 75.93% to 91.46% while the 3DV-HDDA-I algorithm increases coverage from 76.27% to 91.31%. When compared to the unweighted Voronoi graph-based algorithm, the 3DV-HDDA algorithm improves the coverage from 80.19% to 91.46% while the 3DV-HDDA-I algorithm improves the coverage from 72.25% to 91.31%. Further, the energy consumption of the proposed algorithms after 60 iterations is smaller than the energy consumption using a virtual force algorithm. Experimental results demonstrate the accuracy and effectiveness of the 3DV-HDDA and the 3DV-HDDA-I algorithms.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2019EBP3111/_p
부
@ARTICLE{e103-b_5_545,
author={Li TAN, Xiaojiang TANG, Anbar HUSSAIN, Haoyu WANG, },
journal={IEICE TRANSACTIONS on Communications},
title={A Weighted Voronoi Diagram-Based Self-Deployment Algorithm for Heterogeneous Directional Mobile Sensor Networks in Three-Dimensional Space},
year={2020},
volume={E103-B},
number={5},
pages={545-558},
abstract={To solve the problem of the self-deployment of heterogeneous directional wireless sensor networks in 3D space, this paper proposes a weighted Voronoi diagram-based self-deployment algorithm (3DV-HDDA) in 3D space. To improve the network coverage ratio of the monitoring area, the 3DV-HDDA algorithm uses the weighted Voronoi diagram to move the sensor nodes and introduces virtual boundary torque to rotate the sensor nodes, so that the sensor nodes can reach the optimal position. This work also includes an improvement algorithm (3DV-HDDA-I) based on the positions of the centralized sensor nodes. The difference between the 3DV-HDDA and the 3DV-HDDA-I algorithms is that in the latter the movement of the node is determined by both the weighted Voronoi graph and virtual force. Simulations show that compared to the virtual force algorithm and the unweighted Voronoi graph-based algorithm, the 3DV-HDDA and 3DV-HDDA-I algorithms effectively improve the network coverage ratio of the monitoring area. Compared to the virtual force algorithm, the 3DV-HDDA algorithm increases the coverage from 75.93% to 91.46% while the 3DV-HDDA-I algorithm increases coverage from 76.27% to 91.31%. When compared to the unweighted Voronoi graph-based algorithm, the 3DV-HDDA algorithm improves the coverage from 80.19% to 91.46% while the 3DV-HDDA-I algorithm improves the coverage from 72.25% to 91.31%. Further, the energy consumption of the proposed algorithms after 60 iterations is smaller than the energy consumption using a virtual force algorithm. Experimental results demonstrate the accuracy and effectiveness of the 3DV-HDDA and the 3DV-HDDA-I algorithms.},
keywords={},
doi={10.1587/transcom.2019EBP3111},
ISSN={1745-1345},
month={May},}
부
TY - JOUR
TI - A Weighted Voronoi Diagram-Based Self-Deployment Algorithm for Heterogeneous Directional Mobile Sensor Networks in Three-Dimensional Space
T2 - IEICE TRANSACTIONS on Communications
SP - 545
EP - 558
AU - Li TAN
AU - Xiaojiang TANG
AU - Anbar HUSSAIN
AU - Haoyu WANG
PY - 2020
DO - 10.1587/transcom.2019EBP3111
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E103-B
IS - 5
JA - IEICE TRANSACTIONS on Communications
Y1 - May 2020
AB - To solve the problem of the self-deployment of heterogeneous directional wireless sensor networks in 3D space, this paper proposes a weighted Voronoi diagram-based self-deployment algorithm (3DV-HDDA) in 3D space. To improve the network coverage ratio of the monitoring area, the 3DV-HDDA algorithm uses the weighted Voronoi diagram to move the sensor nodes and introduces virtual boundary torque to rotate the sensor nodes, so that the sensor nodes can reach the optimal position. This work also includes an improvement algorithm (3DV-HDDA-I) based on the positions of the centralized sensor nodes. The difference between the 3DV-HDDA and the 3DV-HDDA-I algorithms is that in the latter the movement of the node is determined by both the weighted Voronoi graph and virtual force. Simulations show that compared to the virtual force algorithm and the unweighted Voronoi graph-based algorithm, the 3DV-HDDA and 3DV-HDDA-I algorithms effectively improve the network coverage ratio of the monitoring area. Compared to the virtual force algorithm, the 3DV-HDDA algorithm increases the coverage from 75.93% to 91.46% while the 3DV-HDDA-I algorithm increases coverage from 76.27% to 91.31%. When compared to the unweighted Voronoi graph-based algorithm, the 3DV-HDDA algorithm improves the coverage from 80.19% to 91.46% while the 3DV-HDDA-I algorithm improves the coverage from 72.25% to 91.31%. Further, the energy consumption of the proposed algorithms after 60 iterations is smaller than the energy consumption using a virtual force algorithm. Experimental results demonstrate the accuracy and effectiveness of the 3DV-HDDA and the 3DV-HDDA-I algorithms.
ER -