The Group Mobility Model Optimized by the D* Obstacle Avoidance Algorithm

In Ad Hoc Networks, the Reference Point Group Mobility Model (RPGM) is widely used to simulate the coordinated movement of nodes. However, traditional RPGM models often assume an idealized environment for node movement, overlooking the presence of obstacles and lacking effective obstacle avoidance mechanisms. This limitation reduces the model's adaptability in complex real-world scenarios. To address this issue, this paper proposes a D* algorithm-optimized RPGM model. In this model, the D* algorithm is capable of real-time obstacle avoidance path planning, allowing the nodes in the group to dynamically bypass obstacles during movement. Simulation experiments demonstrate that this model not only enhances obstacle avoidance capabilities but also maintains the coordinated movement of nodes, significantly improving network stability and robustness.

Authors

Institutions

Publication Details

Published
2024-10-11
DOI
https://doi.org/10.1109/cbase64041.2024.10824382
Primary Topic
Mobile Agent-Based Network Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Group Mobility Model Optimized by the D* Obstacle Avoidance Algorithm

Simon X. Yang, Wei Wang, Luyan Xu, Lan Feng
Mobile Agent-Based Network Management
article

The Group Mobility Model Optimized by the D* Obstacle Avoidance Algorithm

Simon X. Yang, Wei Wang, Luyan Xu, Lan Feng
article en

Abstract

In Ad Hoc Networks, the Reference Point Group Mobility Model (RPGM) is widely used to simulate the coordinated movement of nodes. However, traditional RPGM models often assume an idealized environment for node movement, overlooking the presence of obstacles and lacking effective obstacle avoidance mechanisms. This limitation reduces the model's adaptability in complex real-world scenarios. To address this issue, this paper proposes a D* algorithm-optimized RPGM model. In this model, the D* algorithm is capable of real-time obstacle avoidance path planning, allowing the nodes in the group to dynamically bypass obstacles during movement. Simulation experiments demonstrate that this model not only enhances obstacle avoidance capabilities but also maintains the coordinated movement of nodes, significantly improving network stability and robustness.

Xi'an Polytechnic University (CN)
Reduced inequalities
Openalex Percentile: Top 70%
Mobile Agent-Based Network Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.