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Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications.

Sabitri Poudel1, Sangman Moh1

  • 1Department of Computer Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Korea.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary

This study introduces a hybrid path planning (HPP) algorithm for unmanned aerial vehicle (UAV)-aided wireless sensor networks (UWSNs). The HPP algorithm ensures the shortest, collision-free path for UAVs in emergency scenarios, improving data collection efficiency.

Keywords:
artificial bee colonycollision avoidancedata gatheringdelay minimizationpath planningprobabilistic roadmapunmanned aerial vehiclewireless sensor network

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Area of Science:

  • Computer Science
  • Robotics
  • Wireless Communication

Background:

  • Unmanned aerial vehicle (UAV)-aided wireless sensor networks (UWSNs) utilize UAVs as mobile sinks for data collection, extending network lifetime and mitigating the energy-hole problem.
  • Timely and safe path planning for UAVs is critical in emergency applications for efficient data collection and transfer to the base station (BS).
  • Navigating complex, obstacle-ridden environments poses significant challenges for UAV pathfinding in UWSNs.

Purpose of the Study:

  • To propose a hybrid path planning (HPP) algorithm for efficient data collection in emergency UWSN environments.
  • To ensure the shortest, collision-free path for UAVs, optimizing data gathering operations.
  • To enhance the overall performance of UAV-aided wireless sensor networks in time-sensitive applications.

Main Methods:

  • The proposed HPP algorithm integrates the probabilistic roadmap (PRM) algorithm for designing the shortest trajectory map.
  • An optimized artificial bee colony (ABC) algorithm is employed to refine path constraints within a three-dimensional environment.
  • The hybrid approach combines PRM's mapping capabilities with ABC's optimization for robust pathfinding.

Main Results:

  • Simulation results demonstrate that the proposed HPP algorithm significantly outperforms conventional PRM and ABC schemes.
  • The HPP scheme achieves superior performance in terms of reduced flight time and energy consumption.
  • The algorithm shows marked improvements in convergence time and overall flight path efficiency compared to existing methods.

Conclusions:

  • The developed HPP algorithm provides an effective solution for efficient and safe UAV path planning in emergency UWSNs.
  • The hybrid approach offers a significant advancement in optimizing UAV-based data collection under challenging environmental conditions.
  • The findings highlight the potential of HPP for enhancing the reliability and performance of UWSNs in critical applications.