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

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.