Cloud-Assisted UAV Data Collection for Multiple Emerging Events in Distributed WSNs.

Area of Science:

• Computer Science
• Electrical Engineering
• Robotics

Background:

• Unmanned Aerial Vehicles (UAVs) are increasingly used with Wireless Sensor Networks (WSNs) for flexible data collection.
• Existing research often overlooks event-driven factors and focuses primarily on system architecture and flight path planning.
• This gap necessitates novel strategies for efficient data gathering in dynamic environments.

Purpose of the Study:

• To propose a cloud-assisted data gathering strategy for UAV-based WSNs that accounts for emerging events.
• To develop an optimized UAV flight and data acquisition sequence for WSN clusters.
• To enhance the efficiency and data integrity of UAV-WSN systems.

Main Methods:

• Development of a cloud-assisted strategy for UAV-based WSN data collection.
• Implementation of an algorithm for optimizing UAV flight trajectories and data acquisition sequences.
• Validation through simulations and real-world experiments in a farm environment.

Main Results:

• The proposed methodology significantly outperforms conventional approaches in reducing flight time and energy consumption.
• The strategy ensures high integrity of data acquisition, even during emerging events.
• Real-world experiments demonstrated less than half the flight time and near-perfect data integrity compared to traditional methods.

Conclusions:

• The cloud-assisted strategy offers a more efficient and reliable solution for data gathering in UAV-based WSNs, particularly for event monitoring.
• Optimizing flight paths and data acquisition sequences in conjunction with cloud support is crucial for improving system performance.
• This approach addresses key limitations in current UAV-WSN systems, paving the way for more effective real-world applications.