Energy-Efficient Trajectory Planning for Smart Sensing in IoT Networks Using Quadrotor UAVs

Area of Science:

• Robotics and Automation
• Wireless Communications
• Internet of Things (IoT)

Background:

• Quadrotor unmanned aerial vehicles (UAVs) are crucial for mobile data collection in Internet of Things (IoT) networks.
• Optimizing UAV energy consumption is vital for extending flight duration and network coverage.
• Existing methods often overlook detailed UAV dynamics for energy modeling.

Purpose of the Study:

• To develop an accurate energy consumption model for quadrotor UAVs considering their dynamics.
• To propose and evaluate a novel trajectory design algorithm for energy-efficient data collection.
• To enhance the operational efficiency of UAV-assisted IoT networks.

Main Methods:

• A mathematically tractable model for real-time UAV energy consumption, including motor and aerodynamic dynamics.
• A fly-circle-communicate (FCC) trajectory algorithm using Dubins curves for energy-saving circular flights.
• Integration with the Traveling Salesman Problem (TSP) algorithm for optimal user visit sequencing.

Main Results:

• The proposed energy model accurately characterizes UAV power usage.
• Circular flight trajectories were found to be more energy-efficient than hover flights.
• The FCC algorithm demonstrated a 1-10% reduction in energy consumption compared to hover-communicate (HC).

Conclusions:

• The FCC trajectory design is an effective strategy for reducing UAV energy consumption in IoT sensing networks.
• This approach enhances UAV flight duration and expands the service range of mobile sensing networks.
• The study provides a foundation for more efficient UAV-assisted IoT system design.