A Collaborative UAV-WSN Network for Monitoring Large Areas

Area of Science:

• Computer Science
• Robotics
• Environmental Monitoring

Background:

• Large-scale monitoring systems increasingly rely on Wireless Sensor Networks (WSNs) for data acquisition.
• Integrating Unmanned Aerial Vehicles (UAVs) with WSNs enhances monitoring area and system performance.
• Self-configuration is crucial for efficient data acquisition in dynamic, large-scale environments.

Purpose of the Study:

• To present a novel hybrid UAV-WSN network for self-configured, efficient environmental data acquisition.
• To develop an optimal trajectory generation scheme for UAVs in heterogeneous multi-agent systems.
• To design a sensor localization and clustering method for maximizing ground coverage and communication efficiency.

Main Methods:

• A heterogeneous multi-agent scheme for self-configuration and optimal reference trajectory generation.
• Mixed-integer programming to optimize UAV trajectories considering interdicted regions, way-points, communication time, and path length.
• A sensor localization and clustering algorithm utilizing UAV-to-ground sensor communication for optimal coverage.

Main Results:

• Demonstrated improvements in network efficiency and data collection metrics through the proposed algorithms.
• Validated the practical value of the algorithms via simulation and a realistic WSN-UAV test-bed.
• Achieved optimized UAV trajectories and effective sensor clustering for enhanced environmental monitoring.

Conclusions:

• The proposed hybrid UAV-WSN system significantly enhances environmental data acquisition efficiency.
• The developed trajectory optimization and sensor clustering methods offer practical solutions for large-scale monitoring.
• The integration of UAVs and WSNs, with intelligent self-configuration, represents a valuable advancement in monitoring technology.