Hierarchical Analysis Process for Belief Management in Internet of Drones

Area of Science:

• Robotics and Distributed Systems
• Artificial Intelligence
• Information Fusion

Background:

• Group awareness is crucial for efficient mission planning in spatially distributed collaborative entities.
• The Internet of Things (IoT) has enhanced collaborative tracking and event recognition.
• Existing IoT approaches for group awareness struggle with information inaccuracy among distributed entities.

Purpose of the Study:

• To propose a novel belief-management-based model for collaborative Internet of Drones (IoD).
• To enhance decision-making processes by managing uncertainty in shared information.
• To provide group awareness as a service within collaborative drone networks.

Main Methods:

• Development of a belief-management model for IoD.
• Integration of Hierarchical Analysis Process (AHP) for decision-making.
• Application of Subjective Logic (SL) for representing and combining opinions from multiple sources.

Main Results:

• The proposed model enables drones to select appropriate operators for managing information uncertainty.
• Improved accuracy in shared information for collaborative drone operations.
• Demonstrated effectiveness in purely collaborative drone networks.

Conclusions:

• The belief-management model effectively addresses information inaccuracy in collaborative IoD.
• AHP and SL provide a robust framework for managing distributed opinions and uncertainty.
• The approach enhances group awareness as a service for collaborative drone entities.