Leveraging LLM Decision-Making in the Internet of Drone Things (IoDT) Ecosystem

The Internet of Drone Things (IoDT) advances autonomous drone operations by integrating live sensor inputs with environmental and situational awareness and intelligent decision-making capabilities. The full capabilities of IoDT remain limited by the difficulties of dynamic task assignment and path optimization, along with adaptive decision-making, when operating in complex environments such as disaster relief and smart agriculture. Traditional task-scheduling techniques have difficulty adapting to real-time changes caused by dynamic constraints such as weather variations, battery limitations, and drone malfunctions. We present an LLM-based task scheduling framework that uses Large Language Models (LLMs) to improve task prioritization performance and path planning accuracy while minimizing operational failures. We combine heuristic algorithms (A*, Dijkstra) with decision-making processes driven by LLMs to allow drones to adapt to environmental changes while optimizing efficiency and resource consumption. Integrating LLM technology into IoDT operations results in up to 95% task completion rates and improves the scenario completion time by up to 42%, while adding reasonable computational overhead. Our framework demonstrates improved task adaptability, battery efficiency, and stronger system resilience against non-LLM baselines during disaster relief and package delivery operations. Our research shows that LLM-based IoDT task management has transformative potential, leading to the development of more innovative and autonomous drone ecosystems.