Year: 2025
Type: article
Abstract: 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.
DOI: 10.1145/3778168
Institutions: University of North Texas, University of Stuttgart
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
Funder: National Security Agency
Awards: G2123397585, G7845798442
OpenAlex ID: W4416854594