A Novel Agentic AI and DQN Approaches for Unmanned Drone Route Combinatorial Optimisation in Distributed Edge Cloud Networks
DOI:
https://doi.org/10.21015/vtse.v14i1.2292Abstract
This study presents a novel agentic artificial intelligence (Agentic-AI) framework, combined with Deep Q-Network (DQN) reinforcement learning, to solve the combinatorial optimization problem of unmanned drone route planning, specifically designed for smart-city surveillance and road–traffic assessment. The system is designed to integrate autonomous decision-making, context-aware sensing, and dynamic route adaptation to efficiently manage large-scale urban monitoring tasks. To evaluate real-world management of large-scale urban monitoring tasks efficiently in the Karachi Smart City environment in various metropolitan zones, including Saddar, Nazimabad, Gulshan-e-Iqbal, North Karachi, Clifton, Korangi and adjoining high-density regions. These areas exhibit computationally and communication-intensive congestion patterns. Communication-intensive congestion patterns and irregular road conditions make them ideal for testing adaptive, multimodal data, such as vehicle density, traffic flow ratio, road surface deterioration, and broken-road occurrences, while interacting with distributed edge nodes for real-time processing and data exchange. The simulation testbeds show that the proposed methods have higher accuracy of data collection by 98%, processing time by 31% and minimize the resource consumption as compared to existing studies.
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