Adaptive UAV deployment for enhanced connectivity in disaster-stricken emergency networks: A multi-objective approach

Abstract

The work in this research aims to help in cases where a sudden natural calamity strikes. The compromised communication systems are unable to offer the essential services needed. This is a critical situation where vulnerable individuals urgently need access to emergency services through the unmanned aerial vehicle (UAV) network. The main hurdle here is quickly figuring out how to link the disaster area location to the base station. This study presents a dynamic UAV-assisted framework that utilizes multi-objective optimization for adaptive deployment, distinct from conventional static base station or single-layered UAV network methods. This research proposes a UAV solution that fulfills various objectives within ad hoc networks for emergency assistance. The study assumes the initial and the target locations are known. The study then introduces a communication relay and the necessary networking, effectively reducing the time it takes for the UAV to connect. The proposed approach dynamically optimizes UAV positioning and path planning, ensuring efficient connectivity under uncertain conditions. It then presents a multi-objective search algorithm for finding the exact point to assist in the disaster area and guides the UAVs to various paths for ultimate goals. Unlike existing strategies, this method enhances UAV adaptability, reduces energy consumption, and optimizes real-time deployment.

Additionally, the proposed method selects branching nodes, maximizing available paths and reducing network costs for communication in a research environment. This approach significantly improves network resilience and adaptability compared to traditional UAV deployment strategies. The simulations produce a 20 % reduction in network time, a 15 % increase in efficiency, and a 25 % reduction in UAV deployment compared with existing methods. The real-world experimental test produced a power consumption of 150 W, generally between 200–400 W. The experimental test verifies the numerical simulation results and demonstrates the proposed approach's effectiveness, showcasing its superiority in real-world disaster response scenarios.