Energy Efficient UAV-Assisted Emergency Communication with Reliable Connectivity and Collision Avoidance

TL;DR

This paper introduces an energy-efficient, adaptive UAV routing protocol using improved Q-learning to enhance emergency communication reliability, collision avoidance, and network resilience during disaster response.

Contribution

It proposes the IQMR protocol, a novel routing mechanism that optimizes energy use and connectivity without predefined UAV paths, outperforming existing protocols in simulations.

Findings

Energy consumption reduced by over 32% compared to existing protocols.

Data throughput increased by up to 93%.

Demonstrates adaptability to dynamic system conditions.

Abstract

Emergency communication is vital for search and rescue operations following natural disasters. Unmanned Aerial Vehicles (UAVs) can significantly assist emergency communication by agile positioning, maintaining connectivity during rapid motion, and relaying critical disaster-related information to Ground Control Stations (GCS). Designing effective routing protocols for relaying crucial data in UAV networks is challenging due to dynamic topology, rapid mobility, and limited UAV resources. This paper presents a novel energy-constrained routing mechanism that ensures connectivity, inter-UAV collision avoidance, and network restoration post-UAV fragmentation while adapting without a predefined UAV path. The proposed method employs improved Q learning to optimize the next-hop node selection. Considering these factors, the paper proposes a novel, Improved Q-learning-based Multi-hop Routing (IQMR) protocol. Simulation results validate IQMRs adaptability to changing system conditions and superiority over QMR, QTAR, and QFANET in energy efficiency and data throughput. IQMR achieves energy consumption efficiency improvements of 32.27%, 36.35%, and 36.35% over QMR, Q-FANET, and QTAR, along with significantly higher data throughput enhancements of 53.3%, 80.35%, and 93.36% over Q-FANET, QMR, and QTAR.