학술논문
Balancing Time and Energy Efficiency by Sizing Clusters: A New Data Collection Scheme in UAV-Aided Large-Scale Internet of Things
Document Type
Periodical
Source
IEEE Internet of Things Journal IEEE Internet Things J. Internet of Things Journal, IEEE. 11(6):9355-9367 Mar, 2024
Subject
Language
ISSN
2327-4662
2372-2541
2372-2541
Abstract
Unmanned aerial vehicle (UAV)-aided large-scale Internet of Things (UAV-LIoT) are widely used but lack a balanced data collection (DC) scheme. To address this, we propose DC- nonorthogonal multiple access (NOMA), a new DC scheme that combines machine learning clustering with NOMA. We introduce an optimization algorithm for peak density clustering and a new LIoT clustering method. Our approach dynamically adjusts cluster size and formulates the energy-time efficiency problem as a tradeoff between energy minimization and data rate maximization. We propose a heuristic algorithm based on NOMA and an intracluster DC protocol. Experimental results show that DC- NOMA achieves balanced DC time, energy efficiency, load balance, and network lifespan extension, outperforming its benchmarks.