Work

Poster: Optimizing ABS Deployment via LSTM-based Mobility and Data Traffic Demand Prediction

Year: 2024

Type: conference-paper

Abstract: This paper proposes a proactive aerial base station (ABS) deployment framework for hotspots that optimizes the placement of ABSs based on mobility and data traffic demand prediction using a LSTM model. We design a greedy-based ABS deployment algorithm to solve the data rate maximization problem, which is known to be NP-hard. Simulation results demonstrate that the proposed algorithm achieves a higher data rate with fewer ABSs compared to benchmark methods.

Source: ACM Conference on Embedded Networked Sensor Systems (SenSys)

DOI: 10.1145/3666025.3699424

Authors: Jeehee Nam, Anna Cho, Jihyun Park, Chang Kyung Kim, SuKyoung Lee

Institution: Yonsei University

Language: English

Topic: UAV Applications and Optimization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

Funders: National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea