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Jeehee Nam

dblp:389/4049 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0001-1259-0197ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Vehicular, aerial and satellite networks · 77% Cellular and mobile networks · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Vehicular, aerial and satellite networks › UAV deployment
aerial base station deployment
0.812024
Poster: Optimizing ABS Deployment via LSTM-based Mobility and Data Traffic Demand Prediction · SenSys 2024
Cellular and mobile networks › mobility management
mobility prediction
0.212024
Poster: Optimizing ABS Deployment via LSTM-based Mobility and Data Traffic Demand Prediction · SenSys 2024

Methods — techniques the papers use, named apart from their topics

greedy algorithm · 0.8LSTM · 0.8
YearPublicationVenuePosition
2024 Poster: Optimizing ABS Deployment via LSTM-based Mobility and Data Traffic Demand Prediction
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.
Jeehee Nam, Anna Cho, Chang Kyung Kim
SenSys1