EDBT 2026 Demo / reviewers in the wild / expert
Jeehee Nam
dblp:389/4049
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Vehicular, aerial and satellite networks › UAV deployment
aerial base station deployment |
0.8 | 1 | 2024 | Poster: Optimizing ABS Deployment via LSTM-based Mobility and Data Traffic Demand Prediction · SenSys 2024 |
Cellular and mobile networks › mobility management
mobility prediction |
0.2 | 1 | 2024 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Poster: Optimizing ABS Deployment via LSTM-based Mobility and Data Traffic Demand PredictionabstractThis 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 |
SenSys | 1 |