Demo Data from EDBT 2026 / reviewers in the wild / expert
Wuping Mao
dblp:375/6388
· DBLP profile ↗
4ranked-venue papers
0as first author
4since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 2 · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 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 |
Content delivery and video streaming · 44% Edge and fog computing · 44% Cellular and mobile networks · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Content delivery and video streaming › caching › distributed caching
cooperative caching |
1.0 | 1 | 2026 | Edge Collaborative Caching Strategy in Space-Air-Ground Integrated Networks · IEEE Trans. Commun. 2026 |
Edge and fog computing › edge caching
cooperative edge caching |
1.0 | 1 | 2026 | Edge Collaborative Caching Strategy in Space-Air-Ground Integrated Networks · IEEE Trans. Commun. 2026 |
Cellular and mobile networks › 6g
space-air-ground integrated network |
0.3 | 1 | 2026 | Edge Collaborative Caching Strategy in Space-Air-Ground Integrated Networks · IEEE Trans. Commun. 2026 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Edge Collaborative Caching Strategy in Space-Air-Ground Integrated Networks
Wuping Mao, Songtao Guo, Guiyan Liu |
IEEE Trans. Commun. | 3 |
| 2025 | Meta learning-based deep reinforcement learning algorithm for task offloading in dynamic vehicular network
Tengxiang Jing, Wuping Mao, Guanyu Liu |
Eng. Appl. Artif. Intell. | 5 |
| 2024 | Edge computing offloading strategy for space-air-ground integrated network based on game theory
Wuping Mao, Guanyu Liu, Bingchuan Guo |
Comput. Networks | 2 |
| 2024 | Edge computing collaborative offloading strategy for space-air-ground integrated networksabstractSummary Due to geographical factors, it is impossible to build large‐scale communication network infrastructure in remote areas, which leads to poor network communication quality in these areas and a series of delay‐sensitive tasks cannot be timely processed and responded. Aiming at the problem of limited coverage in remote areas, the space‐air‐ground integrated networks (SAGIN) combined with mobile edge computing (MEC) can provide low latency and high reliability transmission for offloading delay‐sensitive tasks for users in remote areas. Considering the strong limitation of satellite resources in the space‐ground integrated network and insufficient energy of local user equipment, firstly, a satellite‐UAV cluster‐ground three‐layer edge computing network architecture is proposed in this paper. Under the condition that the delay requirements of various ground tasks are met, the task offloading problem is transformed into a Stackelberg game between ground user equipment and edge servers. In addition, it is proved that the existence of Nash equilibrium in non‐cooperative game between ground user equipment by using potential game. Finally, a Nash equilibrium iterative offloading algorithm based on Stackelberg game (NEIO‐SG) is proposed to find the optimal offloading strategy for tasks to minimize the system offloading cost and the optimal forwarding percentage strategy for offloading tasks to maximize the utility function of the edge server. Simulation results show that compared to other baseline algorithms, NEIO‐SG reduces the total system latency during task offloading by about 13 and the energy consumption of the edge server by about 35. Biqun Xiang, Anhua Wang, Wuping Mao |
Concurr. Comput. Pract. Exp. | 4 |