Demo Data from SIGIR 2026 / reviewers in the wild / expert
Tianchun Gan
dblp:417/9068
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 since 2021Software engineering, systems software and programming languages · 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 |
Edge and fog computing · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Distributed systems · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Edge and fog computing › mobile edge computing
computation offloading |
1.0 | 1 | 2026 | DIMO: Constrained Computing Power Information Dissemination with Multi-hop Task Offloading · INFOCOM 2026 |
Edge and fog computing › mobile edge computing › computation offloading › cooperative offloading
multi-hop task offloading |
1.0 | 1 | 2026 | DIMO: Constrained Computing Power Information Dissemination with Multi-hop Task Offloading · INFOCOM 2026 |
Distributed systems › distributed communication
data dissemination |
1.0 | 1 | 2026 | DIMO: Constrained Computing Power Information Dissemination with Multi-hop Task Offloading · INFOCOM 2026 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | DIMO: Constrained Computing Power Information Dissemination with Multi-hop Task Offloading
Shan Zhang 0001, Tianchun Gan, Zhiyuan Wang 0004, Hongbin Luo |
INFOCOM | 4 |
| 2025 | Backoff Reveals Priority: A Distributed Status-Aware Response Method for Device-Assisted Task OffloadingabstractAcquiring the status information of candidate servers is important for decision making in edge computing, which is however extremely challenging and time-consuming in high-dense device-assisted task offloading scenarios. In this paper, we propose a Distributed Status-aware response (DiSar) method to balance the status acquisition and task execution latency by utilizing incomplete but necessary status information. Specifically, each candidate server device set a backoff timer based on their computing and transmission status independently, which enables low-latency servers to rapidly respond while avoiding wireless transmission collisions. A salient-driven status mapping algorithm is proposed for the backoff timer setting, and the performances of DiSar are analyzed theoretically. Accordingly the influencing parameters are further optimized to rapidly fit the dynamic offloading environment, whereby the performances of DiSar are guaranteed. Simulation results demonstrate that DiSar can find the optimal server device with 95% probability while reducing the information acquisition latency by 96.33%, compared with centralized complete information acquisition. In addition, the overall task offloading latency can be reduced by 11.63% to 52.67%, using DiSar compared with the state-of-theart methods. Tianchun Gan, Shan Zhang 0001, Zhiyuan Wang 0004, Hongbin Luo |
ICWS | 1 |