Demo Data from EDBT 2026 / reviewers in the wild / expert
Jayashree Subramanian
dblp:59/11343
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Computer networks · 1 · 1 first-author
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 |
Wireless networking · 30% Routing and switching · 30% Internet architecture and protocols · 30% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Routing and switching › routing protocol
flooding protocol |
0.1 | 1 | 2012 | UFlood: High-throughput flooding over wireless mesh networks · INFOCOM 2012 |
Internet architecture and protocols
network coding |
0.1 | 1 | 2012 | UFlood: High-throughput flooding over wireless mesh networks · INFOCOM 2012 |
Wireless networking
wireless mesh network |
0.1 | 1 | 2012 | UFlood: High-throughput flooding over wireless mesh networks · INFOCOM 2012 |
Content delivery and video streaming
bitrate adaptation |
0.0 | 1 | 2012 | UFlood: High-throughput flooding over wireless mesh networks · INFOCOM 2012 |
Methods — techniques the papers use, named apart from their topics
testbed evaluation · 0.1random network coding · 0.1distributed heuristic · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Application of fuzzy cognitive maps for crack categorization in columns of reinforced concrete structures
Vijayalakshmi Senniappan, Jayashree Subramanian, Elpiniki I. Papageorgiou, Suji Mohan |
Neural Comput. Appl. | 2 |
| 2012 | UFlood: High-throughput flooding over wireless mesh networksabstractThis paper proposes UFlood, a flooding protocol for wireless mesh networks. UFlood targets situations such as software updates where all nodes need to receive the same large file of data, and where limited radio range requires forwarding. UFlood's goals are high throughput and low airtime, defined respectively as rate of completion of a flood to the slowest receiving node and total time spent transmitting. The key to achieving these goals is good choice of sender for each transmission opportunity. The best choice evolves as a flood proceeds in ways that are difficult to predict. UFlood's core new idea is a distributed heuristic to dynamically choose the senders likely to lead to all nodes receiving the flooded data in the least time. The mechanism takes into account which data nearby receivers already have as well as internode channel quality. The mechanism includes a novel bit-rate selection algorithm that trades off the speed of high bit-rates against the larger number of nodes likely to receive low bitrates. Unusually, UFlood uses both random network coding to increase the usefulness of each transmission and detailed feedback about what data each receiver already has; the feedback is critical in deciding which node's coded transmission will have the most benefit to receivers. The required feedback is potentially voluminous, but UFlood includes novel techniques to reduce its cost. The paper presents an evaluation on a 25-node 802.11 test-bed. UFlood achieves 150% higher throughput than MORE, a high-throughput flooding protocol, using 65% less airtime. UFlood uses 54% less airtime than MNP, an existing efficient protocol, and achieves 300% higher throughput. Jayashree Subramanian, Robert Morris 0005, Hari Balakrishnan |
INFOCOM | 1 |