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Jayashree Subramanian

dblp:59/11343 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Routing and switching › routing protocol
flooding protocol
0.112012
UFlood: High-throughput flooding over wireless mesh networks · INFOCOM 2012
Internet architecture and protocols
network coding
0.112012
UFlood: High-throughput flooding over wireless mesh networks · INFOCOM 2012
Wireless networking
wireless mesh network
0.112012
UFlood: High-throughput flooding over wireless mesh networks · INFOCOM 2012
Content delivery and video streaming
bitrate adaptation
0.012012
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
YearPublicationVenuePosition
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 networks
abstract
This 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
INFOCOM1