Demo Data from EDBT 2026 / reviewers in the wild / expert
Arvin Asokan
dblp:173/7669
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
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 architecture, parallel and distributed computing, and storage systems
1 paper |
Cloud and datacenter computing · 61% Embedded and real-time systems · 30% Distributed systems · 9% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cloud and datacenter computing › cloud applications
cloud robotics |
0.2 | 1 | 2016 | Low latency bounty hunting and geographically adjacent server configuration for real-time cloud control · ICRA 2016 |
Cloud and datacenter computing
edge and fog computing |
0.2 | 1 | 2016 | Low latency bounty hunting and geographically adjacent server configuration for real-time cloud control · ICRA 2016 |
Embedded and real-time systems
real-time control |
0.2 | 1 | 2016 | Low latency bounty hunting and geographically adjacent server configuration for real-time cloud control · ICRA 2016 |
Methods — techniques the papers use, named apart from their topics
geographically adjacent server selection · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Low latency bounty hunting and geographically adjacent server configuration for real-time cloud controlabstractThis paper explores the feasibility and proposes a method on how to obtain high frequency real-time controllers operating in loop with physical robot hardware over a geographically adjacent and bounty hunting cloud server architecture. Having the cloud in the loop has many purposes including increasing computation, decreasing “on robot” power usage, reducing overall robot weight etc. Today when robots use the cloud in loop it is typically for sharing information, high level planning, and other non-real-time tasks. All of the balancing and stability algorithms stay onboard the robot. What if we could run high frequency real-time loops over the cloud? The better question is how would we do that? As with any real-time system latency is a big factor in the application and operation frequency. This paper shows that with a geographically adjacent and bounty hunting server approach it is feasible to obtain high frequency real-time control in loop with with a physical robot over the cloud. The feasibility of such a system running in-loop on humanoids and wheeled robots are explored and tested. Daniel M. Lofaro, Arvin Asokan |
ICRA | 2 |