Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Caleb Irondi

dblp:325/5403 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-8458-2612ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 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 architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 100%
Human-computer interaction and pervasive computing
1 paper
Immersive interaction · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed systems › distributed data processing
distributed data fusion
0.612022
Consensus Based Networking of Distributed Virtual Environments · IEEE Trans. Vis. Comput. Graph. 2022
Distributed systems › distributed interactive applications › collaborative computing
distributed virtual environments
0.612022
Consensus Based Networking of Distributed Virtual Environments · IEEE Trans. Vis. Comput. Graph. 2022
Immersive interaction
virtual reality
0.212022
Consensus Based Networking of Distributed Virtual Environments · IEEE Trans. Vis. Comput. Graph. 2022

Methods — techniques the papers use, named apart from their topics

distributed averaging · 1.1continuous authority · 1.1
YearPublicationVenuePosition
2022 Consensus Based Networking of Distributed Virtual Environments
abstract
Distributed virtual environments (DVEs) are challenging to create as the goals of consistency and responsiveness become contradictory under increasing latency. DVEs have been considered as both distributed transactional databases and force-reflection systems. Both are good approaches, but they do have drawbacks. Transactional systems do not support Level 3 (L3) collaboration: manipulating the same degree-of-freedom at the same time. Force-reflection requires a client-server architecture and stabilisation techniques. With Consensus Based Networking (CBN), we suggest DVEs be considered as a distributed data-fusion problem. Many simulations run in parallel and exchange their states, with remote states integrated with continous authority. Over time the exchanges average out local differences, performing a distribued-average of a consistent, shared state. CBN aims to build simulations that are highly responsive, but consistent enough for use cases such as the piano-movers problem. CBN's support for heterogeneous nodes can transparently couple different input methods, avoid the requirement of determinism, and provide more options for personal control over the shared experience. Our work is early, however we demonstrate many successes, including L3 collaboration in room-scale VR, 1000's of interacting objects, complex configurations such as stacking, and transparent coupling of haptic devices. These have been shown before, but each with a different technique; CBN supports them all within a single, unified system.
Sebastian Friston, Elias Griffith, David Swapp, Simon J. Julier, Caleb Irondi, Fred P. Jjunju, Ryan Ward, Alan Marshall 0001, Anthony Steed
IEEE Trans. Vis. Comput. Graph.5