Abstract
As the size of the sensor network grows, synchronization starts to become the main bottleneck for distributed computing. As a result, efforts in several areas have been focused on the convergence analysis of asynchronous computational methods. In this work, we aim to cross-pollinate distributed graph filters with results in parallel computing to provide guarantees for asynchronous graph filtering. To alleviate the possible reduction of convergence speed due to asynchronous updates, we also show how a slight modification to the graph filter recursion, through operator splitting, can be performed to obtain faster convergence. Finally, through numerical experiments the performance of the discussed methods is illustrated.
| Original language | English |
|---|---|
| Title of host publication | 2019 IEEE Data Science Workshop (DSW) |
| Subtitle of host publication | Proceedings |
| Place of Publication | Piscataway |
| Publisher | IEEE |
| Pages | 115-119 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-1-7281-0708-0 |
| ISBN (Print) | 978-1-7281-0709-7 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | 2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States Duration: 2 Jun 2019 → 5 Jun 2019 |
Conference
| Conference | 2019 IEEE Data Science Workshop, DSW 2019 |
|---|---|
| Country/Territory | United States |
| City | Minneapolis |
| Period | 2/06/19 → 5/06/19 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- asynchronous filtering
- distributed signal processing
- edge-variant graph filters
- graph filters
- graph signal processing