Grant: Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Carleton University
$82,500.00
Data Packets Routing in Satellite Networks
FY 2019-20 · 2020-03-31
Summary
To deliver packets between non-neighboring satellites using intermediate satellites, efficient routing protocols are required. There exist a high number of routing protocols in the literature, however, they were not designed for the highly dynamic, three-dimensional, fully distributed, and heterogeneous environment of the envisioned satellite networks. As the satellite network changes rapidly, it is necessary to keep learning the topology of the network in order to make the correct routing decisions. In this regard, online machine learning methods will play a significant role in improving the accuracy of routing decisions.
In this project the team will use machine learning to determine routing algorithms for LEO satellite networks.
Department
Program
Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Recipient
Location
Ottawa, Ontario, Canada
Recipient Type
Academia
Business Number
118838937
Type
Grant
Source
Proactive Disclosure - Grants and Contributions
As published
Additional Information
This agreement has been amended 1 time(s). The end date of this agreements has been modified by 119 days. The total amended value is 90,000 dollars.
Additional Information (French)
Cet accord a été modifié 1 fois La date de fin du présent accord a été changé de 119 jours. La valeur totale des modifications est de 90 000 dollars.
Agreement End Date
2021-03-31
Agreement Number
947364
Agreement Start Date
2020-03-31
Agreement Title
Data Packets Routing in Satellite Networks
Agreement Title (French)
Routage des paquets de données sur réseaux satellitaires
Agreement Type
Grant
Agreement Value
$82,500.00
Amendment Number
0
Description
To deliver packets between non-neighboring satellites using intermediate satellites, efficient routing protocols are required. There exist a high number of routing protocols in the literature, however, they were not designed for the highly dynamic, three-dimensional, fully distributed, and heterogeneous environment of the envisioned satellite networks. As the satellite network changes rapidly, it is necessary to keep learning the topology of the network in order to make the correct routing decisions. In this regard, online machine learning methods will play a significant role in improving the accuracy of routing decisions.
In this project the team will use machine learning to determine routing algorithms for LEO satellite networks.
Description (French)
Des protocoles de routage efficaces sont nécessaires pour pouvoir transférer des paquets entre des satellites éloignés en utilisant des satellites intermédiaires. De nombreux protocoles de routage ont été décrits, mais ils ne sont pas conçus pour l’environnement hautement dynamique, tridimensionnel, entièrement distribué et hétérogène des réseaux par satellite envisagés. Le réseau par satellite se modifiant rapidement, il est nécessaire d’acquérir en continu des données sur sa topologie de manière à décider correctement du routage à effectuer. À cet égard, les méthodes d’apprentissage automatique contribueront grandement à l’amélioration de la précision des décisions de routage. Dans ce projet, l'équipe utilisera l'apprentissage automatique pour déterminer les algorithmes de routage pour les réseaux de satellites LEO.
Expected Results
In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems.
Expected Results (French)
À court terme, les résultats prévus seront des collaborations renforcées entre l'industrie, le milieu universitaire et le gouvernement pour soutenir l'excellence en recherche. À moyen terme, les résultats attendus seront le développement de nouvelles technologies potentiellement perturbatrices avec des collaborateurs. À long terme, trouver des solutions collaboratives aux défis des politiques publiques et créer des systèmes d'innovation plus solides.
Federal Riding
Ottawa Centre
Federal Riding (French)
Ottawa-Centre
Federal Riding Number
35075
NAICS Code
541710
Organization Code
nrc-cnrc
Organization
National Research Council Canada | Conseil national de recherches Canada
Program Name
Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Program Name (French)
Programme de collaboration en science, en technologie et en innovation - Initiatives de collaboration en R-D
Program Purpose
Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas.
Program Purpose (French)
Collaborer à des programmes multipartites de recherche-développement dans le but de profiter de la synergie entre la recherche transformatrice à haut risque et à haut rendement et le potentiel de découvertes scientifiques et de percées technologiques révolutionnaires dans des secteurs d’intervention prioritaires.
Recipient Business Number
118838937
Recipient City
Ottawa
Recipient Country
CA
Recipient Legal Name
Carleton University
Recipient Operating Name
Carleton University
Recipient Postal Code
K1S 5B6
Recipient Province or Territory
ON
Recipient Type
S
Reference Number
172-2019-2020-Q4-947364