Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2024 IFIP Networking Conference, IFIP Networking 2024 |
| Seiten | 555-561 |
| Seitenumfang | 7 |
| ISBN (elektronisch) | 9783903176638 |
| Publikationsstatus | Veröffentlicht - 3 Juni 2024 |
Abstract
The significance of the freshness of sensor and control data at the receiver side, often referred to as Age of Information(AoI), is fundamentally constrained by contention for limited network resources. Evidently, network congestion is detrimental for AoI, where this congestion is partly self-induced by the sensor transmission process in addition to the contention from other transmitting sensors. In this work, we devise a decentralized AoI-minimizing transmission policy for a number of sensor agents sharing capacity-limited, non-FIFO duplex channels that introduce random delays in communication with a common receiver. By implementing the same policy, however with no explicit inter-agent communication, the agents minimize the expected AoI in this partially observable system. We cater to the partial observability due to random channel delays by designing a bootstrap particle filter that independently maintains a belief over the AoI of each agent. We also leverage mean-field control approximations and reinforcement learning to derive scalable and approximately optimal solutions for minimizing the expected AoI collaboratively.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Hardware und Architektur
- Entscheidungswissenschaften (insg.)
- Informationssysteme und -management
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
2024 IFIP Networking Conference, IFIP Networking 2024. 2024. S. 555-561.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Collaborative Optimization of the Age of Information Under Partial Observability
AU - Tahir, Anam
AU - Cui, Kai
AU - Alt, Bastian
AU - Rizk, Amr
AU - Koeppl, Heinz
N1 - Publisher Copyright: © 2024 IFIP.
PY - 2024/6/3
Y1 - 2024/6/3
N2 - The significance of the freshness of sensor and control data at the receiver side, often referred to as Age of Information(AoI), is fundamentally constrained by contention for limited network resources. Evidently, network congestion is detrimental for AoI, where this congestion is partly self-induced by the sensor transmission process in addition to the contention from other transmitting sensors. In this work, we devise a decentralized AoI-minimizing transmission policy for a number of sensor agents sharing capacity-limited, non-FIFO duplex channels that introduce random delays in communication with a common receiver. By implementing the same policy, however with no explicit inter-agent communication, the agents minimize the expected AoI in this partially observable system. We cater to the partial observability due to random channel delays by designing a bootstrap particle filter that independently maintains a belief over the AoI of each agent. We also leverage mean-field control approximations and reinforcement learning to derive scalable and approximately optimal solutions for minimizing the expected AoI collaboratively.
AB - The significance of the freshness of sensor and control data at the receiver side, often referred to as Age of Information(AoI), is fundamentally constrained by contention for limited network resources. Evidently, network congestion is detrimental for AoI, where this congestion is partly self-induced by the sensor transmission process in addition to the contention from other transmitting sensors. In this work, we devise a decentralized AoI-minimizing transmission policy for a number of sensor agents sharing capacity-limited, non-FIFO duplex channels that introduce random delays in communication with a common receiver. By implementing the same policy, however with no explicit inter-agent communication, the agents minimize the expected AoI in this partially observable system. We cater to the partial observability due to random channel delays by designing a bootstrap particle filter that independently maintains a belief over the AoI of each agent. We also leverage mean-field control approximations and reinforcement learning to derive scalable and approximately optimal solutions for minimizing the expected AoI collaboratively.
KW - age-of-information
KW - mean-field control
KW - network resources
KW - partial observability
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85202429618&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2312.12977
DO - 10.48550/arXiv.2312.12977
M3 - Conference contribution
SN - 979-8-3503-9060-5
SP - 555
EP - 561
BT - 2024 IFIP Networking Conference, IFIP Networking 2024
ER -