Details
| Original language | English |
|---|---|
| Article number | 102412 |
| Number of pages | 20 |
| Journal | Performance evaluation |
| Volume | 164 |
| Early online date | 19 Mar 2024 |
| Publication status | Published - May 2024 |
Abstract
Age-of-information is a metric that quantifies the freshness of information obtained by sampling a remote sensor. In signal-agnostic sampling, sensor updates are triggered at certain times without being conditioned on the actual sensor signal. Optimal update policies have been researched and it is accepted that periodic updates achieve smaller age-of-information than random updates. We contribute a study of a signal-aware policy, where updates are triggered randomly by a defined sensor event. By definition, this implies random updates and as a consequence inferior age-of-information. Considering a notion of deviation-of-information as a signal-aware metric, our results show, however, that event-triggered systems can perform equally well as time-triggered systems while causing smaller mean network utilization. We use the stochastic network calculus to derive bounds of age- and deviation-of-information that are exceeded at most with a small, defined probability. We include simulation results that confirm the tail decay of the bounds. We also evaluate a hybrid time- and event-triggered policy where the event-triggered system is complemented by a minimal and a maximal update interval.
Keywords
- Age-of-Information, Max-plus systems, Remote state estimation, Stochastic network calculus
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Mathematics(all)
- Modelling and Simulation
- Computer Science(all)
- Hardware and Architecture
- Computer Science(all)
- Computer Networks and Communications
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In: Performance evaluation, Vol. 164, 102412, 05.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Age- and deviation-of-information of hybrid time- and event-triggered systems
T2 - What matters more, determinism or resource conservation?
AU - Noroozi, Mahsa
AU - Fidler, Markus
N1 - Funding Information: This work has been supported in part by the German Research Foundation (DFG) under grant FI 1236/9-1.
PY - 2024/5
Y1 - 2024/5
N2 - Age-of-information is a metric that quantifies the freshness of information obtained by sampling a remote sensor. In signal-agnostic sampling, sensor updates are triggered at certain times without being conditioned on the actual sensor signal. Optimal update policies have been researched and it is accepted that periodic updates achieve smaller age-of-information than random updates. We contribute a study of a signal-aware policy, where updates are triggered randomly by a defined sensor event. By definition, this implies random updates and as a consequence inferior age-of-information. Considering a notion of deviation-of-information as a signal-aware metric, our results show, however, that event-triggered systems can perform equally well as time-triggered systems while causing smaller mean network utilization. We use the stochastic network calculus to derive bounds of age- and deviation-of-information that are exceeded at most with a small, defined probability. We include simulation results that confirm the tail decay of the bounds. We also evaluate a hybrid time- and event-triggered policy where the event-triggered system is complemented by a minimal and a maximal update interval.
AB - Age-of-information is a metric that quantifies the freshness of information obtained by sampling a remote sensor. In signal-agnostic sampling, sensor updates are triggered at certain times without being conditioned on the actual sensor signal. Optimal update policies have been researched and it is accepted that periodic updates achieve smaller age-of-information than random updates. We contribute a study of a signal-aware policy, where updates are triggered randomly by a defined sensor event. By definition, this implies random updates and as a consequence inferior age-of-information. Considering a notion of deviation-of-information as a signal-aware metric, our results show, however, that event-triggered systems can perform equally well as time-triggered systems while causing smaller mean network utilization. We use the stochastic network calculus to derive bounds of age- and deviation-of-information that are exceeded at most with a small, defined probability. We include simulation results that confirm the tail decay of the bounds. We also evaluate a hybrid time- and event-triggered policy where the event-triggered system is complemented by a minimal and a maximal update interval.
KW - Age-of-Information
KW - Max-plus systems
KW - Remote state estimation
KW - Stochastic network calculus
UR - http://www.scopus.com/inward/record.url?scp=85189552217&partnerID=8YFLogxK
U2 - 10.1016/j.peva.2024.102412
DO - 10.1016/j.peva.2024.102412
M3 - Article
AN - SCOPUS:85189552217
VL - 164
JO - Performance evaluation
JF - Performance evaluation
SN - 0166-5316
M1 - 102412
ER -