TY - GEN
T1 - Age of Loop for Wireless Networked Control Systems Optimization
AU - Maia de Sant Ana, Pedro
AU - Marchenko, Nikolaj
AU - Popovski, Petar
AU - Soret, Beatriz
PY - 2021/9/16
Y1 - 2021/9/16
N2 - Joint design of control and communication in Wireless Networked Control Systems (WNCS) is a promising approach for future wireless industrial applications. In this context, Age of Information (AoI) recently has been proposed as a metric that is more representative than communication latency in conduct of systems with a sense-compute-actuate cycle. Nevertheless, AoI is commonly defined for a single communication direction, Downlink or Uplink, which does not capture the closed-loop dynamics. In this paper, we extend the concept of AoI by defining a new metric, Age of Loop (AoL), relevant for closed-loop WNCS problems. The AoL is defined as the time elapsed since the piece of information causing the latest action or state (depending on the selected time origin) was generated. We use the proposed metric to learn the WNCS latency and freshness bounds, and apply such learning methodology to minimize the long-term WNCS cost with the least amount of bandwidth. We show that, using the AoL, we can learn the control system requirement and use this information to optimize network resources.
AB - Joint design of control and communication in Wireless Networked Control Systems (WNCS) is a promising approach for future wireless industrial applications. In this context, Age of Information (AoI) recently has been proposed as a metric that is more representative than communication latency in conduct of systems with a sense-compute-actuate cycle. Nevertheless, AoI is commonly defined for a single communication direction, Downlink or Uplink, which does not capture the closed-loop dynamics. In this paper, we extend the concept of AoI by defining a new metric, Age of Loop (AoL), relevant for closed-loop WNCS problems. The AoL is defined as the time elapsed since the piece of information causing the latest action or state (depending on the selected time origin) was generated. We use the proposed metric to learn the WNCS latency and freshness bounds, and apply such learning methodology to minimize the long-term WNCS cost with the least amount of bandwidth. We show that, using the AoL, we can learn the control system requirement and use this information to optimize network resources.
UR - http://www.scopus.com/inward/record.url?scp=85118446190&partnerID=8YFLogxK
U2 - 10.1109/PIMRC50174.2021.9569366
DO - 10.1109/PIMRC50174.2021.9569366
M3 - Article in proceeding
SN - 978-1-7281-7587-4
T3 - I E E E International Symposium Personal, Indoor and Mobile Radio Communications
BT - 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
PB - IEEE (Institute of Electrical and Electronics Engineers)
T2 - 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
Y2 - 13 September 2021 through 16 September 2021
ER -