TY - CHAP
T1 - A Minmax Utilization Algorithm for Network Traffic Scheduling of Industrial Robots
AU - Wang, Yantong
AU - Friderikos, Vasilis
AU - Andraos, Sebastian
N1 - Funding Information:
This research is partially funded by the Innovate UK Automation of Network edge Infrastructure & Applications with aRtificiAl intelligence ukANIARA project, which is part of the EU Celtic Next project ANIARA (www.celticnext.eu/project-ai-net-aniara).
Publisher Copyright:
© 2022 IEEE.
PY - 2022/8/11
Y1 - 2022/8/11
N2 - Emerging 5G and beyond wireless industrial virtualized networks are expected to support a significant number of robotic manipulators. Depending on the processes involved, these industrial robots might result in significant volume of multi-modal traffic that will need to traverse the network all the way to the (public/private) edge cloud, where advanced processing, control and service orchestration will be taking place. In this paper, we perform the traffic engineering by capitalizing on the underlying pseudo-deterministic nature of the repetitive processes of robotic manipulators in an industrial environment and propose an integer linear programming (ILP) model to minimize the maximum aggregate traffic in the network. The task sequence and time gap requirements are also considered in the proposed model. To tackle the curse of dimensionality in ILP, we provide a random search algorithm with quadratic time complexity. Numerical investigations reveal that the proposed scheme can reduce the peak data rate up to 53.4% compared with the nominal case where robotic manipulators operate in an uncoordinated fashion, resulting in significant improvement in the utilization of the underlying network resources.
AB - Emerging 5G and beyond wireless industrial virtualized networks are expected to support a significant number of robotic manipulators. Depending on the processes involved, these industrial robots might result in significant volume of multi-modal traffic that will need to traverse the network all the way to the (public/private) edge cloud, where advanced processing, control and service orchestration will be taking place. In this paper, we perform the traffic engineering by capitalizing on the underlying pseudo-deterministic nature of the repetitive processes of robotic manipulators in an industrial environment and propose an integer linear programming (ILP) model to minimize the maximum aggregate traffic in the network. The task sequence and time gap requirements are also considered in the proposed model. To tackle the curse of dimensionality in ILP, we provide a random search algorithm with quadratic time complexity. Numerical investigations reveal that the proposed scheme can reduce the peak data rate up to 53.4% compared with the nominal case where robotic manipulators operate in an uncoordinated fashion, resulting in significant improvement in the utilization of the underlying network resources.
KW - Industrial Robots
KW - Industry 4.0
KW - Integer Linear Programming
KW - Network Optimization
KW - Network Traffic Scheduling
UR - http://www.scopus.com/inward/record.url?scp=85137275890&partnerID=8YFLogxK
U2 - 10.1109/ICC45855.2022.9838487
DO - 10.1109/ICC45855.2022.9838487
M3 - Conference paper
AN - SCOPUS:85137275890
T3 - IEEE International Conference on Communications
SP - 2936
EP - 2941
BT - ICC 2022 - IEEE International Conference on Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Communications, ICC 2022
Y2 - 16 May 2022 through 20 May 2022
ER -
