A Minmax Utilization Algorithm for Network Traffic Scheduling of Industrial Robots
This addresses network congestion for industrial robots in 5G environments, though it is incremental as it builds on existing traffic engineering methods.
The paper tackles the problem of scheduling network traffic for industrial robots in 5G networks by proposing an integer linear programming model and a random search algorithm to minimize peak data rates, achieving up to a 53.4% reduction compared to uncoordinated operation.
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.