A time-sensitive network scheduling algorithm based on improved ant colony optimization

Cyber-physical system (CPS) is the core technology of Industry 4.0. The deterministic behaviors of the CPS require real-time deterministic guarantee. Therefore, this paper improves the ant colony optimization (ACO) into a scheduling algorithm for time-triggered flows in time-sensitive network (TSN), a standard developed by the IEEE 802.1 Working Group that fully satisfies the strict end-to-end delay requirements of industrial applications. Simulation results show that the improved ACO (IACO) can schedule the time-triggered flows in the TSN excellently, and outperform the traditional ACO in convergence speed, optimization ability and the proneness to local optimum trap. To sum up, this paper provides an effective real-time guarantee for the TSNs. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.

Automated summary

This paper improves the ant colony optimization into a scheduling algorithm for time-triggered flows in time-sensitive networks, providing effective real-time guarantee for the TSNs.