Deep Learning-Based Scheduling Scheme for IEEE 802.15.4e TSCH Network

Abstract

IEEE 802.15.4e time-slotted channel hopping (TSCH) is one of the most reliable resources of the Industrial Internet of Things (IIoT). TSCH operates on the slot-frame structure consisting of multiple channel-offsets and multiple slot-offsets. It is gaining acceptance due to its simple architecture and consume low power in industrial applications. The performance of TSCH is mainly dominated by the media access control (MAC) mechanism, which covers the refitment, enumeration, composition, and data transmission. However, in many cases, the data transmission schedules are not accurately prescribed. Therefore, most researchers are trying to define many pragmatic scenarios of scheduling. Their fundamental approach is to schedule TSCH network in a centralized way while framing scheduling based on network performance such as throughput and delay. In this work, a deep learning (DL)-based scheme has been proposed. TSCH network schedules for links to cell assignment of a slot-frame can be constructed as a maximum edge weighted bipartite matching approach. In this paper, we design bipartite edge weight to be composed of throughput and delay, and we use the Hungarian algorithm for proper cell assignment. With the Hungarian scheduling algorithm, we generate the training data and train a deep neural network (DNN) accordingly. In the simulation, we consider a simple TSCH network with 5 nodes where 12 links are formulated, and we consider 16 cells for the link assignment. The simulation results show that the proposed deep learning-based scheduling scheme can provide performance similar to the Hungarian algorithm-based scheduling scheme with above 90% accuracy and nearly 80% execution time reduction.

Hierarchical Cross Traffic Scheduling Based on Time-Aware Shapers for Mobile Time-Sensitive Fronthaul Network

“…Update Slot ( 5) ELSE ( 6) Frame_number←get_Frame_number(S); // Determine the number of frames to be scheduled according to the number of waiting frames in S (7) (8) FOR (i = 1; i++; i < Frame_number) (9) Calculate the Slot set that can transmit the all frame; (10) END FOR (11) FOR (i = 1; i++; i < Frame_number) (12) Slot M ← get_feasible_slot(Slot) //Select the largest data frame to be scheduled, and select the transmissible slot with the most matching length (13) Update Slot (14) According Slot M to get W.start // W.start of each frame can be calculated (15) END FOR // The above is to select the timeslot and get W.start. Next, determine the incoming queue Qm (16) FOR (i = 1; i++; i <Q_number) // The secondary queue contains four queues.…”

“…The TSN scheduling mechanisms are roughly divided into two categories, namely, synchronous traffic scheduling and asynchronous traffic scheduling [6][7][8][9][10][11][12][13][14][15][16]. At present, synchronous traffic scheduling mainly focuses on gate control lists (GCLs) and TAS-based joint routing.…”