MAC for Machine Type Communications in Industrial IoT -- Part II: Scheduling and Numerical Results

TL;DR

This paper presents a centralized scheduling scheme for machine-type communications in industrial IoT, combining analytical device assignment and neural network-based parameter tuning to meet strict QoS requirements efficiently.

Contribution

It introduces a two-step scheduling approach that integrates analytical algorithms and deep learning for scalable, accurate MAC protocol configuration in industrial IoT scenarios.

Findings

Supports 1000 devices with 3000 packets/sec load

Achieves <0.5ms average delay for high-priority devices

Maintains <1% collision probability among 50 high-priority devices

Abstract

In the second part of this paper, we develop a centralized packet transmission scheduling scheme to pair with the protocol designed in Part I and complete our medium access control (MAC) design for machine-type communications in the industrial internet of things. For the networking scenario, fine-grained scheduling that attends to each device becomes necessary, given stringent quality of service (QoS) requirements and diversified service types, but prohibitively complex for a large number of devices. To address this challenge, we propose a scheduling solution in two steps. First, we develop algorithms for device assignment based on the analytical results from Part I, when parameters of the proposed protocol are given. Then, we train a deep neural network for assisting in the determination of the protocol parameters. The two-step approach ensures the accuracy and granularity necessary for satisfying the QoS requirements and avoids excessive complexity from handling a large number of devices. Integrating the distributed coordination in the protocol design from Part I and the centralized scheduling from this part, the proposed MAC protocol achieves high performance, demonstrated through extensive simulations. For example, the results show that the proposed MAC can support 1000 devices under an aggregated traffic load of 3000 packets per second with a single channel and achieve <0.5ms average delay and <1% average collision probability among 50 high priority devices.