Document Type
Article
Language
eng
Publication Date
6-15-2021
Publisher
Institute of Electrical and Electronics Engineers
Source Publication
IEEE Internet of Things Journal
Source ISSN
2327-4662
Abstract
In the second part of this article, 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 < 0.5 ms average delay and < 1% average collision probability among 50 high priority devices.
Recommended Citation
Gao, Jie; Li, Mushu; Zhuang, Weihua; Shen, Xuemin; and Li, Xu, "MAC for Machine-Type Communications in Industrial IoT—Part II: Scheduling and Numerical Results" (2021). Electrical and Computer Engineering Faculty Research and Publications. 648.
https://epublications.marquette.edu/electric_fac/648
ADA Accessible Version
Comments
Accepted version. IEEE Internet of Things Journal, Vol. 8, No. 12 (June 15, 2021): 9958-9969. DOI. © 2021 The Institute of Electrical and Electronics Engineers. Used with permission.