Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm

Rajawat, Anand Singh; Goyal, S. B.; Chauhan, Chetan; Bedi, Pradeep; Prasad, Mukesh; Jan, Tony

doi:10.3390/electronics12010217

Open AccessArticle

Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm

by

Anand Singh Rajawat

¹

,

S. B. Goyal

^2,*

,

Chetan Chauhan

¹,

Pradeep Bedi

³,

Mukesh Prasad

^4,*

and

Tony Jan

^5,*

¹

School of Computer Sciences & Engineering, Sandip University, Nashik 422213, India

²

Faculty of Information Technology, City University, Petaling Jaya 46100, Malaysia

³

School of Computing Science and Engineering, Galgotias University, Greater Noida 203201, India

⁴

School of Computer Science, Faculty of Engineering, I.T, University of Technology Sydney, Sydney 2007, Australia

⁵

Centre for Artificial Intelligence Research and Optimization, Design and Creative Technology Vertical, Torrens University, Sydney 2007, Australia

^*

Authors to whom correspondence should be addressed.

Electronics 2023, 12(1), 217; https://doi.org/10.3390/electronics12010217

Submission received: 2 November 2022 / Revised: 9 December 2022 / Accepted: 23 December 2022 / Published: 1 January 2023

(This article belongs to the Special Issue Novel Methods for Dependable IoT Edge Applications)

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Abstract

Agile product development cycles and re-configurable Industrial Internet of Things (IIoT) allow more flexible and resilient industrial production systems that can handle a broader range of challenges and improve their productivity. Reinforcement Learning (RL) was shown to be able to support industrial production systems to be flexible and resilient to respond to changes in real time. This study examines the use of RL in a wide range of adaptive cognitive systems with IIoT-edges in manufacturing processes. We propose a cognitive adaptive system using IIoT with RL (CAS-IIoT-RL) and our experimental analysis showed that the proposed model showed improvements with adaptive and dynamic decision controls in challenging industrial environments.

Keywords: 5G; Industrial Internet of Things; D2D; M2M; reinforcement learning algorithms

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MDPI and ACS Style

Rajawat, A.S.; Goyal, S.B.; Chauhan, C.; Bedi, P.; Prasad, M.; Jan, T. Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm. Electronics 2023, 12, 217. https://doi.org/10.3390/electronics12010217

AMA Style

Rajawat AS, Goyal SB, Chauhan C, Bedi P, Prasad M, Jan T. Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm. Electronics. 2023; 12(1):217. https://doi.org/10.3390/electronics12010217

Chicago/Turabian Style

Rajawat, Anand Singh, S. B. Goyal, Chetan Chauhan, Pradeep Bedi, Mukesh Prasad, and Tony Jan. 2023. "Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm" Electronics 12, no. 1: 217. https://doi.org/10.3390/electronics12010217

APA Style

Rajawat, A. S., Goyal, S. B., Chauhan, C., Bedi, P., Prasad, M., & Jan, T. (2023). Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm. Electronics, 12(1), 217. https://doi.org/10.3390/electronics12010217

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm

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