Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm

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

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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.
Original languageEnglish
JournalElectronics (Switzerland)
Issue number217
Publication statusPublished - 1 Jan 2023


  • 5g
  • Industrial Internet of Things
  • D2D
  • Reinforcement learning algorithms


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