Cognitive Automation for Smart Decision-Making in Industrial Internet of Things

Classical automated schemes in the industrial Internet of Things (IIoT) are challenged by the problems related to huge record storage and the way they respond. To properly manage the manufacturing settings, cognitive systems aim to find a way to efficiently adapt their actions based on uncertainty management and sensory data. However, due to the lack of existing IT integration, cognitive systems are not fully exploited by organizations. In this article, we provide a novel decision-making process in industrial informatics during information transmission, manufacturing, and storing records through the simple additive weighting and analytic hierarchy process. The proposed mechanism is analyzed and validated rigorously using various sensing and decision-making parameters against a baseline solution in industrial parameter settings. The simulation results suggest that the proposed mechanism leads to 87% efficiency in terms of better detection of the sensor node, decision-making, and alteration of transmitted data during analyses of product manufacturing in the IIoT.

[1]  Azzedine Boukerche,et al.  Modeling and Analysis of a Shared Edge Caching System for Connected Cars and Industrial IoT-Based Applications , 2020, IEEE Transactions on Industrial Informatics.

[2]  Jaime Lloret,et al.  An Intelligent Algorithm for Resource Sharing and Self-Management of Wireless-IoT-Gateway , 2020, IEEE Access.

[3]  Fatih Kurugollu,et al.  MARINE: Man-in-the-Middle Attack Resistant Trust Model in Connected Vehicles , 2020, IEEE Internet of Things Journal.

[4]  Boudewijn R. Haverkort,et al.  Smart Industry: How ICT Will Change the Game! , 2017, IEEE Internet Comput..

[5]  Thomas L. Saaty,et al.  Models, Methods, Concepts & Applications of the Analytic Hierarchy Process , 2012 .

[6]  Christina F. Rusnock,et al.  Simulation-Based Evaluation of Adaptive Automation Revoking Strategies on Cognitive Workload and Situation Awareness , 2017, IEEE Transactions on Human-Machine Systems.

[7]  M. Shamim Hossain,et al.  Energy-Aware Green Adversary Model for Cyberphysical Security in Industrial System , 2020, IEEE Transactions on Industrial Informatics.

[8]  Kijoon Chae,et al.  On-Device AI-Based Cognitive Detection of Bio-Modality Spoofing in Medical Cyber Physical System , 2019, IEEE Access.

[9]  Yang Lu,et al.  Industry 4.0: A survey on technologies, applications and open research issues , 2017, J. Ind. Inf. Integr..

[10]  Khaled Salah,et al.  Trust management in social Internet of vehicles: Factors, challenges, blockchain, and fog solutions , 2019, Int. J. Distributed Sens. Networks.

[11]  Dietmar Dietrich,et al.  Cognitive Automation—Survey of Novel Artificial General Intelligence Methods for the Automation of Human Technical Environments , 2012, IEEE Transactions on Industrial Informatics.

[12]  Ashutosh Sharma,et al.  A trust management scheme to secure mobile information centric networks , 2020, Comput. Commun..

[13]  Jörg Thomaschewski,et al.  Empowering User Interfaces for the Industry 4.0 , 2016 .

[14]  Dmitry Podkopaev,et al.  Simple additive weighting - A metamodel for multiple criteria decision analysis methods , 2016, Expert Syst. Appl..

[15]  Zeshui Xu,et al.  Hesitant Fuzzy Linguistic VIKOR Method and Its Application in Qualitative Multiple Criteria Decision Making , 2015, IEEE Transactions on Fuzzy Systems.

[16]  Ying Zhang,et al.  A Knowledge-Based Approach for Multiagent Collaboration in Smart Home: From Activity Recognition to Guidance Service , 2020, IEEE Transactions on Instrumentation and Measurement.

[17]  Jaime Lloret,et al.  Context-Aware Cloud Robotics for Material Handling in Cognitive Industrial Internet of Things , 2018, IEEE Internet of Things Journal.

[18]  Lei Shu,et al.  Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges , 2018, IEEE Access.

[19]  Wei-Jen Lee,et al.  Design of an Industrial IoT-Based Monitoring System for Power Substations , 2019, 2019 IEEE/IAS 55th Industrial and Commercial Power Systems Technical Conference (I&CPS).

[20]  Simon Haykin,et al.  Artificial Intelligence Communicates With Cognitive Dynamic System for Cybersecurity , 2019, IEEE Transactions on Cognitive Communications and Networking.

[21]  M. Shamim Hossain,et al.  Enforcing Position-Based Confidentiality With Machine Learning Paradigm Through Mobile Edge Computing in Real-Time Industrial Informatics , 2019, IEEE Transactions on Industrial Informatics.

[22]  Qi Xuan,et al.  Modern Food Foraging Patterns: Geography and Cuisine Choices of Restaurant Patrons on Yelp , 2018, IEEE Transactions on Computational Social Systems.

[23]  Mark Nixon,et al.  Toward Cloud-Assisted Industrial IoT Platform for Large-Scale Continuous Condition Monitoring , 2019, Proceedings of the IEEE.

[24]  Clark Borst,et al.  Strategic Conformance: Overcoming Acceptance Issues of Decision Aiding Automation? , 2016, IEEE Transactions on Human-Machine Systems.

[25]  Juergen Jasperneite,et al.  The Future of Industrial Communication: Automation Networks in the Era of the Internet of Things and Industry 4.0 , 2017, IEEE Industrial Electronics Magazine.

[26]  Ashutosh Sharma,et al.  A Secure Communicating Things Network Framework for Industrial IoT using Blockchain Technology , 2019, Ad Hoc Networks.

[27]  Arash Ajoudani,et al.  A Capability-Aware Role Allocation Approach to Industrial Assembly Tasks , 2019, IEEE Robotics and Automation Letters.

[28]  Arun Kumar Sangaiah,et al.  Energy Consumption in Point-Coverage Wireless Sensor Networks via Bat Algorithm , 2019, IEEE Access.