Artificial Intelligence for Threat Detection and Analysis in Industrial IoT: Applications and Challenges

2021 ◽  
pp. 1-6
Author(s):  
Hadis Karimipour ◽  
Franaz Derakhshan
Keyword(s):  
Artificial Intelligence ◽  
Threat Detection ◽  
Iot Applications ◽  
Industrial Iot

Role of machine learning and deep learning in securing 5G-driven industrial IoT applications

2021 ◽  
pp. 102685
Author(s):  
Parjanay Sharma ◽  
Siddhant Jain ◽  
Shashank Gupta ◽  
Vinay Chamola
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Iot Applications ◽  
Industrial Iot

Optimizing Blockchain Networks with Artificial Intelligence: Towards Efficient and Reliable IoT Applications

2020 ◽  
pp. 299-321 ◽  
Author(s):  
Furqan Jameel ◽  
Uzair Javaid ◽  
Biplab Sikdar ◽  
Imran Khan ◽  
George Mastorakis ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Iot Applications

Machine Learning for Industrial IoT Systems

2021 ◽  
pp. 336-358
Author(s):  
Mona Bakri Hassan ◽  
Elmustafa Sayed Ali Ahmed ◽  
Rashid A. Saeed
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Operations Management ◽  
Industrial Applications ◽  
Smart Devices ◽  
Iot Applications ◽  
Industrial Iot ◽  
Effective Decision ◽  
Effective Decision Making

The use of AI algorithms in the IoT enhances the ability to analyse big data and various platforms for a number of IoT applications, including industrial applications. AI provides unique solutions in support of managing each of the different types of data for the IoT in terms of identification, classification, and decision making. In industrial IoT (IIoT), sensors, and other intelligence can be added to new or existing plants in order to monitor exterior parameters like energy consumption and other industrial parameters levels. In addition, smart devices designed as factory robots, specialized decision-making systems, and other online auxiliary systems are used in the industries IoT. Industrial IoT systems need smart operations management methods. The use of machine learning achieves methods that analyse big data developed for decision-making purposes. Machine learning drives efficient and effective decision making, particularly in the field of data flow and real-time analytics associated with advanced industrial computing networks.

Making IoT Run

2020 ◽  
pp. 377-397
Author(s):  
Peter Schott ◽  
Torben Schaft ◽  
Stefan Thomas ◽  
Freimut Bodendorf
Keyword(s):  
Artificial Intelligence ◽  
Production Systems ◽  
Smart Manufacturing ◽  
Maturity Level ◽  
Smart Systems ◽  
Industrial Iot ◽  
Increasing Demand ◽  
Manufacturing Environments ◽  
Self Learning

This article describes how today's manufacturing environments are characterized by an increasing demand for individual products and constantly more product variants. Concomitant, developments in the fields of IT, robotics and artificial intelligence allow the realization of smart systems, which means networked, self-learning, self-regulating and versatile production systems to control this complexity. These developments are referred to as industrial IoT that is acknowledged as “next big thing” in production. Firms face the challenge of lacking guidelines for implementing IoT solutions. Neither the technological prerequisites nor generally applicable procedures for realizing an appropriate technological maturity level of the system-to-be exist. Addressing this deficit, a framework is introduced which systematically implements IoT within manufacturing. The framework presents a guideline for the establishment of structural system understanding, the determination of the target system's technological maturity level from a customer's perspective and, building on this, design implications for smart manufacturing.

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