Safety-Related Wireless Communication via RF Modules for Industrial IoT Applications

2018 ◽  
pp. 96-112
Author(s):  
Samer Telawi ◽  
Ali Hayek ◽  
Josef Börcsök
Keyword(s):  
Wireless Communication ◽  
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

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.

Design and Optimization of LNA Amplifier Based on HEMT GaN for X-Band Wireless-Communication and IoT Applications

Silicon ◽  
2020 ◽  
Author(s):  
Abdelhamid Helali ◽  
Moujahed Gassoumi ◽  
Malek Gassoumi ◽  
Hassen Maaref
Keyword(s):  
Wireless Communication ◽  
Design And Optimization ◽  
Iot Applications ◽  
X Band

scholarly journals On the Use of LoRaWAN for Indoor Industrial IoT Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Michele Luvisotto ◽  
Federico Tramarin ◽  
Lorenzo Vangelista ◽  
Stefano Vitturi
Keyword(s):  
Internet Of Things ◽  
Communication Systems ◽  
Ieee 802.15.4 ◽  
High Reliability ◽  
General Description ◽  
Iot Applications ◽  
Industrial Internet ◽  
Industrial Iot ◽  
Monitoring Applications ◽  
Industrial Monitoring

Low-Power Wide-Area Networks (LPWANs) have recently emerged as appealing communication systems in the context of the Internet of Things (IoT). Particularly, they proved effective in typical IoT applications such as environmental monitoring and smart metering. Such networks, however, have a great potential also in the industrial scenario and, hence, in the context of the Industrial Internet of Things (IIoT), which represents a dramatically growing field of application. In this paper we focus on a specific LPWAN, namely, LoRaWAN, and provide an assessment of its performance for typical IIoT employments such as those represented by indoor industrial monitoring applications. In detail, after a general description of LoRaWAN, we discuss how to set some of its parameters in order to achieve the best performance in the considered industrial scenario. Subsequently we present the outcomes of a performance assessment, based on realistic simulations, aimed at evaluating the behavior of LoRaWAN for industrial monitoring applications. Moreover, the paper proposes a comparison with the IEEE 802.15.4 network protocol, which is often adopted in similar application contexts. The obtained results confirm that LoRaWAN can be considered as a strongly viable opportunity, since it is able to provide high reliability and timeliness, while ensuring very low energy consumption.

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