Development of an Internet of Things (IoT) based Lockout/Tagout (LOTO) device for Accident Prevention in Manufacturing Industries

In this era of technology advancement and industrial modernization, the concepts introduced by the German promised a great benefits and widely unexplored opportunities for application in industries. Industry 4.0 concept is one of the heavily discussed topics for academic researcher and practitioners. In this era of consumerism, manufacturing industries are required to manufactured products and services of the highest quality in order to retain competitiveness in the consumers market. The concepts of Smart Factory, Cyber-Physical System as well as Internet of Things and Services offers very capable opportunities and also downside challenges for quality management in manufacturing sectors. Thus, this paper present and discussed the opportunities and challenges in implementation of Industry 4.0 for quality management which triggered by a practical insight of an electronic production and service company in Austria. The findings of the recent study challenges are formed by the three key characteristics of Industry 4.0 which are horizontal, vertical and end-to-end manufacturing integration.

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Cooperative Approaches to Data Sharing and Analysis for Industrial Internet of Things Ecosystems

Applied Sciences ◽

10.3390/app11167547 ◽

2021 ◽

Vol 11 (16) ◽

pp. 7547

Author(s):

Henning Baars ◽

Ann Tank ◽

Patrick Weber ◽

Hans-Georg Kemper ◽

Heiner Lasi ◽

...

Keyword(s):

Internet Of Things ◽

Data Sharing ◽

Value Creation ◽

Manufacturing Industries ◽

Industrial Internet Of Things ◽

Manufacturing Environment ◽

Novel Approach ◽

Industrial Internet ◽

It Resources

The collection and analysis of industrial Internet of Things (IIoT) data offer numerous opportunities for value creation, particularly in manufacturing industries. For small and medium-sized enterprises (SMEs), many of those opportunities are inaccessible without cooperation across enterprise borders and the sharing of data, personnel, finances, and IT resources. In this study, we suggest so-called data cooperatives as a novel approach to such settings. A data cooperative is understood as a legal unit owned by an ecosystem of cooperating SMEs and founded for supporting the members of the cooperative. In a series of 22 interviews, we developed a concept for cooperative IIoT ecosystems that we evaluated in four workshops, and we are currently implementing an IIoT ecosystem for the coolant management of a manufacturing environment. We discuss our findings and compare our approach with alternatives and its suitability for the manufacturing domain.

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DLSTM-HHO: Enhanced Deep Learning Framework for Malware Detection at the Edge of the Iot System

10.21203/rs.3.rs-713566/v1 ◽

2021 ◽

Author(s):

Santhadevi D ◽

B

Keyword(s):

Performance Evaluation ◽

Deep Learning ◽

Internet Of Things ◽

Cyber Security ◽

Malware Detection ◽

Manufacturing Industries ◽

Apache Spark ◽

Learning Framework ◽

Proposed Model ◽

Confidential Data

Abstract Internet of Things (IoT) technology has a dynamic atmosphere due to incorporating multiple smart peripherals, which provide autonomous homes, cities, manufacturing industries, medical domain, etc.; however, a threat by cyber security is still at constant risk, and it gets much attention in researches. Cyber issues in the IoT environment are usually coming due to intruder’s malware activity. This kind of malware affects the confidential data of users in the IoT environment. In this research, a novel framework is implemented with the association of an improved deep LSTM with Harris Hawk Optimization (DLSTM-HHO). This framework is highly improved by adopting an Apache Spark technique for pre-processing IoT dataset. An Apache Spark replaces the traditional data pre-processing, which provides more efficiency to this model for detecting the malware at the edge of the IoT environment. The implementation of this framework is done in the MATLAB2020a platform with Apache Spark. The proposed model provides better performance evaluation in terms of accuracy is at 98%, and the F1-Score at 98.5%.

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Internet of Things based Accident Prevention and Detection System

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.4.11171 ◽

2018 ◽

Vol 7 (2.4) ◽

pp. 85 ◽

Cited By ~ 2

Author(s):

K Meena ◽

C Amuthadevi ◽

G Sai Kumar

Keyword(s):

Internet Of Things ◽

Detection System ◽

Human Life ◽

Accident Prevention ◽

Ambulance Service ◽

Motion Sensor ◽

Vehicle Performance ◽

Police Station ◽

Recent Growth ◽

Novel Approach

Recent growth in technology makes our life faster and easier. This development in technologies leads to enhance the traffic hazards. In this paper, Internet of Things based accident prevention and detection system is proposed to deduce accidents and save human life. The vehicle performance has been continuously monitored for safety purposes. This technique is annoying to maintain speed balance to avoid accident and provide safety to the driver. Hence, a novel approach is proposed to avoid accidents and save the victims while accidents occur. Sensors are exploited to give alarm 'ON' when distance between two vehicles is too short. If accident happens, then the camera is automatically turned on and captures the images around 180 degree angles. This emergency alert information including the location is transferred to nearest police station, ambulance service and relatives through GSM modem. The major component of the project includes a Arduino, motion sensor, touch sensors, relay and GSM modem.

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How the Industrial Internet of Things Changes Business Models in Different Manufacturing Industries

Digital Disruptive Innovation - Series on Technology Management ◽

10.1142/9781786347602_0006 ◽

2019 ◽

pp. 139-168

Author(s):

Christian Arnold ◽

Daniel Kiel ◽

Kai-Ingo Voigt

Keyword(s):

Internet Of Things ◽

Business Models ◽

Manufacturing Industries ◽

Industrial Internet Of Things ◽

Industrial Internet

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Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

Sensors ◽

10.3390/s22010224 ◽

2021 ◽

Vol 22 (1) ◽

pp. 224

Author(s):

Parkash Tambare ◽

Chandrashekhar Meshram ◽

Cheng-Chi Lee ◽

Rakesh Jagdish Ramteke ◽

Agbotiname Lucky Imoize

Keyword(s):

Quality Management ◽

Internet Of Things ◽

Performance Measurement ◽

Industry 4.0 ◽

Manufacturing Industries ◽

Data Driven ◽

Smart Manufacturing ◽

Manufacturing Companies ◽

Industrial Standards

The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology (ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed.

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RFID Protocol Design, Optimization, and Security for the Internet of Things

10.1049/pbce112e ◽

2017 ◽

Author(s):

Alex X. Liu ◽

Muhammad Shahzad ◽

Xiulong Liu ◽

Keqiu Li

Keyword(s):

Internet Of Things ◽

Design Optimization ◽

Protocol Design ◽

The Internet ◽

The Internet Of Things ◽

Rfid Protocol

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Multimedia system and database simulation based on internet of things and cloud service platform

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189253 ◽

2020 ◽

pp. 1-12

Author(s):

Zhang Caiqian ◽

Zhang Xincheng

Keyword(s):

Internet Of Things ◽

Cloud Service ◽

Multimedia System ◽

The Internet ◽

Multimedia Database ◽

Cloud Platform ◽

Service Platform ◽

Device Management ◽

Cloud Service Platform ◽

The Internet Of Things

The existing stand-alone multimedia machines and online multimedia machines in the market have certain deficiencies, so they cannot meet the actual needs. Based on this, this research combines the actual needs to design and implement a multi-media system based on the Internet of Things and cloud service platform. Moreover, through in-depth research on the MQTT protocol, this study proposes a message encryption verification scheme for the MQTT protocol, which can solve the problem of low message security in the Internet of Things communication to a certain extent. In addition, through research on the fusion technology of the Internet of Things and artificial intelligence, this research designs scheme to provide a LightGBM intelligent prediction module interface, MQTT message middleware, device management system, intelligent prediction and push interface for the cloud platform. Finally, this research completes the design and implementation of the cloud platform and tests the function and performance of the built multimedia system database. The research results show that the multimedia database constructed in this paper has good performance.

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