Identification of Heavy Drinking by Using IoT Devices and Artificial Intelligence

2021 ◽  
pp. 203-214
Author(s):  
Karan Gupta ◽  
Ritin Behl
Keyword(s):  
Artificial Intelligence ◽  
Heavy Drinking ◽  
Iot Devices

Artificial Intelligence Applications for Industry 4.0: A Literature-Based Study

2021 ◽  
pp. 1-29
Author(s):  
Mohd Javaid ◽  
Abid Haleem ◽  
Ravi Pratap Singh ◽  
Rajiv Suman
Keyword(s):  
Artificial Intelligence ◽  
Industry 4.0 ◽  
Decisive Role ◽  
Vital Role ◽  
Data Types ◽  
Process Data ◽  
Automation Systems ◽  
Recent Developments ◽  
Iot Devices

Artificial intelligence (AI) contributes to the recent developments in Industry 4.0. Industries are focusing on improving product consistency, productivity and reducing operating costs, and they want to achieve this with the collaborative partnership between robotics and people. In smart industries, hyperconnected manufacturing processes depend on different machines that interact using AI automation systems by capturing and interpreting all data types. Smart platforms of automation can play a decisive role in transforming modern production. AI provides appropriate information to take decision-making and alert people of possible malfunctions. Industries will use AI to process data transmitted from the Internet of things (IoT) devices and connected machines based on their desire to integrate them into their equipment. It provides companies with the ability to track their entire end-to-end activities and processes fully. This literature review-based paper aims to brief the vital role of AI in successfully implementing Industry 4.0. Accordingly, the research objectives are crafted to facilitate researchers, practitioners, students and industry professionals in this paper. First, it discusses the significant technological features and traits of AI, critical for Industry 4.0. Second, this paper identifies the significant advancements and various challenges enabling the implementation of AI for Industry 4.0. Finally, the paper identifies and discusses significant applications of AI for Industry 4.0. With an extensive review-based exploration, we see that the advantages of AI are widespread and the need for stakeholders in understanding the kind of automation platform they require in the new manufacturing order. Furthermore, this technology seeks correlations to avoid errors and eventually to anticipate them. Thus, AI technology is gradually accomplishing various goals of Industry 4.0.

IoT ecosystem faces fragmented future

2019 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Supply Chain ◽  
Internet Of Things ◽  
Data Privacy ◽  
The Internet ◽  
Privacy And Security ◽  
Content Type ◽  
Iot Devices ◽  
The Internet Of Things

Subject IoT ecosystem. Significance The market for the Internet of Things (IoT) or connected devices is expanding rapidly, with no manufacturer currently forecast to dominate the supply chain. This has fragmented the emerging IoT ecosystem, triggering questions about interoperability and cybersecurity of IoT devices. Impacts Firms in manufacturing, transportation and logistics and utilities are expected to see the highest IoT spending in coming years. The pace of IoT adoption is inextricably linked to that of related technologies such as 5G, artificial intelligence and cloud computing. Data privacy and security will be the greatest constraint to IoT adoption.

scholarly journals A Fog Computing Architecture with Multi-Layer for Computing-Intensive IoT Applications

2021 ◽  
Vol 11 (24) ◽  
pp. 11585
Author(s):  
Muhammad Muneeb ◽  
Kwang-Man Ko ◽  
Young-Hoon Park
Keyword(s):  
Artificial Intelligence ◽  
Data Analysis ◽  
New Technologies ◽  
Fog Computing ◽  
Layered Architecture ◽  
Computing Platform ◽  
Computing Paradigm ◽  
Iot Devices ◽  
Analysis System ◽  
Data Analysis System

The emergence of new technologies and the era of IoT which will be based on compute-intensive applications. These applications will increase the traffic volume of today’s network infrastructure and will impact more on emerging Fifth Generation (5G) system. Research is going in many details, such as how to provide automation in managing and configuring data analysis tasks over cloud and edges, and to achieve minimum latency and bandwidth consumption with optimizing task allocation. The major challenge for researchers is to push the artificial intelligence to the edge to fully discover the potential of the fog computing paradigm. There are existing intelligence-based fog computing frameworks for IoT based applications, but research on Edge-Artificial Intelligence (Edge-AI) is still in its initial stage. Therefore, we chose to focus on data analytics and offloading in our proposed architecture. To address these problems, we have proposed a prototype of our architecture, which is a multi-layered architecture for data analysis between cloud and fog computing layers to perform latency- sensitive analysis with low latency. The main goal of this research is to use this multi-layer fog computing platform for enhancement of data analysis system based on IoT devices in real-time. Our research based on the policy of the OpenFog Consortium which will offer the good outcomes, but also surveillance and data analysis functionalities. We presented through case studies that our proposed prototype architecture outperformed the cloud-only environment in delay-time, network usage, and energy consumption.

scholarly journals ІДЕНТИФІКАЦІЙНІ ДАНІ: ПРАВОВІ МЕХАНІЗМИ РОЗРОБКИ КЛАСИФІКАТОРІВ

2021 ◽  
Author(s):  
О. В. Костенко ◽  
Keyword(s):  
Artificial Intelligence ◽  
Information And Communication Technologies ◽  
Driving Forces ◽  
Modern Society ◽  
Communication Technologies ◽  
Legal Rights ◽  
Technological Revolution ◽  
Modern Development ◽  
Information And Communication ◽  
Iot Devices

Modern society has entered into a full-scale implementation of the scientific and technological revolution 4.0 and economic globalization. One of the driving forces of the new scientific and technological revolution is the development of information and communication technologies and the introduction of technologies for the transmission and use of information. Today, the problem of legal support for the management of the confidentiality of data used to identify subjects and objects by their unique attributes is relevant. The degree of solving the problem of managing the processes of digital identification data is one of the main factors in the modern development of crossborder e-economy and trade. There is a situation when in Ukraine in all spheres of public life modern information and communication technologies are rapidly introduced in the actual absence of legal institutions for the management of identification and personal data, biometrics, IoT devices and artificial intelligence. A significant complication for the development and operation of identification data management systems is the lack of a single strategy in this area, socio-legal model of public relations, a single classifier of identification data and a single scheme of identification of subjects by identification data, mechanisms for legal rights and responsibilities. projects, legal procedures for biometric identification, methods of identification of IoT devices and artificial intelligence.

scholarly journals AIoT with PUF: A Concrete Security

2020 ◽  
Vol 9 (7) ◽  
pp. 476-481
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Machine Intelligence ◽  
Customer Relationships ◽  
Physical Unclonable Functions ◽  
Security Problem ◽  
Fast Solution ◽  
Iot Devices ◽  
Efficiency And Productivity ◽  
Exchange Data

Artificial Intelligence in contrast to Natural Intelligence also known as Machine Intelligence is intelligence revealed by machine. It is the science and engineering of making machines intelligent. Therefore, it is a technique that makes a machine work like humans. The IOT Internet of Things is a network of internet-connected objects which can connect and exchange data. The combination of AI and IoT called AIoT is the combination of Artificial Intelligence and Internet of Things to achieve more efficient IoT operations. When Artificial Intelligence is added to IoT it means that the devices can analyze data and make decisions and act accordingly without the intervention of humans. The combination of AI and IOT has several advantages like saving money, building deeper customer relationships, increased operational efficiency and productivity and enhanced security and safety. This research paper focuses on what is AIoT, its applications and challenges and further, it also focuses on AIoT security concern and how can we solve the security problem with the use of PUF which is hardware security which is a simple and fast solution for security purpose. PUF is also more compatible with AIoT gadgets. Attacks on IoT devices are on the upsurge. Physical Unclonable functions (PUFs) are recognized as a robust and mild-weight way for AIoT

scholarly journals Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge

2020 ◽  
Vol 2 (11) ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Rob Walton ◽  
Max Van Kleek ◽  
Rafael Mantilla Montalvo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Academic Literature ◽  
The Past ◽  
Technological Advances ◽  
New Process ◽  
Technical Systems ◽  
Iot Devices ◽  
Cyber Risk

AbstractWe explore the potential and practical challenges in the use of artificial intelligence (AI) in cyber risk analytics, for improving organisational resilience and understanding cyber risk. The research is focused on identifying the role of AI in connected devices such as Internet of Things (IoT) devices. Through literature review, we identify wide ranging and creative methodologies for cyber analytics and explore the risks of deliberately influencing or disrupting behaviours to socio-technical systems. This resulted in the modelling of the connections and interdependencies between a system's edge components to both external and internal services and systems. We focus on proposals for models, infrastructures and frameworks of IoT systems found in both business reports and technical papers. We analyse this juxtaposition of related systems and technologies, in academic and industry papers published in the past 10 years. Then, we report the results of a qualitative empirical study that correlates the academic literature with key technological advances in connected devices. The work is based on grouping future and present techniques and presenting the results through a new conceptual framework. With the application of social science's grounded theory, the framework details a new process for a prototype of AI-enabled dynamic cyber risk analytics at the edge.

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