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Construction of Industrial Internet of Things Based on MQTT and OPC UA Protocols

Construction of Industrial Internet of Things Based on MQTT ...

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IEEE international conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Huichao Shi Li Niu Jinhao Sun College of Information Science and Technology Beijing University of Chemical Technology Beijing China Division for Digital Manufacturing and Flexible Assembly AVIC Manufacture Technology Institute Beijing China

ISBN: (数字)9781728170053

ISBN: (纸本)9781728170060

At present, the Internet of Things has become a hot area of global concern and is considered to be another major scientific and technological innovation after the Internet. The Internet of Things can be analyzed from two aspects, one is to access the Internet, and the other is the connection between things. The Industrial Internet of Things, as a new type of industrial ecosystem, collects resources and data during the industrial production process by using miniature low-cost controllers and high-bandwidth wireless networks to achieve flexible configuration of manufacturing raw materials and improve resource utilization. The Industrial Internet of Things not only includes traditional software elements, but also requires hardware controllers and sensors, as well as cloud service platforms, to ultimately achieve intelligent manufacturing. The Industrial Internet of Things based on MQTT adopts the method of upper computer (WeChat applet) + server (Alibaba Cloud Server) + lower computer (WiFi module ESP8266 NodeMcu) to realize data collection and control of industrial production process. Based on OPC UA protocol, The Industrial Internet of Things, by designing an intelligent node device based on embedded Linux system, uses Qt to build the OPC UA protocol. OPC UA is a C / S architecture real-time database framework. By building an OPC UA Client on a handheld terminal device, An OPC UA Serve is set up on the smart terminal device, and the information collected by the handheld terminal is sent to the smart terminal. At the same time, multiple smart terminals are connected through the OPC UA protocol to perform data interaction and information transfer. At the same time, an edge computing algorithm is embedded in the smart terminal, and a large amount of data collected is processed for analysis, screening, and calculation. At the same time, the processed information is transmitted or processed. Reduce data interactions between data sources and data centers, and reduce the am

关键词： Protocols Servers Internet of Things Tools Sensors Process control Cloud computing

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IoT and Cloud computing based smart Water Metering System

IoT and Cloud Computing based Smart Water Metering System

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Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), 2020 international conference on

作者： Aritra Ray Shreemoyee Goswami A. K. C. School of Information Technology University of Calcutta Kolkata India

ISBN: (数字)9781728165752

ISBN: (纸本)9781728165769

This paper focuses on the developmental and implementation methodology of smart water meter based on Internet of Things (IoT) and Cloud computing equipped with machine learning algorithms, to differentiate between normal and excessive water usage at industrial, domestic and all other sectors having an abundance of water usage, both for Indian and worldwide context. Recognizing that intelligent metering of water has the potential to alter customer engagement of water usage in urban and rural water supplies, this paper fosters for sustainable water management, a need of the present. With shrinking reserves of clean water resources worldwide, it is becoming cumbersome to cater for this resource to masses in the coming years on a consistent basis. Using our smart water meter, water resources can be managed efficiently and an optimum use could save water for the future generations. Sensors will provide for real time monitoring of hydraulic data, automated control and alarming from Cloud platform in case of events such as water leakages, excessive usage, etc. Analysis of the same will help in taking meaningful actions. Thus we do propose for a smart water metering technology that can be utilized by Indian citizens, and worldwide, to curb wastage of water. With an ease of monitoring and visualization of the data through the Cloud platform combined with machine learning based tools to detect excess water consumption, the server-less architecture we propose can be easily adopted and implemented in a large scale.

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Harnessing Low-Fidelity data to Accelerate Bayesian Optimization via Posterior Regularization

Harnessing Low-Fidelity Data to Accelerate Bayesian Optimiza...

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international conference on Big data and smart computing (BIGCOMP)

作者： Bin Liu School of Computer Science Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Bayesian optimization (BO) is a powerful paradigm for derivative-free global optimization of a black-box objective function (BOF) that is expensive to evaluate. However, the overhead of BO can still be prohibitive for problems with highly expensive function evaluations. In this paper, we investigate how to reduce the required number of function evaluations for BO without compromise in solution quality. We explore the idea of posterior regularization to harness low fidelity (LF) data within the Gaussian process upper confidence bound (GP-UCB) framework. The LF data can arise from previous evaluations of an LF approximation of the BOF or a related optimization task. An extra GP model called LF-GP is trained to fit the LF data. We develop an operator termed dynamic weighted product of experts (DW-POE) fusion. The regularization is induced by this operator on the posterior of the BOF. The impact of the LF GP model on the resulting regularized posterior is adaptively adjusted via Bayesian formalism. Extensive experimental results on benchmark BOF optimization tasks demonstrate the superior performance of the proposed algorithm over state-of-the-art.

关键词： Optimization Hafnium Adaptation models Bayes methods Heuristic algorithms data models Acceleration

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High Performance data Engineering Everywhere

High Performance Data Engineering Everywhere

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smart data Services (SMDS), IEEE international conference on

作者： Chathura Widanage Niranda Perera Vibhatha Abeykoon Supun Kamburugamuve Thejaka Amila Kanewala Hasara Maithree Pulasthi Wickramasinghe Ahmet Uyar Gurhan Gunduz Geoffrey Fox Digital Science Center Bloomington IN USA Luddy School of Informatics Computing and Engineering IN USA Indiana University Alumni IN USA University of Moratuwa Sri Lanka

ISBN: (数字)9781728187778

ISBN: (纸本)9781728187785

The amazing advances being made in the fields of machine and deep learning are a highlight of the Big data era for both enterprise and research communities. Modern applications require resources beyond a single node's ability to provide. However this is just a small part of the issues facing the overall data processing environment, which must also support a raft of data engineering for pre- and post-data processing, communication, and system integration. An important requirement of data analytics tools is to be able to easily integrate with existing frameworks in a multitude of languages, thereby increasing user productivity and efficiency. All this demands an efficient and highly distributed integrated approach for data processing, yet many of today's popular data analytics tools are unable to satisfy all these requirements at the same time. In this paper we present Cylon, an open-source high performance distributed data processing library that can be seamlessly integrated with existing Big data and AI/ML frameworks. It is developed with a flexible C++ core on top of a compact data structure and exposes language bindings to C++, Java, and Python. We discuss Cylon's architecture in detail, and reveal how it can be imported as a library to existing applications or operate as a standalone framework. Initial experiments show that Cylon enhances popular tools such as Apache Spark and Dask with major performance improvements for key operations and better component linkages. Finally, we show how its design enables Cylon to be used cross-platform with minimum overhead, which includes popular AI tools such as PyTorch, Tensorflow, and Jupyter notebooks.

关键词： data analysis C++ languages System integration Tools Big data data engineering Libraries

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Way Forward to Digital Society – Digital Transformation of Msmes from Industry 4.0 to Industry 5.0

Way Forward to Digital Society – Digital Transformation of ...

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international conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)

作者： Kavita Bhatt S. Mohan Kumar School of Engineering and Technology CMR University Bangalore India

Digital Transformation is a new buzz word in industrial world which transforms the physical world into virtual space. It leverages the implementation of digital technologies to accelerate the growth of the industry/business. It refers to automation, digitalization of process, infrastructure, organization, management with the help of Industry 4.0 technologies like Cloud computing, 5G& 6G, analytics, Big data, Additive manufacturing, Artificial Intelligence, augmented reality, cyber-physical system, smart sensors, IoT Platforms, cloud computing. The field is continuously evolving and shaping the future of Medium, Small and micro industries (MSMEs). Digital Transformation (DX) provides opportunities and possibilities to MSMEs to accelerate their growth via recent disruption in technologies and its attached benefits. The industry is shifting towards virtual work from mechanized systems leveraging benefits of industry 4.0 practices like AI, big data, intelligent network system, machine learning, strong network connection and making effective use of digital transformation. Since end users' requirements and consumption of goods keep increasing product industry 5.0 is gaining much attention which refers to the integration of Artificial Intelligence matching to the level of human capabilities. This paper discusses the evolution of industry 4 and emerging I4.0 technology, major drivers and factors impacting the implementation of I4.0 practices. It also covers the paradigm shift in how next-generation industry is getting shifted with the help of I5.0. It highlights the prospects of the development of technologies that is contributing to the transition of Industry 4.0 to Industry 5.0. This study provides a high-level overview of industry 4.0, its emergences and emerging technologies and transition from Industry 4.0 to Industry 5.0. This paper also highlights the main components of Industry 5.0 and how digital transformation can be moved from machine to human-machine collaborati

关键词： Industries Productivity Cloud computing intelligent networks Computational modeling Transforms Big data

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Automated Water-Gate Controlling System for Paddy Fields

Automated Water-Gate Controlling System for Paddy Fields

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Advancements in computing (ICAC), 2019 international conference on

作者： W.A.K.L. Sanjula K.T.W. Kavinda M.A.K. Malintha W.M.D.L. Wijesuriya Shashika Lokuliyana Rajitha de Silva Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9781728184128

ISBN: (纸本)9781728184135

The Internet of things (IoT) is an attractive technology being used in almost every aspect of the world today. It allows connecting any of the objects that want to communicate with each other and to transfer data without human interaction. In this paper proposed system is discussed on canal automation for a smart irrigation system using IoT concepts. Automated Water Gate Controlling System collects few environmental factors through a smart module embedded with sensors to communicate with the water gate of the paddy field. Cloud computing is used to store a large amount of data gathered by the sensor module. All real-time sensed data are processed and demonstrated on a web dashboard with a convenient graphical user interface along with a rain and reservoir water level prediction analysis to the users. Automation supports distance monitoring and controlling by allowing minimum human intervention. This paper provides a solution for traditional irrigation systems to confront rural farming interferences and global climatic changes via a cloud-based automated wireless communication system to water their paddy fields, monitor them and smartly control them.

关键词： Temperature sensors Irrigation Logic gates Reservoirs Internet of Things Monitoring intelligent sensors

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A Distributed data Mining and Cloud Analysis for Predictive Gas Level, Dynamic Booking, and smart Energy Optimization

A Distributed Data Mining and Cloud Analysis for Predictive ...

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Advances in data Engineering and intelligent computing Systems (ADICS), international conference on

作者： D. Sugumar R. Ramadevi J. Prasad N. Mohankumar D.C. Joy Winnie Wise S. Velmurugan Department of Commerce Care College of Arts and Science Trichy Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Saveetha University Saveetha Nagar Chennai Tamil Nadu India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The state-of-the-art Internet of Things (IoT) innovation known as the Autonomous Gas Reservoir Management System (AGaRMS) totally changes the most common way of overseeing gas supplies. AGaRMS uses sensors that monitor gas levels and a distributed architecture that forecasts patterns in gas use to ensure that gas refills occur at the right time. Complex AI calculations examine verifiable information, current weather patterns, and usage patterns to estimate when gas will run out. This, then, prompts robotized booking sales to be transported off the supplier. Likewise, AGaRMS appeals to the savvy progression of energy, which empowers strong gas usage. The framework's flexibility and versatility make it reasonable for use in private and business settings, further developing production network productivity, reducing working expenses, and improving the client experience. The AGaRMS system offers a smart method for managing gas leaders that work with gathering effective power energy practices and streamlining gas provisioning processes. AGaRMS optimizes the gas supply chain and improves the user experience by catering to a wide range of residential and industrial applications with its flexible and scalable design.

关键词： Supply chains Weather forecasting Reservoirs Aerodynamics User experience Internet of Things Predictive analytics

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A Management Information Systems Framework for Sustainable Cloud-Based smart E-Healthcare Research Information Systems in Bangladesh

A Management Information Systems Framework for Sustainable C...

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intelligent Technologies (ACOIT), Asian conference on

作者： Khandakar Rabbi Ahmed Rakibul Islam Md Ariful Alam Mir Araf Hossain Rivin Mahfuz Alam Md Shafiqur Rahman Miyan Research Institute International University of Business Agriculture and Technology Uttara Model Town Dhaka Bangladesh Dept. of Mechatronics Engineering Rajshahi University of Engineering and Technology (RUET) Kazla Rajshahi Bangladesh Dept. of MBA in Business Analytics International American University (IAU) 3440 Wilshire Blvd STE 1000 Los Angeles CA United States Dept. of MBA in Management Information System International American University (IAU) 3440 Wilshire Blvd STE 1000 Los Angeles CA United States Dept. of Mathematics Louisiana Tech University 201 Mayfield Ave Ruston LA United States

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Cloud technology provides a practical and suitable way to manage the massive amount of information and resources in the global era. Information technology offers a strong possibility of accelerating smart healthcare research information sharing through cloud technology at a low cost and with limited resources. Health care information is directly related to health providers, but there are minimal options to get available health care records in Bangladesh. Unfortunately, no data centre can handle the volume of health information being transferred to and from multiple healthcare providers through the Internet. The primary purpose of our Research is to introduce sustainable Cloud-Based smart E-Healthcare Research Information Systems (CSERIS) and to adopt them in Bangladesh. Throughout this article, we represent vital research tools in healthcare records and provider data, investigation, and healthcare system integration. We present a list of several possible answers, the best service, and more opportunities to implement (CSERIS). In this Research, we tried to assess different aspects and strengths, suitability, feasibility, weaknesses, opportunities, challenges, and strategies for the Bangladeshi health sector and enterprises. Researchers may also use (CSERIS) to get all process data for further analysis, Research, and development. Our solution is more straightforward, user-friendly, and robust than older methods.

关键词： Cloud computing Costs Software as a service Information sharing Medical services System integration Management information systems Usability Information technology Research and development

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Roger that! Learning How Laypersons Teach New Functions to intelligent Systems

Roger that! Learning How Laypersons Teach New Functions to I...

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international conference on Semantic computing

作者： Sebastian Weigelt Vanessa Steurer Tobias Hey Walter F. Tichy Karlsruhe Institute of Technology Institute for Program Structures and Data Organization Karlsruhe Germany

ISBN: (数字)9781728163321

ISBN: (纸本)9781728163338

intelligent systems are rather smart today but still limited to built-in functionality. To break through this barrier, future systems must allow users to easily adapt the system by themselves. For humans the most natural way to communicate is talking. But what if users want to extend the systems' functionality with nothing but natural language? Then intelligent systems must understand how laypersons teach new skills. To grasp the semantics of such teaching sequences, we have defined a hierarchical classification task. On the first level, we consider the existence of a teaching intent in an utterance; on the second, we classify the distinct semantic parts of teaching sequences: declaration of a new function, specification of intermediate steps, and superfluous information. We evaluate twelve machine learning techniques with multiple configurations tailored to this task ranging from classical approaches such as naïve-bayes to modern techniques such as bidirectional LSTMs and task-oriented adaptations. On the first level convolutional neural networks achieve the best accuracy (96.6%). For the second task, bidirectional LSTMs are the most accurate (98.8%). With the additional adaptations we are able to improve both classifications distinctly (up to 1.8%).

关键词： Task analysis Semantics Neural networks Machine learning intelligent systems Training

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NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion

NAttack! Adversarial Attacks to bypass a GAN based classifie...

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IEEE international conference on Big data Security on Cloud (BigdataSecurity), High Performance and smart computing (HPSC) and intelligent data and Security (IDS)

作者： Aritran Piplai Sai Sree Laya Chukkapalli Anupam Joshi Department of Computer Science University of Maryland Baltimore County Baltimore USA

ISBN: (数字)9781728168739

ISBN: (纸本)9781728168746

With the recent developments in artificial intelligence and machine learning, anomalies in network traffic can be detected using machine learning approaches. Before the rise of machine learning, network anomalies which could imply an attack, were detected using well-crafted rules. An attacker who has knowledge in the field of cyber-defence, could make educated guesses to sometimes accurately predict which particular features of network traffic data the cyber-defence mechanism is looking at. With this information, the attacker can circumvent a rulebased cyber-defense system. However, after the advancements of machine learning for network anomaly, it is not easy for a human to understand how to bypass a cyber-defence system. Recently, adversarial attacks have become increasingly common to defeat machine learning algorithms. In this paper, we show that even if we build a classifier and train it with adversarial examples for network data, we can use adversarial attacks and successfully break the system. We propose a Generative Adversarial Network (GAN) based algorithm to generate data to train an efficient neural network based classifier, and we subsequently break the system using adversarial attacks.

关键词： adversarial attacks network intrusion GANs Classifiers Artificial Intelligence gallium nitrate Machine Learning traffic network bypasses telecommunication traffic network data Machine learning algorithms based Bioinvasion HUMAN INTRUSION Water encroachment intrusions TRAIN

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