Machine Learning Paradigm For Internet Of Things Applications

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Machine Learning Paradigm for Internet of Things Applications

Shalli Rani,R. Maheswar,G. R. Kanagachidambaresan,Sachin Ahuja,Deepali Gupta
Machine Learning Paradigm for Internet of Things Applications Book PDF

Author : Shalli Rani,R. Maheswar,G. R. Kanagachidambaresan,Sachin Ahuja,Deepali Gupta
Publisher : John Wiley & Sons
Page : 308 pages
File Size : 49,8 Mb
Release : 2022-02-16
Category : Computers
ISBN : 9781119763475

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Machine Learning Paradigm for Internet of Things Applications by Shalli Rani,R. Maheswar,G. R. Kanagachidambaresan,Sachin Ahuja,Deepali Gupta Pdf

MACHINE LEARNING PARADIGM FOR INTERNET OF THINGS APPLICATIONS As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT’s potential and this book brings clarity to the issue. Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems. Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store ‘contextualized marketing’, and intelligent transportation systems. Readers will gain an insight into the integration of machine learning with IoT in these various application domains.

Machine Learning in Cognitive IoT

Neeraj Kumar,Aaisha Makkar
Machine Learning in Cognitive IoT Book PDF

Author : Neeraj Kumar,Aaisha Makkar
Publisher : CRC Press
Page : 328 pages
File Size : 52,8 Mb
Release : 2020-08-20
Category : Computers
ISBN : 9781000767971

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Machine Learning in Cognitive IoT by Neeraj Kumar,Aaisha Makkar Pdf

This book covers the different technologies of Internet, and machine learning capabilities involved in Cognitive Internet of Things (CIoT). Machine learning is explored by covering all the technical issues and various models used for data analytics during decision making at different steps. It initiates with IoT basics, its history, architecture and applications followed by capabilities of CIoT in real world and description of machine learning (ML) in data mining. Further, it explains various ML techniques and paradigms with different phases of data pre-processing and feature engineering. Each chapter includes sample questions to help understand concepts of ML used in different applications. Explains integration of Machine Learning in IoT for building an efficient decision support system Covers IoT, CIoT, machine learning paradigms and models Includes implementation of machine learning models in R Help the analysts and developers to work efficiently with emerging technologies such as data analytics, data processing, Big Data, Robotics Includes programming codes in Python/Matlab/R alongwith practical examples, questions and multiple choice questions

Big Data, IoT, and Machine Learning

Rashmi Agrawal,Marcin Paprzycki,Neha Gupta
Big Data IoT and Machine Learning Book PDF

Author : Rashmi Agrawal,Marcin Paprzycki,Neha Gupta
Publisher : CRC Press
Page : 319 pages
File Size : 41,5 Mb
Release : 2020-09-01
Category : Computers
ISBN : 9781000098280

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Big Data, IoT, and Machine Learning by Rashmi Agrawal,Marcin Paprzycki,Neha Gupta Pdf

The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools. This book consists of two sections: Section I contains the topics related to Applications of Machine Learning, and Section II addresses issues about Big Data, the Cloud and the Internet of Things. This brings all the related technologies into a single source so that undergraduate and postgraduate students, researchers, academicians and people in industry can easily understand them. Features Addresses the complete data science technologies workflow Explores basic and high-level concepts and services as a manual for those in the industry and at the same time can help beginners to understand both basic and advanced aspects of machine learning Covers data processing and security solutions in IoT and Big Data applications Offers adaptive, robust, scalable and reliable applications to develop solutions for day-to-day problems Presents security issues and data migration techniques of NoSQL databases

Fog Computing

Assad Abbas,Samee U. Khan,Albert Y. Zomaya
Fog Computing Book PDF

Author : Assad Abbas,Samee U. Khan,Albert Y. Zomaya
Publisher : John Wiley & Sons
Page : 616 pages
File Size : 54,7 Mb
Release : 2020-04-21
Category : Technology & Engineering
ISBN : 9781119551690

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Fog Computing by Assad Abbas,Samee U. Khan,Albert Y. Zomaya Pdf

Summarizes the current state and upcoming trends within the area of fog computing Written by some of the leading experts in the field, Fog Computing: Theory and Practice focuses on the technological aspects of employing fog computing in various application domains, such as smart healthcare, industrial process control and improvement, smart cities, and virtual learning environments. In addition, the Machine-to-Machine (M2M) communication methods for fog computing environments are covered in depth. Presented in two parts—Fog Computing Systems and Architectures, and Fog Computing Techniques and Application—this book covers such important topics as energy efficiency and Quality of Service (QoS) issues, reliability and fault tolerance, load balancing, and scheduling in fog computing systems. It also devotes special attention to emerging trends and the industry needs associated with utilizing the mobile edge computing, Internet of Things (IoT), resource and pricing estimation, and virtualization in the fog environments. Includes chapters on deep learning, mobile edge computing, smart grid, and intelligent transportation systems beyond the theoretical and foundational concepts Explores real-time traffic surveillance from video streams and interoperability of fog computing architectures Presents the latest research on data quality in the IoT, privacy, security, and trust issues in fog computing Fog Computing: Theory and Practice provides a platform for researchers, practitioners, and graduate students from computer science, computer engineering, and various other disciplines to gain a deep understanding of fog computing.

Machine Learning Approach for Cloud Data Analytics in IoT

Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Monika Mangla,Suneeta Satpathy,Sirisha Potluri
Machine Learning Approach for Cloud Data Analytics in IoT Book PDF

Author : Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Monika Mangla,Suneeta Satpathy,Sirisha Potluri
Publisher : John Wiley & Sons
Page : 528 pages
File Size : 50,5 Mb
Release : 2021-07-14
Category : Computers
ISBN : 9781119785859

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Machine Learning Approach for Cloud Data Analytics in IoT by Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Monika Mangla,Suneeta Satpathy,Sirisha Potluri Pdf

Machine Learning Approach for Cloud Data Analytics in IoT The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology. Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.

Deep Learning for Internet of Things Infrastructure

Uttam Ghosh,Mamoun Alazab,Ali Kashif Bashir,Al-Sakib Khan Pathan
Deep Learning for Internet of Things Infrastructure Book PDF

Author : Uttam Ghosh,Mamoun Alazab,Ali Kashif Bashir,Al-Sakib Khan Pathan
Publisher : CRC Press
Page : 240 pages
File Size : 41,7 Mb
Release : 2021-09-30
Category : Computers
ISBN : 9781000431957

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Deep Learning for Internet of Things Infrastructure by Uttam Ghosh,Mamoun Alazab,Ali Kashif Bashir,Al-Sakib Khan Pathan Pdf

This book promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of deep learning (DL)–based data analytics of IoT (Internet of Things) infrastructures. Deep Learning for Internet of Things Infrastructure addresses emerging trends and issues on IoT systems and services across various application domains. The book investigates the challenges posed by the implementation of deep learning on IoT networking models and services. It provides fundamental theory, model, and methodology in interpreting, aggregating, processing, and analyzing data for intelligent DL-enabled IoT. The book also explores new functions and technologies to provide adaptive services and intelligent applications for different end users. FEATURES Promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of DL-based data analytics of IoT infrastructures Addresses emerging trends and issues on IoT systems and services across various application domains Investigates the challenges posed by the implementation of deep learning on IoT networking models and services Provides fundamental theory, model, and methodology in interpreting, aggregating, processing, and analyzing data for intelligent DL-enabled IoT Explores new functions and technologies to provide adaptive services and intelligent applications for different end users Uttam Ghosh is an Assistant Professor in the Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA. Mamoun Alazab is an Associate Professor in the College of Engineering, IT and Environment at Charles Darwin University, Australia. Ali Kashif Bashir is a Senior Lecturer/Associate Professor and Program Leader of BSc (H) Computer Forensics and Security at the Department of Computing and Mathematics, Manchester Metropolitan University, United Kingdom. Al-Sakib Khan Pathan is an Adjunct Professor of Computer Science and Engineering at the Independent University, Bangladesh.

Green Internet of Things and Machine Learning

Roshani Raut,Sandeep Kautish,Zdzislaw Polkowski,Anil Kumar,Chuan-Ming Liu
Green Internet of Things and Machine Learning Book PDF

Author : Roshani Raut,Sandeep Kautish,Zdzislaw Polkowski,Anil Kumar,Chuan-Ming Liu
Publisher : John Wiley & Sons
Page : 279 pages
File Size : 53,7 Mb
Release : 2022-01-10
Category : Computers
ISBN : 9781119793120

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Green Internet of Things and Machine Learning by Roshani Raut,Sandeep Kautish,Zdzislaw Polkowski,Anil Kumar,Chuan-Ming Liu Pdf

Health Economics and Financing Encapsulates different case studies where green-IOT and machine learning can be used for making significant progress towards improvising the quality of life and sustainable environment. The Internet of Things (IoT) is an evolving idea which is responsible for connecting billions of devices that acquire, perceive, and communicate data from their surroundings. Because this transmission of data uses significant energy, improving energy efficiency in IOT devices is a significant topic for research. The green internet of things (G-IoT) makes it possible for IoT devices to use less energy since intelligent processing and analysis are fundamental to constructing smart IOT applications with large data sets. Machine learning (ML) algorithms that can predict sustainable energy consumption can be used to prepare guidelines to make IoT device implementation easier. Green Internet of Things and Machine Learning lays the foundation of in-depth analysis of principles of Green-Internet of Things (G-IoT) using machine learning. It outlines various green ICT technologies, explores the potential towards diverse real-time areas, as well as highlighting various challenges and obstacles towards the implementation of G-IoT in the real world. Also, this book provides insights on how the machine learning and green IOT will impact various applications: It covers the Green-IOT and ML-based smart computing, ML techniques for reducing energy consumption in IOT devices, case studies of G-IOT and ML in the agricultural field, smart farming, smart transportation, banking industry and healthcare. Audience The book will be helpful for research scholars and researchers in the fields of computer science and engineering, information technology, electronics and electrical engineering. Industry experts, particularly in R&D divisions, can use this book as their problem-solving guide.

Machine Learning and IoT for Intelligent Systems and Smart Applications

Madhumathy P.,M.Vinoth Kumar,R. Umamaheswari
Machine Learning and IoT for Intelligent Systems and Smart Applications Book PDF

Author : Madhumathy P.,M.Vinoth Kumar,R. Umamaheswari
Publisher : Unknown
Page : 227 pages
File Size : 45,5 Mb
Release : 2021-11
Category : Biomedical engineering
ISBN : 1032047259

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Machine Learning and IoT for Intelligent Systems and Smart Applications by Madhumathy P.,M.Vinoth Kumar,R. Umamaheswari Pdf

"The fusion of AI and IoT enables the systems to be predictive, prescriptive, and autonomous, and this convergence has evolved the nature of emerging applications from being assisted to augmented, and ultimately to autonomous intelligence. This book discusses algorithmic applications in the field of machine learning and IoT with pertinent applications. It further discusses challenges and future directions in the machine learning area and develops understanding of its role in technology, in terms of IoT security issues. Pertinent applications described include speech recognition, medical diagnosis, optimizations, predictions, and security aspects. Features: Focuses on algorithmic and practical parts of the artificial intelligence approaches in IoT applications. Discusses supervised and unsupervised machine learning for IoT data and devices. Presents an overview of the different algorithms related to Machine learning and IoT. Covers practical case studies on industrial and smart home automation. Includes implementation of AI from case studies in personal and industrial IoT. This book aims at Researchers and Graduate students in Computer Engineering, Networking Communications, Information Science Engineering, and Electrical Engineering"--

Heterogenous Computational Intelligence in Internet of Things

Pawan Singh,Prateek Singhal,Pramod Kumar Mishra,Avimanyou K. Vatsa
Heterogenous Computational Intelligence in Internet of Things Book PDF

Author : Pawan Singh,Prateek Singhal,Pramod Kumar Mishra,Avimanyou K. Vatsa
Publisher : CRC Press
Page : 376 pages
File Size : 49,9 Mb
Release : 2023-10-23
Category : Computers
ISBN : 9781000967944

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Heterogenous Computational Intelligence in Internet of Things by Pawan Singh,Prateek Singhal,Pramod Kumar Mishra,Avimanyou K. Vatsa Pdf

We have seen a sharp increase in the development of data transfer techniques in the networking industry over the past few years. We can see that the photos are assisting clinicians in detecting infection in patients even in the current COVID-19 pandemic condition. With the aid of ML/AI, medical imaging, such as lung X-rays for COVID-19 infection, is crucial in the early detection of many diseases. We also learned that in the COVID-19 scenario, both wired and wireless networking are improved for data transfer but have network congestion. An intriguing concept that has the ability to reduce spectrum congestion and continuously offer new network services is providing wireless network virtualization. The degree of virtualization and resource sharing varies between the paradigms. Each paradigm has both technical and non-technical issues that need to be handled before wireless virtualization becomes a common technology. For wireless network virtualization to be successful, these issues need careful design and evaluation. Future wireless network architecture must adhere to a number of Quality of Service (QoS) requirements. Virtualization has been extended to wireless networks as well as conventional ones. By enabling multi-tenancy and tailored services with a wider range of carrier frequencies, it improves efficiency and utilization. In the IoT environment, wireless users are heterogeneous, and the network state is dynamic, making network control problems extremely difficult to solve as dimensionality and computational complexity keep rising quickly. Deep Reinforcement Learning (DRL) has been developed by the use of Deep Neural Networks (DNNs) as a potential approach to solve high-dimensional and continuous control issues effectively. Deep Reinforcement Learning techniques provide great potential in IoT, edge and SDN scenarios and are used in heterogeneous networks for IoT-based management on the QoS required by each Software Defined Network (SDN) service. While DRL has shown great potential to solve emerging problems in complex wireless network virtualization, there are still domain-specific challenges that require further study, including the design of adequate DNN architectures with 5G network optimization issues, resource discovery and allocation, developing intelligent mechanisms that allow the automated and dynamic management of the virtual communications established in the SDNs which is considered as research perspective.

Machine Learning Paradigms

George A. Tsihrintzis,Lakhmi C. Jain
Machine Learning Paradigms Book PDF

Author : George A. Tsihrintzis,Lakhmi C. Jain
Publisher : Springer Nature
Page : 429 pages
File Size : 52,7 Mb
Release : 2020-07-23
Category : Computers
ISBN : 9783030497248

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Machine Learning Paradigms by George A. Tsihrintzis,Lakhmi C. Jain Pdf

At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.

Intelligence of Things: AI-IoT Based Critical-Applications and Innovations

Fadi Al-Turjman,Anand Nayyar,Ajantha Devi,Piyush Kumar Shukla
Intelligence of Things AI IoT Based Critical Applications and Innovations Book PDF

Author : Fadi Al-Turjman,Anand Nayyar,Ajantha Devi,Piyush Kumar Shukla
Publisher : Springer Nature
Page : 244 pages
File Size : 49,8 Mb
Release : 2021-10-28
Category : Technology & Engineering
ISBN : 9783030828004

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Intelligence of Things: AI-IoT Based Critical-Applications and Innovations by Fadi Al-Turjman,Anand Nayyar,Ajantha Devi,Piyush Kumar Shukla Pdf

This book presents recent technologies that explore artificial intelligence (AI) and its scope in Internet of Things (IoT) enabled areas for productivity and the betterment of society. The book aims at targeting audiences of several disciplines to share research, suggest solutions, and future trends in the field of AI using IoT. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both aspects to give a holistic understanding of the AI paradigm for IoT. The book focuses on timely topics related to the field of AI enabled IoT applications at large. The book consists of four major parts: fundamentals, theoretical discussion, critical applications, and the learning algorithms. These contents shall include the basics, types, tools, and techniques of AI. Finally, applications of AI enabled IoT in several areas are presented including health, security, climate change, agricultural engineering, bioinformatics, biomedicine, smart applications, natural language processing, social and economic implications of AI enabled IoT, as well as robotics, sustainability, risk management, seismic data processing, smart grid management, text analysis, security, privacy, and ethics.

Hands-On Deep Learning for IoT

Md. Rezaul Karim
Hands On Deep Learning for IoT Book PDF

Author : Md. Rezaul Karim
Publisher : Packt Publishing Ltd
Page : 298 pages
File Size : 51,7 Mb
Release : 2019-06-27
Category : Computers
ISBN : 9781789616064

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Hands-On Deep Learning for IoT by Md. Rezaul Karim Pdf

Implement popular deep learning techniques to make your IoT applications smarter Key FeaturesUnderstand how deep learning facilitates fast and accurate analytics in IoTBuild intelligent voice and speech recognition apps in TensorFlow and ChainerAnalyze IoT data for making automated decisions and efficient predictionsBook Description Artificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL). With an increase in investments in smart cities, smart healthcare, and industrial Internet of Things (IoT), commercialization of IoT will soon be at peak in which massive amounts of data generated by IoT devices need to be processed at scale. Hands-On Deep Learning for IoT will provide deeper insights into IoT data, which will start by introducing how DL fits into the context of making IoT applications smarter. It then covers how to build deep architectures using TensorFlow, Keras, and Chainer for IoT. You’ll learn how to train convolutional neural networks(CNN) to develop applications for image-based road faults detection and smart garbage separation, followed by implementing voice-initiated smart light control and home access mechanisms powered by recurrent neural networks(RNN). You’ll master IoT applications for indoor localization, predictive maintenance, and locating equipment in a large hospital using autoencoders, DeepFi, and LSTM networks. Furthermore, you’ll learn IoT application development for healthcare with IoT security enhanced. By the end of this book, you will have sufficient knowledge need to use deep learning efficiently to power your IoT-based applications for smarter decision making. What you will learnGet acquainted with different neural network architectures and their suitability in IoTUnderstand how deep learning can improve the predictive power in your IoT solutionsCapture and process streaming data for predictive maintenanceSelect optimal frameworks for image recognition and indoor localizationAnalyze voice data for speech recognition in IoT applicationsDevelop deep learning-based IoT solutions for healthcareEnhance security in your IoT solutionsVisualize analyzed data to uncover insights and perform accurate predictionsWho this book is for If you’re an IoT developer, data scientist, or deep learning enthusiast who wants to apply deep learning techniques to build smart IoT applications, this book is for you. Familiarity with machine learning, a basic understanding of the IoT concepts, and some experience in Python programming will help you get the most out of this book.

Recent Advances in Internet of Things and Machine Learning

Valentina E. Balas,Vijender Kumar Solanki,Raghvendra Kumar
Recent Advances in Internet of Things and Machine Learning Book PDF

Author : Valentina E. Balas,Vijender Kumar Solanki,Raghvendra Kumar
Publisher : Springer Nature
Page : 340 pages
File Size : 50,8 Mb
Release : 2022-02-14
Category : Technology & Engineering
ISBN : 9783030901196

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Recent Advances in Internet of Things and Machine Learning by Valentina E. Balas,Vijender Kumar Solanki,Raghvendra Kumar Pdf

This book covers a domain that is significantly impacted by the growth of soft computing. Internet of Things (IoT)-related applications are gaining much attention with more and more devices which are getting connected, and they become the potential components of some smart applications. Thus, a global enthusiasm has sparked over various domains such as health, agriculture, energy, security, and retail. So, in this book, the main objective is to capture this multifaceted nature of IoT and machine learning in one single place. According to the contribution of each chapter, the book also provides a future direction for IoT and machine learning research. The objectives of this book are to identify different issues, suggest feasible solutions to those identified issues, and enable researchers and practitioners from both academia and industry to interact with each other regarding emerging technologies related to IoT and machine learning. In this book, we look for novel chapters that recommend new methodologies, recent advancement, system architectures, and other solutions to prevail over the limitations of IoT and machine learning.

Convergence of Deep Learning and Artificial Intelligence in Internet of Things

Ajay Rana,Arun Kumar Rana,Sachin Dhawan,Sharad Sharma,Ahmed A Elngar
Convergence of Deep Learning and Artificial Intelligence in Internet of Things Book PDF

Author : Ajay Rana,Arun Kumar Rana,Sachin Dhawan,Sharad Sharma,Ahmed A Elngar
Publisher : CRC Press
Page : 329 pages
File Size : 53,9 Mb
Release : 2022-12-20
Category : Computers
ISBN : 9781000822083

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Convergence of Deep Learning and Artificial Intelligence in Internet of Things by Ajay Rana,Arun Kumar Rana,Sachin Dhawan,Sharad Sharma,Ahmed A Elngar Pdf

This book covers advances and applications of smart technologies including the Internet of Things (IoT), artificial intelligence, and deep learning in areas such as manufacturing, production, renewable energy, and healthcare. It also covers wearable and implantable biomedical devices for healthcare monitoring, smart surveillance, and monitoring applications such as the use of an autonomous drone for disaster management and rescue operations. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas such as electrical engineering, electronics and communications engineering, computer engineering, and information technology. • Covers concepts, theories, and applications of artificial intelligence and deep learning, from the perspective of the Internet of Things. • Discusses powers predictive analysis, predictive maintenance, and automated processes for making manufacturing plants more efficient, profitable, and safe. • Explores the importance of blockchain technology in the Internet of Things security issues. • Discusses key deep learning concepts including trust management, identity management, security threats, access control, and privacy. • Showcases the importance of intelligent algorithms for cloud-based Internet of Things applications. This text emphasizes the importance of innovation and improving the profitability of manufacturing plants using smart technologies such as artificial intelligence, deep learning, and the Internet of Things. It further discusses applications of smart technologies in diverse sectors such as agriculture, smart home, production, manufacturing, transport, and healthcare.

Smart Network Inspired Paradigm and Approaches in IoT Applications

Mohamed Elhoseny,Amit Kumar Singh
Smart Network Inspired Paradigm and Approaches in IoT Applications Book PDF

Author : Mohamed Elhoseny,Amit Kumar Singh
Publisher : Springer
Page : 253 pages
File Size : 52,5 Mb
Release : 2019-07-20
Category : Computers
ISBN : 9789811386145

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Smart Network Inspired Paradigm and Approaches in IoT Applications by Mohamed Elhoseny,Amit Kumar Singh Pdf

This book gathers high-quality research articles and reviews that reflect the latest advances in the smart network-inspired paradigm and address current issues in IoT applications as well as other emerging areas. Featuring work from both academic and industry researchers, the book provides a concise overview of the current state of the art and highlights some of the most promising and exciting new ideas and techniques. Accordingly, it offers a valuable resource for senior undergraduate and graduate students, researchers, policymakers, and IT professionals and providers working in areas that call for state-of-the-art networks and IoT applications.