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2020 | Buch

IoT Fundamentals: Definitions, Architectures, Challenges, and Promises Kapitel lesen Erstes Kapitel lesen

Intelligent Internet of Things

From Device to Fog and Cloud

herausgegeben von: Farshad Firouzi, Krishnendu Chakrabarty, Sani Nassif

Verlag: Springer International Publishing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik"

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Über dieses Buch

This holistic book is an invaluable reference for addressing various practical challenges in architecting and engineering Intelligent IoT and eHealth solutions for industry practitioners, academic and researchers, as well as for engineers involved in product development. The first part provides a comprehensive guide to fundamentals, applications, challenges, technical and economic benefits, and promises of the Internet of Things using examples of real-world applications. It also addresses all important aspects of designing and engineering cutting-edge IoT solutions using a cross-layer approach from device to fog, and cloud covering standards, protocols, design principles, reference architectures, as well as all the underlying technologies, pillars, and components such as embedded systems, network, cloud computing, data storage, data processing, big data analytics, machine learning, distributed ledger technologies, and security. In addition, it discusses the effects of Intelligent IoT, which are reflected in new business models and digital transformation. The second part provides an insightful guide to the design and deployment of IoT solutions for smart healthcare as one of the most important applications of IoT. Therefore, the second part targets smart healthcare-wearable sensors, body area sensors, advanced pervasive healthcare systems, and big data analytics that are aimed at providing connected health interventions to individuals for healthier lifestyles.

Inhaltsverzeichnis

Frontmatter

IoT Building Blocks

Frontmatter
Chapter 1. IoT Fundamentals: Definitions, Architectures, Challenges, and Promises
Abstract
The Internet is everywhere and touched almost every corner of the globe affecting our lives in previously unimagined ways. As a living entity, the Internet is constantly evolving, and now, an era of widespread connectivity through various smart devices (i.e., things) that connect with the Internet has begun. This paradigm change is generally referred to as the Internet of Things (IoT). Welcoming IoT will bring significant benefits to economies and businesses as it enables greater innovation and productivity. On the other hand, the rapid adoption of IoT presents new challenges regarding connectivity, security, data processing, and scalability. Because the IoT world is vast and versatile, it cannot be viewed as a single technology. IoT looks more like an umbrella covering many protocols, technologies, and concepts that depend on specific industries. In this chapter, we will seek to look at the history of IoT, more clearly define it, and review its terms and concepts. We will also review vertical IoT markets and higher-level use cases that have successfully adopted IoT solutions. We will also discuss the details of the business implications, business models, and opportunities of IoT. Finally, the complete IoT stack and reference architectures from smart objects, to the networks, to the cloud, and finally the applications where information is leveraged are explained.
Farshad Firouzi, Bahar Farahani, Markus Weinberger, Gabriel DePace, Fereidoon Shams Aliee
Chapter 2. The Smart “Things” in IoT
Abstract
Things (devices) are the key components of IoT systems that have continually evolved in various aspects of the user interface, form factor, performance, energy consumption, and security. Every new generation of IoT devices harnesses more intelligence to enable novel applications in IoT systems. As a result, IoT designers have defined an architectural design platform to build smart IoT things. In this chapter, we first provide a definition of smart things. Then, we continue with an overview of relevant architectural components included in typical smart things. In the following sections, we provide detailed examples of the architectural components. Various types of sensors and actuators are explained. The general architecture of microcontrollers, in particular ARM Cortex-M, is examined. Finally, we provide a general view of the input/output interfaces, programming models, and real-time operating systems for IoT smart things.
Farshad Firouzi, Bahar Farahani, Mahdi Nazm Bojnordi
Chapter 3. Engineering IoT Networks
Abstract
Networks inside IoT applications can be very complicated, merging several different standards in order to achieve the common communication objective. This chapter presents the main reference communication scenarios and architectures, as well as the primary standards behind them. To better describe network standards, the well-known ISO/OSI model is adopted as reference, and the basic network terminology is introduced. We present several widespread standards such as Bluetooth, IEEE 802.15.4-based technologies, LoRaWAN, Sigfox, and, among cellular standards, NB-IoT and LTE-M. Then application-level technologies are discussed. Finally, we discuss the aspects to be considered in the design of the communication part of IoT applications, including localization aspects.
Enrico Fraccaroli, Davide Quaglia
Chapter 4. Architecting IoT Cloud
Abstract
Cloud computing and the Internet of Things (IoT) are distinct technologies that significantly affect our everyday lives. IoT is made up of small real-world things, with limited processing and storage capacity, which are widely distributed. These characteristics raise concerns regarding performance and connectivity. Conversely, as a more mature technology, Cloud computing is able to address some of these issues through virtually limitless storage and processing capability. Therefore, over the past few years, Cloud and IoT technologies have been integrated to have the best of these two complementary worlds. This chapter presents the fundamentals of Cloud computing, as well as the details of IoT Cloud layers including data ingestion, data processing, data storage, data visualization, and IoT applications.
Farshad Firouzi, Bahar Farahani
Chapter 5. Machine Learning for IoT
Abstract
Machine learning stems from a discipline with a long history, namely, artificial intelligence, which uses statistical techniques to equip computer systems with the ability to "learn" from data, rather than following pre-programmed rules. Over the past few years, machine learning has become one of the workhorses of information technology and has become enmeshed in, albeit usually hidden, every aspect of daily lives. From image recognition to voice recognition, and from predictive maintenance in production lines to health diagnosis systems, the use of machine learning have been embedded everywhere, which invariably accelerates the advancement of most technologies and their applications accordingly.
This chapter intends to provide readers with an overview of machine learning. We will first discuss some fundamentals of probability theory, statistics, and linear algebra, since they are the basics that many machine learning solutions must rely on to become amenable. Next, we provide several use cases of machine learning in IoT solutions. Finally, we will discuss the details of two main categories in machine learning, namely, supervised learning and unsupervised learning.
Farshad Firouzi, Bahar Farahani, Fangming Ye, Mojtaba Barzegari
Chapter 6. Big Data
Abstract
We live in the data age and big data technologies allow to harness data, uncover hidden patterns and correlations in data, discover insights, and improve decision making. Although the Internet of Things (IoT) and big data have been evolved separately, they are closely intertwined. Indeed, the role of big data in IoT is tremendous as IoT is projected to include billions of connected devices, producing a massive amounts of data within a few years. In this context, big data analytics is a key enabler for unlocking the untapped potential of the IoT and fueling a wide range of data-driven products, services, and business processes. Apache Hadoop is the most prominent and used tool in big data. Hadoop is an open source framework that enables distributed processing of large data sets across commodity clusters. It is designed to scale with built-in high availability and reliability. Additional open source projects have been built around the original Hadoop implementation with the addition of Apache Spark. Spark is the next hype in the industry among the big data tools which was designed to address the shortcomings of Hadoop. Spark provides primitives for in-memory cluster computing which can speed jobs that run on the Hadoop data processing platform. This chapter discusses the details of top Big Data processing frameworks being used today enabling automatic and scalable insight discovery from large and complex data. In particular, we explain the origins of Hadoop and Apache Spark, their functionalities, architectures, and practical applications.
Natasha Balac
Chapter 7. Intelligent and Connected Cyber-Physical Systems: A Perspective from Connected Autonomous Vehicles
Abstract
Cyber-physical systems (CPS) have broad applications in the automotive, avionics, robotics, healthcare, and power grid, where the cyber components involving information processing and networking closely interact with the physical processes. Conventionally, there is a separate design flow of CPS. For instance, control algorithms managing the physical dynamics are designed using model-based approaches, without considering details of the cyber implementation platforms. Modern CPS are getting increasingly intelligent and connected. A new design methodology taking all the layers of CPS and their interplays into account is being developed, aiming for assurance of safety and security, as well as high robustness and resource efficiency. This chapter presents the technical background of CPS, with an emphasis on the cyber and physical interactions, corresponding to the new design methodology. Case studies on connected autonomous vehicles (CAVs) are used to illustrate the most recent development in CPS.
Wanli Chang, Simon Burton, Chung-Wei Lin, Qi Zhu, Lydia Gauerhof, John McDermid
Chapter 8. Distributed Ledger Technology
Abstract
Distributed ledger technology (DLT) has attracted tremendous attention from industry and academia. A wide scope of research and product development activities have emerged in recent years and are expected to impact every aspect of our lives. This chapter covers prominent DLTs and their applications. The chapter focuses on blockchain and directed acyclic graphs (DAGs). Applications discussed include finance, healthcare, identity management, supply chain, and energy. The chapter also discusses the Internet of Things (IoT), the weaknesses of current IoT system implementations, and how blockchain and IoT can be integrated to overcome the weaknesses of IoT. The chapter also touches on token economics and enterprise-level DLT applications. Finally, the vulnerabilities of blockchain are discussed.
Xing Liu, Bahar Farahani, Farshad Firouzi
Chapter 9. Emerging Hardware Technologies for IoT Data Processing
Abstract
Fast and energy-efficient data processing has become a critical need for various forms of computing in the era of Internet of things (IoT). Emerging IoT applications demand for increasingly high data collection rates and significant computational requirements that often do not fit in the stringent power envelopes of the existing IoT devices. Data centers and cloud servers are then used to empower the IoT systems by performing massive data processing on behalf of the IoT users. Recent years have witnessed many significant challenges for big data processing in IoT systems. This section provides an overview of main architectural challenges for data processing in IoT systems and explains a number of recent innovations for addressing the challenges. The rest of the section examines two example memory architectures for accelerating data-intensive applications in the IoT nodes and data centers. The first architecture is a memory subsystem based on the emerging nonvolatile memory technologies that enables energy-efficient neural network acceleration in memory arrays. The memory system is capable of performing ordinary data storage in the future IoT nodes, as well as significantly accelerating certain operations for binary neural network workloads. The second architecture is a memory-centric accelerator specifically designed to perform large-scale data clustering using the k-median algorithm. The accelerator is suitable for IoT data centers, where large data is collected from the IoT nodes and clustered in the cloud servers. The architecture has shown significant energy-efficiency and performance potentials for gene expression analysis from the healthcare sector and document clustering used for data mining in web applications.
Mahdi Nazm Bojnordi, Payman Behnam
Chapter 10. IoT Cyber Security
Abstract
As Internet of things (IoT) devices grow more and more popular, IoT cyber security becomes ever more pertinent to ensure the confidentiality, integrity, and authenticity of the device and the data it may have access to. IoT systems are composed of many areas, from the physical device to the cloud to its users, there are numerous areas that must be taken into consideration when attempting to secure an IoT system. This chapter builds from the ground up specificities about IoT cyber security. It details how to identify threats, provides tools which can be used to identify threats, identifies ways to secure vulnerable areas of an IoT system, and tools for monitoring system components.
Brian Russell

IoT Technologies for Smart Healthcare

Frontmatter
Chapter 11. Healthcare IoT
Abstract
The interaction between technology and healthcare has a long history. However, recent years have witnessed the quick growth and appropriation of the Internet of Things (IoT) paradigm, the advent of miniature wearable biosensors, and research advances in Big Data techniques for effective manipulation of large, multiscale, multimodal, distributed, and heterogeneous data sets. This advancement has created new opportunities in customized healthcare services. The IoT has signaled a paradigm change on the horizon for healthcare, bringing advantages such as accessibility, availability, personalized content, and cost-efficient delivery. While Healthcare IoT has greatly increased the number of possibilities for meeting healthcare needs, there are still several challenges that must be addressed in order to create adaptable, appropriate, power-efficient, and safe systems that address health needs. Empowering this transformation will require the software and hardware communities to come together to achieve extensive technological advancements. This chapter addresses all these important aspects of novel IoT technologies for smart healthcare – wearable sensors, body area sensors, advanced pervasive healthcare systems, and Big Data analytics. It identifies new perspectives and highlights compelling research issues and challenges such as scalability, interoperability, device-network-human interfaces, and security. Finally, the feasibility of healthcare IoT is investigated by a novel case study, ECG-based arrhythmia detection, based on deep learning and convolutional neural network (CNN) methods distributed across Edge-Fog-Cloud.
Bahar Farahani, Farshad Firouzi, Krishnendu Chakrabarty
Chapter 12. Biomedical Engineering Fundamentals
Abstract
This chapter introduces the concept of bioelectricity and biomechanics. The descriptions of several specific biosensors are also included in this chapter. The main aim of this chapter is to provide an interdisciplinary work related to measurement, analysis, and classification of biomedical signals using signal processing techniques for clinical diagnosis purpose. Some applications for the diagnosis of various diseases are also included in this chapter.
Ram Bilas Pachori, Vipin Gupta
Chapter 13. Smart Learning Using Big and Small Data for Mobile and IOT e-Health
Abstract
In this chapter, we provide a snapshot of the state-of-the-art research in mobile and IOT e-health studies that leverage AI technologies for making sense of personal health measurement and assessment, as well as for delivering situational, actionable insights in care flows. In recent years, the proliferation of consumer and pervasive health technologies has enabled a whole new generation of sensor-based precision measurement technologies and mobile ecological momentary assessments that are able to capture patient-specific characteristics in context [3–5]. The captured physiomes (i.e., a collection of quantitative and integrated descriptions of the functional behavior of the physiological state of an individual [1]) can help detect physiological macro-phenotypes such as inflammatory response and fatigue [8], as well as critical conditions such as seizure and atrial fibrillation [6, 7]. The accumulated longitudinal records of such phonemes are also expected to capture patterns that can help distinguish individual physiological differences, e.g., being insulin-sensitive or insulin-resistant, which will make a difference in disease diagnosis and prognosis [8].
Pei-Yun Sabrina Hsueh, Xinyu Hu, Ying Kuen Cheung, Dominik Wolff, Michael Marschollek, Jeff Rogers
Backmatter
Metadaten
Titel
Intelligent Internet of Things
herausgegeben von
Farshad Firouzi
Krishnendu Chakrabarty
Sani Nassif
Copyright-Jahr
2020
Electronic ISBN
978-3-030-30367-9
Print ISBN
978-3-030-30366-2
DOI
https://doi.org/10.1007/978-3-030-30367-9

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