Survelliance of Type I and II Diabetic Subjects on Physical Characteristics

The Delhi and NCR healthcare systems are rapidly registering electronic health records and diagnostic information available electronically. Furthermore, clinical analysis is rapidly advancing, and large quantities of information are examined and new insights are part of the analysis of this technology experienced as big data. It provides tools for storing, managing, studying, and assimilating large amounts of robust, structured, and unstructured data generated by existing medical organizations. Recently, data analysis data have been used to help provide care. The present study aimed to analyse diabetes with the latest IoT and big data analysis techniques and its correlation with stress (TTH) on human health. The authors have tried to include age, gender, and insulin factor and its correlation with diabetes. Overall, in conclusion, TTH cases increasing with age in case of males and not following the pattern of diabetes variation with age, while in the case of females, TTH pattern variation is the same as diabetes (i.e., increasing trend up to age of 60 then decreasing).

IoT Device Onboarding, Monitoring, and Management

In establishing a healthy environment for connectivity devices, it is essential to ensure that privacy and security of connectivity devices are well protected. The modern world lives on data, information, and connectivity. Various kinds of sensors and edge devices stream large volumes of data to the cloud platform for storing, processing, and deriving insights. An internet of things (IoT) system poses certain difficulties in discretely identifying, remotely configuring, and controlling the devices, and in the safe transmission of data. Mutual authentication of devices and networks is crucial to initiate secure communication. It is important to keep the data in a secure manner during transmission and in store. Remotely operated devices help to monitor, control, and manage the IoT system efficiently. This chapter presents a review of the approaches and methodologies employed for certificate provisioning, device onboarding, monitoring, managing, and configuring of IoT systems. It also examines the real time challenges and limitations in and future scope for IoT systems.

Big Data IoT Analytics for Smart Cities With Cloud Computing Technique

Currently, cities are being reconstructed to smart cities that use an information and communication technology (ICT) framework alongside the internet of things (IoT) technology to increase efficiency and also share information with the public, helping to improve the quality of government services citizens' welfare. This large, diverse set of information called big data is obtained by ICT and IoT technologies from smart cities. This information does not have any meaning of its own but a high potential to make use of smart city services. Therefore, the information collected is mined and processed through use of big data analytic techniques. The environmental footprints in smart cities can be monitored and controlled with the help of ICT. Big data analytic techniques help enhance the functionalities of smart cities and the 4G and 5G network provides strong connectivity for professional devices.

Machine and Deep Learning Techniques in IoT and Cloud

For centuries, the concept of a smart, autonomous learning machine has fascinated people. The machine learning philosophy is to automate the development of analytical models so that algorithms can learn continually with the assistance of accessible information. Machine learning (ML) and deep learning (DL) methods are implemented to further improve an application's intelligence and capacities as the quantity of the gathered information rises. Because IoT will be one of the main sources of information, data science will make a significant contribution to making IoT apps smarter. There is a rapid development of both technologies, cloud computing and the internet of things, considering the field of wireless communication. This chapter answers the questions: How can IoT intelligent information be applied to ML and DL algorithms? What is the taxonomy of IoT's ML and DL and profound learning algorithms? And what are real-world IoT data features that require data analytics?

Emerging Trends and Techniques in Cloud-Based Data Analytics

The new trending technologies such as big data and cloud computing are in line with social media applications due to their fast growth and usage. The big data characteristic makes data management challenging. The term big data refers to an immense collection of both organised and unorganised data from various sources, and nowadays, cloud computing supports in storing and processing such a huge data. Analytics are done on huge data that helps decision makers to take decisions. However, merging two conflicting design principles brings a challenge, but it has its own advantage in business and various fields. Big data analytics in the cloud places rigorous demands on networks, storage, and servers. The chapter discusses the importance of cloud platform for big data, importance of analytics in cloud and gives detail insight about the trends and techniques adopted for cloud analytics.

Impact of Deep Learning Techniques in IoT

Deep learning models can achieve more accuracy sometimes that exceed human-level performance. It is crucial for safety-critical applications such as driverless cars, aerospace, defence, medical research, and industrial automation. Most of the deep learning methods mimic the neural network. It has many hidden layers and creates patterns for decision making and it is a subset of machine learning that performs end-to-end learning and has the capability to learn unsupervised data and also provides very flexible, learnable framework for representing the visual and linguistic information. Deep learning has greatly changed the way and computing devices processes human-centric content such as speech, image recognition, and natural language processing. Deep learning plays a major role in IoT-related services. The amalgamation of deep learning to the IoT environment makes the complex sensing and recognition tasks easier. It helps to automatically identify patterns and detect anomalies that are generated by IoT devices. This chapter discusses the impact of deep learning in the IoT environment.

Big Data Analytics and Its Applications in IoT

Big data and the internet of things (IoT) are two major ruling domains in today's world. It is observed that there are 2.5 quintillion bytes of data created each day. Big data defines a very huge amount of data in terms of both structured and unstructured formats. Business intelligence and other application domains that have high information density use big data analytics to make predictions and better decisions to improve the business. Big data analytics is used to analyze a high range of data at a time. In general, big data and IoT were built on different technologies; however, over a period of time, both of them are interlinked to build a better world. Companies are not able to achieve maximum benefit, just because the data produced by the applications are not utilized and analyzed effectively as there is a shortage of big data analysts. For real-time IoT applications, synchronization among hardware, programming, and interfacing is needed to the greater extent. The chapter discusses about IoT and big data, relation between them, importance of big data analytics in IoT applications.

Best Practices

The internet of things (IoT) is rapidly changing our society to a world where every “thing” is connected to the internet, making computing pervasive like never before. It is increasingly becoming a ubiquitous computing service, requiring huge volumes of data storage and processing. Unfortunately, due to the lack of resource constraints, it tends to adopt a cloud-based architecture to store the voluminous data generated from IoT application. From a security perspective, the technological revolution introduced by IoT and cloud computing can represent a disaster, as each object might become inherently remotely hackable and, as a consequence, controllable by malicious actors. This chapter focus on security considerations for IoT from the perspectives of cloud tenants, end-users, and cloud providers in the context of wide-scale IoT proliferation, working across the range of IoT technologies. Also, this chapter includes how the organization can store the IoT data on the cloud securely by applying different Access control policies and the cryptography techniques.

The Age of Autonomous Internet of Things Devices

The age of autonomous sensing has dominated almost every industry today. Our lives have been engaged with multiple sensors embedded in our smartphones to achieve sensing of all sorts starting from proximity sensing to social sensing. Our possessions (cars, fridges, oven) have sensors embedded in them. The art of autonomous IoT has shifted from a mere detection of events or changes in the environment to dominant systems for social sensing, big data analytics, and smart things. Recently, sensing systems have adapted connectivity resulting in input mechanisms for big data analytics and smart systems resulting in pervasive systems. Currently, a range of sensors has come to existence, for example, mobile phone sensors that measure blood pressure at patients' figure tip, or the sensors that be used to detect deforestation. In this chapter, the authors provide a technical view upon which autonomous IoT devices can be implemented and enlist opportunities and challenges of the same.

Real-Time Problems to Be Solved by the Combination of IoT, Big Data, and Cloud Technologies

With the advancement of computation power and internet revolution, IoT, big data, and cloud computing have become the most prevalent technologies in present time. Convergence of these three technologies has led to the development of new opportunities and applications which solve the real time problems in the most efficient way. Though cloud computing and big data have an inherent connection between them, IoT plays a major role of a data source unit. With the explosion of data, cloud computing is playing a significant role in the storage and management. However, the main concern that accompanies IoT are the issues related to privacy, security, power efficiency, computational complexities, etc. Misinterpretation of data and security limitations are the bottlenecks of big data whereas the limitations of cloud computing involve network connection dependency, limited features, technical issues, and security. The chapter considers use cases to address their real time problems and discusses about how to solve these issues by combining these technologies.