Securing IoT and Big Data

Next Generation Intelligence

Vijayalakshmi Saravanan, Alagan Anpalagan, T. Poongodi, Firoz Khan, Vijayalakshmi Saravanan, Alagan Anpalagan, T. Poongodi, Firoz Khan

• Publisher: CRC Press
• Year: 2020
• Print ISBN: 9780367432881
• eBook ISBN: 9781000258530
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

This book covers IoT and Big Data from a technical and business point of view. The book explains the design principles, algorithms, technical knowledge, and marketing for IoT systems.

It emphasizes applications of big data and IoT. It includes scientific algorithms and key techniques for fusion of both areas. Real case applications from different industries are offering to facilitate ease of understanding the approach. The book goes on to address the significance of security algorithms in combing IoT and big data which is currently evolving in communication technologies.

The book is written for researchers, professionals, and academicians from interdisciplinary and transdisciplinary areas. The readers will get an opportunity to know the conceptual ideas with step-by-step pragmatic examples which makes ease of understanding no matter the level of the reader.

1Foundation of Big Data and Internet of Things

Applications and Case Study

Vijayalakshmi Saravanan,

Rochester Institute of Technology, USA

Mark Nuneviller,

Rochester Institute of Technology, USA

Anju S. Pillai and

Amrita Vishwa Vidyapeetham, India

Alagan Anpalagan

Ryerson University, Canada

1.1Introduction to Big Data and Internet of Things

The healthcare sector is currently experiencing a data overload derived from the digitization revolution. There are multiple streams of data entering healthcare systems through electronic health records. Healthcare data originally was kept in the form of hard copies but following the recent trends, health systems are racing to digitize these large amounts of patient health data. The information includes medical diagnoses, prescriptions, allergy data, demographics, clinical narratives, and the clinical laboratory results [1]. With the change to digitization, we have observed improvements in healthcare systems with data being widely available, resulting in reduced examinations, fewer ambiguities caused by illegible handwriting, and fewer missing or lost patient records. Patient care has immensely improved with healthcare systems becoming more organized [2].

Electronic health records are not the only data that is being recorded; with advancements in healthcare technology, multiple instruments have transformed into digital formats. Like electronic health records, which were originally static, X-ray films, scripts, and so on all went digital. Small electronics, such as heart rate monitors, EKG, and oxygen sensors, and large electronics, such as MRI machines, that are continuously recording data are all now digitized. Keeping all these streams of data organized is a serious challenge, especially with each type of data being different. The challenge of the healthcare sector is how to keep everything, including MRI images, sensory data, and patient health records, organized and interlinked. The historical relational databases that currently exist will struggle in maintaining structured and unstructured data [3].

Data analytics revolve around the five Vs of data characteristics. These are volume, velocity, variety, value, and veracity (shown in Figure 1.1), which will be the main challenges healthcare must overcome in order to become dominant in data organization and analytics [4]. Healthcare systems are continuously creating and accumulating data at an exponential rate. These systems need to store this large volume of data and access it at any time with a high rate of velocity. This leads to another data characteristic: the velocity that will be needed to analyse the data in real-time. Real-time monitoring of aspects such as blood pressure, operating room anaesthesia measurements, and heart monitors is crucial, as they can be the difference between life and death. Variety is a big challenge in healthcare, since the data streams come from multiple structured and unstructured sources [5]. Health systems will have to understand and manage these streams of data, such as MRI and X-ray images, sensory data, and genomic data. Healthcare applications need to be efficient in combining and converting various forms of data, including conversion from structured to unstructured data or other creative data storage solutions. Veracity is the final challenge that healthcare systems need to overcome. Is the data reliable and captured correctly? Is the data secured from outside intrusion/attack? In order to ensure these, a methodology and solution need to be constructed to tackle the challenges.

FIGURE 1.1The Five Vs of big data.

There are additional challenges in the healthcare context [6]. These include the high cost of overhauling the whole IT infrastructure that exists currently in the healthcare system. Not many healthcare systems have the money or the resources to dedicate to this kind of technological overhaul. Not only is the cost high, but also resources and a number of inputs are required to pull off a successful project. The healthcare systems will have to organize doctors, scientists, software engineers, and data scientists; all come together and bring a full technological construction, which is a huge challenge in itself.

The end goal of healthcare applications should be to successfully maintain multiple streams of data and grow to include genomic data to provide individualized healthcare treatment for patients and the highest quality of care possible. With the digitization and combining of data in order to use big data, healthcare sectors, ranging from doctors’ offices to large hospital groups, can receive significant advantages. Data would be more readily available, enabling fast disease detection, so starting the right treatment within a short span of time would now be possible. These technical advancements would also lead to improvements in health status, billing fraud would be detected more quickly, and health systems would increase profits and reduce waste and redundant costs. Overall disease patterns and outbreaks would be heavily analysed to improve surveillance and speedy response. Health systems can use this data to deliver faster targeted vaccinations such as the yearly influenza strain. Data collected would turn into actionable information to meet requirements, deliver services, and both forecast and prevent crises for the benefit of mankind. Enhanced data and analytics would benefit patients, most of whom are the largest consumers of health assets and can be provided with factual and accurate information to make informed decisions, proactively handle their own health, select and track healthier practices, and identify treatments in real-time.

Genomics, not to be confused with genetics, is a new science [7]. Genomics is the study of how the entire genome (the unique genetic code that serves as a blueprint for all living things) is expressed in the physical characteristics of an organism. Genetics is an older discipline and tracks how traits are inherited from generation to generation. Genomics is young because genomes could not be sequenced u...

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Table of Contents
• Preface
• Acknowledgements
• List of Editors
• List of Contributors
• Chapter 1: Foundation of Big Data and Internet of Things: Applications and Case Study
• Chapter 2: Securing IoT with Blockchain: Challenges, Applications, and Techniques
• Chapter 3: IoT and Big Data Using Intelligence
• Chapter 4: Compulsion for Cyber Intelligence for Rail Analytics in IoRNT
• Chapter 5: Big Data and IoT Forensics
• Chapter 6: Integration of IoT and Big Data in the Field of Entertainment for Recommendation System
• Chapter 7: Secure and Privacy Preserving Data Mining and Aggregation in IoT Applications
• Chapter 8: Real-Time Cardiovascular Health Monitoring System Using IoT and Machine Learning Algorithms: Survey
• Index