Preface

      The Internet-of-Things (IoT) interconnects humans with uniquely identifiable embedded computing devices within the existing internet infrastructure. It has emerged as a powerful and promising technology, and though it has significant technological, social, and economic impacts, it also poses new security and privacy challenges. Compared with the traditional internet, the IoT has various embedded devices, mobile devices, a server, and the cloud, with different capabilities to support multiple services. The pervasiveness of these devices represents a huge attack surface. And since the IoT connects cyberspace to physical space, known as a cyber-physical system, IoT attacks not only have an impact on information systems, but also affect physical infrastructure, the environment, and even human security. Nowadays, the IoT has received massive attention for applications in different domains, the healthcare sector being one of them. A healthcare system serves society by taking care of its citizens’ physiological and neurological conditions through sensors by amassing information on their current health conditions and passing it along to the healthcare center for necessary actions. Accordingly, physicians can examine these health conditions and take the steps required to prevent the deterioration of the patient’s health. The purpose of this book is to help achieve a better integration between the work of researchers and practitioners in a single medium for capturing state-of-the-art IoT solutions in healthcare applications to address how to improve the proficiency of wireless sensor networks (WSNs) in healthcare. It explores possible automated solutions in everyday life, including the structures of healthcare systems built to handle large amounts of data, thereby improving clinical decisions; which is why this book will prove invaluable to professionals who want to increase their understanding of recent challenges in the IoT-enabled health-care domain. The separate chapters herein address various aspects of the IoT system, such as design challenges, theory, various protocols, and implementation issues, and also include several case studies. Furthermore, this book has been designed for both undergraduate students and researchers to easily understand and apply IoT in the healthcare domain.

       About the Book

      Smart Healthcare System: Security and Privacy Aspects covers the introduction, development, and applications of smart healthcare models that represent the current state-of-the-art of various domains. The primary focus will be on theory, algorithms, and their implementation targeted at real-world problems. It will deal with different applications to give the practitioner a flavor of how IoT architectures are designed and introduced into various situations. More particularly, this volume consists of 14 chapters contributed by authors well-versed in the subject who are devoted to reporting the latest findings on smart healthcare system design.

      Chapter 1 explores a framework that can use real-time electroencephalogram (EEG) signals from multiple channels to predict the occurrence of an epileptic seizure. A selected number of EEG channels are provided as input to the system, and the corresponding epileptic seizure state is recorded at every second. A hybrid artificial neural network with a support vector machine-based classification is created as a simulation of real-time dynamic predictions in this system.

      Chapter 2 discusses the critical factors to be considered in mHealth applications, such as mobility awareness, location-based medication, data, distance, and measurement protection for eHealth. Most of the mHealth apps operate with the patient’s background, which involves disease and environmental observation. Many problems face creating these applications, such as protection, smartness, decision-making, application size, and timely actions. This study presents the health sector dilemma by using it fuzzy logic for changes in health. For the health application to enhance well-being, all features addressed in this chapter are imperative.

      Chapter 3 includes the design of a decision-making framework that gathers, preprocesses, and analyzes data from IoT-based healthcare systems and produces comprehensive information reports for better diagnosis. It implements data preprocessing methods, such as data washing, munging, normalization, elimination, and noisy data removal. The integration of the IoT with data analytics technologies results in healthcare systems becoming smarter and smarter. In the preliminary stage alone, the collected IoT data, such as pulse rate, temperature, oxygen level, and heart rate from connected devices, can be used to analyze the need and severity using appropriate machine learning techniques. Multi-criteria decision-making (MCDM) strategies, such as SMART, WPM, and TOPSIS, are often used to create comprehensive, insightful diagnostic reports at the conclusion of the development process.

      In Chapter 4, the proposed work deals with touch and native voice-assisted prototype design and development to allow intuitive communication and interaction between health professionals and patients affected by severe acute respiratory infection (SARI), who are dependent on a ventilator and admitted for quarantine treatment. It also ensures that the multilingual capacity to communicate effectively in most of the ten Indian languages is established so that patients are relieved of pain, etc., as health professionals answer their queries. Touch-based gesture patterns can be effectively used as an interactive module in this prototype and let doctors frequently track and react to ICU patient inquiries by updating it to easily communicate the patient’s emotions or pains to caregivers. The planned prototype would be made available and public in an open source software repository.

      Chapter 5 discusses the critical importance, especially in developing countries, of identifying the cause of a pandemic, such as COVID-19, and monitoring the spread of the disease. Included in our proposed system presented in this chapter is a network model that incorporates wireless body sensors, wearable devices, and cloud computing to manage patient data in the form of text or images, or cloud voice. To keep track of the real-time data, a cell phone application is installed along with a website.

      In Chapter 6, Healthcare 4.0 technologies are adopted so that patients can be tracked remotely for surgical operations. Biosensors are also adopted in handheld gadgets. The proposed framework uses machine learning techniques to analyze the data obtained by the sensors. This method gathers the medical records of patients for review. It is challenging to provide a bed for treatment in the current COVID-19 pandemic situation, especially in developing and highly populated countries. Thus, the proposed Healthcare 4.0 system is designed to move therapies with a high-precision disease detection rate and testing from hospitals to patients’ homes.

      Chapter 7 explains why even though smart technology offers several healthcare benefits, the same systems have a more significant effect on both confidentiality and security. Hacks on other frameworks, personal security risks, privacy threats, data eavesdropping, etc., are potential threats. Therefore, together with a cloud server, the framework proposed in this chapter uses the wireless body area network