Intelligent Internet of Things for Healthcare and Industry

The healthcare industry has been one of the fastest to accept the Internet of Things; the main reason for this development is that assimilating IoT features into medical devices significantly improves the superiority and efficiency of the service, carrying a particularly high value for the aged patients in prolonged situations and those demanding continuous observations. Nowadays, a number of IoT-enabled medical devices exist in the market for assistance and correct measurement of information related to our health, such as remote temperature checking for vaccinations, medical data transmitting methods, air quality sensors, drug efficiency tracking, vital science data sizing, sleep monitor systems, medicine refill reminder technologies, and remote care biometric scanners for sleep and safety tools for children. The first section of the chapter consisted of the usage and development of IoT devices in the healthcare system for better care of patients. The second section of the chapter is also comprised of the framework of IoT for healthcare, role and importance of IoT-enabled device in healthcare, and operation of IoT in healthcare components and Internet of Medical Things. The third section of the chapter contained a reported work which covered the IoT along with its different learning models, including machine and deep learning in different healthcare components. The main section of the chapter covered the comparative analysis based on the types of disease detection, datasets used with learning techniques, and performance parameters.

With the expansion of technology, the Internet of Things (IoT) comprises keen gadgets, smart devices, wireless sensors, and systems that are a combination of updated innovations. Recently, low-power, low-latency gadgets are the most demanding devices in the healthcare sector. In this term, the network structure has changed a lot with the progress of wireless communications. Moreover, some research work explores that the next-generation network structure will be dependable on IoT, which signifies that the embedded devices can communicate with each other instantly. The arrangement of wireless and low-power, low-latency devices in medical science for IoT devices will have a life-changing impact on the healthcare system. This chapter will concentrate on various communication protocols and low-power, low-latency healthcare IoT devices. Furthermore, commonly IoT communication protocols, with an emphasis on the main features and behaviors of various metrics of power consumption, security spreading, data rate, and other features will also be distinguished, which will help in further research endeavors to select the correct convention for various applications.

Research in emotion recognition is important in many domains, from industrial ergonomics and health diagnostics to neuromarketing and affective computing. Emotion recognition from facial expression can be performed using image processing of face photos or using directly attached sensors reading the physiological signals from the muscular system governing the facial expression. We describe our experiments with the recognition of affectively induced facial expressions using non-intrusive on-body electromyography (EMG) sensors from the MySignals biosignal acquisition platform. For induction of the affective reaction, we use three different visual stimuli (movie clips) aimed to induce neutral, positive, and negative reactions. The results from 14 subjects show that 97.6% accuracy of emotion recognition was achieved using the covariance matrix features and K-nearest neighbor (KNN) classifier.

The emergence of coronavirus (COVID-19) is currently a challenge that has physical, economic, social, and pedagogical boundaries, thus gaining global attention. The emergence of new trends in technologies contributed to the commencement of the Internet of Things (IoT), which is gaining worldwide attention as well as becoming available for monitoring, diagnosing, forecasting, and preventing emerging communicable diseases. IoT in the medical organization is advantageous and has enabled appropriate control of individuals with COVID-19 by using interconnected wearable sensors and networks. IoT is an evolving area of investigation in infectious disease epidemiology. However, the augmented dangers of communicable diseases transmitted through worldwide integration and the pervasive availability of smart types of machinery, including the interrelatedness of the world, require its utilization for monitoring, averting, predicting, and managing transmittable viruses. This has helped in reducing the circulation rate in the hospital and increasing patient satisfaction. Therefore, this chapter discusses the overall applications of IoT during the COVID-19 pandemic. Also, the significant applications of IoT, challenges, and opportunities of deploying the technologies during the outbreak are presented. This can be of help to identify symptoms and provides better treatment for the outbreak. Taking into account the current situation worldwide, smart disease monitoring systems focused on IoT can be significantly advanced in an attempt to combat the next contagion. With the development of smartphones, wearable devices, and Internet access, IoT’s role will limit the spread of the pandemic by collecting and analyzing data already gathered. These technologies also help to provide an automated and efficient warning system that allows early and timely identification of COVID-19, thus reducing mortality and preventing global spread.

The advancement of information technology is demonstrated by the emergence of the Internet of Things, which impacts many areas, such as healthcare and health services. The Internet of Things has grown its branches in nearly all areas due to its enormous characteristics. Due to the current pandemic that has created a gap between doctors and their patients, an intelligent health monitoring system for exact and precise tracking of a patient’s health is crucial. Various health monitoring systems are being developed on the button of current growths in the Internet of Things. However, there exist some shortcomings in the likes of range of connectivity, cost, and portability. This study aims to develop an enhanced Internet of Things-based patients’ health monitoring system consisting of four vital sensors: temperature sensor, heartbeat sensor, pulse sensor, and UV sensor. These sensory units are attached to the Arduino Nano board, and the data gotten from these sensors are stored in a ThingSpeak cloud with the ESP8266–01 Wi-Fi communication module. The proposed solution is fully tested on 25 live patients, and the overall results with respect to the output of the sensors with given live temperature, heartbeat, pulse rate, and UV readings showed an automated response of sensors and significant improvements to the availability of patients’ vitals in real time with a minimum average response time of 7 s. In conclusion, our experimental results showed an advancement in existing studies regarding the number of measured parameters, deployment environment, and response time.

The healthcare system that depends on the Internet of Things (IoT) assists individuals and aids their vital everyday life activities. The affordability and user-friendliness of the usage of IoT start revolutionizing healthcare services. The IoT and its related technologies have emerged as the most preferred use cases in the healthcare system. Hence, the security and privacy issues associated with smart healthcare systems and their participated entities make its general acceptance still a dream. Also, as a remote patient monitoring system using IoT-based devices are the increase in privacy, popularity, accessibility, security concerns of logging and the transfer of medical data continue arising. Therefore, this chapter proposes IoT-based blockchain-protected medical data utilizing a smart contract for secure analysis and management of wearable sensors based on the Ethereum protocol. The integration of blockchain will counter the security and privacy issues that may arise using IoT-based wearable devices in the healthcare monitoring system, thus facilitating safe and secure storage of patients’ information in the utilization of IoT devices in the healthcare system. IoT-based healthcare can be employed to enhance the well-being of patients, make the healthcare system more effective, and help respond quickly to emergencies.

The unrelenting quest of humans to make computers a rational decision-making and reasoning machine to perform operations much like humans led to the development of Computational Intelligence. The health industry has been endowed with medical decision-making procedures, which include but are not limited to diagnosis, automated image infusion, and most importantly computer-aided surgery and therapy. The prime example of how CI has found relevance in contemporary research is the prediction of breast cancer using particle swarm optimization. Despite the large strides in technology, the potential of Computational Intelligence remains largely untapped. Much similar is the case with the Internet of Medical Things. With benefits that include simultaneous reporting and monitoring, end-to-end connectivity, affordability, data assortment, live feed, and tracking with remote medical assistance, it is rapidly being used to discover, gather, diagnose, analyze, and research upon patients’ data.

This chapter would aim to provide a comprehensible and concise understanding of Computational Intelligence-based schemes in the biomedical field and would also focus on challenges to the integration of Computational Intelligence in medical applications. Furthermore, the chapter would explore the ubiquitous nature of CI in the biomedical field and seeks to shed light on the emerging trends in the CI-based health sector and the Internet of Medical Things (IoMT). Finally, the chapter would conclude with prospects that lie ahead in the Computational Intelligence-based biomedical industry.

The Internet of Medical Things (IoMT) is increasing the accuracy, the reliability, and the production capability of electronic devices by playing a very important part in the industry of healthcare. The available medical resources and services related to the healthcare are working to get an interconnection with each other by the digital healthcare system by the contribution of the researchers. Sensors, wearable devices, medical devices, and clinical devices are all connected to form an ecosystem of the Internet of Medical Things. The different applications of healthcare are enabled by the Internet of Medical Things to reduce the healthcare costs, to attend the medical responses on time, and also to help in increasing the quality of the medical treatment. The healthcare industry is transformed by the Internet of Medical Things as it delivers targeted and personalized medical care, and it also seamlessly enables the communication of medical data. Devices used in the medical field and their application are connected to the system of healthcare with the help of the digital world.

With the changes caused by the constant evolution of the Internet of Things, the healthcare area could not be left behind. With the introduction of 5G technology and modern devices, a better, real-time remote healthcare monitoring is shaping up to become the next step toward the future of medical treatment, and this is only becoming more present in a world, where at any time things might change and a presential form of monitoring can be difficult to achieve, as was the case with COVID-19. In this chapter, we take a look at some of the work that has been done in this area, as well as some projects and technologies planned for the future, in the hope to better understand how this technology works and what can be expected from it. In this chapter, the most recent papers on this field are reviewed, offering the reader a summary, providing an accessible entry point for those interested in delving into it.

Early prediction of diabetes is often needed for a clinically effective outcome due to the existence of a relatively long asymptomatic period. Because of its long-term asymptomatic period, about 50% of the people suffering from diabetes are unidentified. It is only possible to make an early diagnosis of diabetes by thoroughly examining both common and less common signs, which may be identified timely at different stages from initiation of the disease to diagnosis. To detect early diabetes and to avoid serious effects, it is possible to track in real time due to the advancement of information technologies. The chapter suggests the Internet of Medical Things (IoMT) with a fog-assisted healthcare system for early diabetes prediction (or emergency) and notification for remote patients. The system continuously monitors the information for data analysis to track physiological signals and contextual information through notified alert messages to service providers and end users in real time. Boosting techniques for the risk prediction model of the disease have been well considered by many researchers. In this work, gradient boosting (GB) algorithm is proposed to predict the early symptoms of diabetes with higher classification accuracy. The experimental results demonstrate the enhanced performance of the proposed system compared to machine learning (ML) in terms of precision, recall, and F1-score.

The recent worldwide outbreak Corona Virus Disease 2019 (COVID-19) had an impact on our daily lifestyle enormously. The enterprises have reconsidered their work practices to cope up with the new scenario and a severe impact has been observed in different sectors including manufacturing, shipping and distribution of products. Different Internet of Things (IoT) infrastructures allowed us to obtain some better solutions throughout the decade and it also became one of the most researched areas due to the convergence with other emerging technologies like artificial intelligence, cloud computing, etc. In the pandemic era also, IoT has proved its efficiency in different online platforms like e-medicine, e-learning, online shopping, etc. On the other hand, the global trend is moving towards sustainability and hence sustainable solutions in different sectors are in high demand. Though IoT helps to reduce energy consumption in different applications, still environmental concern regarding IoT components has been raised recently since the modern electronics and IoT devices are often difficult to recycle. In this chapter, sustainability of different IoT infrastructures will be reviewed and their applicability in appropriate domains will be analysed for the post-COVID-19 era. Different challenges will be attempted to be identified for future redress satisfying the customer requirement and a possible future direction will be provided based upon the current research findings.

Autism spectrum disorder (ASD) is one among the psychiatric disorders that has devastating impact among the growth of children. Early diagnosis and proper care help to control the functional deterioration of ASD-affected children. Using the noninvasive fMRI data, a diagnosis model is being proposed. The challenge of the huge dimension in the fMRI data is addressed by proposing a semi-wrapper feature selection model for ensemble classification algorithms. A novel reinforced rider optimization algorithm is proposed for the semi-wrapper feature selection. Along with the ASD diagnosis, RROA is also tested using wrapper feature selection model with KNN algorithm over generic medical datasets. It is observed that the proposed algorithm surpasses the existing algorithms over 60% of the medical datasets and provides an accuracy of 75% over the ASD diagnosis.

Though the definition of smart city may not be very crystallized till date, rather leaving many opportunities for further debates and discussions, the smart city typically refers to digital and ICT-based innovation so as to provide efficient urban services and new economic opportunities for users [1].

Green cloud computing is an emerging technology. Cloud computing is already well accepted in the industry. Green cloud computing brings green computing into the cloud computing environment. The goal of this collaborative technology is to provide environment sustainability while providing cloud services in a cost-effective manner. Traditional data centers consume huge electricity generated from fossil fuels that increases the carbon footprint in the environment. Green cloud computing will ensure efficient service delivery with minimal energy consumption. Several security concerns also arise with new technology. Those security concerns and some solutions were discussed in this book chapter.

The widening of the Internet of Things (IoT) in healthcare and industry opens new vectors of service. Specific in healthcare, IoT enhances patient measurement monitoring and data analytics. Thanks to automation, such devices collect, interpret, and make recommendations to the patient in a short time and with minimum engagement. However, focused on customer service, several security concerns are occurring. The damage caused by malware intrusion can be extremely high in case of affecting a person’s health and life. Besides, the privacy of data plays an important role in hiding the existence of any vulnerabilities that can be exploited. This chapter provides an overview of privacy issues and possible attacks in healthcare smart IoT, a discussion of responsible parties after data leakage and guidelines how to avoid them, and ways of enhancing privacy and security in the healthcare industry.

The Internet of Things (IoT) is confronting a massive paradigm shift with all new technologies which have tremendous potential to upgrade the lifestyle to a brand new level. It is a versatile concept with wide-ranging applications in the Industry 4.0 revolution, including but not limited to home automation, scientific research, military equipment, consumer industry, health facilities, and automotive driving. With the advent of 5G and 6G, IoT promises high-speed communication and control over greater distances with better reliability and security. While IoT has already proved its value in today’s scenario, by far, the most remarkable advancements are in the automotive sector. Self-driving cars are already hitting the roads, and the navigation system is getting better with time, thanks to machine learning (ML), artificial intelligence (AI), and federated learning (FL). This chapter aims to present a clear and concise idea on Intelligent IoT and its applications in the automotive industry. Various types of remote communications, their classifications, and characteristics are discussed in detail. A brief discussion about image recognition with a field of view for automotive driving is presented systematically. AI and its various components such as ML, deep neural network (DNN), deep learning (DL), transfer learning, and deep convolutional networks (DCN) are explained and summarized. The intersections of image processing, pattern recognition and computer vision, machine learning, and IoT for locomotives’ remote and automatic functioning are reviewed. Modern devices like smartphones and smartwatches are analyzed, and security issues arising thereof are enumerated. Current and future challenges are also listed in this chapter. Finally, the chapter is concluded with a state-of-the-art future research direction of Intelligent IoT.

Adoption of Industrial Internet of Things (IIoT) can transform how industries operate and integrate IIoT to optimize the use of its assets and autonomously predict the failures and its maintenance processes by upholding the security with increased connectivity. IIoT thrive to provide smart security for cyber-physical systems and production processes in industries. The tremendous potential of IIoT has considerably reduced the workloads of professionals in the healthcare sector. IoT solutions provide assured time and energy to attend patients who are affected with chronic conditions and they can be monitored at home. IIoT vision in the world of healthcare is referred to as Internet of Healthcare Things (IoHT) or Internet of Medical Things (IoMT). Major use case of IIoT is to solve the interoperability challenges in collecting and analyzing patient data securely, to help in predicting and preventing adverse health conditions of patient, and to reduce the likelihood of patients’ readmission in hospitals. Managed security service providers (MSSPs) in industries and enterprises handle operational technologies which can be expected to be well-versed in the safety of workers and the quality of product. IIoT adopters empower the industries and enterprises with a secured setup and use their connected devices such as robotics, medical devices, and software-defined production processes with better efficiency and reliability in their operations.