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Since its inception, Healthcare 4.0 has empowered the integration of advanced technologies to create and improve the quality of healthcare services. The delivery of healthcare services has come a long way from physical appointments with doctors to remote health monitoring and disease prediction, surgery assistive systems. This advancement has only been possible because of the integration of cutting-edge technologies like Tele-healthcare, software-defined networking and many more, with healthcare systems. In this survey, we have targeted some of the pioneering research works that could contribute significantly to the future development of Healthcare 4.0 systems. We have identified the significant research gaps and presented the modern state-of-the-art of healthcare systems, introducing the Healthcare IoT Application and Service Stacks. We have also discussed the latest paradigm of Wireless Body Area Networks, emphasizing its significance and how it can contribute to the development of next-generation healthcare applications using emerging technologies like Machine Learning, Blockchain, Cloud Computing, Internet of things, Edge/ Fog Computing, Tele-healthcare, Big Data Analytics, Software-Defined Networking and many more. We have performed a comparative study of different architectural implementations considering their advantages, shortcomings, and quality-of-service requirements. We emphasize the importance of the different emerging technologies in detail, discussing the opportunities available and their potential to create better healthcare solutions that can provide superior service quality. Finally, we highlight the fundamental need for establishing security and privacy in future healthcare systems. Overall, this survey provides a strong outlook into the development of the future of healthcare 4.0. [ABSTRACT FROM AUTHOR]

The uprising of Internet of Things (IoT) has dramatically influenced the world's technology in terms of interoperability, connectivity and interconnectivity with help of smart sensors, devices, objects, data and applications. General population aging, dearth of healthcare resources and upsurge in healthcare costs makes IoT advancements in healthcare all the more essential in order to confront these challenges. The revolution in IoT healthcare is redeveloping the healthcare sector in every aspect – social, technical and economical. This article presents a comprehensive survey on upcoming technologies helpful in healthcare 4.0 systems where the major focus is on emerging technologies like fog computing, cloud computing, machine learning and Bigdata analytics and that are all based on IoT based healthcare applications. In addition, the authors also provided an exhaustive survey on Wide Body Area Network (WBAN)-based IoT health care systems and discussed their network topology, architecture, platform, services and their applications. In addition, this study analyses IoT healthcare security challenges, possible threats, attack taxonomies and how blockchain technology can be helpful in countering these challenges. Lastly, exhaustive state-of-the-art technologies, challenges identified so far and possible future scope of this domain is also discussed in this survey. [ABSTRACT FROM AUTHOR]

Epilepsy is a psychosocial neurological disorder, which emerges as a major threat to public health. In this age of the internet of things, the smart diagnosis of epilepsy has gained huge research attention with machine learning-based seizure detection in cloud-fog assisted environments. The present paper also proposes a cloud-fog integrated smart neurocare approach, which performs a temporal analysis of raw electroencephalogram (EEG) signals using deep learning to detect the occurrence of epileptic seizures. This patient-independent approach makes use of single-channel EEG signals to achieve real-time and computationally efficient seizure detection at fog layer devices. It employs a maximum variance-based channel selection procedure to select only one channel of raw scalp EEG signals, followed by their filtering and segmentation into various short-duration temporal segments. To analyse EEG patterns, these segments are further fed to the proposed models of convolutional neural network, recurrent neural network and stacked autoencoder deep learning classifiers. The performance analysis through simulation results evidently reveals that the proposed convolutional neural network-based temporal analysis approach performs better than other approaches. It realises an optimum accuracy of 96.43%, sensitivity of 100% and specificity of 93.33% for 30s duration EEG segments of CHB-MIT dataset and achieves 100% accuracy, sensitivity and specificity values for EEG segments of Bonn dataset for 23.6s EEG segments. Thus, the proposed convolutional neural network-based approach emerges as an appropriate method for rapid and accurate detection of epileptic seizures in fog-cloud integrated environment. [ABSTRACT FROM AUTHOR]

The rapid proliferation and progress of Wireless Sensor Networks (WSN) and Internet of Things (IoT) has conducted to the formation of a gigantic amount of data and a growing need to multiple new services and resources. In spite of the main role of Cloud computing in solving these issues, IoT applications need more reduced latency with mobility support and location awareness. To overcome the mentioned limits, a new concept favouring the integration of Fog computing onto IoT is increasingly utilized. It is a motivating scheme that offers a timely task execution and data management at the network edge, in a distributed way, with the collaboration of nearby nodes. In this paper, we provide a complete study of the integration of Fog onto IoT. We discuss the various challenges that are facing the Fog/IoT paradigm and specify the main contributions that have been proposed to overcome these challenges. We give also an insight on the relationship between IoT/Fog integration concept, and other leading technologies. The open issues that need more investigation are highlighted in this paper to identify clearly the research gaps in the area of Fog computing integration onto IoT. [ABSTRACT FROM AUTHOR]

The Internet of Things has the potential of transforming health systems through the collection and analysis of patient physiological data via wearable devices and sensor networks. Such systems can offer assisted living services in real-time and offer a range of multimedia-based health services. However, service downtime, particularly in the case of emergencies, can lead to adverse outcomes and in the worst case, death. In this paper, we propose an e-health monitoring architecture based on sensors that relies on cloud and fog infrastructures to handle and store patient data. Furthermore, we propose stochastic models to analyze availability and performance of such systems including models to understand how failures across the Cloud-to-Thing continuum impact on e-health system availability and to identify potential bottlenecks. To feed our models with real data, we design and build a prototype and execute performance experiments. Our results identify that the sensors and fog devices are the components that have the most significant impact on the availability of the e-health monitoring system, as a whole, in the scenarios analyzed. Our findings suggest that in order to identify the best architecture to host the e-health monitoring system, there is a trade-off between performance and delays that must be resolved. [ABSTRACT FROM AUTHOR]