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Preface ix
List of Acronyms xiii
Introduction xvii
Chapter 1 Healthcare Internet of Things: The State of the Art 1
1.1 H-IoT network landscape 2
1.2 Technology emergence in H-IoT 10
1.2.1 Edge computing in H-IoT 12
1.2.2 Software-defined network in H-IoT 16
1.3. Learned lessons and neglected opportunities .. 28
1.3.1 Learned lessons 28
1.3.2 Neglected opportunities 37
1.4 Conclusion 38
Chapter 2. A Novel Network Infrastructure Concept .. 39
2.1 Introduction 39
2.2 Related work 44
2.2.1 Software-defined network 45
2.2.2 5G novel network perspective 45
2.2.3 Software-defined infrastructure 46
2.2.4 Software-defined vehicular network 47
2.2.5 Software-defined unmanned aerial vehicular network 48
2.2.6 Other potential software-defined hardware blocks 49
2.3 The evolution of SDI 50
2.3.1 Research gap 50
2.3.2 SDI extension 51
2.3.3 Proposal of our architecture 52
2.3.4 Suggested technology adjustments 54
2.3.5 Prospective interaction within the SDI ecosystem 56
2.3.6 Software-defined infrastructure's benefit recap 57
2.4 Unified functional model formalization 58
2.4.1 Description of the model 58
2.4.2 Structure of the model 59
2.5 Description of the experiments 64
2.5.1 Implementation details 66
2.5.2 Approach 1: opportunistic coverage enhancement 71
2.5.3 Approach 2: connection recovery 72
2.5.4 Approach 3: self-assisted coverage deployment and data transportation 74
2.5.5 Approach 4: priority orchestration in high-density network 76
2.6 Improvement evaluation approaches 78
2.6.1 Opportunistic coverage enhancement 78
2.6.2 Connection recovery 80
2.6.3 Self-assisted coverage deployment and data transportation 82
2.6.4 Priority orchestration in high-density network 83
2.7 Conclusion 86
Chapter 3 SMART Connection Migration 89
3.1 Introduction 89
3.2 Related work 92
3.2.1 Virtual machine migration 93
3.2.2 Container migration 93
3.2.3 TCP-based connection migration 94
3.2.4 QUIC-based connection migration 95
3.2.5 Motivation 96
3.3 Solution design 97
3.3.1 Sequence diagram comparison 97
3.3.2 System design 98
3.3.3 Prospective impact 99
3.4 Performance evaluation 100
3.4.1 Experimental environment 100
3.4.2 Experiment scenario 101
3.4.3 Influence of delay on the connection migration process 103
3.4.4 The influence of migration frequency on the connection migration time 103
3.5 Conclusion 104
Chapter 4 Generic Adaptive Deep Learning-based Multipath Scheduler Selector 107
4.1 Introduction 107
4.2 Related work 110
4.2.1 Multipath transport protocols 110
4.2.2 Multipath scheduling algorithms 111
4.2.3 Multipath scheduling performance over heterogeneous paths 113
4.3 Prototype design 114
4.3.1 Scheduler selector paradigm 114
4.3.2 Prototype design 115
4.4 Simulated evaluation 117
4.4.1 Experiment setup 117
4.4.2 Initial dataset analysis 119
4.4.3 Traditional machine learning's performance evaluation 121
4.4.4 Deep learning's performance evaluation 123
4.5 Practical evaluation 125
4.5.1 Brief overview of GADaM 125
4.5.2 Extensible modular scheduler evaluating framework 128
4.5.3 Experiment environment 129
4.5.4 Trial run 132
4.5.5 Actual run 136
4.5.6 In-house static environment 138
4.5.7 Metro line environment 139
4.5.8 In-vehicle environment 140
4.6 Limitations 143
4.7 Conclusion 145
Conclusions and Perspectives 147
References 151
Index 171
Digital change has been particularly challenging in healthcare...
da Fonseca et al. (2021)
The Internet of Things (IoT) is the technology that enables the link between the world's objects and their identities in the digital space. After two decades of development, 15 billion things will be connected to the Internet by 2025, according to CISCO. This indicates the IoT's development capability and exposes various potential issues and opportunities with such dramatic growth. The upward trend in the IoT's number of devices is predicted to continue increasing even further because of the technology's broad adoption in various industries: manufacturing, logistics, smart driving, smart homes, smart cities, etc.
Among numerous domains influenced by the IoT paradigm, there is a crucial domain that consistently improves the quality of human life and also receives significant research attention from the community, which is eHealth. The eHealth paradigm describes the healthcare services supported by digital processes, technology and communication. Throughout its history, the eHealth transition has significantly improved healthcare services - automating redundant tasks, unifying digital health records, increasing patient satisfaction, lowering costs, etc. - by digitizing healthcare procedures. However, various trivial tasks that need to be included require the next level of innovation, such as large-scale health monitoring, telemedicine and telehealth. The emergence of the IoT paradigm in the eHealth domain thus leads to a branch formulation in the literature: healthcare Internet of Things (H-IoT). The emergence of this domain promises the deployment of ubiquitous multilayer connected smart devices that enable potential solutions and open new opportunities for future use cases.
To investigate existing research challenges in the field, especially toward the eHealth use case, we conduct a thesis and present our work in this book.
In the H-IoT branch, the critical factor that fuels the success of the ecosystem is delivering vital healthcare data in real time, especially in urgent circumstances. The main reason is that the prehospital stage is vital to maintaining the patients' conditions before treating them at the healthcare facilities. During this phase, time and communication continuity are essential to allow on-the-scene healthcare providers to deliver proper first aid to patients. Therefore, we oriented our solutions first to cope with such critical circumstances to satisfy its strict communication requirements and preserve the holism of our proposed solutions. However, their applicable prospects are feasible and unlimited in the general IoT context or even in less competitive scenarios and other domains.
Generally, the system architecture in the H-IoT domain comprises three layers: Things, Communication and Processing. Despite many existing studies, most have focused on the healthcare-related computing tasks at the Things and Processing layers. Some might investigate the Communication layer to improve the network performance. Such improvement might happen at the gateway endpoint but rarely influences the network ecosystem. The main reason for such limitation comes from a factor that could easily be ignored by researchers. Most of the research has always been conducted with a common presumption: the communication infrastructure is static throughout the communication process and could not be influenced by a third-party actor.
However, this assumption might no longer fully reflect the typical network conditions. The principal factors leading to this situation are the complex Internet operations, the software-defined network's advancements, the rise in mobile users and the global deployment of the 5G network. To be more explicit, the complex Internet operations and the rise in mobile users cause variations in the network infrastructure while the transmission is happening. On the other hand, the global 5G deployment and SDN developments enable advanced features such as network function virtualization and network slicing. These features allow third-party actors to create a separate network from the Internet infrastructure. They could also join, operate, manage and share their assigned infrastructure with other participants. In short, the prospect of the Internet and the features it brought with it have evolved through time and invalidated the presumed legacy of a static H-IoT communication process. Acknowledging the situation, we are motivated to update the H-IoT's Communication layer with the latest network advancements (multiple network interfaces and cutting-edge transport protocol - QUIC). The following points specify our objectives in this work:
As mentioned in the previous discussion, our objective is to renovate the Communication layer in the H-IoT domain. Therefore, this book studies the latest opportunities offered by cutting-edge network technology. Briefly introducing our novel solutions, the following top-down view summarizes our contributions in this book:
- Software-defined infrastructure (SDI): a novel paradigm attempts to deal with network hardware heterogeneity by unifying the network infrastructure into one coherent logical view. The paradigm can provide dedicated solutions for critical network use cases, especially emergency H-IoT, by its central management capability and universal accessibility to the network devices. We formulated a unified functional model that allows the proposed paradigm to optimize the overall network performance or prioritize critical traffic in urgent situations. We also present four experimental approaches to evaluate the proposed paradigm's efficiency. Our obtained simulation results also confirmed the paradigm's network orchestrating capabilities and its effectiveness in improving network performances for vital data transmission.
- SMART connection migrations (SCM): an approach that takes advantage of the unified logical network overview empowered by the SDI paradigm to maintain a seamless network experience for emergency vehicles to transmit vital healthcare data. The proposed method not only enables the ability to ensure vital transmission continuity and optimize overall latency for onboard devices to edge computing servers, but also succeeds in reducing the required duration to execute the mobile device's connection handover in a simulation environment by 50%.
- Generic adaptive deep-learning-based multipath scheduler selector (GADaM): an intelligent approach that manages multiple network interfaces to provide the best network performance for the network terminal in highly dynamic network conditions without the SDI controller's assistance. In the simulation and practical environments, the "scheduler selector" deep-learning model achieved a high performance in selecting the most suitable scheduler based on multiple interfaces' network characteristics. In particular, the practical results also proved the model's capability to outperform the SOTA in some specific scenarios without prior knowledge.
Having discussed the contributions of the book, we now present its organization. First of all, Chapter 1 covers the H-IoT SOTA. This chapter analyzes and classifies the surveyed papers to present the lessons learned and the opportunities neglected in the literature. Afterward, considering the given context and assisting the reader's navigation, we structure our contributions into three dedicated chapters regarding a general system's top-down view.
After the first chapter's overview, Chapter 2 introduces the first contribution of SDI, which influences the whole healthcare network infrastructure with its top-down overview. Initially, we present the literature's limited vision of the software-defined paradigm. Then, we investigate the research gap in Chapter 1. Later, we construct a new paradigm to fill the missing gap and enable future research. We also present a unified functional model with specified converged conditions and a simulation framework to concretize our proposed paradigm for prospective performance evaluations. Four distinguished experimental scenarios dedicated to supporting healthcare transport are presented to evaluate the novel paradigm's performance. The results confirm SDI's effectiveness by significantly improving the selected network metrics (bandwidth, delay and packet loss) versus the SOTA in healthcare vital data transmission scenarios.
Taking advantage of SDI's accessibility and knowledge about the healthcare network ecosystem, Chapter 3 presents an intelligent approach toward seamless and transparent network experiences for a healthcare endpoint: SCM. The approach aims to bridge the gap in the SOTA Internet connection concept that primarily focuses on static infrastructure but not highly dynamic and flexible network infrastructure, whose criteria are mobile healthcare transport scenarios. By employing...
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