Integrating Cloud, Fog, and Edge Computing in Healthcare: Federated Learning and Blockchain Approaches

Inhaltsverzeichnis

1. Frontmatter

2. Introduction to Cloud, Fog, and Edge Computing in Healthcare

   Naween Kumar, Sahani Pooja Jaiprakash, Choudhary Shyam Prakash

   Abstract

   Cloud, fog, and edge computing paradigms are revolutionizing healthcare by improving real-time/on-demand data storage/processing/analytics, enabling effective, efficient, and affordable health services. Cloud computing offers a scalable infrastructure for telemedicine, artificial intelligence-based diagnosis, e-health, and storing/archiving huge health data. Fog computing and edge computing solve the problems of latency and bandwidth that sometimes arise with cloud solutions by data processing near the source. Fog computing processes data locally and in a decentralized manner at intermediate nodes; applications such as predictive analytics and remote patient monitoring benefit from quicker response times. Edge computing increases efficiency by performing real-time processing directly on medical devices, ensuring faster decision-making in critical healthcare situations. These technologies maximize healthcare delivery, enhance data security, and promote seamless interoperability. This paper highlights how cloud, fog, and edge computing technologies enhance emergency care systems, personalized therapies, and medical research while examining the fundamental concepts, advantages, and challenges of these technologies in the healthcare industry.

3. Addressing the Challenges in Federating Edge Resources

   Ankit Dubey, Subham Sharma, Khushboo Devi, Shailendra Pratap Singh, Balamurugan Balusamy, Prithi Samuel

   Abstract

   Edge computing is transforming the traditional approach to data processing and workload management by shifting computational tasks closer to the point of data generation. A network of decentralized nodes such as IoT devices, micro data centers and mobile edge infrastructure collaborate among themselves to find the cheapest paths for the workloads to process in federated edge environment. Unlike classical centralized cloud architecture, federated edge provides computational power at multiple autonomous domains and improves latencies reduction, fault tolerance, system resilience, and scalability. However, these benefits come at a price, as this paradigm brings a number of hurdles to the table regarding heterogeneity of devices, varying network characteristics, resource limitation, security risk, and cross collaboration with heterogeneous systems. This chapter discusses Edge Resource Federation (ERF), a multi-domain coordination framework that renders fast and seamless integration, workload balancing, as well as cooperative resource management of distributed edge resources. It discusses the foundation technical issues to federate hardware and software components at the edge including the heterogeneity, the need for real time decision making when network conditions change, and the security risks arising from the decentralized data processing at the edge environment. It also explores state of the art solutions like KubeEdge, StarlingX and ETSI MEC (Multiaccess Edge Computing) standard for orchestration and security enforcement in federated edge networks. Secondly, the chapter outlines best practices of operationalizing massive edge deployments including promulgating edge native Service Level Agreements (SLAs), powering the edge minimally by scheduling according to energy usage, and implementing zero trust security principles. The distribution strategy is used to ensure workload is present, reliable, secure and the compliance with geopolitical data sovereignty regulations. Finally, the chapter shows real world case study where federated edge computing has been used in the field of healthcare, autonomous systems and next generation telecommunications. The applications are illustrative examples of federated medical imaging models to aid stroke faster diagnosis, remote patient monitoring enabled with AI driven analytics at the edge, 5G network slicing for dynamic bandwidth allocation, digital twin technology to enable precision driven simulation in smart infrastructure. This chapter gives a complete view on how ERF is revolutionizing modern computing architectures by addressing both the challenge and the technological progress in federated edge computing. On the edge, federated learning, quantum-secure communication protocol, and federated edge computing will certainly play an increasing role in the development of the federated edge computing, which will facilitate the real time, privacy protecting, and even globally scalable distributed computations.

4. Integrating IoT + Fog + Cloud Infrastructures: System Modeling and Research Challenges

   Chanchal Ahlawat, Shivani Tufchi, Priyanka Chandani, Neha Yadav, Naween Kumar

   Abstract

   The integration of IoT, Fog Computing (FC), and Cloud Computing (CC) is revolutionizing healthcare by enhancing real-time monitoring, data processing, and resource management. IoT enables seamless connectivity among medical devices, facilitating real-time patient monitoring and remote healthcare services. Cloud computing provides scalable storage and computational power, ensuring efficient data management and analytics. However, latency-sensitive healthcare applications face challenges due to the centralized nature of cloud infrastructure. Fog computing mitigates these challenges by bringing computational resources closer to the data source, reducing latency, and improving response times. This chapter explores the synergy of these technologies in the smart healthcare environment, discussing system modeling techniques, applications, and research challenges. Various system modeling approaches, including analytical models, Petri nets, and Markov chains, are analyzed to optimize performance, resource allocation, and service quality. Applications of smart healthcare, which include remote monitoring, predictive analytics, and AI-driven healthcare solutions, are discussed in this chapter. After all the significant steps have been taken, existing problems in data security, interoperability, and resource management still remain to be addressed. This chapter concludes by revealing some future research directions: the role of big data analytics, Tactile Internet, and the Internet of Nano Things in forming next-generation smart healthcare systems. The seamless integration of IoT, FC, and CC promises to enhance healthcare efficiency, reduce costs, and improve patient outcomes, driving innovation in the digital health ecosystem.

5. BC and Federated Learning for the Management and Orchestration of Network Slices in 5G, Fog, Edge, and Clouds

   Richa Golash, Shahnawaz Ahmad, Bhawana, Naween Kumar

   Abstract

   The demand for secure, scalable, and low-latency data management solutions has intensified the integration of 5G networks, Edge computing, and Internet of Things (IoT)-driven technologies. Modern systems, characterized by distributed devices and sensitive data, face privacy, interoperability, and resource optimization challenges. Traditional centralized approaches struggle to address these issues due to vulnerabilities in security, high communication overhead, and regulatory constraints. This chapter explores integrating BC and Federated Learning (FL) technologies as a transformative paradigm to manage network slices in 5G, Fog, Edge, and Cloud environments, focusing on healthcare applications. Blockchain (BC) offers decentralized, tamper-proof data storage and Transaction (Tx) validation through consensus mechanisms like Proof of Work (PoW) and Proof of Stake (PoS). At the same time, FL enables collaborative Machine Learning (ML) without centralized data aggregation. Together, they form FedBLOC—a hybrid architecture that enhances trust, security, and efficiency in distributed systems. The paper systematically examines BC fundamentals, FL algorithms, and network slicing in 5G, highlighting their synergistic potential. Network slicing, which creates virtualized, application-specific networks, benefits from BC’s immutability and FL’s privacy-preserving model training, enabling real-time healthcare services like remote patient monitoring and Artificial Intelligence (AI)-driven diagnostics. By addressing challenges such as data security, incentive mechanisms, and system resilience, the integration of these technologies paves the way for robust, decentralized healthcare ecosystems. The chapter also discusses practical implementations, including secure medical record sharing, federated AI models for disease detection, and dynamic resource allocation in smart hospitals. This work underscores the critical role of BC and FL in advancing secure, efficient, and scalable solutions for next-generation networks and healthcare systems.

6. AI-Driven Healthcare Optimization: Integrating Fog and Edge Computing for Secure and Real-Time Medical Solutions

   Shahnawaz Ahmad, Shahadat Hussain, Mohd Aquib Ansari, Naween Kumar

   Abstract

   The advancement of new technology has compelled modern healthcare organizations through the intelligent computing modeling to enhance efficiency in processing healthcare delivery outcomes and health care organization efficiency. This chapter focuses on the application of Fog and Edge Computing integrated with Machine Learning (ML) to solve major healthcare optimization problems. It explores the decentralized computing paradigms which support real-time analytics for various use cases such as remote patient monitoring and emergency response, which leads to lower latency and increased response times. To reduce data privacy and cybersecurity threats, measures, including encryption and an ML-based anomaly detection system, are proposed. The chapter also expounds on new and creative approaches in achieving better compatibility across various healthcare systems through the approach of applying data harmonization technique built on ML and establishes tactics for developing scalable structures based on dynamic resource management in fog and edge settings. The development of adaptive ML models for providing individualized healthcare services including accurate diagnosing and treatment planning is also investigated. Furthermore, this chapter provides a detailed summary of algorithms and frameworks such as Federated Learning, Blockchain Technology, and Edge-Fog-Cloud computing that collectively permit safe and efficient real-time healthcare solutions. Each of these technologies is illustrated through ECG anomaly, medical IoT security, and patient risk prediction use cases. The chapter also considers the modern developments in artificial intelligence approaches to data analysis in the form of Data Mining, Modeling and Optimization techniques in personalized medicine, and resources management. The value of this integrative approach is to transform the current healthcare model to secure, efficient and personalized one through novel computational models.

7. A Lightweight AI-Enabled Container Middleware for Edge Cloud Architectures

   Naween Kumar, Subham Sharma, Ankit Dubey, Khushboo Devi, Balamurugan Balusamy

   Abstract

   For current edge computing architectures, intelligent, adaptive middleware is indispensable for bridging distributed applications with distributed infrastructure to allow localized processing, convenient data exchange, workload management and enforcement of security, in heterogeneous edge environments. Given that edge computing brings data processing closer to the data source to cut edge latency, bandwidth provisioning and dependence on centralized cloud infrastructure, a complex ecosystem of dynamic edge devices has to be managed and fluctuating workloads needs to be suspended, a middleware built on top of such AI complexity is now needed to be relied upon for resilience, scalability and interoperability. By combining the use of machine learning models, containerization technologies, e.g., Docker and Kubernetes along with event-driven orchestration mechanisms, these next generation platforms help with predictive resource allocation, intelligent task scheduling, automated load balancing and proactive anomaly detection all necessary to maintain high availability and robust cybersecurity posture in real-time applications. This paradigm is implemented in A Lightweight AI-Enabled Container Middleware for Edge Cloud Architectures where they provide a secure and efficient solution of lightweight containers and container orchestration as intelligent middleware for edge cloud ecosystems. Further growth of edge computing within these sectors like healthcare, autonomous vehicles, industrial automation and IoT-based smart environments requires additional advances in such middleware and turns it into a fundamental part required to facilitate low latency, context aware decision-making as well as to optimize performance for distributed, decentralized edge networks.

8. Revolutionizing Healthcare Data Management in Fog Environments with Machine Learning

   Shivani Tufchi, Chanchal Ahlawat, Priyanka Chandani, Neha Yadav, Naween Kumar

   Abstract

   Health Data Management (HDM) is a crucial component of today’s healthcare involving the effective collection, storage, and analysis of patient data. With the increasing volume of large-scale medical data, conventional cloud-based systems encounter latency, security, and real-time processing challenges. Fog computing has been seen as a viable paradigm, overcoming the limitations of centralized cloud infrastructures and edge devices, with localized data processing, low latency, and high security. This chapter explores how fog computing and machine learning techniques can be combined for healthcare applications to support improved decision-making, predictive analytics, and real-time monitoring. Specifically, supervised learning techniques aid in disease diagnoses and risk predictions unsupervised learning enables anomaly detection and patient segmentation, semi-supervised learning improves medical image analysis with limited labeled data, and reinforcement learning optimizes treatment planning and resource allocation. These approaches enhance diagnostic accuracy, reduce latency in healthcare decision-making, and improve patient care efficiency. Furthermore, challenges in health data management, such as interoperability, scalability, and data privacy, are presented alongside implementation approaches for secure and efficient healthcare analytics.

9. Predictive Analysis in Healthcare to Support Fog Application Deployment

   Naween Kumar, Subham Sharma, Ankit Dubey, Vaibhav Saini, Sahani Pooja Jaiprakash, Balamurugan Balusamy

   Abstract

   Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies have rapidly advanced, bringing drastic positive changes to modern healthcare by enhancing data driven decision-making, real time patient monitoring, and predictive diagnostics. Predictive analytics utilized within fog computing is one of the most promising developments in this field, as fog computing is a decentralized computing architecture that can process data from the source nearer as opposed to using only cloud based infrastructures. It enables healthcare institutions to improve clinical decision support continuity, optimize resource allocation, increase response time for emergencies and early disease detection at a low latency. Served by AI and ML, the predictive analytics utilizes complex computational models like the decision trees, random forests, support vector machines, deep learning, and reinforcement learning to examine large medical datasets to predict disease risks, to form the best possible treatment plans for patients and ultimately enhance patient outcomes in critical situations like detection of sepsis in Intensive Care Units (ICUs) and AI-based stroke prediction through real time imaging analysis. On the other hand, fog computing processes healthcare data at the edge of the network to reduce the dependency on cloud infrastructure, decrease the data transmission delay and appraise healthcare data at the edge of the network in real time to facilitate real time AI driven diagnostics and remote health monitoring especially in areas where there is no network coverage. Although the advantages of predictive analytics in the fog-based healthcare system, however, the implementation of predictive analytics in fog based healthcare system faces many challenges such as data privacy issues, computational resource limitations, and the issues of interoperability as well as biases in AI based medical predictions. For the success of the implementation of AI powered predictive analytics inside the fog computing environment, the main problem to resolve is to ensure the compliance with data protection regulations such as HIPAA and GDPR and address the constraints of Computing Devices at the edge, as well as the development of standard frameworks for secure healthcare data management in fog computing environments. Although these obstacles have to be overcome, further research into privacy protecting AI models, federated learning techniques, and modalities of blockchain based health data management systems are necessary to achieve that goal. Ultimately, the fusion of predictive analytics with fog computing has the potential to revolutionize healthcare by enhancing diagnostic accuracy, reducing hospital readmissions, and improving overall healthcare efficiency, making it a crucial innovation in the future of medical technology. Looking ahead, emerging technologies such as 5G connectivity, federated learning, blockchain-based secure data exchanges, and edge AI acceleration will further enhance the capabilities of predictive healthcare analytics in fog computing. These advancements will enable seamless remote diagnostics, privacy-preserving AI model training, and intelligent real-time health monitoring systems. As healthcare systems continue to evolve toward AI-driven, decentralized computing paradigms, the integration of predictive analytics with fog computing will play a pivotal role in shaping the future of precision medicine, telehealth, and smart hospital infrastructures. By addressing existing challenges and harnessing cutting-edge technological innovations, predictive analytics in fog-based healthcare has the potential to revolutionize patient care, streamline clinical workflows, and establish an intelligent, real-time, and data-driven healthcare ecosystem. This paper aims to provide a deep, technical, and practical understanding of how predictive analytics supports fog computing deployment in healthcare, offering valuable insights into its applications, benefits, limitations, and future directions in advancing medical AI and digital health transformation.

10. Using Machine Learning for Protecting the Security and Privacy of Internet of Medical Things (IoMT) Systems

    Himanshu Sharma, Prabhat Kumar, Gulshan Shrivastava, Kavita Sharma, Amit Bhola

    Abstract

    Internet of Medical Things (IoMT) is the new generation of medical devices. It unleashes a confluence of technology that transforms the healthcare industry, where it facilitates in remote diagnostics, personalized treatment, and real-time health monitoring. These interconnected devices generate vast amounts of sensitive data, which, when unprotected, are prone to breaches. However, these devices' resource-constrained design and heterogeneous communication protocols lead to heterogeneous IoMT systems, making them vulnerable to substantial security attacks. Improvised approaches are needed because these problems are often beyond what conventional security mechanisms can adequately tackle. Machine learning might mitigate these issues by enabling IoMT devices to detect anomalies, anticipate risks, and effectively respond to attacks. The chapter discusses ML techniques and frameworks developed to protect IoMTs from cyberattacks. Federated learning and edge computing safeguard distributed IoMT networks and ensure the confidentiality of patient information. Data breaches, unauthorized access, denial-of-service attacks, and their implications for IoMT systems are outlined in detail. This chapter focuses on encryption, anonymization, and differential privacy during data sharing and usage. Many case studies are researched to test ML-based security solutions on IoMT systems in real life so that best practices and implementation methodologies are followed. This chapter reports how emerging technologies can bolster the IoMT systems to withstand advanced threats. Machine learning, cloud, fog, and edge computing should be incorporated throughout all the components of an IoMT system to provide a robust and flexible security paradigm. We also discuss how blockchain technology combined with ML can enhance data reliability and visibility in IoMT ecosystems. It will enable researchers, medical practitioners, and engineers to better protect IoMT systems from cyber threats by providing compelling analysis and detailing practical recommendations. It looks ahead to integrating machine learning with modern computing paradigms to secure and privacy-protect healthcare applications.

11. Fog Computing Realization for Healthcare-Based Big Data Analytics with Machine Learning

    Dhananjay Kumar, Arun Sharmila, Naween Kumar

    Abstract

    Healthcare analytics has changed due to integrating Big Data with Machine Learning (ML) and Fog Computing, allowing real-time decisions and predictive analysis to produce better patient results. Modern healthcare requires innovative solutions to alleviate the current cloud architecture limitations, particularly because of the exponential increment of health data. Such data from EHRs, medical imaging systems, and IoT devices create difficulties because of the systems’ complexity in managing volume, velocity, variety, veracity, and value. Healthcare analytics in traditional systems faces multiple barriers because it requires paying high costs and dealing with data silos, as well as security problems and latency issues. It lacks consistent standards for processing information. Fog Computing implements decentralization through source-based data processing, thus reducing reliance on centralized cloud platforms and supporting real-time medical decisions. Machine learning fulfills its revolutionary function by using supervised and unsupervised learning and deep learning methods to facilitate analytical clinical care, disease prediction, and automated diagnostic procedures. The chapter discusses the primary principles of Big Data healthcare and Fog Computing’s system infrastructure and analytics value while showing how Machine Learning operates in medical diagnosis systems. The analysis includes discussions about actual case studies and anticipated research routes, technical approaches to developing fog-based ML systems, privacy measures, and ethical aspects.

12. Exploiting Fog Computing and Federated Learning in Health Monitoring

    Jaishree Jain, Updesh Kumar Jaiswal, Mani Dublish, Shailendra Pratap Singh, Mradul Kumar Jain

    Abstract

    In order to protect sensitive patient data across heterogeneous computer platforms, the federated learning (FL) technique has emerged in systems. In this work, we present the Federated Health Fog framework, which was meticulously created to address dispersed learning issues in IoT-enabled healthcare systems with limited resources, especially those that are energy-efficient and delay-sensitive. Conventional federated learning methods suffer from high compute requirements and communication costs. Their inability to gather global data on a single server is the main cause of this, leading to inefficient training models. By promoting a carefully positioned role in the network, we offer a novel solution to these issues. To optimize, greedy heuristic technique is employed to serve for every cloud. For three benchmark algorithms examined in this paper, the Federated Health Fog system dramatically reduces energy usage by 57.98, 34.36, and 35.37%, as well as communication delay by 87.01, 26.90, and 71.74%. The outcomes of our tests unequivocally demonstrate that Fed Health Fog is successful in lowering the quantity of global aggregation cycles when contrasted with cutting-edge substitutes. These results demonstrate how Federated Health Fog can revolutionize federated learning for applications that are resource-constrained and sensitive to delays in IoT contexts.

13. Federated Learning: A Paradigm Shift in Healthcare Data Privacy

    Anjuli Goel, Chander Prabha

    Abstract

    Data privacy has become a significant problem in the healthcare sector due to the growing computerization of healthcare information and information-driven health studies. Sensitive patient data must be protected against breaches and illegal access since these situations might have serious ethical and legal repercussions. Data security and privacy are compromised by traditional AI-driven healthcare systems’ reliance on centralized data gathering, which compiles private health information into one central database for training machine learning (ML) models. This issue is addressed by Federated Learning (FL), which permits several healthcare institutions to collaborate to get knowledge from distributed content without exchanging it. The application of federated learning in healthcare clinical trial research, therapeutic customization, and disease prediction. But FL by itself has drawbacks including high communication costs, computational inefficiency, and security risks like model inversion attacks. To improve FL's security and efficiency, cloud and fog computing are essential. For aggregating FL model updates, cloud computing offers high processing power and large-scale storage, while fog computing makes real-time AI processing possible at the network edge, nearer to hospital networks, medical IoT devices, and mobile health apps. This chapter describes the most advanced methods currently in use for protecting patient privacy using federated learning.

14. Fog Computing Model for Evolving Smart Healthcare Applications

    Vinayak Gupta, Yajnaseni Dash, Sudhir C. Sarangi, Shailendra Pratap Singh, Naween Kumar

    Abstract

    Emerging as a key paradigm in contemporary smart healthcare systems, fog computing provides distributed data processing capability that help to reduce the inherent latency and bandwidth constraints of cloud computing. Emphasizing their importance to changing healthcare environments, this chapter investigates the fundamental ideas, architectures, and models of fog computing. Examined closely are many fog computing models—client–server, peer-to-peer, publish-subscribed architectures—each suited to certain operational settings and latency needs. Virtual machines (VMs), Docker, Kubernetes, and serverless computing—among other virtualization and containerization technologies—help fog networks allocate resources more effectively and scalable. The conversation also covers hardware deployment issues, defining how edge servers, intelligent gateways, and micro-data centers may maximize computing processes. Software-defined networking (SDN) among other communication models is examined for their part in dynamic routing, adaptive bandwidth allocation, and congestion avoidance. Using real-world applications of fog computing in healthcare, a focused case study shows its efficiency in predictive analytics and real-time patient monitoring. In fog computing, security is top priority and calls for sophisticated cryptographic methods, blockchain-based authentication, artificial intelligence-driven anomaly detection, Trusted Execution Environments (TEE) to protect private medical information. While these security systems improve data integrity and privacy, reaching total confidence in distributed fog architectures remains difficult, especially in mission-critical applications subject to strict regulatory criteria. By tackling these technological aspects, this chapter offers a whole picture of how fog computing shapes solutions for next-generation healthcare. It underlines the requirement of ongoing innovation in networking, security, and computational methodologies to guarantee the resilience, dependability, and compliance of fog-based healthcare systems.

15. Testing Perspectives of Fog-Based IoMT Applications with Federated Learning

    Aparna Baboo, Sachikanta Dash, Sasmita Padhy, Prithi Samuel

    Abstract

    Wearable devices, smart medical systems, and sensors can serve as the sources of huge amounts of health data. The typical use of cloud computing includes issues of security, bandwidth limitations, and high latency. Fog computing was created to address those problems by having the processing of data executed at the network edge, thus trimming down on the latencies, improving both the safety and the economy of bandwidth. Fog-based IoMT systems are functional in real time but also allow offline operation, ideal for remote monitoring of patients, smart ICUs, and health devices. Testing is essential for fog-related IoMT systems because these systems are distributed by nature. It assures rapid processing of health data, compatibility between devices, cybersecurity, and scalability for big datasets. Federation Learning (FL), a distributed artificial intelligence method, maximizes security and privacy by training machine learning models locally in IoMT devices without sharing raw data. FL tests for IoMT model accuracy as well as security resilience, synchronizing and computational economy. In fog-based IoMT, different FL testing methods include testing the accuracy and convergence of the model, security and privacy, synchronization and communication efficiency, and performance scalability. Homomorphic encryption, secure multi-party computation, federated averaging, and adaptive communication enable a successful incorporation of FL with IoMT. Therefore, with these advanced technologies, fog-based solutions will ensure robust, safe and fast ways for improvements in healthcare.

16. Legal Aspects of Operating IoMT Applications in the Fog Computing

    Soumya Ranjan Mishra, Sachikanta Dash, Sasmita Padhy, Prithi Samuel

    Abstract

    The Internet of Medical Things (IoMT) with fog computing brings a change to healthcare by enhanced patient monitoring, individualized treatment, and real-time data processing. But this paradigm shift has raised some serious ethical and legal questions, especially concerning data privacy and security as well as regulatory compliance. The distributed nature of fog computing challenges compliance with regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), thereby necessitating strong data governance measures such as compliance monitoring, encryption, and access control. Some of the other ethical issues that need to be addressed in order to ensure equitable access and transparency include patient autonomy, informed consent, and data ownership. On the other hand, the advent of AI in the IoMT intensifies the challenges of algorithmic bias and accountability. Thus, to facilitate effective deployment, strong legal injunctions, and ethical oversight coupled with multidisciplinary cooperation among technologists, medical doctors, and legislators will be required. Both innovation and compliance will allow IoMT in fog computing to reach its highest potential while being mindful of patient rights, data security, and trust in digital healthcare ecosystems. The foundation for drastically reducing cyber risks lies with frequent legal assessments, risk assessments, and security updates. Clear systems of patient consent, interoperability frameworks, and cross-border data flow rules should be respected. A culture of compliance with the engagement of legal professionals fosters accountability. Ethical issues like data ownership, transparency of artificial intelligence, and equitable access remain paramount. The forward-thinking integration of legal, technical, and ethical safeguards within fog computing ensures compliant, secure, and transparent IoMT deployment.

17. Future Directions: Towards a Unified Healthcare Computing Framework

    Shalini Kumari, Chander Prabha

    Abstract

    The Unified Healthcare Computing Framework (UHCF) provides a system that handles urgent healthcare technology fragmentation problems. Medical and technical achievements have failed to end the existence of fragmented healthcare systems. The various healthcare systems function independently, leading to patient care disruptions and information-based development based on advancements. The information researchers propose combines multiple separate databases into a unified system structure with analytical features, application-specific tools, and management instruments into a single operational system. The healthcare systems must implement evidence-based methods to activate true system dynamics and continuous health management platforms. The discussion evaluates initial deployment tests showing value in using this framework, yet large obstacles persist that hinder implementation steps. The implementation process faces important obstacles from organizational structures combined with technical specifications. The UHCF presents itself as an innovative healthcare operational model. The system provides healthcare professionals with clinical advantages and patient empowerment as its main application benefits. Healthcare systems cannot deploy actual computing ability in their current operational state.

18. Smart Healthcare Analytics by Integrating Machine Learning and Fog Computing

    V. Jeevika Tharini, A. Aruna, A. Sathya

    Abstract

    The advancement in sensory equipment fuels the expansion of the Internet of Things (IoT) across the globe. The usage of diversified applications and IoT devices generates massive sensor data which in turn need analytics or intelligent processing system. IoT, Machine Learning, and Fog Computing are utilized to achieve healthcare assistance and diagnosis progression in diverse aspects. Machine Learning is a kind of data analysis technique that retrieves the needed information without performing any explicit program. This chapter focuses on two diverse paradigms such as machine learning and fog computing which concern the processing and analysis of sensor data. The taxonomy of Machine Learning algorithms, significant aspects of Machine Learning in IoT based Big data analytics and applications of Machine Learning based IoT techniques are elaborated. This chapter also elucidates fog computing and its tiered computing architecture for healthcare. The major issues and the future research directions of IoT Data analysis are also explained in this chapter.