Internet of Things (IoT) is an emerging technology that is taking the current technology to the next phase of connectivity by connecting any device to the internet. It may be a mobile phone device, a speaker or even a connected vehicle. The number of IoT-based devices people use is increasing day by day. The quantity of smart gadgets people purchase on both offline and online platforms has increased to a significant number in the past few years. This research article proposes social networking between IoT devices, by the interconnection of IoT devices based on a Cloud computing method. Through cloud computing, number of Edge platform-based, IoT devices can be connected to a common cloud called Fog computing, which in turn connects to the Cloud network. The social networking of IoT devices enables users to switch and share data between their IoT devices. Various computational programs and block chain techniques develop applications that allow the host network to communicate with IoT devices with secure access to the data shared by all IoT devices connected to the Cloud without any data loss or security breach. 

IoT based Social Device Network with Cloud Computing Architecture

With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and Quality-of-Service (QoS) standards. Recent developments in federated learning (FL) have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this article, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unraveling the complexities of designing reliable and scalable FL models. This article outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state of the art and identifying open problems and future research directions. 

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

Background Hypertension is the most common modifiable risk factor for cardiovascular diseases in South Asia. Machine learning (ML) models have been shown to outperform clinical risk predictions compared to statistical methods, but studies using ML to predict hypertension at the population level are lacking. This study used ML approaches in a dataset of three South Asian countries to predict hypertension and its associated factors and compared the model's performances. Methods We conducted a retrospective study using ML analyses to detect hypertension using population-based surveys. We created a single dataset by harmonizing individual-level data from the most recent nationally representative Demographic and Health Survey in Bangladesh, Nepal, and India. The variables included blood pressure (BP), sociodemographic and economic factors, height, weight, hemoglobin, and random blood glucose. Hypertension was defined based on JNC-7 criteria. We applied six common ML-based classifiers: decision tree (DT), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), logistic regression (LR), and linear discriminant analysis (LDA) to predict hypertension and its risk factors. Results Of the 8,18,603 participants, 82,748 (10.11%) had hypertension. ML models showed that significant factors for hypertension were age and BMI. Ever measured BP, education, taking medicine to lower BP, and doctor's perception of high BP was also significant but comparatively lower than age and BMI. XGBoost, GBM, LR, and LDA showed the highest accuracy score of 90%, RF and DT achieved 89 and 83%, respectively, to predict hypertension. DT achieved the precision value of 91%, and the rest performed with 90%. XGBoost, GBM, LR, and LDA achieved a recall value of 100%, RF scored 99%, and DT scored 90%. In F1-score, XGBoost, GBM, LR, and LDA scored 95%, while RF scored 94%, and DT scored 90%. All the algorithms performed with good and small log loss values <6%. Conclusion ML models performed well to predict hypertension and its associated factors in South Asians. When employed on an open-source platform, these models are scalable to millions of people and might help individuals self-screen for hypertension at an early stage. Future studies incorporating biochemical markers are needed to improve the ML algorithms and evaluate them in real life.

Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries

Internet of Things (IoT) is an emerging technology that is taking the current technology to the next phase of connectivity by connecting any device to the internet. It may be a mobile phone device, a speaker or even a connected vehicle. The number of IoT-based devices people use is increasing day by day. The quantity of smart gadgets people purchase on both offline and online platforms has increased to a significant number in the past few years. This research article proposes social networking between IoT devices, by the interconnection of IoT devices based on a Cloud computing method. Through cloud computing, number of Edge platform-based, IoT devices can be connected to a common cloud called Fog computing, which in turn connects to the Cloud network. The social networking of IoT devices enables users to switch and share data between their IoT devices. Various computational programs and block chain techniques develop applications that allow the host network to communicate with IoT devices with secure access to the data shared by all IoT devices connected to the Cloud without any data loss or security breach.

—With the advent of the Internet of Things (IoT), Artiﬁcial Intelligence (AI), and Machine Learning (ML)/Deep Learning (DL) algorithms, the data-driven medical application has emerged as a promising tool for designing reliable and scalable diagnostic and prognostic models from medical data. This has attracted a great deal of attention from academia to industry in recent years. This has undoubtedly improved the quality of healthcare delivery. However, these AI-based medical applications still have poor adoption due to their difﬁculties in satisfying strict security, privacy, and quality of service standards (such as low latency). Moreover, medical data are usually fragmented and private, making it challenging to generate robust results across populations. Recent developments in Federated Learning (FL) have made it possible to train complex machine-learned models in a distributed manner. Thus, FL has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and security concerns. To this end, this survey paper highlights the current and future of FL technology in medical applications where data sharing is a signiﬁcant burden. It also review and discuss the current research trends and their outcomes for designing reliable and scalable FL models. We outline the general FL’s statistical problems, device challenges, security, privacy concerns, and its potential in the medical domain. Moreover, our study is also focused on medical applications where we highlight the burden of global cancer and the efﬁcient use of FL for the development of computer-aided diagnosis tools for addressing them. Additionally, the recent literature revealed that FL models ensure a high level of robustness and generalization compared to the traditional data-driven medical applications. We hope that this review serves as a checkpoint that sets forth the existing state-of-the-art works in a thorough manner and offers open problems and future research directions for this ﬁeld.

pdf/federated-learning-for-medical-applications-a-taxonomy-iptod1oh.pdf

Making resources closer to the user might facilitate the integration of new technologies such as edge, fog, cloud computing, and big data. However, this brings many challenges shall be overridden when distributing a real?time stream processing, executing multiapplication in a safe multitenant environment, and orchestrating and managing the services and resources into a hybrid fog/cloud federation. In this article, first, we propose a business process model and notation (BPMN) extension to enable the Internet of Things (IoT)?aware business process (BP) modeling. The proposed extension takes into consideration the heterogeneous IoT and non?IoT resources, resource capacities, quality of service constraints, and so forth. Second, we present a new IoT?fog?cloud based architecture, which (i) supports the distributed inter and intralayer communication as well as the real?time stream processing in order to treat immediately IoT data and improve the entire system reliability, (ii) enables the multiapplication execution within a multitenancy architecture using the single sign?on technique to guarantee the data integrity within a multitenancy environment, and (iii) relies on the orchestration and federation management services for deploying BP into the appropriate fog and/or cloud resources. Third, we model, by using the proposed BPMN 2.0 extension, smart autistic child and coronavirus disease 2019 monitoring systems. Then we propose the prototypes for these two smart systems in order to carry out a set of extensive experiments illustrating the efficiency and effectiveness of our work.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/spe.2924

IoT‐fog‐cloud based architecture for smart systems: Prototypes of autism and COVID‐19 monitoring systems

The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19 infected people and consequently limit the pandemic spread, this paper entails a framework integrating the machine learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a novel smart COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, lung ultrasound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a service (federated MLaaS); (i) the distributed batch MLaaS that is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream MLaaS, which is installed into a hybrid fog-cloud environment for a short-term decision-making. The stream MLaaS uses a shared federated prediction model stored into the cloud, whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream ML algorithms from the Python's libraries. The evaluation considers both the quantitative (i.e., performance in terms of accuracy, precision, root mean squared error, and F1 score) and qualitative (i.e., quality of service in terms of server latency, response time, and network latency) metrics to assess these algorithms. This evaluation shows that the stream ML algorithms have the potential to be integrated into the COVID-19 prognosis allowing the early predictions of the suspected COVID-19 cases.

/pdf/hybrid-based-framework-for-covid-19-prediction-via-federated-4r9nrzmuom.pdf

Hybrid-based framework for COVID-19 prediction via federated machine learning models

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IoT‐fog‐cloud based architecture for smart systems: Prototypes of autism and COVID‐19 monitoring systems

Hybrid-based framework for COVID-19 prediction via federated machine learning models