S-Nav: Safety-Aware IoT Navigation Tool for Avoiding COVID-19 Hotspots

In this article, we present a [Formula Omitted]-learning-enabled safe navigation system—S-Nav—that recommends routes in a road network by minimizing traveling through categorically demarcated COVID-19 hotspots. S-Nav takes the source and destination as inputs from the commuters and recommends a safe path for traveling. The S-Nav system dodges hotspots and ensures minimal passage through them in unavoidable situations. This feature of S-Nav reduces the commuter's risk of getting exposed to these contaminated zones and contracting the virus. To achieve this, we formulate the reward function for the reinforcement learning model by imposing zone-based penalties and demonstrate that S-Nav achieves convergence under all conditions. To ensure real-time results, we propose an Internet of Things (IoT)-based architecture by incorporating the cloud and fog computing paradigms. While the cloud is responsible for training on large road networks, the geographically aware fog nodes take the results from the cloud and retrain them based on smaller road networks. Through extensive implementation and experiments, we observe that S-Nav recommends reliable paths in near real time. In contrast to state-of-the-art techniques, S-Nav limits passage through red/orange zones to almost 2% and close to 100% through green zones. However, we observe 18% additional travel distances compared to precarious shortest paths.

A review of edge computing in healthcare Internet of things: Theories, practices and challenges

The pandemic has forced industries to move immediately their critical workload to the cloud in order to ensure continuous functioning. As cloud computing expansions pace and organisations strive for methods to increase their network, agility and storage, edge computing has shown to be the best alternative. The healthcare business has a long history of collaborating with cutting-edge information technology, and the Internet of Things (IoT) is no exception. Researchers are still looking for substantial methods to collect, view, process, and analyze data that can signify a quantitative revolution in healthcare as devices become more convenient and smaller data become larger. To provide real-time analytics, healthcare organisations frequently deploy cloud technology as the storage layer between system and insight. Edge computing, also known as fog computing, allows computers to perform important analyses without having to go through the time-consuming cloud storage process [15, 16]. For this form of processing, speed is key, and it may be crucial in constructing a healthcare IoT that is useful for patient interaction, inpatient treatment, population health management and remote monitoring. We present a thorough overview to highlight the most recent trends in fog computing activities related to the IoT in healthcare. Other perspectives on the edge computing domain are also offered, such as styles of application support, techniques and resources [17]. Finally, necessity of edge computing in era of Covid-19 pandemic is addressed. © The Institution of Engineering and Technology 2022.

Computer-aided methods for combating Covid-19 in prevention, detection, and service provision approaches.

The infectious disease Covid-19 has been causing severe social, economic, and human suffering across the globe since 2019. The countries have utilized different strategies in the last few years to combat Covid-19 based on their capabilities, technological infrastructure, and investments. A massive epidemic like this cannot be controlled without an intelligent and automatic health care system. The first reaction to the disease outbreak was lockdown, and researchers focused more on developing methods to diagnose the disease and recognize its behavior. However, as the new lifestyle becomes more normalized, research has shifted to utilizing computer-aided methods to monitor, track, detect, and treat individuals and provide services to citizens. Thus, the Internet of things, based on fog-cloud computing, using artificial intelligence approaches such as machine learning, and deep learning are practical concepts. This article aims to survey computer-based approaches to combat Covid-19 based on prevention, detection, and service provision. Technically and statistically, this article analyzes current methods, categorizes them, presents a technical taxonomy, and explores future and open issues.

Real-time scanning and tracking of health records through a centralized ecosystem based on Internet of Things and fog computing

Rapid improvements in healthcare services and affordable IoT in the past decade have been a big help in dealing with the issue of fewer medical facilities. Unfortunately, some people still choose not to get immunized, thus fear and reluctance remain a part of human existence despite widespread vaccination initiatives. Therefore, it is important to screen this group of potential spreaders as soon as possible since they may become infected and transfer viruses to others. It is in this context that the pharmaceutical sector might benefit from highly developed health monitoring systems. This work has created and tested a multi-node architecture based on Fog computing to perform real-time initial screening and recording of such individuals, therefore addressing the demand and reducing the unpredictability of the scenario. In addition to capturing photographs of the subject's face, the suggested device also recorded the subject's current body temperature and GPS locations. As an added bonus, the suggested system could upload information to a remote server over the internet. To test the viability of the proposed system, a thorough examination of the existing work environment was carried out, including implementation and evaluations. From the results of the statistical analysis, it was seen that the suggested IoT Fog-enabled ecosystem may be put to good use. © 2023 IEEE.

Research and Application of 5G Edge AI in Medical Industry

With the development of 5G and AI technology, the infectious virus detection framework system based on the combination of 5G MEC and medical sensors can effectively assist in the intelligent detection and control of influenza viruses such as COVID-19. Employing the edge computing and 5G+MEC model, the virus AI model is trained for the collected influenza virus data. Then the virus AI model can be used to evaluate the virus patients on the local edge computing service platform. Therefore, this paper introduces an algorithm and resource allocation, which uses 5G functions (especially, low latency, high bandwidth, wide connectivity, and other functions) to achieve local chest X-ray or CT scan images to detect COVID-19. Meanwhile, this paper also compares the computational efficiency of different algorithms in the 5G edge AI-based infectious virus detection framework, in this way to select the best algorithm and resource allocation. © 2022 IEEE.

BOSS: A new QoS aware blockchain assisted framework for secure and smart healthcare as a service

The latest epidemic of COVID‐19 has significantly impacted both human capital and the global economy, contributing to pandemics and severe global crises. Research into the creation and propagation of the disease is desperately needed. The Internet of Things, cloud computing, and artificial intelligence offer modern technology for real‐time processing for multiple applications such as healthcare applications, transport, traffic control, and so on blockchain is an evolving technology that will dramatically boost transaction protection in finance, supply chain, and other transaction networks. A stable and latency‐sensitive Quality of Service framework for COVID‐19 is the need of an hour. The purpose of this paper is to combine Fog computing and Artificial Intelligence with smart health to establish a reliable platform for early‐stage detection of COVID‐19 infection. A new ensemble‐based classifier is proposed to detect COVID‐19 patients. This research offers a blockchain platform to analyse how the unrelated cases of the COVID‐19 virus can be tracked and identified using peer‐to‐peer, time stamping, and the shared storage advantages of blockchain. In addition to growing patient loyalty, this would effectively enhance the consistency, flexibility, productivity, performance, and effectiveness of healthcare services. The idea of blockchain is used to establish security for the whole framework. Different implementations measure the efficiency of the suggested system. The performance of the proposed framework is evaluated in terms of delay, network usages, RAM usages, and energy consumption. On the other hand, the classifier is evaluated in terms of classifier accuracy, recall, precision, kappa static, and root mean square error. The result shows the performance of the proposed framework and classifier is always better than the traditional frameworks and classifiers. [ FROM AUTHOR] Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A Secure Blockchain-Assisted Access Control Scheme for Smart Healthcare System in Fog Computing

IoT-based smart healthcare system allows doctors to monitor and diagnose patients remotely, which can greatly ease overcrowding in the hospitals and disequilibrium of medical resources, especially during the rage of COVID-19. However, the smart healthcare system generates enormous data which contains sensitive personal information. To protect patients’privacy, we propose a secure blockchain-assisted access control scheme for smart healthcare system in fog computing. All the operations of users are recorded on the blockchain by smart contract in order to ensure transparency and reliability of the system. We present a blockchain-assisted Multi-Authority Attribute-Based Encryption (MA-ABE) scheme with keyword search to ensure the confidentiality of the data, avoid single point of failure and implement fine-grained access control of the system. IoT devices are limited in resources, therefore it is not practical to apply the blockchain-assisted MA-ABE scheme directly. To reduce the burdens of IoT devices, We outsource most of the computational tasks to fog nodes. Finally, the security and performance analysis demonstrate that the proposed system is reliable, practical, and efficient. IEEE

A Distributed Data Mesh Paradigm for an Event-based Smart Communities Monitoring Product.

The recent pandemic events in Thailand, Covid-19 in 2018, demonstrated the need for an event-based smart monitoring system. While a distributed multi-level architecture has emerged as an architecture of choice for a larger-scale smart event-based system that requires better latency, security, scalability, and reliability, a recently introduced data mesh paradigm can add a few additional benefits. The paradigm enables each district to become an event-based smart monitoring mesh and handle its analytics and monitoring workload. Districts can form a set of domains in a network of event-based smart community monitoring systems and provide data products for others during a crisis. This paper presents a distributed data mesh paradigm for an event-based smart monitoring product in a given community with predefined domains. The paper presents smart monitoring as a data product between domains. Key considerations for designing an event-based smart monitoring data product are given. The author introduces three possible domains necessary for creating a smart monitoring system in each community. Each domain creates a data product for a given domain and shares data between domains. Finally, a three-layer analytics architecture for a smart monitoring product in each domain and a use case is presented.

Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

In healthcare systems, the Internet of Things (IoT) innovation and development approached new ways to evaluate patient data. A cloud-based platform tends to process data generated by IoT medical devices instead of high storage, and computational hardware. In this paper, an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography (CT) images of patients with pneumonia, Covid-19, tuberculosis (TB), and cancer. Firstly, the CT images are captured and transmitted to the fog node through IoT devices. In the fog node, the image gets modified into a convenient and efficient format for further processing. advanced encryption Standard (AES) algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating systems. Finally, the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning models. Herein different pre-trained deep learning architectures (Inception-ResNet-v2, VGG-19, ResNet-50) used transfer learning is adopted for feature extraction. The softmax of heterogeneous base classifiers assists to make individual predictions. As a meta-classifier, the ensemble approach is employed to obtain final optimal results. Disease predicted image is consigned to the recurrent neural network with long short-term memory (RNN-LSTM) for severity analysis, and the patient is directed to seek therapy based on the outcome. The proposed method achieved 98.6% accuracy, 0.978 precision, 0.982 recalls, and 0.974 F1-score on five class classifications. The experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective. © 2023 CRL Publishing. All rights reserved.

A Collaborative Fog-Based Healthcare Intrusion Detection Security Using Blockchain and Machine Learning

Over recent years, the outbreak of Covid-19 has infected more than a billion people. Due to this crisis, the healthcare industry is revolutionizing using the Internet of Health Things (IoHT). As a result, the increasing number of distributed connected objects, their heterogeneity, and mobility have led to a dramatic expansion in the volume of medical data, consequently, a considerable increase in cybercrime. However, the security of the healthcare system must be considered a top priority. According to the policy principles of cybersecurity intrusion detection systems (IDS) are effective and indispensable security tools. We propose in this paper a collaborative distributed fog-based intrusion detection system reinforced by using blockchain to ensure trust and reliability between Fog nodes, and machine learning (ML) approaches with the effective open-source Catboost benefiting from the GPU library to get a record detection and computation time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Designing Fog Device Network for Digitization of University Campus

With the evolving digitization, services of Cloud and Fog make things easier which is offered in form of storage, computing, networking etc. The importance of digitalization has been realized severely with the home isolation due to COVID-19 pandemic. Researchers have suggested on planning and designing the network of Fog devices to offer services nearby the edge devices. In this work, Fog device network design is proposed for a university campus by formulating a mathematical model. This formulation is used to find the optimal location for the Fog device placement and interconnection between Fog devices and the Cloud (Centralized Information Storage). The proposed model minimizes the deployment cost and the network traffic towards Cloud. The IBM CPLEX optimization tool is used to evaluate the proposed multi-objective optimization problem. Classical multi-objective optimization method, i.e., Weighted Sum approach is used for the purpose. The experimental results exhibit optimal placement of Fog devices with minimum deployment cost. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Time to forge ahead: The Internet of Things for healthcare

Situated at the intersection of technology and medicine, the Internet of Things (IoT) holds the promise of addressing some of healthcare's most pressing challenges, from medical error, to chronic drug shortages, to overburdened hospital systems, to dealing with the COVID-19 pandemic. However, despite considerable recent technological advances, the pace of successful implementation of promising IoT healthcare initiatives has been slow. To inspire more productive collaboration, we present here a simple—but surprisingly underrated—problem-oriented approach to developing healthcare technologies. To further assist in this effort, we reviewed the various commercial, regulatory, social/cultural, and technological factors in the development of the IoT. We propose that fog computing—a technological paradigm wherein the burden of computing is shifted from a centralized cloud server closer to the data source—offers the greatest promise for building a robust and scalable healthcare IoT ecosystem. To this end, we explore the key enabling technologies that underpin the fog architecture, from the sensing layer all the way up to the cloud. It is our hope that ongoing advances in sensing, communications, cryptography, storage, machine learning, and artificial intelligence will be leveraged in meaningful ways to generate unprecedented medical intelligence and thus drive improvements in the health of many people. © 2022 Chongqing University of Posts and Telecommunications

Networking Data and Intelligent Management in the Post-COVID19 Era: A Report on APNOMS 2021

This article presents a report on APNOMS 2021, which was held on September 8–10, 2021 in Tainan, Taiwan. The theme of APNOMS 2021 was "Networking Data and Intelligent Management in the Post-COVID19 Era.”. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Decentralized Authentication Mechanism for Mobile Ad hoc Networks

Covid 19 has dramatically changed people's lives around the world. It has shut down schools, companies and workplaces, forcing individuals to stay at home and comply to quarantine orders. Thus, individuals have resorted to the Internet as a means for communicating and sharing information in different domains. Unfortunately, some communities are still unserved by commercial service providers. Mobile Adhoc Network (MANET) can be used to fill this gap. One of the core issues in MANET is the authentication of the participating nodes. This mechanism is a fundamental requirement for implementing access control to network resources by confirming a user's identity. In recent years, security experts worldwide proposed distributed authentication for MANET due to the lack of a central authority to register and authenticate nodes. In this article, decentralized authentication based on the technology of fog computing and the concept of the blockchain is proposed. The evaluation of this mechanism satisfies the diverse security requirements and strongly protects the networks from attacks.

Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring.

The new coronavirus disease (COVID-19) has increased the need for new technologies such as the Internet of Medical Things (IoMT), Wireless Body Area Networks (WBANs), and cloud computing in the health sector as well as in many areas. These technologies have also made it possible for billions of devices to connect to the internet and communicate with each other. In this study, an Internet of Medical Things (IoMT) framework consisting of Wireless Body Area Networks (WBANs) has been designed and the health big data from WBANs have been analyzed using fog and cloud computing technologies. Fog computing is used for fast and easy analysis, and cloud computing is used for time-consuming and complex analysis. The proposed IoMT framework is presented with a diabetes prediction scenario. The diabetes prediction process is carried out on fog with fuzzy logic decision-making and is achieved on cloud with support vector machine (SVM), random forest (RF), and artificial neural network (ANN) as machine learning algorithms. The dataset produced in WBANs is used for big data analysis in the scenario for both fuzzy logic and machine learning algorithm. The fuzzy logic gives 64% accuracy performance in fog and SVM, RF, and ANN have 89.5%, 88.4%, and 87.2% accuracy performance respectively in the cloud for diabetes prediction. In addition, the throughput and delay results of heterogeneous nodes with different priorities in the WBAN scenario created using the IEEE 802.15.6 standard and AODV routing protocol have been also analyzed. Fog-Cloud architecture-driven for IoMT networks • An IoMT framework is designed with important components and functions such as fog and cloud node capabilities. •Real-time data has been obtained from WBANs in Riverbed Modeler for a more realistic performance analysis of IoMT. •Fuzzy logic and machine learning algorithms (RF, SVM, and ANN) are used for diabetes predictions. •Intra and Inter-WBAN communications (IEEE 802.15.6 standard) are modeled as essential components of the IoMT framework with all functions.

COVID-19 Spread Detection and Controlling with Fog-based Infection Probability Evaluation Model

COVID-19 has created a pandemic worldwide, paused the path of building the future, and is still ongoing without any long-term solution. The time taken in vaccine distribution is too slow compared to the spread of COVID-19. Hence, it is important to be aware and take precautions on time without delaying and waiting for long-duration after getting infected with the virus. Technology nowadays is more advanced than ever before. Almost everyone has access to at least one mobile device with internet connection. Therefore, we propose a Fog Server (FS) based system that helps create awareness about the spread of COVID-19 within the surroundings of an individual, utilizing the concept of Hidden Markov Model (HMM) and Bluetooth contact tracing in polynomial computational time complexity. Moreover, we evaluate the effectiveness of the proposed model through real-world data analysis on different simulation settings. © 2023 ACM.

Decentralized Authentication Mechanism for Mobile Ad hoc Networks

Covid 19 has dramatically changed people's lives around the world. It has shut down schools, companies and workplaces, forcing individuals to stay at home and comply to quarantine orders. Thus, individuals have resorted to the Internet as a means for communicating and sharing information in different domains. Unfortunately, some communities are still unserved by commercial service providers. Mobile Adhoc Network (MANET) can be used to fill this gap. One of the core issues in MANET is the authentication of the participating nodes. This mechanism is a fundamental requirement for implementing access control to network resources by confirming a user's identity. In recent years, security experts worldwide proposed distributed authentication for MANET due to the lack of a central authority to register and authenticate nodes. In this article, decentralized authentication based on the technology of fog computing and the concept of the blockchain is proposed. The evaluation of this mechanism satisfies the diverse security requirements and strongly protects the networks from attacks. © 2022 Scientific Association for Infocommunications. All rights reserved.

MIC-Net: A deep network for cross-site segmentation of COVID-19 infection in the fog-assisted IoMT.

The automatic segmentation of COVID-19 pneumonia from a computerized tomography (CT) scan has become a major interest for scholars in developing a powerful diagnostic framework in the Internet of Medical Things (IoMT). Federated deep learning (FDL) is considered a promising approach for efficient and cooperative training from multi-institutional image data. However, the nonindependent and identically distributed (Non-IID) data from health care remain a remarkable challenge, limiting the applicability of FDL in the real world. The variability in features incurred by different scanning protocols, scanners, or acquisition parameters produces the learning drift phenomena during the training, which impairs both the training speed and segmentation performance of the model. This paper proposes a novel FDL approach for reliable and efficient multi-institutional COVID-19 segmentation, called MIC-Net. MIC-Net consists of three main building modules: the down-sampler, context enrichment (CE) module, and up-sampler. The down-sampler was designed to effectively learn both local and global representations from input CT scans by combining the advantages of lightweight convolutional and attention modules. The contextual enrichment (CE) module is introduced to enable the network to capture the contextual representation that can be later exploited to enrich the semantic knowledge of the up-sampler through skip connections. To further tackle the inter-site heterogeneity within the model, the approach uses an adaptive and switchable normalization (ASN) to adaptively choose the best normalization strategy according to the underlying data. A novel federated periodic selection protocol (FED-PCS) is proposed to fairly select the training participants according to their resource state, data quality, and loss of a local model. The results of an experimental evaluation of MIC-Net on three publicly available data sets show its robust performance, with an average dice score of 88.90% and an average surface dice of 87.53%.

Adopting effective hierarchal IoMTs computing with K-efficient clustering to control and forecast COVID-19 cases.

The Internet of Medical Things (IoMTs) based on fog/cloud computing has been effectively proven to improve the controlling, monitoring, and care quality of Coronavirus disease 2019 (COVID-19) patients. One of the convenient approaches to assess symptomatic patients is to group patients with comparable symptoms and provide an overview of the required level of care to patients with similar conditions. Therefore, this study adopts an effective hierarchal IoMTs computing with K-Efficient clustering to control and forecast COVID-19 cases. The proposed system integrates the K-Means and K-Medoids clusterings to monitor the health status of patients, early detection of COVID-19 cases, and process data in real-time with ultra-low latency. In addition, the data analysis takes into account the primary requirements of the network to assist in understanding the nature of COVID-19. Based on the findings, the K-Efficient clustering with fog computing is a more effective approach to analyse the status of patients compared to that of K-Means and K-Medoids in terms of intra-class, inter-class, running time, the latency of network, and RAM consumption. In summary, the outcome of this study provides a novel approach for remote monitoring and handling of infected COVID-19 patients through real-time personalised treatment services.

Indoor Access Control System Through Symptomatic Examination Using IoT Technology, Fog Computing and Cloud Computing

The pandemic experienced in the last two years in the world has led people to be much more careful in their social relations, keeping their social distance and using hygienic prevention measures. However, when it is necessary to enter crowded closed environments, people feel insecure and are more afraid of contagion. This situation leads to the need for measures to control access to public places in order to prevent infection and to reinforce people’s confidence. Various devices and solutions exist to control access, ranging from card-based identification to biometric sensors. However, they have shortcomings detected during the pandemic, such as the need to touch elements or the types of computing used, which can compromise security and/or response times. The solution proposed in this article integrates the best of these by incorporating facial recognition using neural networks, the presence or absence of a mask and medical Internet of Things (IoT) devices to monitor pulse, blood oxygen and body temperature. All this technology is used to check whether the person’s access is safe for them and others. The data collection process in this system has proven to be efficient thanks to fog computing, which reduces latency times and prevents the user’s data from being accessed by third parties while maintaining their privacy. © 2022, Springer Nature Switzerland AG.