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The issue of traffic mismanagement is getting bad to worse as the trend of urbanization is increasing and posing significant threats to the urban areas. This paper addresses this issue of traffic mismanagement by proposing a Fog-Cloud centric Internet of Things (IoT)-based collaborative framework that enables machine learning-based situation-aware traffic management. This framework monitors the traffic dynamics at the fog layer for providing real-time analytics to the cloud and enabling the traffic situation-aware routing of the vehicles. Whereas the framework at the cloud layer determines the situation-aware analytics by predicting Waiting Time (WaT), Waiting Queue Length (WQueL) and next green phase duration (G) for each Traffic Movement Signal Point (TMSP) based on the real-time data and analytics provided by the fog layer. The road-infrastructure hosted fog layer classifies the In-schedule Reachability Status (IRS) that determines the ability of the vehicle to reach the respective TMSP down the lane before the start of the next green phase. After classifying all such vehicles on a lane by the fog layer, the cloud layer predicts the optimal duration of the next green phase at that respective TMSP to reduce the traffic congestion on the respective junction point and subsequently provides these area-wide analytics to the vehicle hosted fog nodes and traffic controllers on the respective junction points. The vehicles use these analytics for enabling situation-aware vehicle routing functionality by selecting a time optimized path and enabling balanced traffic load on all possible paths. The framework employs Logistic Regression (LR) for IRS classification at the fog layer and Artificial Neural Network (ANN) for traffic prediction at the respective TMSP at the cloud layer. The result depiction acknowledges LR's efficiency compared to other employed classifiers in terms of various statistical parameters. The optimal performance of ANN at the learning rate (LeR) of 0.1, momentum rate (MoR) of 0.95, and 500 epochs depict the prediction efficiency of ANN as compared to other used prediction approach. The contribution of this paper is two functionalities-based working of fog computing for providing real-time and situation-aware traffic analytics, adaptive traffic movement phase planning, time optimized navigation, and optimal traffic load balancing. [ABSTRACT FROM AUTHOR]

COVID-19 outbreak has caused a devastating impact on the daily lives of people, public health, and economic progress of infected countries. It has become a leading cause of substantial mortality and morbidity around the world. The emergence of new variants of virus has posed severe challenges for humanitarian society. Information and Communication Technology (ICT) has played a vital role in this pandemic and offered various promising innovations to control its dissemination. The current research study presents a scientometric analysis on the literature of ICT-assisted COVID-19 research. In this paper, ICT has been classified into six major categories; artificial intelligence and medical imaging, mobile technology, ubiquitous computing, big data analytics, social media platforms, and printing technology. It extensively examines the role of these technologies in COVID-19 by applying various empirical approaches such as co-citation analysis, publication and citation behavior analysis, participating nations, and knowledge mapping of scientific literature using visualization tool CiteSpace. Furthermore, it provides a visual approach to identify developing paths, evolution trends, research hotspots, cluster analysis, and potential future directions in medical informatics. [ABSTRACT FROM AUTHOR]

Dengue is a mosquito-borne pandemic viral infection, which transmits to humans from Female Aedes albopictis or Aedes agypti mosquitoes. It progressively deteriorates the health of infected individuals and poses a high threat of human morbidity and mortality. This paper proposes an intelligent healthcare system which identifies, monitors, and alerts dengue virus (DeV) infected individuals and other stakeholders in real-time and control the DeV infection outbreak using cloud computing, internet of things and fog computing paradigms. The proposed system uses Naive Bayesian Network (NBN) for diagnosing the possibly DeV infected individuals and generating real-time alerts for suggesting and alerting the concerned stakeholders for taking on-time necessary actions at the fog subsystem. The proposed system also uses Social Network Analysis at the cloud subsystem, to provide Global Positioning Systems (GPS)-based global risk assessment of the DeV infection on Google Maps (Google-based web map service) and control DeV infection outbreak. The analysis of the experimental results acknowledges the efficiency of the NBN-based DeV infection diagnosis, alert generation, and GPS-based risk assessment functionality, of the proposed system, via various statistical measures and experimental approaches. [ABSTRACT FROM AUTHOR]

During disasters, individuals witness various dreadful scenes and situations. These scenes and situations generate a sense of panic and cause panic attacks to individuals. Panic attacks cause various disorders like Panic Disorder, Post Traumatic Stress Disorder, and Anxiety Disorder, etc. On the other side, smart cities are becoming the mainstay for urbanization. Hence, the increasing incidents of disruptions due to disasters require the smart cities to adopt emergency response and resilience as the most critical dimension for its design so that the disaster-related risks can be prevented and controlled. This dimension of smart city design helps in minimizing the disruption, human, and socio-economic loss through Information and Communication Technologies (ICT) and called smart disaster management. In this paper, a Fog-Cloud centric Internet of Things (IoT)-based cyber physical framework is proposed, which prioritizes the evacuation of the panicked stranded individuals and provides timely medical support. The physical subsystem of the framework acquires data from stranded individuals and disaster-affected environment and provides various information services to the respective stakeholders (evacuation personnel and stranded individuals). Whereas, the cyber subsystem of the proposed framework, initially at the Fog layer classifies the Panic Health Status (PHS) of the stranded individuals in real-time based on the acquired health data and analyzes the novelty of the data for avoiding unnecessary data traffic to Cloud. After PHS diagnosis, the cyber subsystem uses Bayesian Belief Network (BBN) to monitor the panic health sensitivity of the stranded panicked individuals using disaster-related health and environmental data, at the Cloud layer. This subsystem also builds the evacuation map using acquired disaster-related environmental data at the Cloud layer. Based on the evacuation map and monitored panic health sensitivity of the individuals, the subsystem prepares evacuation strategy, which prioritizes the evacuation of the stranded individuals. The vital points of this proposed framework are the immediate panic-related diagnostic and curative alert generation to the mobile devices of the stranded individuals from the Fog layer, and the preparation of the evacuation strategy based on the evacuation map and panic health sensitivity monitoring of the stranded individuals, at the Cloud layer. The experimental evaluation of the proposed framework depicts the classification efficiency of Support Vector Machine (SVM) for classifying the PHS of the stranded individuals in real-time, and efficiency of Data Novelty Analysis (DNA) for avoiding unnecessary data traffic, at the Fog layer. The experimental evaluation also acknowledges the efficiency of evacuation map building using Unmanned Aerial Vehicles (UAVs), and panic health sensitivity monitoring using BBN at the Cloud layer. [ABSTRACT FROM AUTHOR]

The integration of smart grids with Advanced Metering Infrastructure (AMI) has bridged the realms of the Internet of Vehicles (IoV) and Electric Vehicles (EVs), yet challenges persist in managing EV Battery Range and charging infrastructure effectively. This paper presents an innovative IoV-based framework tailored for EVs, with a specific focus on forecasting electricity consumption in a Vehicle-to-Grid (V2G) scenario. By exploring the hurdles surrounding electric vehicle usage, the research lays the foundation for Electric Vehicles (EVs). The proposed IoV model optimizes IoT sensor utilization through a fog layer and employs a Back Propagation (BP) neural network for battery State of Charge (SoC) estimation, integrating Principal Component Analysis (PCA) for data dimensionality reduction. Leveraging substantial computing capabilities, the cloud layer predicts Electricity Consumption Data (ECD) associated with EVs in V2G scenarios. Performance evaluation metrics like Akaike Information Criteria (AIC), Mean Error (ME), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) are assessed across state-of-the-art forecasting algorithms. Incorporating EV potential into the optimal model reveals a significant 10% reduction in electricity demand. This research advances IoV-based frameworks, offering insights to enhance EV efficiency within the broader energy infrastructure. • An innovative Internet of Vehicles (IoV) framework for depicting electricity demand on the demand side in V2G scenarios. • In-depth experimentation and evaluation of the state-of-the-art approaches of clustering and forecasting. • The devised model successfully projected a 10% decrease in demand side for consumers. [ABSTRACT FROM AUTHOR]

Disasters around the world have adversely affected every aspect of life and panic-health of stranded persons is one such category. An effective and on-time evacuation from disaster-affected areas can avoid any panic-related health problems of the stranded persons. Although the nature of disasters differ in terms of how they occur, the evacuation of stranded persons faces approximately same set of issues related to the communication, time-sensitive computation and energy efficiency of the devices operated in the disaster-affected areas. In this paper, a cyber physical system (CPS) is proposed that takes into account various challenges of the disaster evacuation, so an efficient on-time and orderly evacuation of stranded panicked persons could be realized. The system employs fog-assisted mobile and UAV devices for time-sensitive computation services, data relaying and energy-aware computation. The system uses a fog-assisted two-factor energy-aware computation approach using data reduction, which enables the energy-efficient data reception and transmission (DRecTrans) operations at the fog nodes and compensates to extend the period for other functionalities. The data reduction at fog devices employs Novel Events Identification (NEI) and Principal Component Analysis (PCA) for detecting consecutive duplicate traffic and data summarization of high dimensional data, respectively. The proposed system operates in two spaces: physical and cyber. Physical space facilitates real-world data acquisition and information sharing with the concerned stakeholders (stranded persons, evacuation teams and medical professionals). The cyber space houses various data-analytics layers and comprises of two subspaces: fog and cloud. The fog space helps in providing real-time panic-health diagnostic and alert services and enables the optimized energy consumption of devices operate in disaster-affected areas, whereas the cloud space facilitates the monitoring and prediction of panic severity of the stranded persons, using a conditional probabilistic model and seasonal auto regression integrated moving average (SARIMA), respectively. Cloud space also facilitates the disaster mapping for converging the evacuation map to the actual situation of the disaster-affected area, and geographical population analysis (GPA) for the identification of the panic severity-based critical regions. The performance evaluation of the proposed CPS acknowledges its Logistic Regression-based panic-well being determination and real-time alert generation efficiency. The simulated implementation of NEI and PCA depicts the fog-assisted energy efficiency of the DRecTrans operations of the fog nodes. The performance evaluation of the proposed CPS also acknowledges the prediction efficiency of the SARIMA and disaster mapping accuracy through GPA. The proposed system also discusses a case study related to the pandemic disaster of coronavirus disease 2019 (COVID-19), where the system can help in panic-based selective testing of the persons, and preventing panic due to distressing period of COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

The biggest challenges for several universities and educational institutions throughout the COVID‐19 pandemic are online and e‐learning infrastructure availability. The incorporation of Information and Communication Technologies in the domain of disaster management is one such dimension, which focuses on the sustainability of human beings regarding the handling of unexpected pandemics. Therefore, it is important to evaluate the panic well‐being of the student in this situation. Moreover, virtual reality technology provides a virtual classroom environment that improves the skill and knowledge of the students at remote sites. In this paper, a fog‐assisted cyber physical system is proposed that deals with the various aspects of the panic well‐being of the student, including the virtual reality platform for remote learning. The proposed system utilizes the concepts of physical and cyberspace. The physical space facilitates real‐time data acquisition, and cyberspace determines and predicts the panic well‐being of the student. The performance assessment of the proposed model acknowledges the efficiency of the virtual learning system and panic well‐being determination and prediction. The proposed system also discussed a virtual learning system that provides a virtual classroom environment to the students at remote sites and reduces the panic due to stressful times during the COVID‐19 pandemic. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) has emerged as the most effective approach for COVID-19 detection, but privacy concerns hinder its performance due to limited data sharing by medical institutions. To address this challenge, Federated Learning (FL) enables collaborative model training without compromising individual data privacy. However, existing approaches in this domain often prioritize factors like energy consumption, latency, and privacy over improving model accuracy and stability. To overcome these limitations, a novel approach called FL-inspired DL-assisted Data Analysis (FDDA) is proposed. FDDA utilizes aural and image-based data to identify COVID-19 symptoms, which are then analyzed using the Dynamic Integration Approach (DIA) to determine an individual’s health status. The proposed solution achieves impressive accuracy rates of 97.78 and 98.58% for aural and image-based samples, respectively. Additionally, the DIA demonstrates an overall accuracy of 98.86% in symptom identification, making it a highly reliable decision-making tool. To ensure training stability, a dynamic factor is continuously updated as the model deepens its layers. The proposed solution exhibits remarkable reliability with an accuracy of 96.78% even when one of the data modalities is missing during testing. It outperforms existing baselines in terms of model training effectiveness, precision, and stability. By prioritizing accuracy and stability while addressing privacy concerns, FDDA presents a promising approach for COVID-19 detection in a federated learning setting. [ABSTRACT FROM AUTHOR]

The past five decades have witnessed the unprecedented contribution of arboviral diseases towards global morbidity and disability. It is primarily attributed due to unplanned urbanization, population explosion and globalization. Out of these, dengue is considered the most important arboviral disease because of its predominant growth in the past. The presented study explores the immense potential of Internet of things (IoT), fog and cloud computing for providing technology-based healthcare solutions for dengue virus (DENV) infection. In this paper, a hierarchical healthcare computing system for controlling DENV infection using fog–cloud-assisted IoT is proposed. This system provides a real-time remote diagnosis of DENV infection in individuals and monitors and predicts their health sensitivity during its infection period. The system uses fog computing to diagnose the DENV infection status of the individuals using |$k$| -means clustering and generates immediate diagnostic alerts to individuals, at the fog layer. Furthermore, the system uses cloud computing to monitor and predict the probabilistic health sensitivity of the DENV-infected individuals using Bayesian belief network and artificial neural network, respectively, at the cloud layer. The prediction of health sensitivity in the proposed system helps the infected individuals and healthcare agencies in determining the health vulnerability of DENV-infected individuals and preventing severe or permanent health losses in the future. The proposed system is experimentally evaluated using well-defined approaches, which conform to its validity and applicability. The results obtained from the experimental evaluations of the proposed system acknowledge the performance superiority and high efficiency of the system in delivering DENV-related healthcare services in real time. [ABSTRACT FROM AUTHOR]

The advancements in information and communication technologies have propelled the efforts of various nations toward a disaster-ready society. This progression has even conceived a new term called as smart disaster management (SDM). This profound interest and popularity of SDM among the nations in recent years has led to a considerable number of research publications in this domain. Henceforth, the high relevance and growth of research require the overall study of the structure and development in this field, which can be well understood through quantitative approaches. This article presents a bidimensional scientometric study of SDM research from the perspective of various categories of disasters and research areas. This study is based on an extensive bibliographic data of the period 2009–2018 retrieved from Scopus. It exhibits various empirical approaches to study the evolution, status quo, and output of SDM research. This article provides extensive insights into the publication patterns, citation patterns, and keywords co-occurrence analysis for technological trends of the SDM research. This scientometric study identifies significant categories, research areas, nations, and institutions for SDM research from various perspectives. In whole, this article presents an overall and better understanding of various patterns, trends, and other factors as the foundation for the future research directives and collaborations in the domain of SDM research. [ABSTRACT FROM AUTHOR]

Hydro-meteorological hazards have an immense effect on this planet throughout the history of disasters from prehistoric to recent times. Despite numerous studies on hydro-meteorological hazards, however, little attention has been paid to the role of ICT in the management of these hazards. Emerging techniques of ICT (Information and Communication Technology) have encouraged the researchers to incorporate these techniques for managing hydro-meteorological hazards. Through a scientometric analysis of the bibliographic data retrieved from Scopus for 2010 to 2019 observation period, this study reveals the pattern of research on hydro-meteorological hazards and the role of various information and communication technologies in the management of these hazards worldwide. This study provides a framework for visualizing the growth of publications, role of ICT in hazards management, citation pattern, countries collaboration and most influential countries, journals and articles. The further analysis evaluates the prominent authors in each category and co-occurrence of key research areas. The results of research outputs show that remote sensing, GIS & artificial intelligence are rapidly grown technologies in the field of hydro-meteorological hazard management over the last 10 years. Since 2013 IoT, cloud computing & big data are emerging technologies in this domain. This scientometric study provides directions for future research in ICT assisted hydro-meteorological hazard management. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic highlights the need for effective and non-intrusive methods to monitor the well-being of elderly individuals in their homes, especially for early detection of potential viral infections. Conspicuously, the present paper develops a Multi-scaled Long Short Term Memory (Ms-LSTM) model for the routine health monitoring of elderly patients to detect COVID-19. The proposed method offers home-based health diagnostics through urine analysis by leveraging the IoT-Fog-Cloud paradigm. Mainly, the proposed model constitutes a four-layered architecture: data acquisition, fog layer, cloud layer, and interface layer. Each layer serves distinct functionalities and provides specific services, thereby collectively enhancing the overall effectiveness of the model. The statistical results of the study demonstrate the superior performance of the proposed Ms-LSTM model in comparison to state-of-the-art methods, including Artificial Neural Networks (ANN), K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Random Forest, and LSTM. Further, the proposed model attains a mean temporal efficiency of 39.23 seconds. It exhibits high reliability (92.97%), stability (70.06%), and predictive accuracy (93.25%). • The emergence of new viruses has put health systems under immense pressure. • The current article offers an IFC health framework for health monitoring. • It endeavours to offer an innovative IFC-assisted intelligent toilet system. • It envisions advancing healthcare to achieve better home-based care outcomes for the elderly. [ABSTRACT FROM AUTHOR]

The anomalies on highways impose wear-tear which may cause degradation in prolonging of the vehicles. Although, past research has played an important role in detecting potholes and speed bumps with high accuracy. This study proposes an Internet of Vehicles (IoV) based framework for detecting road anomalies using Multidimensional Dynamic Time Warping (MDTW) for improving the F1 score and sensitivity. Furthermore, the vehicle degradation index caused by road anomalies is established. The system employs a Three-layer architecture comprising IoV, Fog, and cloud computing, utilizing their inherent capabilities as needed for the specific task at hand. The data is gathered using the IoV system, which includes an Arduino MEGA 2560 equipped with an ESP8266, M8N GPS, and MPU-6050. The fog layer computes the vehicle's jerk impact using onboard capabilities and a complementary filter for bandwidth optimization. The cloud layer, with powerful computing capabilities, evaluates the proposed MDTW method against existing techniques for detecting potholes and speed bumps, serving as a potential backbone for the model. The proposed approach achieved an F1 score of 0.8934 and 0.8852 for the studied datasets. A correlation was found between prolonging the lifespan of a vehicle and the frequency of jerks experienced during two trips, as indicated by the Vehicle Degradation Index with an average score of 0.7212. To summarize, the proposed framework can be used not only in the automobile industry, where production can be customized based on demographic factors but also in low-cost automatic monitoring and repair of roads. • A novel Internet of Vehicle (IoV) based Framework for Vehicle Degradation caused by road Anomalies. • Proposed a novel approach for jerk classification using MDTW clustering. • Proposed SVM-MDTW algorithm achieved better precision with an F1 score of 0.8852 to 0.8934. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has led to a substantial loss of human life throughout the world and has a tremendous impact on healthcare services. Industry 4.0 technologies have established effective supply chain management towards the fulfillment of customized demands in the healthcare field. In addition, the internet of things, artificial intelligence, big data analytics, and 3D printing have been extensively used to combat the COVID-19 pandemic and assist in providing value-added services in the healthcare sector. Henceforth, this paper presents a scientometric analysis on the literature of aforementioned Industry 4.0 technologies in the context of COVID-19. It provides extensive insights into co-citation and co-occurrence analysis of high cited publications, participating countries, influential authors, prolific journals, and keywords using the CiteSpace tool. The analyses reveal that China has produced the highest research outputs, although India is the most collaborative country in this field. The current research hotspots include supply chain, 4D printing, and social distancing technologies. Furthermore, it explores emerging trends, intellectual structure of publications, research frontiers, and potential research directions for further work in the Industry 4.0 assisted healthcare domain. [Display omitted] • Industry 4.0 technologies have emerged in the healthcare domain as an area of growing interest in academia and industry discipline. • Artificial intelligence, big data analytics, internet of things and 3D printing have been extensively used to control the COVID-19 spread and assist in providing value-added services in the healthcare sector. • It explores emerging trends, intellectual structure of publications, and potential research directions for further work in the Industry 4.0 assisted healthcare domain. [ABSTRACT FROM AUTHOR]

A correction is presented to the article "A Scientometric Review of Global Research on Smart Disaster Management" published in 2020 issue of the periodical.

The disasters often lead to panic among people resulting into life threatening crowd movements. An efficient and systematic evacuation can mitigate the impact of devastation on human life. The evacuation, on the wake of extreme events, requires delay-sensitive computation and energy conserving mechanisms for power constrained data collection devices. In this paper, an IoT based Cloud-Fog assisted layered evacuation system is proposed for mass evacuation during emergency situation. The system utilizes fog computing paradigm to evaluate the panic health state in real-time using fuzzy K-nearest neighbour (FK-NN) method. The energy conserving mechanisms at fog layer performs data selection and data reduction to reduce the amount of data transmission to upper layer. Spatio-temporal Data Selection layer chooses the data for further transmission on the basis of its spatio-temporal analysis. Data reduction component reduces the dimensions of data using Principal Component Analysis (PCA). The cloud layer examines the future temporal health state of evacuees by employing Holt-Winters method. Geographic Analysis layer at cloud determines the priority evacuation status of person on the basis of current and future temporal panic state. Moreover, it specifies the priority evacuation status of region using the count of currently panicked and predicted health status. Experimentation proves the efficiency of proposed system at various levels. • This paper contributes an approach to diagnose on-time panic health state of the persons at fog layer. • This paper provides an approach for transmitting the multi-attribute data to cloud using an energy conserving method. • This paper predicts the future panic health state of the persons on the basis of current and past panic health states. • This paper provides an approach to prioritize the evacuation of people based on the panic health-based geographic analysis of the regions. [ABSTRACT FROM AUTHOR]