Quantum Machine Learning (QML) merges Quantum Computing (QC) and Artificial Intelligence (AI) to solve problems that are intractable by classical computers. At the same time, domains such as finance, healthcare, manufacturing, and transportation domains are transitioning into Internet of Things (IoT)-enabled cyber physical systems generating streams of heterogeneous data with high dimensionality and volume. This work presents a unified scientometric and qualitative review of QML across these sectors. A PRISMA-based screening is followed by a Keyword Co-occurrence Cluster Analysis (KCCA), a Keyword Burst Analysis (KBA), and a Citation Burst Analysis (CBA) using CiteSpace on a Scopus-derived corpus of literature. An evidence-based Technology Readiness Level (TRL) framework is introduced to assess the technology’s maturity. Our findings indicate that the volume of publications has increased after 2022, with healthcare dominating the volume and manufacturing the impact. Furthermore, a clear trend towards the development of hybrid quantum classical architectures is present across all domains. Security applications such as anomaly detection are the most developed QML entry point to IoT systems. All four domains are at a TRL of 3-4, indicating that QML research is still at the concept-validation stage. Challenges include hardware noise, barren plateaus, absence of standardized benchmarks, and encoding heterogeneous data from IoT devices for training QML applications.

Assessing groundwater resources is crucial for managing water contamination and preventing illnesses. Effective water quality evaluation requires continuous real-time parametric data collection. Previous systems have failed to incorporate unpredictability and flexibility, relying on static models that lead to errors. This study introduces a novel Virtual Twin-inspired Water Quality System (VTWQS) for real-time water quality assessment, addressing these limitations and providing quantitative insights into health risks. The validity of the method is confirmed using experimental data from the Maheru surveillance post in Punjab, India. Results indicate strong performance in water quality evaluation, with an overall prediction accuracy of 94.14%, recall of 91.47%, precision of 93.74%, and f-measure of 92.37%. The system also demonstrates lower computing latency, higher reliability, and increased dependability, making it an effective choice for accurate water quality forecasts.

Artificial Intelligence (AI) is widely used in the healthcare sector, including healthcare administration, predictive analytics for medical outcomes, decision-making processes, and diagnoses. While AI has advanced to rival human proficiency in certain healthcare tasks, its implementation is limited due to perceived opacity and lack of trust. To address this limitation, this study introduces an innovative approach to interpretable deep learning for detecting Respiratory Syncytial Virus (RSV). The proposed technique aggregates audio and image data features to generate a cumulative probabilistic outcome and employs the SHapley Additive exPlanations (SHAP) technique for transparent explanations. Two public datasets, Coswara and Virufy, were used to train and evaluate the approach. The technique outperformed existing techniques by achieving an overall Accuracy, Precision, Recall, and F1-Score of 97.56%, 98.78%, 95.87%, and 97.86%, respectively. After generating probabilistic predictions, each prognosis is explained using the SHAP technique that considers both localized and global perspectives. In this manner, the proposed approach offers explanations for predictions, increasing trust in AI applications in the healthcare sector.

Post-quantum cryptography (PQC) is under development to guard against the threats of quantum computers by implementing a new class of cryptosystems. In this direction, much work has been done since 2006, which has led to many publications. Hence, this study presents an overview of PQC research through scientometric analysis of the data containing 1611 publications published from 2006 to 2023, retrieved from the Scopus database. The analysis identifies growth, trends, leading countries, and significant publications, providing insights into impactful PQC research. It also demonstrates a significant rise in publications after 2015, and the United States is a highly productive country. Furthermore, this study also discusses the managerial view, which can assist technology managers in understanding its impact on global and local markets. The findings of this analysis can be a valuable resource for researchers, policymakers, and stakeholders interested in the future of cryptography and other potential impacts of quantum computing.

The evolution of vehicular research into an effectuating area like the Internet of Vehicles (IoV) was verified by technical developments in hardware. The integration of the Internet of Things (IoT) and Vehicular Ad-hoc Networks (VANET) has significantly impacted addressing various problems, from dangerous situations to finding practical solutions. During a catastrophic collision, the vehicle experiences extreme turbulence, which may be captured using Micro-Electromechanical systems (MEMS) to yield signatures characterizing the severity of the accident. This study presents a three-layer design, with the data collecting layer relying on a low-power IoT configuration that includes GPS and an MPU 6050 placed on an Arduino Mega. The fog layer oversees data pre-processing and other low-level computing operations. With its extensive computing capabilities, the farthest cloud layer carries out Multidimensional Dynamic Time Warping (MDTW) to identify accidents and maintains the information repository by updating it. The experimentation compared the state-of-the-art algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest Tree (RFT) using threshold-based detection with the proposed MDTW clustering approach. Data collection involves simulating accidents via VirtualCrash for training and testing, whereas the IoV circuitry would be utilized in actual real-life scenarios. The proposed approach achieved an F1-score of 0.8921 and 0.8184 for rear and head-on collisions.

In the constantly changing realm of logistics and transportation management, the incorporation of Information and Communication Technology (ICT) has catalyzed transformative paradigm shifts in the approach and resolution of Vehicle Route Optimization (VRO). The current scientometric research paper embarks on a comprehensive exploration of the scholarly endeavors acquired from the Scopus database in the realm of ICT-assisted vehicle route optimization, spanning 2014–2023. The scientometric implications of the article encompass several pivotal dimensions, including publication patterns, (author, country, institution) co-authorship, geographical distribution, Document Co-citation network Analysis (DCA) and top articles based on betweenness centrality corresponding to each opted category of the current knowledge domain. A meticulous examination of the analyses revealed a significant research impact in the pervasive computing and communication technology categories. The co-authorship analysis presenting the interconnectedness of collaborative efforts across countries, authors, and institutions highlights the authors and universities of China and the United States as dominant players in the domain. The DCA elucidates research themes, including intelligent transportation systems, unmanned aerial vehicle-based wireless sensor networks, electric vehicle-based sustainable VRO, and vehicular ad-hoc networks. These themes underscore the current research trajectories within the field. Notably, quantum computing and blockchain emerged as prominent technologies. Overall, the study unveils the transformative impact of ICT on VRO, highlighting the key themes, future research directions and a collaborative research community poised for substantial innovation in this area of research.

Innovations in Internet of Things (IoT) technology have significantly enhanced the service qualities of power grid organizations by incorporating smart energy distribution techniques. Conspicuously, the current study presents an effective approach for distributing power load in smart homes using IoT-Edge technology by addressing efficient allocation and real-time energy demand. Specifically, the research focuses on evaluating the spatial-temporal efficiency of power grid sub-stations for distributing energy using edge computing. An optimal distribution of power is achieved by estimating the Spatial-Temporal Utilization Index (STUI) using a Quantum Computing-inspired approach for each smart home based on real-time energy usage. Additionally, an Automated Quantum-inspired Neural Network (AQNN) model is developed to predict the spatial-temporal allocation of energy for power grid sub-stations. For validation, a 60-day simulation of four smart homes in a controlled environment is conducted. Comparison with state-of-the-art data assessment methodologies demonstrates the superiority of the proposed technique for Temporal Delay (5.9ms), Optimization Performance (Precision (95.15%), Sensitivity (89.75%), Coverage (95.55%) and Specificity (92.99%)), Reliability (92.65%) and Stability (70%).

Digital Twins (DT), specialized simulated modeling that has been popularized in the industrial domain, are starting to be implemented in the domain of healthcare with significant success. Individualized Internet of Things (IoT) models also have various applications in healthcare, from developing medicine to optimizing treatment. These advancements have the potential to integrate and analyze data from different sources. A Digital Twin-inspired IoT-assisted framework is introduced to analyze irregular physical, visual, and behavioral events of individuals with anxiety disorders in academic environments. The framework utilizes quantum probability techniques to determine irregularities and performs Temporal Data Mining (TDM) to frame requested data granules. These granules are then forwarded to the proposed Multi-level Bi-Gated Recurrent Unit (ML-Bi-GRU) for Health Severity Index (HSI) determination. A smart warning deliverance approach is also proposed to notify caregivers of assistive care. The proposed solution’s health irregularity and severity determination is evaluated on a real-time dataset with a total of 35730 instances. The methodology’s efficacy in the domain of smart healthcare is defined through a case study.

During any emergency, a donation is considered a moral responsibility all over the globe. The lack of transparency and oversight in charity donations hurts people’s enthusiasm to donate. Donors are distrustful about how their funds are utilized. The use of blockchain technology (BCT) will provide a solution to make the donation procedure more viable. It is a distributed technology that offers a secure and transparent environment by avoiding the involvement of third parties between contributors and charities. This article proposed a blockchain-based donation mechanism for the convenience of charity organizations, donors, and beneficiaries during disasters, pandemics such as Covid-19, and other emergencies. All transactions can be traced in blockchain, giving donors visibility into where and how their funds are utilized. This article contributes to improving donations’ openness to strengthen public interest in donations and encourage BCT in charity. Ethereum blockchain is used to implement the proposed framework and provides a convenient donation platform. Smart contracts are used to make donations, which build trust between contributors, beneficiaries, and charity organizations. The blockchain-based donation method saves time, lowers donation costs, and eliminates the chances of dubious campaign funds. This study will contribute to improving emergency recovery efforts.

Yellow fever is a vigorous, phlebotomic, vector-borne disease that poses a significant public health threat in regions with high mosquito density and inadequate vaccination coverage. The disease's toxic phase is lethal, making prompt identification and control measures crucial. The emergence of the latest technologies and data analytics techniques, such as edge-cloud computing, data analytics, and machine learning/deep learning, has played a pivotal role in revolutionizing remote healthcare services. Henceforth, applying the abovementioned technologies leads to improvements in the response time, service quality, and location awareness of healthcare systems. Relative to this context, we propose an intelligent fuzzy-centric fog–cloud-assisted healthcare framework to identify and control yellow fever epidemics. Initially, at the fog layer, singular value decomposition is used for data dimensionality reduction analysis and the Fuzzy-C mean clustering (FCM) algorithm is leveraged to get rigorous results. Moreover, for better results and to focus on time-series patterns, the deep interval type 2 fuzzy Bi-LSTM model is proposed at the cloud layer to generate a yellow fever severity index and visualize each yellow fever region based on self-organized maps. In addition, we propose an alert generation mechanism to facilitate real-time decision-making. Finally, results show that the proposed system yields significant efficacy, compared with other state-of-the-art methodologies.

The rapid proliferation of quantum information technologies, spanning theoretical investigations to practical experiments, has generated a number of research papers and documents in quantum algorithms. Consequently, the current research serves as a gateway for interested readers to comprehend the status quo of quantum algorithms, with a specific focus on vehicular network optimization. It aims to explore the research patterns and latest trends by analyzing the dataset sourced from the Scopus and Web of Science databases. The scientometric implications offer valuable insights into publication patterns, keyword co-occurrence, author co-citation, country collaboration, and burst reference. These analyses delineate the temporal progression, prominent research topics, emerging research areas, leading collaborative nations, prolific authors, and research trends within this knowledge domain. The results reveal that smart power grids, traveling salesman problem, electric vehicle charging, battery life estimation, and air traffic control are emerging research areas. Similarly, quantum approximate optimization algorithms, adiabatic quantum computing, quantum-inspired evolutionary algorithms, and quantum annealing emerge as prominent quantum algorithms employed for vehicular network optimization problems. In addition, systematic literature analysis is objectively conducted to discern key insights, research challenges and future research directions in the current knowledge domain.

Abstract 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 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 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.

Information and Communication Technologies (ICT) are becoming indispensable nowadays for the healthcare industry. The utilization of ICT in healthcare services has accelerated even faster after the commencement of the COVID-19 outbreak. This study aims to perform a scientometric analysis of scholarly literature on airborne diseases in the discipline of science and technology. It explores the recent advancement of internet technologies in healthcare to control the prevalence of deadly airborne illnesses by applying analytical approaches. It presents publication trends, citation structure, influential sources, co-citation, and co-occurrence network analysis using the CiteSpace tool. It identifies the important research topics, current research hotspots, most active research areas, and leading technologies in this scientific knowledge domain. It inferred significant results from analyses that will benefit researchers and the academic fraternity across the globe to understand the evolving paths and recent scientific progress of ICT in airborne disease management.

Earthquakes are among the most inevitable natural catastrophes. The uncertainty about the severity of the earthquake has a profound effect on the burden of disaster and causes massive economic and societal losses. Although unpredictable, it can be expected to ameliorate damage and fatalities, such as monitoring and predicting earthquakes using the Internet of Things (IoT). With the resurgence of the IoT, an emerging innovative approach is to integrate IoT technology with Fog and Cloud Computing to augment the effectiveness and accuracy of earthquake monitoring and prediction. In this study, the integrated IoT-Fog-Cloud layered framework is proposed to predict earthquakes using seismic signal information. The proposed model is composed of three layers: (i) at sensor layer, seismic data are acquired, (ii) fog layer incorporates pre-processing, feature extraction using fast Walsh–Hadamard transform (FWHT), selection of relevant features by applying High Order Spectral Analysis (HOSA) to FWHT coefficients, and seismic event classification by K-means accompanied by real-time alert generation, (iii) at cloud layer, an artificial neural network (ANN) is employed to forecast the magnitude of an earthquake. For performance evaluation, K-means classification algorithm is collated with other well-known classification algorithms from the perspective of accuracy and execution duration. Implementation statistics indicate that with chosen HOS features, we have been able to attain high accuracy, precision, specificity, and sensitivity values of 93.30%, 96.65%, 90.54%, and 92.75%, respectively. In addition, the ANN provides an average correct magnitude prediction of 75%. The findings ensured that the proposed framework has the potency to classify seismic signals and predict earthquakes and could therefore further enhance the detection of seismic activities. Moreover, the generation of real-time alerts further amplifies the effectiveness of the proposed model and makes it more real-time compatible.

Abstract Wildfires are exorbitantly cataclysmic disasters that lead to the destruction of forest cover, wildlife, land resources, human assets, reduced soil fertility and global warming. Every year wildfires wreck havoc across the globe. Therefore, there is a need of an efficient and reliable system for real-time wildfire monitoring to dilute their disastrous effects. Internet of Things (IoT) has demonstrated remarkable evolution and has been successfully adopted in environmental monitoring domain. Therefore, timely detection and prediction of wildfires is the need of the hour. The proliferation of the IoT has been witnessed in the environment monitoring domain for detection and prediction of several environmental hazards. This research proposes an integrated IoT–fog–cloud framework for real-time detection and prediction of forest fires. Initially, a Bayesian belief network is used to detect the outbreak of wildfire at fog layer followed by real-time alert generation to the forest department offices and fire-fighting stations. Cloud layer-assisted fuzzy-based long-term wildfire prediction and monitoring is responsible for determining the susceptibility of a forest terrain to wildfire outbreak based on wildfire susceptibility index (WSI). Furthermore, WSI is used for risk zone mapping of forest terrains.

Abstract Drought is considered as one of the most extremely destructive natural disasters with catastrophic impact on hydrological balance, agriculture outcome, wildlife habitat and financial budget. Therefore, there is a need for an efficient system to predict and forecast drought situations. There are a number of drought indices to assess the severity of droughts considering different causing factors. Most of them does not take important factors into consideration. Internet of Things (IoT) has demonstrated phenomenal growth and has successfully worked in monitoring environmental conditions. This paper proposes an IoT-enabled fog-based framework for the prediction and forecasting of droughts. At the fog layer, the dimensions of the data are decreased using singular vector decomposition. Artificial neural network with genetic algorithm classifier is used to assess drought severity category to the given event and Holt-Winters method is used to predict the future drought conditions. The proposed system is implemented using datasets from government agencies and it proves its effectiveness in assessing drought severity level.

The healthcare industry is the premier domain that has been significantly influenced by incorporation of Internet of Things (IoT) technology resulting in smart healthcare application. Inspired by the enormous potential of IoT technology, this research provides a framework for an IoT-based smart toilet system, which enables home-based determination of Urinary Infection (UI) efficaciously. The overall system comprises a four-layered architecture for monitoring and predicting infection in urine. The layers include the Urine Acquisition, Urine Analyzation, Temporal Extraction, and Temporal Prediction layers, which enable an individual to monitor his or her health on daily basis and predict UI so that precautionary measures can be taken at early stages. Moreover, probabilistic quantification of urine infection in the form of Degree of Infectiousness (DoI) and Infection Index Value (IIV) were performed for infection prediction based on a temporal Artificial Neural Network. In addition, the presence of UI is displayed to the user based on a Self-Organized Mapping technique. For validation purposes, numerous experimental simulations were performed on four individuals for 60 days. Results were compared with different state-of-the-art techniques for measuring the overall efficiency of the proposed system.

Internet of Things (IoT) is defined as the interconnection of millions of wireless devices to acquire data in a ubiquitous manner. With multiple devices targeting to perceive data over a common platform, it becomes indispensable to analyze accuracy for realizing an optimal IoT environment. Inspired from these aspects, this article presents a novel quantum computing-inspired (IoT-QCiO) optimization technique to maximize data accuracy (DA) in a real-time environment of IoT application. Specifically, the presented model incorporates quantum formalization of sensor-specific parameters to quantify IoT devices in terms of sensors in vicinity (SIV) and optimal sensor space (OSS). The optimality of the presented algorithm is estimated in terms of three key performance indicators of data cost (DC), DA, and data temporal efficiency (DTE). For validation purposes, the proposed algorithm is implemented for monitoring geographical traffic to address vehicular routing problems using 90 WiSense nodes, Raspberry Pi v3, and quantum simulators. Results obtained were compared with several state-of-the-art optimization algorithms. Based on the results, significant improvement was registered for the proposed model in terms of statistical parameters of precision, sensitivity, specificity, and F-measure. Moreover, enhanced values of reliability depict the optimal performance of the proposed approach.

Drought is a catastrophic natural disaster with significant impact on financial stability, hydrological budget, public health, and agricultural productivity. Numerous drought indices have been introduced to quantify the severity of droughts, but the majority of them are incapable of demonstrating the changes in crucial drought causing elements. Internet of Things (IoT) is appropriate to monitor time-critical environmental parameters. This article proposes an energy-efficient cloud-centric system to assess the drought for the current situation and predict for the future time frame. The architecture determines the active and sleep interval of IoT sensors based on the analysis of data variability using the Bartlett test. The dimensionality of the data about drought causing elements is reduced using kernel principal component analysis (KPCA) at the fog layer. The intensity of drought is determined at the cloud layer using naïve-Bayes classifier, and drought severity for different time periods is predicted using the seasonal autoregressive integrated moving average model. Experimentation and performance analysis prove the efficiency of the proposed system in assessing and predicting the drought with better correlations with drought-causing attributes. Furthermore, it shows significant energy savings as compared to other schemes.

Drought is one of the most recurrent natural disasters with cataclysmic effects on water budget, crop production, economic progression and public health. These consequences are magnified by the climate change leading to more intense drought conditions. A number of drought indices have been presented to calibrate the drought severity with its own strengths and limitations. Many of them are region-specific and unable to exhibit the alterations in significant drought inducing elements. Internet of Things (IoT) is well-suited for continuous monitoring, collection and analysis of different environmental phenomena. The dimensionality of the data collected about drought inducing attributes temperature, humidity, precipitation, evapotranspiration, groundwater, soil moisture at different depths, streamflow and season is reduced using PCA (Principal Component Analysis) at fog layer. Cloud layer estimates the drought severity level using Artificial Neural Network (ANN) whose parameters are optimised with Genetic Algorithm (GA) to get more accurate system and ARIMA method is used to forecast the drought for different time frames. Experimentation done on data collected from government websites shows that proposed system performs well in terms of accuracy, sensitivity, specificity, precision and F-measure with values 95.03%, 90.6%, 96.73%, 91.42% and 91.01%.

Hypertension is a chronic disease causing risk of different types of disorders, such as hypertension attacks, cerebrovascular attacks, kidney failure, and cardiovascular diseases. To prevent such risks, statistics related to hypertension have to be monitored and analyzed in real-time. Internet of Things (IoT)-assisted fog health monitoring system can be used to monitor blood pressure (BP) and to diagnose the stage of hypertension in real-time. In this paper, IoT-fog-based healthcare system is proposed for continuous monitoring and analysis of BP statistics to predict hypertensive users. The proposed system initially identifies the stage of hypertension on the basis of user's health parameters collected using IoT sensors at fog layer. After identifying the hypertensive stage, artificial neural network is used for predicting the risk level of hypertension attack in users at remote sites. The vital point of this paper is to continuously generate emergency alerts of BP fluctuation from fog system to hypertensive users on their mobile phones. Finally, analysis results and compiled medical information of each user are stored on cloud storage for sharing with domain experts, such as clinicians, doctors, and personal caregivers. The temporal information generated from fog layer can be utilized for providing precautionary measures and suggestions well on time for patients' wellness. Experimental results reveal that proposed framework achieves low response time, high accuracy, and bandwidth efficiency.

Wildfires are extremely destructive disasters that cause significant loss of lives, forest cover and wildlife. This is due to their uncontrolled, erratic, rapid spread and behaviour. The incidence of wildfires is expected to increase worldwide because of Global Warming. Henceforth, it becomes increasingly important to detect and tackle such fires in their infancy to minimise their adverse effects. IoT technology has shown an exponential growth in recent years. Moreover, deployment of IoT devices to monitor and collect time-critical data is pressing need of hour. This research proposes an effective Fog-IoT centric framework for timely detection of wildfires. The proposed methodology provides an efficient real-time solution to dilute the destruction caused by wildfires. Initially, K-means Clustering is used to detect the wildfire outbreak at fog layer followed by real-time alert generation to the administration and community. Furthermore, cloud layer based Adaptive Neuro Fuzzy Inference System is used for assessing the vulnerability of a forest block to forest fires as well as classifying it into one of the five risk zones based on Forest Fire Vulnerability Index. Implementation results of the proposed framework prove its efficiency in detecting and predicting wildfires. In addition, real-time alert generation further enhances the efficacy of the proposed system.

In recent years, solutions based on Internet of Things (IoT) are gaining impetus in educational institutions. It is observed that student performance evaluation system in education institutions is still manual. The performance score of student in traditional evaluation system is confined to its academic achievements while activity-based performance attributes are overlooked. Moreover, the traditional system fails to capitalise information of each student related to different activities in learning environment. In relation to this context, we propose to facilitate automated student performance evaluation system by exploring ubiquitous sensing capabilities of IoT. The system deduces important results about the performance of the students by discovering daily spatial–temporal patterns. These patterns are based on the data collected by the sensory nodes (objects) in the institution learning environment. The information is generated by applying data mining algorithms for each concerned activity. The automated decisions are taken by management authority for each student using game theory. In addition, to effectively manage IoT-based activity data, tensor-based storage mechanism is proposed. The experimental evaluation compares the student performance score generated by the proposed system with the manual student performance evaluation system. The results depict that the proposed system evaluates the performance of the student efficiently.

Chikungunya virus is an infection which is transmitted to humans by the bite of Aedes agypti and Aedes albopictis mosquitoes. It causes deterioration in health leading to multiorgan failure. Diagnostic tests for this virus are not easily available and affordable in developing countries. The medical technologies are not so efficient in diagnosing and preventing the outbreak of Chikungunya virus. Healthcare services based on a fog-cloud technology are emerging as a proactive and effective solution to provide remote detection and monitoring of users. A fog assisted healthcare system can be used to diagnose users infected by Chikungunya virus in an initial state of their infection so that proper treatment can be given to them on time to enable fast recovery. In this paper, a fog assisted cloud-based healthcare system is designed to diagnose and prevent the outbreak of this virus. Initially, a decision tree is used to classify the category of user's infection depending on his/her health symptoms and diagnostic alerts are immediately generated on the user's mobile phone from fog layer. Furthermore, the state of Chikungunya virus outbreak is represented using temporal network analysis (TNA) on cloud layer using proximity data. Various outbreak metrics from TNA graph are calculated which indicate the probability to receive or transmit Chikungunya infection to human beings. Results depict that J48 decision tree classifier has higher accuracy and lower response time in determining the level of Chikungunya virus infection as compared to other classification algorithms. Moreover, alert generation based on real-time healthcare data further enhances the utility of the proposed system.

Internet of Things (IoT) technology provides a competent and structured approach to handle service deliverance aspects of healthcare in terms of mobile health and remote patient monitoring. IoT generates an unprecedented amount of data that can be processed using cloud computing. But for realtime remote health monitoring applications, the delay caused by transferring data to the cloud and back to the application is unacceptable. Relative to this context, we proposed the remote patient health monitoring in smart homes by using the concept of fog computing at the smart gateway. The proposed model uses advanced techniques and services, such as embedded data mining, distributed storage, and notification services at the edge of the network. Event triggering-based data transmission methodology is adopted to process the patient's real-time data at fog layer. Temporal mining concept is used to analyze the events adversity by calculating the temporal health index of the patient. In order to determine the validity of the system, health data of 67 patients in IoT-based smart home environment was systematically generated for 30 days. Results depict that the proposed Bayesian belief network classifier-based model has high accuracy and response time in determining the state of an event when compared with other classification algorithms. Moreover, decision making based on real-time healthcare data further enhances the utility of the proposed system.

Summary Internet of Things (IoT) in today's smart environments has many applications in gerontology, health care, transportation, and smart cities. Many challenges still exist in developing IoT‐based smart environments. Dynamic generation of action strategies based on multiple IoT object's input is one of the major challenges. In this paper, stochastic Petri nets and game theory are combined to create stochastic game nets (SGNs) for IoT‐based smart environment where each IoT device acts as a player with predefined place and action sets. Complete SGN will be created dynamically using individual sensor SGNs which will make IoT‐based smart environments highly interoperable and scalable. Proposed model is used to predict activities performed by two‐single person in their respective home with more than 70 sensors. Simulation results show suitability of proposed model in smart environments. Proposed model is tested for smart homes, but it can be used in any IoT‐based smart environment. Copyright © 2016 John Wiley & Sons, Ltd.

Summary As a result of varying demands of computing resources by the users on cloud, resource provisioning in cloud computing has come out as a prominent topic of research. Many researchers have focused exclusively on the technical and security aspects of cloud computing, thereby neglecting the efficient provisioning of resources and the necessity of cloud services to be cost effective. Cloud consists of a large number of resources that are allocated to cloud customer's on‐demand. As demands cannot be static and constantly change with time, cloud service providers cannot adopt static provisioning of resources as there are chances of over‐provisioning or under‐provisioning. Therefore, to achieve efficient resource utilization, an optimized strategy that can deploy virtual machines on different physical machines according to resource requirements is the current need of cloud computing. That is, there must be a mechanism by which the total number of active physical nodes can be dynamically changed corresponding to their resource usage rate, thereby providing the efficient utilization of resources. In this paper, a linear regression‐based prediction model is proposed to predict the resource usage based on the number of function points computed from the users' requests. Thereafter, the artificial neural network is also used to predict the future resource requirements more accurately. The predicted resource usage results are used by a resource pool manager to manage the resources and allocate them to the users. The resource pool manager also uses an efficient load‐balancing algorithm to balance the load on each cloud service provider as well as to optimize cloud usage cost. With the help of this prediction model, the decision to allocate or release a virtual machine can be made proactively, thus making the cloud effective in terms of both cost and performance. Copyright © 2014 John Wiley & Sons, Ltd.

Instances of password theft are rapidly growing in number. This is sufficient to shake the confidence of the customer in e-commerce. Authenticating the user on insecure communication channel like internet is an essential primitive and is target of various attacks. Therefore, remote user authentication is one of the most essential requirement for ensuring secure communication in today's ubiquitous computing environment. Smart card-based authentication provide multi-factor authentication for accessing web-based applications. In this paper, an efficient password-based remote user authentication scheme using smart card is proposed. The proposed scheme is an improvement of the scheme proposed by Hsiang and Shih in 2009. Proposed scheme is secure against all well known security attacks and has a low computational cost. The security of the proposed protocol depends upon two security parameters which makes difficult for an attacker to launch attacks on the proposed scheme. Moreover, the user and the server agree on the common session key. Afterwards, all the subsequent messages between the user and the server are encrypted with this session key. Therefore, the attacker cannot get any meaningful authentication information from eavesdropping even in insecure communication channel.

Abstract In the password‐based authentication, password reuse rate increases because the users tend to use the same password with more and more accounts. An average user finds it difficult to remember several complex passwords and hence it is difficult to prevent phishing and dictionary attacks. In 2007, Gouda et al. proposed a single password protocol for HTTP authentication that allows the user to choose a single password of his choice for multiple online accounts on different web servers. It provides to the user the flexibility of changing his password for all accounts on different web servers. However, we found that Gouda et al.'s protocol is not secure against offline dictionary attack, denial of service attack and man‐in‐the‐middle attack in the presence of an active attacker. An improvement to Gouda et al.'s protocol is proposed that resists offline dictionary attack and denial of service attack but cannot resists man‐in‐the‐middle attack. Moreover, it is found that Gouda et al.'s protocol is not repairable for man‐in‐the‐middle attack. In this paper, we present a new single password‐based anti‐phishing protocol that resolves aforementioned problems and is secure against different possible attacks. In this protocol, the client machine's browser generates a dynamic identity and a dynamic password for each login request to the server. The dynamic identity and dynamic password will be different for the same client in different sessions of the Secure Socket Layer (SSL) protocol. The client's password verifier information for different online accounts is stored on the server protected with a hash function, nonce value and the private key of the server. The proposed protocol makes financial transactions more secure on the web as it is practical and efficient. Copyright © 2009 John Wiley & Sons, Ltd.