Deep Learning (DL) has become a leading method for predicting vulnerabilities in gas pipeline networks. However, its effectiveness is limited by privacy concerns, as infrastructure operators often restrict data sharing. Existing state-of-the-art methods predominantly emphasize energy efficiency, latency, and privacy, while paying less attention to improving model accuracy and stability. This gap highlights the need for approaches that simultaneously ensure data privacy and enhance predictive robustness. To address this, a new approach of Federated Learningempowered Data Analysis (FDA) is introduced. FDA leverages both sensor and image-based data to detect pipeline vulnerabilities leading to faults, which are then evaluated using the Dynamic Accumulation Approach (DAM) to assess the structural integrity of the pipelines. The proposed model is validated using real-world IoT data with 57,469 instances. The proposed model demonstrates improved performance in Delay (4.92 ms), Classification Efficiency (97.15%), Vulnerability Efficacy (Precision: 91.53%, Sensitivity: 97.15%, Specificity: 97.68%, F-Measure: 94.03%), and Stability (74%).

This study presents a Digital Twin-enhanced cyber-security framework for real-time anomaly detection and dynamic vulnerability management in intelligent healthcare systems. The framework integrates a hybridBayesian with CNN-LSTMmodel within the DT environment to accurately identify abnormal patient vitals and device behaviors. To secure diagnostic data, a blockchain-based security layer with aReputation-Aware Fault-Tolerant Consensus (RAFTC)mechanism is employed, enabling immutable, decentralized, and tamper-resistant data sharing. Validation on healthcare data demonstrates superior performance, achieving a low latency of 9.25s, high precision (94.78%), sensitivity (96.89%), specificity (96.95%), and F-measure of 95.79%.

The oil and gas sector is witnessing rapid technological transformation through the integration of Internet of Things (IoT)-enabled systems that facilitate real-time monitoring, automation, and data-driven decision-making. Digital Twin (DT) serves as a key IoT application, offering dynamic virtual representations of physical assets to support predictive analytics and operational optimization. The deployment of DT is further strengthened by emerging technologies such as 5G/6G networks, Artificial Intelligence (AI), Machine Learning (ML), and distributed computing paradigms, including edge, cloud, and fog computing. The study conducts a scientometric analysis of IoT-centric technological advancements in the oil and gas domain from 2020 to 2025, with a particular focus on the role of DT. Based on bibliographic data from the Scopus database and visualizations generated using VOSviewer and CiteSpace, the analysis includes descriptive metrics and research impact evaluation, document co-citation analysis, keyword co-occurrence analysis, and country co-citation analysis to map the intellectual and collaborative structure of the field. Research patterns are examined across four operational categories: Intelligent Reservoir Management and Optimization (IRMO), Autonomous Operations and Safety & Environmental Monitoring (AOSEM), Smart Supply Chain and Refinery Optimization (SSCRO), and Cybersecurity and Digital Risk Management (CDRM). Findings reveal that IoT and DT technologies are most influential in IRMO and AOSEM, while SSCRO and CDRM increasingly adopt secure, AI- and blockchain-supported infrastructures. Scholarly contributions are led by China, the United States, and India. The study provides a comprehensive overview of emerging research trajectories and strategic directions driving IoT- and DT-enabled innovation in the oil and gas sector.

The Internet of Things (IoT) enables real-time data acquisition, coordinated decision-making, and system-level automation, making it a key enabler of digital transformation in industrial domains. In complex and distributed environments such as the oil and gas industry, IoT supports the development of scalable, interoperable, and secure digital ecosystems. The paper presents a systematic scientometric review of the IoT-driven digital transformation domain and its relationship with Digital Twin (DT), edge/cloud computing, and artificial intelligence (AI). For this purpose, 978 records published in Scopus-indexed journals between 2015 and 2025 were analyzed using the scientometric tools CiteSpace and VOSviewer. The review includes publication trends, keyword co-occurrence analysis, co-citation analysis, and collaboration analysis. The analysis covers four key areas: operational monitoring, supply chain and logistics, maintenance, and management systems. Results highlight the growing importance of IoT-based architectures, real-time analytics, and distributed intelligence in improving the resilience and efficiency of industrial systems. In addition, key challenges related to interoperability, cross-layer integration, scalability, and security in IoT-enabled DT environments are examined. The identified challenges provide a foundation for future research toward developing secure, robust, and intelligent IoT-based DT systems. The findings offer insights into the evolution of research in the field and highlight directions for future studies on IoT applications in industrial settings.

The growing demand for reliable service delivery and real-time data processing has intensified challenges in efficiently distributing workloads across geographically distributed server nodes in Internet of Things (IoT)-enabled Digital Twin (DT) environments. To address this issue, this study proposes a quantum computing–inspired (QCi) optimization–based framework to enhance load balancing in IoT–DT systems. The approach integrates a novel QCi optimization strategy with a QCi-based predictive neural network to estimate optimal server node allocation by considering real-time server availability and workload dynamics. Extensive evaluations show that the proposed method outperforms Bayesian Belief Networks, conventional Artificial Neural Networks, and Support Vector Machines, achieving a sensitivity of 87.36%, specificity of 94.38%, precision of 91.25%, and coverage of 97.51%. The results demonstrate that combining QCi optimization with DT technology significantly improves processing efficiency, prediction accuracy, and adaptability for real-time IoT workloads.

A Digital Twin (DT) is an innovative area of computer science and technology research that generates an imitation of a realistic system, procedure, or object. The usage of DT technology in innovation across several industries has made it a vital and revolutionary tool. The CiteSpace tool can be utilised for carrying out a scientometric evaluation of the effectiveness of DT technologies in engineering. The current analysis used data acquired through the Scopus repository from 2014 to 2025. To highlight emerging areas of research across various engineering areas, it offers link–walkthrough, keyword co-occurrence and author co-citation analysis of networks. The report highlights the present state of DTs in engineering, their impact on numerous fields and the potential for collaborative research.

Water quality critically affects aquatic life, where even slight changes in chemical or physical conditions can cause stress or health decline. Real-time monitoring is challenging due to dynamic, distributed environments. This study introducesAquaTwinCare, a DT-based virtual replica of aquatic ecosystems for assessing and predicting animal health under pollution stress. The framework integrates environmental factors (dissolved oxygen, pH, temperature, turbidity, pollutants) with biological indicators (respiration, locomotion, stress responses) using hybrid CNNs enhanced with Bayesian inference. To ensure secure, reliable data, it employs a Reputation-Aware Fault-Tolerant Consensus protocol on a consortium blockchain. Validated on 91,845 data instances,AquaTwinCareachieved reduced anomaly detection latency (9.80 s), high precision (84.25%), sensitivity (86.13%), specificity (86.19%), F-measure (85.15%), predictive fidelity (r2= 78%), low error (0.23%), and Mean Error (0.62). Overall, it enables proactive aquatic health management, early pollution-stress warnings, and scalable ecosystem governance.

The healthcare industry is increasingly adopting digital twin technology, a specialized form of simulation modeling that has gained traction in industrial applications. An intelligent architecture inspired by digital twins has been proposed to analyze unusual visual, behavioral, and physiological phenomena in individuals with anxiety disorders within smart office environments. This framework utilizes quantum probability methods for anomaly detection and temporal data mining for data granule framing. Additionally, a novel multilevel Convolutional Neural Network (CNN) is introduced to predict the Health Vulnerability Index, along with a smart alert system designed to notify caregivers of detected health irregularities and provide supportive care. The method was evaluated on a challenging dataset of 59,867 instances, demonstrating superior performance compared to state-of-the-art techniques in several metrics, including Temporal Efficacy (9.2 ms), Classification Efficacy (Precision: 96.29%, Sensitivity: 93.62%, Specificity: 96.73%), Decision-making Efficiency (r2: 78%, Error: 0.30%), and Stability (72%).

The rapid expansion of Internet of Things (IoT) networks has introduced significant security challenges, leaving networked systems increasingly exposed to sophisticated cyber threats. As traditional security approaches struggle to address the emerging challenges, researchers are turning to innovative strategies to safeguard IoT ecosystems. The evolving nature of cyber threats demands adaptive security techniques capable of responding to dynamic attack vectors and mitigating risks in real-time. The aim of the study is to conduct a scientometric analysis of IoT security research published between 2017 and 2024, focusing on identifying emerging trends, influential contributions, and research gaps across key security techniques. A dataset of 1,881 Scopus-indexed papers was analyzed using CiteSpace and VOSviewer, with a specific focus on five major security approaches: AI-driven defense, blockchain-based models, cyber deception technology (CDT), hardware-level protections, and quantum-inspired security incorporating Document Citation Analysis (DCA), Keyword Co-occurrence Analysis, Publication Pattern Growth, Global Research Connectivity, and Author Citation Analysis (ACA). The findings reveal a dominant focus on AI and blockchain, while CDT and QIS remain underexplored. Hardware security, though acknowledged, faces implementation challenges. In conclusion, the study highlights a shift towards decentralized and intelligent security frameworks, emphasizing the need for scalable, lightweight models to meet the evolving demands of IoT ecosystems.

A digital twin (DT) is a precise digital replica of an object or the physical world in which real-time data is input to model, analyze, forecast, and improve key performance indicators. To explore the complex landscape of DT research, the current scientometric review focuses on four pivotal components: architecture, application, middleware, and tools. For the study, the Citespace visualization tool is employed and articles are sourced from the Scopus database, focusing on publications between 2010 and 2024. The initial search yielded 11247 publications, which, after applying filters dataset was reduced to a final dataset of 2214 relevant articles. The view in the current study is centered on the following: keyword co-occurrence network (KCN), country analysis, document co-citation network (DCN), and burst reference analysis. The goal of the current study is to explore the four important components: architecture, applications, middleware, and tools. Limited previous study has explored such components. The current work presents a statistical summary of the development of research on DTs, examining existing patterns and highlighting important scientific discoveries. Finding the most popular trends and patterns of DT technology around the world will assist future researchers gain important knowledge about new sectors and indicate areas that could benefit from more research and development.

The Digital Twin (DT) technology is essential as it facilitates the creation of a virtual representation of physical objects. The evolution of wireless communication technologies (WCT) has been significantly influenced by the emergence of DT technology, resulting in transformative changes across various engineering fields. Therefore, it is essential to conduct an investigation and promote advancements through rigorous scientific analysis to address the global significance, relevance, and rapid growth of the technology. The current study employs the VOSviewer and CiteSpace visualization tools to illustrate the knowledge mapping of the rapidly advancing research in DT technology. The data which is used in the research is extracted from the Scopus database which covers the years 2017 to 2025. It offers insights through Document Co-Citation Analysis, Country Collaboration Network Analysis, and Institution Analysis to identify emerging research areas. The findings highlight the current state of DT technology in WCT in the various engineering fields and identify potential research areas.

Integrating artificial intelligence has the potential to significantly advance digital twin technologies by enhancing data analytics, predictive modeling, and real-time decision-making capabilities. The present scientometric review examines the intersection of artificial intelligence and digital twin technologies, utilizing a comprehensive dataset sourced from Scopus. Initially, 3,526 records were identified and refined to 1,302 through specific filters. For detailed analysis, the records were categorized into four subcategories: architecture, applications, digital twin as middleware, and tools. Using advanced scientometric tools, namely CiteSpace and VOSviewer, the research conducted five distinct analyses: publication pattern, keyword co-occurrence, prominent journals, document co-citation, and author co-citation analysis. The findings reveal significant trends and patterns in the literature, highlighting the evolving landscape of artificial intelligence-integrated digital twins. The study provides insights into the current state of research and identifies key areas for future exploration, contributing to a broader understanding of innovative technology.

The current paper presents a scientometric and systematic analysis of defense evolution driven by emerging technologies, focusing on two broad categories: Internal Defense (Civil) and External Defense (Ground, Aerial, and Naval). The study explores how technologies such as Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), Digital Twin (DT), and next-generation networks (4G, 5G, and 6G) transform defense operations by enhancing efficiency, accuracy, and decision-making capabilities. The analysis integrates three tools: Excel for examining publication and citation trends, CiteSpace for citation network visualisation and co-citation cluster identification, and VOSViewer for keyword co-occurrence mapping and geographic contribution analysis. The dataset, comprising records retrieved from the Scopus database, spans the publication years 2019 to 2024. In addition, a Systematic Literature Review (SLR) was conducted covering English-language peer-reviewed journal and conference articles published between 2016 and 2025. The findings reveal a significant rise in defense-related research, particularly from 2019 onward, driven by the integration of AI-enabled autonomous systems, predictive analytics, and real-time situational awareness. The study concludes with future research directions emphasising the need for 6G-enabled Digital Twins, advanced AI capabilities, and cross-domain interoperability to enhance the adaptability, resilience, and intelligence of modern defense systems.

The convergence of Digital Twin (DT) technology with 5G and 6G networks is revolutionizing various industries by enabling real-time data exchange, advanced automation, and intelligent decision-making. The paper conducts a scientometric analysis of DT integration with 5G/6G, concentrating on four critical domains: Blockchain, the Internet of Things (IoT), autonomous vehicles, and network management. The study begins with an overview of DT technology, followed by a comprehensive literature review and a discussion of the primary research questions. Two analytical tools-Excel and CiteSpace are employed to examine the data. Excel is used to analyze publication trends, domain-specific research output, and prolific authors, offering a quantitative perspective on research progress. CiteSpace, a robust platform for citation and network analysis, is utilized to investigate keyword co-occurrence, geographic distribution, citation structures, and research clusters. Data for the analysis is drawn from the Scopus database, encompassing publications from 2019 to 2024. The results highlight a marked increase in scholarly activity, with substantial contributions from multiple regions and strong interdisciplinary collaboration across the four focus areas. The study concludes by identifying emerging categories and suggesting future research directions in the dynamic and rapidly advancing field.

The rise of digital technology has brought about the era of Industry 4.0, leading to significant changes in various industries. The concept of “Digital Twin (DT)” is crucial as it enables the creation of digital representations of physical assets and processes. In the modern age, it has become an indispensable and transformative tool, revolutionizing how industries operate and innovate. The effectiveness of DT technology can be evaluated through scientometric analysis using VOSviewer, a powerful tool for constructing and presenting bibliometric networks. Research related to Industry 4.0 indicates the growth of DT literature and examines the geographic distribution of research contributions from 2018 to 2024 using the Scopus repository. It also highlights the most notable scholars and their seminal works, emphasizing key contributors, driving innovation, and shaping the future of the field. The findings offer a valuable understanding of the current state of DT study, its impact on 4.0 industries, and the potential for collaborative research.

Digital twin technology is emerging as a key innovation to enhance efficiency and performance in the automobile industry. Despite its potential, several challenges persist, notably the need for accurate performance evaluation and robust predictive capabilities. This article addresses these challenges by presenting a comprehensive model that delineates the vehicle metrics essential for performance evaluation. The novelty of this research lies in the integration of multiple advanced techniques, including a Naive Bayes model for categorizing data segments for quantitative analysis, spatial-temporal mining and regression analysis to abstract temporal data for deeper evaluation, and recurrent neural network (RNN) technology to ensure robust predictive capabilities. Experimental validation in a simulated environment comprising 50 250 data segments demonstrates the efficacy of the proposed model, showing significant improvements in performance metrics. Prediction analysis yields promising results with high Specificity (92.44%), Sensitivity (95.81%), Precision (94.33%), and F1-score (88.33%). Notably, the proposed model achieves temporal efficiency with a minimum time delay of 99.83 s, underscoring its effectiveness in real-time assessment of driverless automobile performance.

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.

The Internet of Things (IoT) has transformed the industrial sector. This study presents a novel framework for real-time evaluation of service quality in residential gas pipeline networks. IoT devices collect critical operational and environmental data, which are processed through a fog-cloud architecture using Bayesian modeling. This enables the calculation of a comprehensive Quality of Service Measure (QSM) and a Service Quality Delivery Value (SQDV) to assess and interpret service performance. A two-level decision-tree model further supports decisions by regulators and end-users. The framework was validated using 73,462 service interaction records, showing significant improvements over existing approaches: reduced data delay (179.01 s), high classification performance (Specificity: 94.22%, Sensitivity: 92.78%, Precision: 93.15%), enhanced decision-making (Accuracy: 95.45%, Error Rate: 1.29%), improved reliability (93.69%), and robust system stability (73.25%).

Cyber twin technology, a successful branch of simulation modeling in business, is now being applied in the healthcare sector. An intelligent architecture inspired by cyber twins is proposed to explore the unique visual, behavioral, and physiological experiences of individuals with anxiety disorders while working in smart office environments. Temporal data mining is utilized for framing data granules, and quantum probability techniques are employed for anomaly detection within the framework. Additionally, a novel multilayer Convolutional Neural Network is introduced to predict a quantifiable Health Vulnerability Index. A smart alert system is included, capable of notifying caregivers of any identified health concerns, enabling timely assistance. To evaluate the effectiveness of the proposed strategy, it was tested on real-world data comprising 82,235 cases. The results demonstrate that the method excels in several key performance metrics: time efficiency (24.6 seconds), classification efficiency (Precision (92.77%), Specificity (92.43%), and Sensitivity (92.82%)), decision-making efficiency ( \(r^{2}=79%\) ), error rate (AAE 0.31%), and stability (75%), surpassing current state-of-the-art methodologies.

The integration of various computational paradigms including the Internet of Things (IoT), and Edge-Cloud platforms, have the potential to enhance the efficiency of evacuation during emergencies. Conspicuously, this study presents an intelligent evacuation framework by integrating the IoT-Edge-Cloud (IEC) computing paradigm. The proposed framework utilizes IoT technology to collect ambient data and track occupant movement based on the location. Edge computing involves the incorporation of a Support Vector Machine (SVM) to identify emergency events. Additionally, it facilitates real-time Spatio-temporal data processing. Furthermore, cloud computing enables the implementation of an evacuation algorithm for efficiently computing a secure and expeditious path based on environmental and occupant data. The presented algorithm generates a comprehensive evacuation map, which serves as a guidance tool to direct individuals toward the designated exit point. Based on experimental simulations, enhanced results were obtained for performance enhancement in terms of Temporal delay (5.23s), Decision-making Efficiency (Precision (95.56%), Sensitivity (96.44%), Specificity (96.97%), F-Measure (96.69%)), Energy Efficiency (4.56mJ), Reliability (92.69%) and Stability (73%).

Smart logistics industry leverages advanced software and hardware systems to enable efficient transmission. The incorporation of smart technologies, including digital twin (DT) and blockchain assesses vulnerabilities in the logistics industry, making them effective for physical attacks by users for stealing and theft control. DT persists a transformative potential in optimizing industrial operations. By bridging the physical and digital worlds, they enable real-time monitoring, predictive analytics, and enhanced decision making, driving innovations in efficiency, security, and sustainability. Conspicuously, the primary objective is to propose an effective logistic monitoring system for ensuring automated theft control. Specifically, the proposed model determines the logistic transmission patterns through secure surveillance using Internet of Things-empowered blockchain technology. Moreover, the deep learning technique of a bi-directional convolutional neural network is used to assess theft and stealing vulnerability by users in real-time for optimal decision making. The proposed approach has been demonstrated to enable accurate real-time analysis of vulnerable behavior. Based on the experimental simulations, the suggested solution effectively facilitates the development of superior logistic monitoring. The performance of the proposed system is evaluated using several statistical metrics, including latency rate (26.15 s), data processing cost, prediction efficiency (accuracy (96.12%), specificity (97.53%), and F-measure (97.25%), reliability (93.34%), and stability (0.74).

National security is one of the premier sectors of research and development in every country. Moreover, government organizations spend large funds to ensure technical advancements in the defense sector. However, major flaws in the routine activities of security personnel have resulted in severe tragedies. Conspicuously, a comprehensive Internet of Things (IoT)-based methodology has been presented to evaluate the integrity of an officer based on professional and personal activities. Specifically, the current study proposes a digital twin-inspired method for evaluating the overall performance of intelligence agency officers (IAOs). The probabilistic digital integrity estimate (DIE) is formalized through data analysis using the Bayesian belief model (BBM). Additionally, the multiscaled long term memory (MLSTM) model has been proposed to predict the overall integral behavior of IAO. The proposed system has been verified over a real-world data set with 42892 instances. Results show that the proposed technique surpasses comparative models in key metrics, such as temporal delay effectiveness (127.79 s), categorization analysis [precision (96.67%), specificity (97.08%), and sensitivity (97.55%)], decision-modeling efficacy [specificity (93.49%), precision (93.49%), and sensitivity (93.69%)], reliability (94.86%), and stability (82.0%).

Medical professionals have devised novel solutions to transform the healthcare industry. Modern technology of digital twins (DTs) can revolutionize medical treatment significantly. The DT technology incorporates digitizing physical entities by constantly monitoring their current status. Conspicuously, a state-of-the-art secure framework for monitoring adults’ physical activity is formulated using the culmination of the DT technology with Internet of Things (IoT)-edge computing, and blockchain technology. The presented framework is designed to discreetly secure the health data of the individual. To identify healthcare vulnerabilities in adults, the present study employs deep learning’s ability to analyze IoT data sequentially. Specifically, a deep learning-assisted multilayered convolutional neural networks (CNNs) and long short-term memory (LSTM) technique is proposed for real-time vulnerability assessment. Additionally, the proposed framework can protect personal healthcare data by using the blockchain technique. For performance validation, numerous simulations were performed over the challenging data set. Based on the results, the proposed methodology can outperform state-of-the-art techniques by registering enhanced values of Temporal Delay Efficacy (120.79 s), Prediction Efficacy (Accuracy (92.24%), Specificity (94.67%), Sensitivity (95.26%), and F-measure (95.69%)), Reliability (91.58%), and Stability (64%).

Over the last decade, Dengue infection has expanded more rapidly than any other viral illness. The current research investigates the vast potential of the Internet of Things (IoT), and Edge–cloud computing in reproving dengue virus (DGN) infection-related technological healthcare solutions. Specifically, a hierarchical healthcare framework is proposed for preventing the spread of DGN using Edge–cloud-assisted IoT technology. The presented system can monitor and forecast an individual’s susceptibility to DGN infection in a ubiquitous manner. Using K-means clustering, the presented system determines an individual’s DGN infection status and generates alert signals in real-time. In addition, the proposed technique employs cloud computing to monitor people healthcare impacted by DGN. Moreover, it ensures probabilistic predictions about susceptibility to the DGN virus using Bayesian belief networks and artificial neural networks. The proposed system can assess health vulnerability, thereby reducing the probability of health loss. The suggested system’s validity and applicability are confirmed by experimental evaluation. The simulation results of the proposed system confirm its optimal performance in terms of temporal delay (14.15 s), classification efficacy [accuracy (91.66%), sensitivity (92.34%), specificity (90.25%), and F-measure (91.12%)], prediction effectiveness [error (0.23), and pearson coefficient (85%)], and stability (72%).

The significance of intelligent transportation is increasing in modern societies. The development of electric mobility is a result of extensive research and industrial needs. Conspicuously, the current study proposes a smart Electric Vehicular (EV) performance system for the transportation industry that uses IoT-Fog-Cloud (IFC) computing technology to provide an effective analysis of domestic and commercial EVs. The system analyzes real-time EV-oriented attributes to present a Performance Analysis Measure (PAM). The framework uses a Bayesian Belief Model (BBM) to classify EV-related attributes in different categories over a temporal scale. Finally, a two-level threshold-based decision tree model is proposed for an overall assessment of the EV. Experimental simulations were performed to validate its effectiveness over challenging datasets with nearly 56365 data instances. Comparative to state-of-the-art techniques, the proposed framework registered enhanced performance for statistical metrics of Delay Assessment (126.68s), Statistical Classification Analysis (Specificity (96.97%), Precision (95.56%), and Sensitivity (96.44%)), Decision-making efficiency (97.53%), Reliability (92.69%), and Stability (0.73).

Internet of Things (IoT) technology has revolutionized the industrial sector. This research article focuses on the development of Food Industry 4.0, which was made possible by advancements in edge-cloud computing and IoT technologies. The study presents an IoT-based smart framework that uses the Bayesian belief network (BBN) on the edge-cloud platform to analyze data in the food industry. The acquired data is assessed to estimate the probability of food quality (PFQ) and evaluate food outlets using the food quality analysis measure (FQAM). Additionally, a Bi-level decision-tree modeling is presented to assess food quality. Food-oriented data security is ensured using blockchain. The proposed model is tested on a complex data set containing data about four restaurants with about 43 520 individual instances. Simulations show effective results of temporal delay (94.41 s), decision-making efficacy (99.64%), classification efficiency (precision (96.67%), specificity (96.97%), and sensitivity (97.55%)), stability (74.25%), and reliability (93.70%).

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.

Panic disorder (PD) is considered one of the destructive ailments, with various individuals experiencing a critical functional disorder. As the range of remission for PD is achieved only between 20% and 50% with the help of regular pharmacotherapy, modern solutions are expected to deal with this issue. By taking the advantage of Internet of Things (IoT), a novel IoT-inspired behaviour monitoring framework is proposed for the analysis of panic disorder in a particular context. A quantum probability-inspired quantification measure is calculated to determine the scale of health irregularity. In addition, Temporal Data Mining (TDM) is performed for the formulation of temporal data granules to measure Individual Health Index (IHI) by utilising the Multi-scaled Gated Recurrent Unit (M-GRU) technique of deep learning. Moreover, a two-phased alert generation approach is proposed for notifying the current health condition of an individual to the concerned caretaker or medical specialist for assistive or medical services. In the comparative analysis, the proposed framework has outperformed the state-of-the-art approaches by achieving a considerable classification accuracy of 96.89% for event determination and 94.14% accuracy for health severity determination. Similarly, a considerable improvement with respect to Specificity, Sensitivity, and F-measure has been observed for the proposed framework.

Advancements in the Internet of Things (IoT) paradigm have greatly improved the quality of services in the electricity industry through the integration of smart energy distribution and dependable electric devices. Conspicuously, the current research introduces a method for managing electricity consumption in smart residences using IoT-Fog technology, focusing on efficient energy allocation and real-time energy needs. The study specifically examines the effectiveness of electricity grid sub-stations in distributing energy using fog computing technology. By utilizing a quantum computing-assisted approach, optimal energy distribution is achieved by calculating a novel Electricity Usage Measure (EUM) based on actual energy usage patterns of smart homes. Furthermore, the Quantumized Neural Network (QiM-NN) technique is developed to forecast the electricity distribution over grid substations. For performance assessment, 4-month data are collected using four smart houses. Comparative analysis with existing data assessment techniques illustrates the effectiveness in terms of Temporal Delay (6.33 ms), Optimization Performance (Specificity (93.00%), Sensitivity (90.86%), Precision (96.66%), Coverage (96.66 %), Reliability (93.76%), and Stability (71%).

Abstract In this pandemic, providing a quality environment is considered one of the essential objectives of smart wellbeing observation. Therefore, the prediction of irregular events has become a fundamental requirement of assistive or clinical consideration. By concentrating on this need, a dew computation-inspired irregular physical event determination solution is presented to determine the symptoms of COVID-19 by analyzing the physical activities of the individuals at their initial stage. The benefit of the proposed solution is enhanced by forwarding the predicted outcomes and their occurrence within a speculated time on a private cloud database to decide the health seriousness. Furthermore, a dynamic decisive-module is introduced to notify medical specialists about the current wellbeing status of the individuals under monitoring. The real-time prediction efficiency of the proposed solution is determined by implementing and calculating the outcomes on both the dew and cloud platforms. The calculated outcomes exhibit the improved viability of the dew platform over the cloud platform by increasing the prediction speed of 46.27% for 40 and 45.54% for 30 frames per second. Moreover, the event prediction performance is justified over the state-of-the-art monitoring arrangements by achieving accuracy (92.88%), specificity (90.87%), sensitivity (88.26%) and F1-measure (89.53%) with the least decision-making delay.

With smart electricity distribution and dependable electric appliances, the revolutionary impact of the Internet of Things (IoT) technology has considerably improved the service-oriented features of the power grid industry. In the current study, a methodology for IoT-based electricity distribution for intelligent homes is described to detect power consumption efficiently. Although effective power resource allocation remains a primary issue for every power grid house, poor energy distribution has significantly influenced everyday living. The current study focuses on the effective distribution of electricity resources by power grid houses over a spatial–temporal basis. Specifically, the spatial–temporal consumption index is calculated for each home in a geographical region based on electricity usage, which enables the effective allocation of power resources. Additionally, an automated game-theoretic decision-making model is proposed to assist power grid house managers in optimizing the spatial–temporal distribution of electricity resources. For validation purposes, a simulated environment is used to monitor four smart houses for 60 days. A comparative analysis with state-of-the-art data assessment methodologies shows that the presented approach is significantly better in terms of statistical parameters of temporal delay (113.24 s), classification efficacy [precision (93.23%), sensitivity (92.34%), and specificity (92.34%)], decision-making efficiency, reliability (88.45%), and stability (72%).

Healthcare is the most pivotal domain of every nation. With the sudden upraise of the COVID-19 pandemic, there has been a major concern for the healthcare industry to provide quality medical services to the common people. Vulnerable healthcare conditions have proven to be fatal for the patients. Conspicuously, it has become indispensable to assess the quality of healthcare services provided by the hospitals. The current article focuses on analyzing healthcare service quality delivered by the hospitals and healthcare centers. Specifically, the presented framework utilizes Internet of Things (IoT) technology to acquire real-time ambient data inside smart hospitals. The quantification of the healthcare service is performed using the Probability of Health Grade (PoHG) to classify data segments using the probabilistic Bayesian belief model. Furthermore, the temporal data abstraction is performed for the numerical analysis of healthcare service quality in terms of the health quality index (HQI). Finally, a 2-player game theory-inspired decision modeling is performed to analyze healthcare quality in a time-sensitive manner. The proposed framework is assessed using a simulated environment where 225325 data segments are analyzed. Results are compared with state-of-the-art techniques in which enhanced performance measures are registered in terms of classification efficacy (93.74%), decision-making efficiency [coefficient of determination (95%), accuracy (97.53%), mean square error (2.01%), and root mean square error (1.95%)], temporal delay (96.62 s), and reliability (91.58%).

Information and communication technology (ICT) empowered by the Internet of Things (IoT) and fog–cloud paradigm has been widely adopted in several domains of logistics, healthcare, and agriculture. Inspired by the enormous benefits of IoT technology, this research proposes a novel notion of smart restaurants for assessing the food quality using the game theory. Specifically, this research presents a smart framework for food quality assessment inside restaurants. Real-time data are acquired using numerous IoT devices for food quality assessment. The data are communicated to the fog nodes backed by the cloud platform. This enables the time-sensitive analysis of food quality for formalizing a quantifiable measure, i.e., food quality estimate (FQE) using the Bayesian modeling technique. FQE presents a quantification factor for assessing the food quality over temporal patterns in terms of the quality support index (QSI). This is followed by the 2-player game model for effective food quality assessment. The presented model is validated by deploying it over four data sets. Based on the comparative analysis with other decision-making techniques, the presented technique has registered superior performance in terms of temporal effectiveness, classification efficacy, statistical efficiency, and reliability.

Smart sports industry presents a novel vision with immense potential for effective decision-making services. Conspicuously, this research presents an Internet of Things-Fog computing inspired game-theoretic decision-making model for provisioning in-depth analysis of athlete performance in a time-sensitive manner. Specifically, sport-oriented parameters are acquired using smart devices using an energy-efficient mechanism, which is further classified and analyzed in terms of quantifiable parameters of the Probability-of-Performability (PoP) and form index value (FIV). Finally, a game-theoretic mathematical model has been proposed between the sports athlete and monitoring officials for effective decision-making services. For validation purposes, the simulation was performed over a challenging data set of four cricket players comprising of 80 120 data instances. Comparative analysis was performed with numerous state-of-the-art analytical techniques. Based on the simulation results, the presented model was able to register enhanced performance in terms of sensitivity (93.14%), specificity (93.97%), precision (94.56%), and f-measure (91.69%). Moreover, improved battery efficiency (25%) and stability (92.79%) were registered for the proposed technique.

Internet of Things (IoT) technology backed by Artificial Intelligence (AI) techniques has been increasingly utilized for the realization of the Industry 4.0 vision. Conspicuously, this work provides a novel notion of the smart sports industry for provisioning efficient services in the sports arena. Specifically, an IoT-inspired framework has been proposed for real-time analysis of athlete performance. IoT data is utilized to quantify athlete performance in the terms of probability parameters of Probabilistic Measure of Performance (PMP) and Level of Performance Measure (LoPM). Moreover, a two-player game-theory-based mathematical framework has been presented for efficient decision modeling by the monitoring officials. The presented model is validated experimentally by deployment in District Sports Academy (DSA) for 60 days over four players. Based on the comparative analysis with state-of-the-art decision-modeling approaches, the proposed model acquired enhanced performance values in terms of Temporal Delay, Classification Efficiency, Statistical Efficacy, Correlation Analysis, and Reliability.

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.