Efficient orchestration in the edge–cloud continuum is essential for reducing energy consumption and meeting latency requirements in large-scale IoT systems. This article presents a hybrid deep reinforcement learning (DRL) and federated learning (FL) framework that dynamically allocates computation across IoT, edge, fog, and cloud layers. The DRL agent learns energy-efficient scheduling strategies through a latency-aware reward design, while FL enables decentralized model training without exposing raw data. Experimental evaluation demonstrates up to 31.6% lower energy consumption and 28.4% latency reduction compared to existing heuristics. Results also show rapid learning convergence within 200 episodes, indicating strong adaptability under changing network and workload conditions. These findings confirm the effectiveness of the proposed framework in improving energy efficiency, latency performance, and scalability for next-generation IoT deployments.

Accurate and efficient lung and colon cancer classification is vital for early detection and treatment planning. Traditional methods require manual effort and expert analysis, leading researchers to explore deep learning models. However, deep learning-based lung and colon cancer classification models face challenges such as generalization, overfitting, gradient vanishing, and hyperparameter tuning. To overcome these challenges, we propose an efficient Deep Attention module and a Residual block-based lung and colon cancer classification Network (DARNet). It comprises three key components such as residual blocks, attention modules, and fully connected layers. Residual blocks (RBs) are utilized to refine the DARNet's ability to learn and capture residual information which allows DARNet to perceive complex patterns and improve accuracy. Attention module (AM) enhances feature extraction and captures useful information in the input data. Finally, to achieve better generalization performance, we employ Bayesian Optimization (BO) to fine-tune the hyperparameters of DARNet. Extensive experimental results indicate that the proposed BO-based DARNet achieved superior performance over competitive models on benchmark lung and colon cancer datasets, with a median accuracy of 98.86% and lower variance.

ABSTRACT An emerging component of smart cities is vehicle‐to‐grid (V2G) technology, which provides a novel approach to scheduling and energy storage. Security threats currently impede V2G's normal operations. V2G security faces two challenges. Current V2G security schemes only consider the static security approach, which is insufficient to handle the problem of advanced persistent attacks and high dynamics in V2G. However, the lack of a unified information modeling technique in present V2G causes problems with security and communication. The aim is to propose a novel technique in task allocation and security analysis based on smart energy management using a machine learning model in V2G architecture. Here, the smart energy management and task allocation are carried out using a hybrid fuel cell model with a deep vector Q‐gradient model. Then, the security analysis of the V2G network is carried out using a multilayer blockchain smart contract‐based federated LSTM model. Experimental analysis is carried out in terms of QoS, energy efficiency, network efficiency, data integrity, and training accuracy. Simulation results are conducted to prove the effectiveness of this proposed method.

Abstract This article examines the impact of utilizing generative artificial intelligence optimizations in automating the content generation process. This instance involves the identification of fraudulent content, which is often characterized by dynamic patterns, in addition to content production. The generated contents are constrained, which limits their dimensionality. In this scenario, duplicated contents are eliminated from the automatic creations. Furthermore, the generated ratios are utilized to discover current patterns with minimized losses and errors, hence enhancing the accuracy of generative contents. Furthermore, while analysing the created patterns, we detect a significant discrepancy in lead durations, resulting in the generation of high scores for relevant information. In order to test the results using generative tools, the adversarial network codes are employed in four scenarios. These scenarios involve generating large patterns and reducing the dynamic patterns with an enhanced accuracy of 97% in the projected model. This is in contrast to the existing approach, which only provides a content accuracy of 77% after detecting fraud.

Abstract The most important and difficult challenge the digital society has recently faced is ensuring data privacy and security in cloud‐based Internet of Things (IoT) technologies. As a result, many researchers believe that the blockchain's Distributed Ledger Technology (DLT) is a good choice for various clever applications. Nevertheless, it encountered constraints and difficulties with elevated computing expenses, temporal demands, operational intricacy, and diminished security. Therefore, the proposed work aims to develop a Decentralized Identifiable Distributed Ledger Technology‐Blockchain (DIDLT‐BC) framework that is intelligent and effective, requiring the least amount of computing complexity to ensure cloud IoT system safety. In this case, the Rabin algorithm produces the digital signature needed to start the transaction. The public and private keys are then created to verify the transactions. The block is then built using the DIDLT model, which includes the block header information, hash code, timestamp, nonce message, and transaction list. The primary purpose of the Blockchain Consent Algorithm (BCA) is to find solutions for numerous unreliable nodes with varying hash values. The novel contribution of this work is to incorporate the operations of Rabin digital data signature generation, DIDLT‐based blockchain construction, and BCA algorithms for ensuring overall data security in IoT networks. With proper digital signature generation, key generation, blockchain construction and validation operations, secured data storage and retrieval are enabled in the cloud‐IoT systems. By using this integrated DIDLT‐BCA model, the security performance of the proposed system is greatly improved with 98% security, less execution time of up to 150 ms, and reduced mining time of up to 0.98 s.

This research examines the influence of digital literacy on preschool children’s school readiness and mental health. The analysis dissects psychological resilience’s role as a mediator among digitally literate-conscious kids. The underlying theory underpinning the literature is a social learning theory, which provides the paradigm lens for effectively accessing and evaluating available digital information. This study measures these proposed assumptions using structural equation modeling techniques. Data collection was carried out in structured questionnaires, and the target population was parents of preschool children. The study used a convenience sampling technique to select a sample of parents based on preschool children under five. The results show that digital literacy among preschoolers is directly and positively related to their school readiness, mental well-being, and resilience. Findings suggest that psychological resilience significantly mediates between children’s digital literacy and school readiness. The findings provide valued insight and further directions for policymakers and educators from developing countries. It offers valuable guidance for teachers and parents of preschool children. Findings will encourage them to allow children to use digital gadgets to build and enhance their understanding of digital information. Preschoolers should also receive implicit and explicit training in the practical and fundamental knowledge of digital technologies for their educational use. In theory, this study contributes to the scientific literature by answering how digital literacy and resilience positively impact children’s school readiness and mental well-being.

Adaptive HCI for education refers to design and development computer interfaces that can dynamically adjust and tailor themselves to individual users’ needs, preferences, and abilities. The goal of...

Beyond 5G-enabled edge computing networking (ECN) will further deploy computing and communication resources to the edge of the networks. Then, edge service demands for Internet of Things (IoT) applications are becoming more and more diverse, while the corresponding routing service capability is limited and not flexible enough to deal with the demands of ECN, which then leads to reducing the inherent routing capability of ECN. It becomes extremely difficult for ECN to support diversified demands and provide diverse IoT applications quickly and flexibly. In this article, we propose a novel and customized deep routing mechanism for IoT applications in ECN, in which the network slicing and deep learning methods are jointly applied and leveraged. First, we design a new ECN architecture that formulates four kinds of network slices to cope with various IoT scenarios, which are eMBB, uRLLC, mMTTC, and backup slices. Second, using these slices, we can customize the ECN environment flexibly, based on which we propose the corresponding routing method for the purpose of fast and efficient service delivery. In particular, the mapping between network slices and the infrastructure is established with the object of maximizing the resource utilization. Then, the routing is designed and customized by using the deep learning model. Lastly, the experimental results show that the deep customized mechanism designed in this article can reduce the average loss rate of the model, decrease the average delay, as well as improve the average resource utilization compared with the existing studies.

In this paper the need of biometric authentication with synthetic data is analyzed for increasing the security of data in each transmission systems. Since more biometric patterns are represented the complexity of recognition changes where low security features are enabled in transmission process. Hence the process of increasing security is carried out with image biometric patterns where synthetic data is created with explainable artificial intelligence technique thereby appropriate decisions are made. Further sample data is generated at each case thereby all changing representations are minimized with increase in original image set values. Moreover the data flows at each identified biometric patterns are increased where partial decisive strategies are followed in proposed approach. Further more complete interpretabilities that are present in captured images or biometric patterns are reduced thus generated data is maximized to all end users. To verify the outcome of proposed approach four scenarios with comparative performance metrics are simulated where from the comparative analysis it is found that the proposed approach is less robust and complex at a rate of 4% and 6% respectively.

Artificial intelligence and machine learning have been transforming the health care industry in many areas such as disease diagnosis with medical imaging, surgical robots, and maximizing hospital efficiency. The Healthcare service market utilizing Artificial Intelligence is expected to reach 45.2 billion U. S. Dollars by 2026 from its current valuation, off $4.9 billion. Diabetic Retinopathy (DR) is a disease that results from complications of type one and Type two diabetes and affects patients' eyes. Diabetic retinopathy, if remains unaddressed, is one of the most serious complications of diabetes, resulting in permanent blindness. The disease has been affecting the lives of 347 million people worldwide. The paper aims to propose a residual network-based deep learning framework for the detection of diabetic retinopathy. The accuracy of our approach is 83% whereas the precision value for checking the absence of DR is 95%.

Abstract In contrast to a conventional virtual machine (VM), a container is a lightweight virtualization technology. Containers are becoming a prominent technology for cloud services because of their portable, scalable, and flexible deployments, especially in the Internet of Things (IoT), smart devices, and fog and edge computing. It is a type of operating system‐level virtualization in which the kernel allows multiple isolated containers to run independently. Container placement (CP) is a nontrivial problem in Container‐as‐a‐Service (CaaS). CP is mapping to a container over virtual machines (VMs) to execute an application. Designing an efficient CP strategy is complex due to several intertwined challenges. These challenges arise from a diverse spectrum of computing resources, like on‐demand and unpredictable fluctuations of IT resources by multiple tenants. In this article, we propose a modified sum‐based container placement algorithm called a multi‐objective optimization‐based container placement algorithm (MSBCPA). In the proposed algorithm, we have considered two metrics: makespan and monetary costs for optimizing available IT resources. We have conducted comprehensive simulation experiments to validate the effectiveness of the proposed algorithm over the CloudSim 4.0 simulator. The proposed optimization algorithm (MSBCPA) aims to minimize the makespan and the execution monetary costs simultaneously. In the simulation, we found that the execution cost and energy consumption cost reduce by 20% to 30% and achieve the best possible cost‐makespan trade‐offs compared to competing algorithms.

Electrocardiogram (ECG) analysis and diagnosis are important auxiliary means for preventing and detecting cardiovascular diseases. Traditional approaches often face challenges due to the sheer volume of data, difficulty in extracting meaningful features, limitations in model complexity, and the requirement for real-time analysis in clinical settings. This paper presents a pioneering approach for automatic ECG diagnosis through the application of a novel Multiscale Deep Neuro-fuzzy Network (MDNFN) structure. The MDNFN is designed to address the complexity of arrhythmia classification by incorporating deep learning and fuzzy logic processing across multiscale feature extraction. To optimize the performance of the MDNFN, an innovative model optimization technique based on the Particle Swarm Optimization (PSO) algorithm is introduced, offering an efficient exploration of the parameter space. Extensive experiments across diverse datasets validate the superior performance of the proposed model compared to existing methods. The MDNFN demonstrates heightened accuracy and robustness, supported by its adaptability to different frequency and time scales inherent in ECG signals. The study establishes the model's efficacy through comprehensive experimentation, providing compelling evidence for its potential application in real-world clinical scenarios.

Medical image analysis plays a crucial role in healthcare systems of Internet of Medical Things (IoMT), aiding in the diagnosis, treatment planning, and monitoring of various diseases. With the increasing adoption of artificial intelligence (AI) techniques in medical image analysis, there is a growing need for transparency and trustworthiness in decision-making. This study explores the application of explainable AI (XAI) in the context of medical image analysis within medical cyber-physical systems (MCPS) to enhance transparency and trustworthiness. To this end, this study proposes an explainable framework that integrates machine learning and knowledge reasoning. The explainability of the model is realized when the framework evolution target feature results and reasoning results are the same and are relatively reliable. However, using these technologies also presents new challenges, including the need to ensure the security and privacy of patient data from IoMT. Therefore, attack detection is an essential aspect of MCPS security. For the MCPS model with only sensor attacks, the necessary and sufficient conditions for detecting attacks are given based on the definition of sparse observability. The corresponding attack detector and state estimator are designed by assuming that some IoMT sensors are under protection. It is expounded that the IoMT sensors under protection play an important role in improving the efficiency of attack detection and state estimation. The experimental results show that the XAI in the context of medical image analysis within MCPS improves the accuracy of lesion classification, effectively removes low-quality medical images, and realizes the explainability of recognition results. This helps doctors understand the logic of the system's decision-making and can choose whether to trust the results based on the explanation given by the framework.

In recent times, there has been a notable rise in the utilization of Internet of Medical Things (IoMT) frameworks particularly those based on edge computing, to enhance remote monitoring in healthcare applications. Most existing models in this field have been developed temperature screening methods using RCNN, face temperature encoder (FTE), and a combination of data from wearable sensors for predicting respiratory rate (RR) and monitoring blood pressure. These methods aim to facilitate remote screening and monitoring of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and COVID-19. However, these models require inadequate computing resources and are not suitable for lightweight environments. We propose a multimodal screening framework that leverages deep learning-inspired data fusion models to enhance screening results. A Variation Encoder (VEN) design proposes to measure skin temperature using Regions of Interest (RoI) identified by YoLo. Subsequently, the multi-data fusion model integrates electronic records features with data from wearable human sensors. To optimize computational efficiency, a data reduction mechanism is added to eliminate unnecessary features. Furthermore, we employ a contingent probability method to estimate distinct feature weights for each cluster, deepening our understanding of variations in thermal and sensory data to assess the prediction of abnormal COVID-19 instances. Simulation results using our lab dataset demonstrate a precision of 95.2%, surpassing state-of-the-art models due to the thoughtful design of the multimodal data-based feature fusion model, weight prediction factor, and feature selection model.

Tongue diagnosis is the kernel method of Traditional Chinese Medicine (TCM), and it has been proved that the condition of the tongue can serve as an indicator of a person's health status. To automatically recognize a person's latent diseases by computer vision technology, getting the tongue segmentation from a picture with high precision has significant importance. However, the precision of tongue segmentation images in most prior methods is not satisfactory, which will inevitably result in misjudging. In this paper, an effective method is proposed for highly precise tongue segmentation, which is combined with an improved U-shaped neural network and an edge refinement post-processing method. The contributions are three-fold. First, a carefully designed data augmentation strategy is imported to prevent the network from over-fitting. Second, an updated U-shaped neural network is designed to segment tongue images with high precision. Third, a post-processing method is imported to refine the edge of the tongue segmentation further. The proposed method achieves competitive performance in almost all experiments on two datasets. Furthermore, the proposed post-processing method can effectively improve all classic neural networks in tongue segmentation, which strongly proves the flexibility and generalization of the proposed method.

The consensus mechanism is crucial to blockchain performance, making it essential to design a mechanism that aligns with the characteristics of the Internet of Things (IoT). This paper focuses on the application of PBFT consensus mechanism in the Internet of things. However, it is found that the current PBFT consensus mechanisms need to address some problems, such as communication overhead, bandwidth occupation and privacy protection. In this paper, we propose a voting sorting clustering mechanism based on group signatures to enhance the PBFT consensus mechanism (IPBFT), ensuring privacy protection between Internet of Things nodes. Finally, the communication efficiency is improved. The voting sorting clustering method reduces the communication probability with the nodes, and decreases the communication overhead and bandwidth occupation. Experimental results show that compared with other mechanisms, the proposed mechanism increases throughput, reduces communication overhead and bandwidth occupation, and alleviates privacy protection problems.

With the gradual increase in market share of electric vehicle (EV), Vehicle to Grid(V2G) has become a new research hotspot in the field of intelligent transportation systems. Its goal is to avoid overloading the power grid due to the simultaneous charging of a large number of electric vehicles. However, when EV is connected to the power grid, V2G will involve a large amount of privacy data exchange. Once these data are leaked, the privacy and security of users will be threatened. Ensuring the secure transmission of user privacy information in V2G is crucial. Therefore, this paper proposes a practical and secure authentication protocol for V2G in intelligent transportation systems. This protocol ensures user login security through three-factor authentication mechanism and then implements authentication based on Chebyshev chaotic maps. Finally, secure communication is carried out through the established key. Security analysis shows that this protocol is secure and can ensure the privacy and security of V2G. Informal security analysis shows that this protocol can meet various security attributes. Functional comparison and performance analysis indicate that the protocol not only has high security but also has low computation and communication overhead.

In the era of large-scale machine learning, largescale clusters are extensively used for data processing jobs. However, the state-of-the-art heuristic-based and Deep Rein-forcement Learning (DRL) based job scheduling mechanisms are facing challenges such as slow training speed and underexploitation of jobs' complex dependencies. We propose MPDA, a Massively Parallel learning and Dependency-Aware scheduling algorithm, consisting of a fast-training mechanism and a novel dependency-aware policy network, GATNetwork, to address these two challenges respectively. The fast-training mechanism is a two-level massively parallel training method that can significantly accelerate the training process and maximally utilize the resources of the cluster. Additionally, its decoupled learning and interacting design enables hybrid-workload training for MPDA, which guarantees the generalization and robustness of MPDA. The GATNetwork exploits the dependencies among stages/jobs using Graph Attention Network (GAT) and Long Short-Term Memory (LSTM) networks to improve the performance of the scheduling policy. The experiments show that MPDA accelerates the training speed by one to two orders of magnitude and achieves better scheduling performance, i.e., lower average job completion time, compared with existing scheduling algorithms.

Summary In order to make networks more adaptable and flexible, software‐defined networking (SDN) is an architecture that abstracts the many, easily distinct layers of a network. By enabling businesses and service providers to react swiftly to shifting business requirements, SDN aims to improve network control. SDN has become an important framework for Internet of Things (IoT) and 5G. Despite recent research endeavors focused on pinpointing constraints within SDN design components, various security attacks persist, including man‐in‐the‐middle attacks, host hijacking, ARP poisoning, and saturation attacks. Overcoming these limitations poses a challenge, necessitating robust security techniques to detect and counteract such attacks in SDN environments. This study is dedicated to developing a method for detecting and mitigating control plane attacks within Software Defined Network Environments utilizing Deep Learning Algorithms. The study presents a deep‐learning‐based approach to identifying malicious hosts within SDN networks, thus thwarting unauthorized access to the controller. Experimental results demonstrate the effectiveness of the proposed model in host classification, exhibiting high accuracy and performance compared to alternative approaches.

Summary One of the practical preferences of cloud service providers is to use specialized physical hosts. In other words, the goal is to place homogeneous virtual machines (VMs) on the physical host according to performance criteria such as energy consumption, resource wastage, and utilization. virtual machine placement (VMP) falls into NP‐hard knapsack problems. To overcome the time complexity, the use of heuristic and metaheuristic methods has attracted the attention of researchers. In this paper, we use an entropy‐based method for VMP for the first time. The proposed method tries to place the VMs on physical machines by considering the type of VMs to minimize entropy. Entropy is a measurable property that is more associated with disorder, randomness, or uncertainty. We use one of the most common entropy criteria called the Gini coefficient. In summary, among the different placement combinations of VMs, those that can minimize the Gini coefficient are preferred. We then solve the multi‐objective problem with the non‐dominated sorting genetic algorithm (NSGA‐III). We also combine this method with differential evolution methods to improve the quality of solutions. Recent research in other engineering fields has shown that combining metaheuristic methods with differential evolution methods increases the rate of convergence toward the optimal solution. The simulation results on the CloudSim simulator, along with statistical analysis, show that the entropy‐based method has a significant improvement over the state‐of‐the‐art methods in terms of significant performance criteria such as utilization, resource wastage, and energy consumption.

Abstract Plant leaf diseases are challenging to categorize due to the complexity of the pattern variations and the high degrees of inter‐class similarity. Plant ailments harm food quality and production. To ensure the quality and quantity of harvests, it is essential to protect plants from disease. Detection of diseases at an early stage is the main and the most complex task for farmers due to common morphological properties like colour, shape, texture, and edges. In this study, a Hybrid Deep Learning model named Hybrid‐Convolutional Support Machine (H‐CSM) based on ‘Support Vector Machine (SVM)’, ‘Convolutional Neural Network (CNN)’ and ‘Convolutional Block Attention Module (CBAM)’ is proposed for the early diagnosis and classification of leaf diseases in plants leaf. The suggested model can initially identify different plant leaf illnesses, although it is not constrained to these. A database of pictures of plant leaves is used to test the suggested method based on different evaluation parameters. The results were highly promising, with an accuracy of up to 98.72% which has been increased by applying better learning methods. Farmers can quickly identify 36 common diseases with a little instruction for 14 plant categories, enabling them to take prompt preventive measures using the proposed method.

Identifying objects in aircraft monitoring systems poses significant challenges due to the presence of extreme loading conditions. Despite the presence of several sensor units, the transmission of precise data to multiple data units is hindered by an increase in time intervals. Therefore, the suggested methodology is specifically developed for the purpose of generating digital replicas for aeronautical applications, wherein an aero transfer function is correlated with the digital twins. Mapping functions are utilized in the monitoring of diverse parameters that are associated with the identification of objects inside data transmission networks, with the aim of minimizing uncertainty. The suggested system model is enhanced by incorporating analytical representations and deep learning methods, resulting in the provision of zero point twin functionalities. The present study investigates the aforementioned integrated procedure through the analysis of four different situations. In these settings, an aero communication tool box is employed to transform the device configuration into simulation outputs. The results obtained from the comparison of these scenarios reveal that the projected model significantly enhances the maintenance period while minimizing data errors.

Sensor ontology is the kernel technique of the Intelligent Sensor System, which provides a structured framework to organize and interpret the knowledge of the Internet of Things (IoT). However, the ontology heterogeneity issue hampers the communication of sensor ontologies. Sensor Ontology Matching (SOM) can find semantically identical entities between two ontologies, which is an effective method to address this issue. However, due to their complicated semantic relationships, it is a challenge to construct an effective Similarity Feature (SF) to distinguish the heterogeneous sensor entities. Although Evolutionary Algorithms (EAs) based matching techniques have shown their effectiveness in the ontology matching field, they suffer from drawbacks such as high computational complexity and expert-dependent solution evaluation. To overcome these drawbacks, this paper proposes a novel Light Genetic Programming (L-GP) to automatically construct SF for SOM. First, a simplified evolutionary mechanism is designed to improve the efficiency of the SOM process. Second, a novel fitness function based on the approximate evaluation metric is introduced to automatically guide the search direction of L-GP. Lastly, a two-stage tournament selection operator is presented to balance the quality and complexity of the solutions, improving the accuracy of the SOM results. The experiment uses ten pairs of real-world SOM tasks to test the performance of L-GP, and the experimental results show that L-GP significantly outperforms state-of-the-art matching techniques.

Common spelling checks in the current digital era have trouble reading languages such as Bengali, which employ English letters differently. In response, we have created a better Bidirectional Encoder Representations from Transformers (BERT)–based spell checker that makes use of a convolutional neural network (CNN) sub-model (Semantic Network). Our novelty, which we term progressive stacking , concentrates on improving BERT model training while expediting the corrective process. We discovered that, when comparing shallow and deep versions, deeper models could require less training time. There is potential for improving spelling corrections with this technique. We categorized and utilized as a test set a 6,300-word dataset that Nayadiganta Mohiuddin supplied, some of which had spelling errors. The most popular terms were the same as those found in the Prothom-Alo artificial error dataset.

World Health Organization (WHO) has identified depression as a significant contributor to global disability, creating a complex thread in both public and private health. Electroencephalogram (EEG) can accurately reveal the working condition of the human brain, and it is considered an effective tool for analyzing depression. However, manual depression detection using EEG signals is time-consuming and tedious. To address this, fully automatic depression identification models have been designed using EEG signals to assist clinicians. In this study, we propose a novel automated deep learning-based depression detection system using EEG signals. The required EEG signals are gathered from publicly available databases, and three sets of features are extracted from the original EEG signal. Firstly, spectrogram images are generated from the original EEG signal, and 3-dimensional Convolutional Neural Networks (3D-CNN) are employed to extract deep features. Secondly, 1D-CNN is utilized to extract deep features from the collected EEG signal. Thirdly, spectral features are extracted from the collected EEG signal. Following feature extraction, optimal weights are fused with the three sets of features. The selection of optimal features is carried out using the developed Chaotic Owl Invasive Weed Search Optimization (COIWSO) algorithm. Subsequently, the fused features undergo analysis using the Self-Attention-based Gated Densenet (SA-GDensenet) for depression detection. The parameters within the detection network are optimized with the assistance of the same COIWSO. Finally, implementation results are analyzed in comparison to existing detection models. The experimentation findings of the developed model show 96% of accuracy. Throughout the empirical result, the findings of the developed model show better performance than traditional approaches.

As a component of the Intelligent Transportation System (ITS), Internet of Vehicles (IoV) is becoming increasingly important in the management and construction of urban transportation as it can provide users with a range of applications related to traffic accident warnings, entertainment information, collaborative driving and real-time road information through communication devices on vehicles. However, with the increasing variety of services in the IoV, the growing demand for user traffic and the advances in Unmanned Aerial Vehicle (UAV) technology, UAV is introduced into the IoV as a solution, which can relieve the pressure on the communication infrastructure in the network, provide emergency communication services and improve the performance of network services. Due to the openness of IoV and the high-speed movement of vehicles, authentication and privacy issues are among the most pressing issues in IoV. Therefore, the paper proposes a secure and effective authentication protocol for UAV-assisted IoV. The protocol utilises elliptic curve cryptography to assure the security of the authentication. The protocol undergoes proof of security, Burrows-Abadi-Needham (BAN) logic analysis and informal security analysis to ensure secure and mutual authentication, and have a good resistance to known attacks. Furthermore, performance analysis and comparison are conducted to evaluate the efficiency of our protocol. The results indicate that our protocol has superior advantages in overhead.

In this paper, a realization technique is designed with a unique analytical model for transmitting multimedia data to appropriate end users. Transmission of multimedia data to all end users through a variety of visualization methods is the foundation of future computer systems. Yet, highly limited system resources prevent the updating of the methods used to manage multimedia data. Hence, a high-end visualization technique where uncertainties are eliminated is required for the visualization process with a multimedia system. As a result, the suggested system incorporates a clustering technique utilizing an analytical framework to ensure a high degree of transmission for all multimedia data. The technical contribution of the proposed method depends on a multimedia visualization process that takes place with high security features by including necessary parametric relationships such as occurrence of jitter, data density points, time period, multimedia storage, data smoothness and distance. For the established parametric relationship the validation methodology is integrated with a hierarchical clustering algorithm, thereby transmitting every clustered data with high security feature, thereby the examined outcomes under five scenarios proves that data security which is represented by simulation outcomes is improved to 88% as compared to the existing approach.

With the rapid development of the new edge discipline sports biomechanics, modelling and simulation of human motion as one of the cutting-edge research topics in sports biomechanics is receiving more and more attention. Based on the establishment of the three-dimensional simulation system model of human knee joint and the analysis of buckling motion, this paper uses the three-dimensional image registration knee joint motion analysis method to carry out the research on knee flexion movement to solve the knee joint injury problem of sprinters. The results showed that medial collateral ligament injury and meniscus injury were more common in all types of knee joint injuries, accounting for 24% respectively 7% and 22.4%. The incidence of lateral collateral ligament injury was relatively small, accounting for only 5.6%. And the research shows that the foot strength curve recognition method based on biomechanics sprinters knee joint simulation model is an advanced and complete strategy to reduce sprinters knee joint injury, and it can achieve better results in sprinters knee joint nursing.

Location estimation is one of the critical requirement for developing smart environment products. Due to huge utilization and accessibility of WiFi infrastructure facility in indoor environments, researchers widely studied this technology to locate users accurately to provide several services instantly. In this research work, a hybrid algorithm namely fuzzy decision tree (FDT) with evolutionary fuzzy clustering methods is adopted for optimal user localization in a closed environment. Here we consider the wireless signal strengths received from the smart phones as predictors and the location of the user as the classification label. The required data for the current research is collected from the physical facility available at an office location in USA. The classification results obtained are promising enough to show that the evolutionary clustering approaches provide good fuzzy clusters for FDT induction with better accuracy.

With the growing emergence of the Internet connectivity in this era of Gen Z, several IoT solutions have come into existence for exchanging large scale of data securely, backed up by their own unique cloud service providers (CSPs). It has, therefore, generated the need for customers to decide the IoT cloud platform to suit their vivid and volatile demands in terms of attributes like security and privacy of data, performance efficiency, cost optimization, and other individualistic properties as per unique user. In spite of the existence of many software solutions for this decision‐making problem, they have been proved to be inadequate considering the distinct attributes unique to individual user. This paper proposes a framework to represent the selection of IoT cloud platform as a MCDM problem, thereby providing a solution of optimal efficacy with a particular focus in user‐specific priorities to create a unique solution for volatile user demands and agile market trends and needs using optimized distance‐based approach (DBA) aided by Fuzzy Set Theory.