Network-in-box (NIB) architectures are designed to improve communication information sharing in an ad hoc manner with limited infrastructure support. These architectures are interoperable, and hence it is capable of providing services based on sixth-generation (6G) communication technologies. Resource allocation for massive machine-type communications in the 6G platform aided for NIB architectures is challenging due to the terahertz and high throughput features. Then it comes to resource allocation, and the most difficult part is figuring out what capacity is to check the availability of work on a project is difficult to determine. In this manuscript, the attuned slicing-dependent concurrent resource allocation (AS-CRA) method is formulated for improving the service reliability of the 6G users in NIB architecture. Learning assisted slicing and concurrent resource allocation process is jointly exploited in this proposed method to improve the users’ service reliability. The output of the learning process is useful in classifying resource allocation and user mapping irrespective of the limited NIB infrastructure support. Virtualization and concurrency in resource allocation are balanced based on the user capacity and network blocking rate to achieve optimality in service responses. The performance of the proposed resource allocation method is verified using simulations, and the performance is verified using the metrics capacity 89.726%, latency 81.32%, resource utilization rate 0.963%, response ratio 92.309, and blocking rate 0.047%.

Intelligent Transportation security requires cooperative credentials for sharing navigation and communication data between the vehicles. However due to the dynamic environment, communication is interrupted by the adversaries, resulting in non-privacy issues. This article introduces an Agreement-induced Data Verification Model (ADVM) for securing vehicular communication against adversaries. The connected vehicles in a grid communicate with each other based on direct and indirect recommendation. This recommendation is based on mutual identity sharing between the vehicles for masked information exchange. Non-replicated and recommendation based verifications are performed using the vector classification learning. In this learning process, the credential validity and communication tolerance amid adversaries are augmented. The constraint-failing vehicles are disconnected from the communication grid, preventing its insecure impact over the communication. The proposed model’s performance is verified using false rate, success ratio, processing time, complexity, and recommendation ratio. For the different vehicles, the proposed model achieves 9.69% less false rate, 10.3% success ratio, 10.49% less processing time, 10.3% less complexity, and 12.87% high recommendation ratio.

Intelligent Transportation Systems (ITSs) rely on environmental information for communication, navigation, and driving assistance. This technology-based interconnected vehicular network provides a wide range of support for heterogeneous real-time applications. However, the network relies on secure and robust information for providing driving application support, which is defaced at times due to fraudulent devices and information. In this article, Graph-Transient Security Method (GTSM) is proposed for improving the cybersecurity features of ITS. The proposed method uses a trusted graph model for identifying reliable infrastructure and neighboring units in communication. The units are verified based on the information exchanged for identifying the frauds through the mutual trust sharing paradigm. In the mutual trust sharing process, fastened classifier learning is employed for assessing the trust of the communicating vehicles and infrastructure units. Based on the output of the classifier learning, a connected trust-based transportation network is constructed. This helps to replace, transform the connected scenario depending on the cybersecurity requirement. The proposed method improves sharing rate by 11.4% and detection ratio by 7.84% and reduces down-time and latency by 11.53% and 10.7% in different sharing intervals.

Intelligent Transportation Systems provide ubiquitous communication for the driving users through heterogeneous interconnections. The heterogeneous interconnections are required for uninterrupted resource sharing. Spontaneous resource availability due to vehicle speed and infrastructure connectivity disturb prompt service utilization. In this manuscript, a Permissible Service Selection and Allocation (PSSA) method is proposed to address spontaneous issues in vehicular communication and connection. This method considers vehicle displacement and minimum interconnection factors in accessing a cloud service. Both factors and their balancing impact are analyzed throughout the vehicle’s service requesting interval. In this process, random forest learning is induced to identify the balancing factors’ adjustments. The service access is probed through the active infrastructure based on the balancing factor. The ordering process of the learning intervals provides ease of service selection and allocation. In this process, reallocation is not preferred due to the random displacement of the vehicles. Therefore, the interval dropouts are reduced in both handoff and non-handoff communication scenarios. Further metrics such as service ratio, delay, and connectivity are used in validating the proposed method’s performance.

During the epidemic of COVID-19, Computed Tomography (CT) is used to help in the diagnosis of patients. Most current studies on this subject appear to be focused on broad and private annotated data which are impractical to access from an organization, particularly while radiologists are fighting the coronavirus disease. It is challenging to equate these techniques since they were built on separate datasets, educated on various training sets, and tested using different metrics. In this research, a deep learning semantic segmentation architecture for COVID-19 lesions detection in limited chest CT datasets will be presented. The proposed model architecture consists of the encoder and the decoder components. The encoder component contains three layers of convolution and pooling, while the decoder contains three layers of deconvolutional and upsampling. The dataset consists of 20 CT scans of lungs belongs to 20 patients from two sources of data. The total number of images in the dataset is 3520 CT scans with its labelled images. The dataset is split into 70% for the training phase and 30% for the testing phase. Images of the dataset are passed through the pre-processing phase to be resized and normalized. Five experimental trials are conducted through the research with different images selected for the training and the testing phases for every trial. The proposed model achieves 0.993 in the global accuracy, and 0.987, 0.799, 0.874 for weighted IoU, mean IoU and mean BF score accordingly. The performance metrics such as precision, sensitivity, specificity and F1 score strengthens the obtained results. The proposed model outperforms the related works which use the same dataset in terms of performance and IoU metrics.

The sixth-generation (6G) communication technology provides a high level of interoperability through terahertz data transfer and latency-less service sharing. Due to its interoperable nature, the integration of heterogeneous networks, such as the Internet of Things (IoT) and cloud radio access networks (CRANs), is performed at ease. This integration is managed using software-defined networks (SDNs) for managing the Quality of Service (QoS) experience of the users, irrespective of the application. This manuscript proposes the service virtualization and flow management framework (SVFMF) for the reliable utilization of resources in the 6G-cloud environment. The imbalance in a service request and response due to overloaded and idle virtual resources is addressed in this framework. For this purpose, this framework endorses service virtualization and user allocation modules for mitigating the drawbacks of imbalanced service allocations. Linear decision making of the service virtualization process helps to reduce the computation and service discovery by identifying overloaded services and performing a reallocation. The purpose of user allocation is to distribute the service requests to the idle service providers to reduce the prolonged wait time of the increasing user requests. The performance of the proposed framework is verified using experimental analyses, for the metrics service discovery and computation time, service failure ratio, and flows. The reliability of SVFMF is proved by varying the density of users, virtual machines, service requests, and user allocation per virtual machine, respectively.

Smart connected vehicles are becoming standardized with the incorporation of information and communication technology. Connected vehicles are employed for surveillance and management of road traffic, navigation assistance, etc., by inheriting different analytical and communication techniques. With the Social Internet of Things (SIoT), interogrowthperable and shared computing models are adopted by the connected vehicles to perform application-specific decisions. By considering the need for computation models in smart connected vehicle networks, this article introduces a shared adaptive computing model (SACM) for improving the reliability of vehicle control and traffic management. This computing model considers multiple features of the in-range vehicles in detecting traffic and providing guided solutions for reliable routing in a smart city environment. This computing model is aided by the conditional support vector machine (SVM) for differentiating the complexity of multiflow data processing from the neighboring vehicles. The physical and connectivity-based factors from the smart vehicle using SVM classification learning improve the decision reliability and reduce the computing time and complexity.

Internet of Things assisted healthcare services grants reliable clinical diagnosis and analysis by exploiting heterogeneous communication and infrastructure elements. Communication is enabled through point-to-point or cluster-to-point between the users and the diagnosis center. In this process, the complication is the resource sharing and diagnosis swiftness invalidating multiple resources. IoT's open and ubiquitous nature results in proactive resource sharing, resulting in delayed transmissions. This manuscript introduces the Redemptive Resource Sharing and Allocation (R2SA) scheme to address this issue. The available health data is accumulated on a first-come-first-serve basis, and the transmitting infrastructure is selected. In this process, the data-to-capacity of the available infrastructure is identified for non-redemptive resource allocation. The extremity of the capacity and unavailability of the resource is then analyzed for parallel processing and allocation. Therefore, the data accumulation and exchange rely on concurrent sharing and resource allocation processes, deferring a better accumulation ratio. The concurrent redemptive selection and sharing reduces transmission delay, improves resource allocation, and reduces transmission complexity. The entire process is managed for transfer learning, data-to-capacity validation, and concurrent recommendation. The first validation knowledge base remains the same/shared for different data accumulation and sharing intervals.

Internet of Things (IoT) assisted healthcare systems are designed for providing ubiquitous access and recommendations for personal and distributed electronic health services. The heterogeneous IoT platform assists healthcare services with reliable data management through dedicated computing devices. Healthcare services' reliability depends upon the efficient handling of heterogeneous data streams due to variations and errors. A Proportionate Data Analytics (PDA) for heterogeneous healthcare data stream processing is introduced in this manuscript. This analytics method differentiates the data streams based on variations and errors for satisfying the service responses. The classification is streamlined using linear regression for segregating errors from the variations in different time intervals. The time intervals are differentiated recurrently after detecting errors in the stream's variation. This process of differentiation and classification retains a high response ratio for healthcare services through spontaneous regressions. The proposed method's performance is analyzed using the metrics accuracy, identification ratio, delivery, variation factor, and processing time.

Vehicular Ad-Hoc Networks (VANETs) provide roadside communication for improving the ease of driving user information exchange. It interconnects hierarchical infrastructure units and other vehicles for real-time traffic and vehicle management applications. The growth of vehicle and information density requires concord information exchange for application responses. In this article, Vehicle-Consensus Routing Management Scheme (VCRMS) is proposed for achieving fair roadside assistance for driving users. The proposed scheme exploits the surrounding vehicle information for infrastructure selection and traffic management. The infrastructure and vehicle information are analyzed for their similarity using deep learning for extracting monotonous decisions. The current and previous decisions are used for succeeding in information selection and traffic information retrieval. This prevents unnecessary data from being congesting the traffic and vehicle management application during driver assistance. The performance shows that the proposed scheme achieves 10.7% high application response, 15.9% less information delay, and 10.7% less traffic for different vehicle velocities.

Cooperative intelligent and autonomous transportation systems rely on intelligent sensing, computing, and actuating technologies for unmanned freight and public movements. The information gained from neighbors and communication infrastructures provides efficient actuation for safe and sustained transportation. This article resolves traffic data management congestion using sixth-generation (6G) communication and computing techniques. Terahertz and machine-type communications are exploited for swift information exchange, bypassing the congestion effects. Congestion occurs when demand for road space exceeds supply. This proposal incorporates prediction-based learning to compute the feasibility of handling traffic information and cooperative intelligent transportation. This model is named Congestion-aware Pre-predictive Data Allocation (CPPDA). The traffic flows causing congestion in the data exchange process are predicted for re-allocation and independent channel utilization. In this learning, the pre-predicted instances are updated with the actual identified utilization-to-congestion rate. Therefore, the congestion-causing channels for sensing are identified with ease, reducing the outage. The outage is examined for a basic inter-vehicle data link. Through the optimal allocation of channels for actuation, cloud-aided resources are utilized to a maximum level, leveraging infrastructure support. In addition to an outage of 10.83%, the response time of 14.75%, congestion factor of 8.2%, computational overhead of 6.4%, and information gain factors of 6.86% are analyzed through a comparative study.

Intelligent Transportation Systems (ITS) is a smart-transportation system for road-side assistance and data exchange support by integrating cloud and wireless networks. ITS facilitates vehicle-to-vehicle and vehicle-to-anything (V2X) data exchanges for satisfying user demands. The rate of big data granting to the vehicular users is interrupted by the fundamental attributes such as mobility and link instability of the vehicles. To address the issues in vehicular data exchange big data, this article introduces displacement-aware service endowment scheme with the benefits of data offloading. Displacement-aware big data endowment ensures responsive availability of vehicle request information despite unfavorable location and density factors. The time congruency in V2V and V2X data exchanges are adopted for minimizing data exchange dropouts. In the data offloading phase, extraneous information and big data responses are detained based on data exchange relevance to improve congestion free big data endowment. The distinct methods work in a co-operative manner to improve big data quality of fast configuring smart vehicles to provide reliable big data in smart city environments.

introduction Share on Guest Editorial Introduction for the Special Section on Deep Learning Algorithms and Systems for Enhancing Security in Cloud Services Editors: Gunasekaran Manogaran Howard University, Washington D.C., USA Howard University, Washington D.C., USAView Profile , Hassan Qudrat-Ullah York University, Toronto, Canada York University, Toronto, CanadaView Profile , Qin Xin University of the Faroe Islands, Faroe Islands University of the Faroe Islands, Faroe IslandsView Profile , Latifur Khan The University of Texas at Dallas, Texas, USA The University of Texas at Dallas, Texas, USAView Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 22Issue 2May 2022 Article No.: 39epp 1–5https://doi.org/10.1145/3516806Online:14 May 2022Publication History 0citation49DownloadsMetricsTotal Citations0Total Downloads49Last 12 Months49Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The Internet of Vehicles (IoV) communication platform provides seamless information exchange facilities in a dynamic mobile city environment. Heterogeneous communication is a common medium for information exchange through autonomous resources distributed and accessed using infrastructure units. Cyber-security is a primary concern in accessing autonomous information from the distributed resources due to anonymity and different types of targeted adversaries. This article proposes token-based authorization and authentication (TAA) for securing IoV communications. The proposed method relies on blockchain technology and random forest learning for authorization and key management for authentication, respectively. In this process, frequent change in tokens and key update features are restricted in a view to maximize the seamlessness in information exchange. Authentication is preceded by knowledge of the data classification without errors to prevent additional overhead. Blockchain-based authorization helps to update specific fields of the tokens to retain the communication ratio by reducing vehicle-to-vehicle losses. The performance of the proposed method is assessed using appropriate simulations for these metrics by varying vehicle density, error rate, and classification sets.

Resource allocation and offloading in green Internet of Things (IoT) relies on the multi-level heterogeneous platforms. The energy expenses of the platform determine the reliability of green IoT based services and applications. This manuscript introduces a decisive energy management scheme for optimal resource allocation and offloading along with energy constraints. This scheme handles both the allocation and energy-cost in a balanced manner through deterministic task offloading. In particular, resource allocation solution for non-delay tolerant green IoT applications is focused by confining the failures of discrete tasks through neural learning. The dropout process augmented with the learning process improves the feasible conditions for resource handling and task offloading among the active IoT service providers. Through extensive simulations the performance of the proposed scheme is analyzed and energy consumption, failure rate, processing, and completion time metrics are used for a comparative study. Further, the optimal utilization and on-demand dissipation of such stored resources help to improve the sustainability of green power and communication technologies in the smart city environment.

Block chain provides an innovative solution to information storage, transaction execution, security, and trust building in an open environment. The block chain is technological progress for cyber security and cryptography, with efficiency-related cases varying in smart grids, smart contracts, over the IoT, etc. The movement to exchange data on a server has massively increased with the introduction of the Internet of Things. Hence, in this research, Splitting of proxy re-encryption method (Split-PRE) has been suggested based on the IoT to improve security and privacy in a private block chain. This study proposes a block chain-based proxy re-encryption program to resolve both the trust and scalability problems and to simplify the transactions. After encryption, the system saves the Internet of Things data in a distributed cloud. The framework offers dynamic, smart contracts between the sensor and the device user without the intervention of a trustworthy third party to exchange the captured IoT data. It uses an efficient proxy re-encryption system, which provides the owner and the person existing in the smart contract to see the data. The experimental outcomes show that the proposed approach enhances the efficiency, security, privacy, and feasibility of the system when compared to other existing methods.

Industrial Internet of Things is focused to improve the performance of smart factories through automation and scalable functions. IoT paradigm, information and communication technology, and intelligent computing are assimilated as a single entity for industrial automation, optimization, sharing and security, and scalability. In a view of the security requirement in smart industry data sharing through IoT, this article introduces a blockchain-assisted secure data sharing (BSDS) model. This model is responsible for administering inbound and outbound security in data acquisition and dissemination. The inbound acquisition is first classified using recurrent learning to identify adverse sequences in data dissemination. In the outbound security measure, end-to-end authentication based on the blockchain information of reputation and sequence differentiation is engaged. The blockchain paradigm controls the data gathering and dissemination instances through the classification and integrity verification in both the industry and processing terminals. For this purpose, the functions of the blockchain are riven for data gathering and monitoring in the smart industry whereas integrity and sequence verification is performed by the nonmining blockchain terminal in the processing environment. The integrated security measures are capable of maximizing the response rate by confining false alarm progression, failure rate, and time delay. Statistical analysis shows that the BSDS achieves a 5.67% high response rate and reduces the failure rate by 2.14%. Further, it achieves 3.12%, maximizes response rate by 6.63%, and reduces delay by 11.91%, respectively.

Smart or electronic healthcare is undergoing rapid change from the traditional specialist and hospital-centered style to a disseminated patient-centered using Internet of Things (IoT). Presently, 4G and other advanced communication standards are utilized in healthcare for intelligent healthcare services and applications. Traffic handling is an essential feature for the flexible interoperability of the internet of things (IoT) with other heterogeneous communication networks. Efficient traffic handling controls latency and communication failures due to random access and collision in cellular network overlay IoT. It is challenging for existing communication technology to achieve the necessities of time-sensitive and very dynamic healthcare applications of the future. In this manuscript, adaptive eNB selection with traffic scheduling (AeS-TS) is proposed to improve the efficiency of IoT-long term evolution (LTE) networks. AeS-Tsworks in two phases: adaptive eNB selection and gateway traffic scheduling. In eNB selection, traffic-aware radio infrastructure selection with the offloading feature is presented. eNB selection is preceded by using a preference function to improve the acceptance rate of incoming IoT traffic and minimize transmission loss. In the traffic scheduling phase, sequential and level-based slot transmission is adapted to improve traffic forwarding quality. The slots are selected by analyzing the error in time function using the recurrent learning process.

Cloud storage is a popular method of storing and sharing data. Numerous cloud service providers offer storage as a service, while others focus on sharing as a service. Many service providers focus their services on collaboration and integration with multiple tools to help with inline commenting and workflow. In recent days, working from home and new normal has attracted secure sharing services for strict access control. Data is new gold there for having the correct type of access control that satisfies the organization’s existing policies is a crucial feature. Also, the demand for extensive data sharing is increasing day by day. December 2020’s cyber-attack on SolarWinds & FireEye is the primary security service provider in the U.S. and worldwide. Organizations are more concerned about their data at rest or in motion. To meet current industry needs, we have demonstrated a secure file sharing framework with a three-prong security strategy by employing next-generation cryptography coupled with proxy re-encryption and splitting data at bit level on multiple heterogeneous clouds. This paper aims to present the idea of split-protocol as a protection technique for security, integrity, and availability of data stored on the public cloud. We have used three encryption techniques for this experiment, namely AES 128-, AES, 256, and RLP, and compared their overheads and timings with different file sizes and types. Implementation and results indicate the feasibility of the proposed architecture without client involvement.

Diabetic retinopathy (DR) is an optical syndrome infecting the eyes’ vision by impairing the retinal blood vessels. Early misdetection of impairment results in hemorrhage, a state in which retinal bleeding occurs. Therefore, initial detection of such bleeding in the retina is identified using intelligent computing and clinical analysis. This analysis helps to improve the precision of detection and requires complex-less time and processing instances. In this article, the regression model for retina feature filtering (RM-FF) is introduced to improve the accuracy of detecting hemorrhages. In this filtering, the complex image is simplified into smaller blocks for classification and conditional verification. Based on conditional verification, the training set is updated recursively to improve the specificity and sensitivity detection process. Using a differential dataset, the proposed detection method assessed using the metrics true positive rate, accuracy, sensitivity, and specificity.

Industry 4.0 is a promising evolution in the field of smart farming by improving the productivity and reducing human intervention to modernize agriculture. This smart paradigm incorporates different levels of the automation from cropping to production yield through sophisticated techniques. Different intelligent computing techniques and communication technologies are augmented with the industry paradigm for improving the efficiency of agriculture systems. This letter introduces information scheduling and optimization framework (ISOF) for optimizing the communication and information layer process in industry 4.0 architecture. Information scheduling and classification of agriculture information are optimized through this framework for reducing process latency and stagnancy. The control flexibility of a smart farm is determined using the latency and stagnancy at the end of yields. The classification part segregates information based on processing and completion time to reduce backlogs through offloading process. The advantage of this framework is that it inherits the advantages of Internet of Things (IoT) and edge computing (EC) technologies with interoperable feature to aid information processing, information classification, offloading, and periodic updates. The performance of the proposed framework is tested in a corn farm and some common metrics, such as delayed information, processing time, audit data, and information distribution, are analyzed for proving the reliability of the framework.

Resource allocation and management in an Internet-of-Things (IoT) paradigm requires precise request and response processing irrespective of its scalability support. Unpredictable traffic patterns and user density demands reliable offloading for handling user request traffic and service response. Considering the need for large-scale IoT in an account of its interoperability and heterogeneous support, this manuscript introduces a response-aware traffic offloading scheme (RTOS) for delay-sensitive user requests. This offloading scheme is supported by a multivariate spline regression machine learning model for classifying traffic for reducing the failure rate. The splines are adaptive based on the classified traffic for performing independent and shared offloading. The computation process for determining the offloading model is inherited from the cyber-physical system (CPS) coupled with the IoT-Cloud architecture. The information from the knowledge base and event logs are exploited for decision making in employing the offloading method for the classified traffic. The simulation analysis of this scheme shows that it is effective in improving the request processing ratio and reducing processing, response time, and delay. The simulation is performed for the varying user density and traffic flows.

No abstract available.

research-article Share on Introduction to the Special Issue on Deep Structured Learning for Natural Language Processing Authors: Gunasekaran Manogaran Big Data Scientist, University of California, Davis, USA Big Data Scientist, University of California, Davis, USAView Profile , Hassan Qudrat-Ullah Professor of Decision Sciences, School of Administrative Studies, York University, Toronto, Canada Professor of Decision Sciences, School of Administrative Studies, York University, Toronto, CanadaView Profile , Qin Xin Full Professor of Computer Science, Faculty of Science and Technology, University of the Faroe Islands, Faroe Islands. Full Professor of Computer Science, Faculty of Science and Technology, University of the Faroe Islands, Faroe Islands.View Profile Authors Info & Claims ACM Transactions on Asian and Low-Resource Language Information ProcessingVolume 20Issue 3May 2021 Article No.: 37epp 1–3https://doi.org/10.1145/3474087Online:01 September 2021Publication History 0citation38DownloadsMetricsTotal Citations0Total Downloads38Last 12 Months38Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

introduction Introduction to the Special Issue on Deep Structured Learning for Natural Language Processing, Part 3 Share on Editors: Gunasekaran Manogaran View Profile , Hassan Qudrat-Ullah View Profile , Qin Xin View Profile Authors Info & Claims ACM Transactions on Asian and Low-Resource Language Information ProcessingVolume 20Issue 5September 2021 Article No.: 72epp 1–3https://doi.org/10.1145/3476464Published:31 August 2021 0citation11DownloadsMetricsTotal Citations0Total Downloads11Last 12 Months11Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Online social networks (OSNs) have become so popular that it has changed the Internet to a more collaborative environment. Now, a third of the world's population participates in OSNs, forming communities, and producing and consuming media in different ways. The recent boom of artificial intelligence technologies provides new opportunities to help improve the processing and mining of social data. In this article, an algorithm that can detect communities in the OSNs using the concepts of granular computing in rough sets is proposed. In this information model, a social network as a rough set granular social network (RGSN) is modeled. A new community detection algorithm named granular-based community detection (GBCD) is implemented. This article also defines and uses two measures, namely, a granular community factor and an object community factor. The proposed algorithm is evaluated on four real-world data sets as well as computer-generated data sets. The model is compared with other state-of-the-art community detection algorithms for the values of modularity, normalized mutual information (NMI), Omega index, accuracy, specificity, sensitivity, and F1-measure. The cumulative performance of the GBCD algorithm is found to be 3.99, which outperforms other state-of-the-art community detection algorithms.

Big data analytics and processing require complex architectures and sophisticated techniques for extracting useful information from the accumulated information. Visualizing the extracted data for real-time solutions is demanding in accordance with the semantics and the classification employed by the processing models. This article introduces fuzzy-optimized data management (FDM) technique for classifying and improving coalition of accumulated information based semantics and constraints. The dependency of the information is classified on the basis of the relationships modeled between the data based on the attributes. This technique segregates the considered attributes based on similarity index boundaries to process complex data in a controlled time. The performance of the proposed FDM is analyzed using a real-time weather forecast dataset consisting of sensor data (observed) and image data (captured). With this dataset, the functions of FDM such as input semantics analytics and classification based on similarity are performed. The metrics classification and processing time and similarity index are analyzed for the varying data sizes, classification instances, and dataset records. The proposed FDM is found to achieve 36.28% less processing time for varying classification instances, and 12.57% high similarity index.

The advent of 5G and beyond networks paves the way for significant innovations in industrial applications and services. It does not only offer broadband services through mobile networks. Indeed it unleashes application layer innovation. Potentially beneficial to millions of users. Though it looks attractive, it possesses very complex network functions proven to be well defined and consistent. This special issue is hosted to address three major drawbacks in 6G-enabled NIB industrial applications. The first is to implement a portable solution that constitutes a core network product that can be easily implemented across small hardware platforms. The second is to find cost-effective solutions, with each core supporting a small network. Finally, finding new innovative paradigms for 6G enabled NIB that can operate in very challenging environments in a qualitative, flexible, and scalable manner.

The intelligent industrial environment developed with the support of the new generation network cyber-physical system (CPS) can realize the high concentration of information resources. In order to carry out the analysis and quantification for the reliability of CPS, an automatic online assessment method for the reliability of CPS is proposed in this article. It builds an evaluation framework based on the knowledge of machine learning, designs an online rank algorithm, and realizes the online analysis and assessment in real time. The preventive measures can be taken timely, and the system can operate normally and continuously. Its reliability has been greatly improved. Based on the credibility of the Internet and the Internet of Things, a typical CPS control model based on the spatiotemporal correlation detection model is analyzed to determine the comprehensive reliability model analysis strategy. Based on this, in this article, we propose a CPS trusted robust intelligent control strategy and a trusted intelligent prediction model. Through the simulation analysis, the influential factors of attack defense resources and the dynamic process of distributed cooperative control are obtained. CPS defenders in the distributed cooperative control mode can be guided and select the appropriate defense resource input according to the CPS attack and defense environment.

Internet of Things (IoT) has gained significant importance due to its flexibility in integrating communication technologies and smart devices for the ease of service provisioning. IoT services rely on a heterogeneous cloud network for serving user demands ubiquitously. The service data management is a complex task in this heterogeneous environment due to random access and service compositions. In this article, a machine learning aided information management scheme is proposed for handling data to ensure uninterrupted user request service. The neural learning process gains control over service attributes and data response to abruptly assign resources to the incoming requests in the data plane. The learning process operates in the data plane, where requests and responses for service are instantaneous. This facilitates the smoothing of the learning process to decide upon the possible resources and more precise service delivery without duplication. The proposed data management scheme ensures less replication and minimum service response time irrespective of the request and device density.

Internet of Medical Things (IoMT) platform serves as an interoperable medium for healthcare applications by connecting wearable sensors, end-users, and clinical diagnosis centers. This interoperable medium provides solutions for disease diagnosis; predicting and monitoring end-user health using physiological vital signs sensed wearable sensor data. The communicating and data exchanging internet of things (IoT) platform imposes latency and overloading uncertainties in the heterogeneous environment. This article introduces cognitive data processing for uncertainty analysis (CDP-UA) to improve WS data management's efficiency. CDP-UA addresses uncertainties in two levels namely aggregation and dissemination of WS data. The uncertainties in synchronizing aggregation and dissemination slot mapping are addressed using classification learning. In the dissemination process overloaded intervals are identified and segregated using regression learning and conditional sigmoid function analysis. The joint learning process helps to classify overloaded and latency-centric dissemination and aggregation instances to improve WS data delivery in the clinical/medical analysis center. The experimental analysis shows that the proposed method is reliable in achieving less uncertainty factor, latency, and overloaded intervals for varying disseminations and sensing intervals.

Intelligent Transportation System (ITS) serves as the on-the wheel communication and service platform for the real-world driving users. Navigation service and traffic information flow among the connected vehicles relies on the available resources and infrastructure units. Appropriate sensing and selection of infrastructure units for seamless navigation responses and information flow in the dynamic environment is facilitated using bio-inspired learning in this article. This method named as ant-inspired recurrent learning model (ARLM) introduced in this article is focused to improve the sensing and response rate of the navigation-based services for the vehicles. This model relies on forward and backward ant agents and recurrent learning for maximizing the navigation service response rate of the vehicles. In this model, the training sets are differentiated on the basis of connection probability and learning depreciation to retain the service rate through different learning iterates. The conditional verification for sensing and retaining active link helps to reduce the sensing and response time irrespective of the varying vehicle and request densities. The performance of the proposed model is verified using suitable experimental analysis and the metrics information flow rate, service sensing, sensing time, response rate, and response time are analyzed for assessing ARLM.

Software defined network (SDN) architectures are assimilated with vehicular communication networks in order to improve the real-time application support for the driving users. Internet of vehicles (IoV) paradigm provides information-centric application support for the road-side users. Information sharing through the road-side units (RSUs) influences the application services due to the frequent change in physical attributes of the vehicles. Considering the application oriented services and information handling in IoV, this article introduces information-centric content management framework (ICMF) for effective information utilization in the vehicular networks. This framework performs data acquisition, smoothing and management process for effective information analysis and better offloading. The proposed framework incorporates the functions of linear vector quantization for classifying acquired information and segregating it for maximum utilization. This quantization is recurrent in both continuous and alternating learning process to improve the reliability of information handling and management. The performance of the proposed framework is verified using simulations and the results prove its efficiency. The proposed framework is found to maximize resource utilization and offloading ratio with less analysis time and overhead. The simulation is verified for the varying density of vehicles, offloading ratio, and communication time, information utilization.

Internet of Things (IoT) has gain the importance with the growing applications in the fields of ubiquitous and context-aware computing. In IoT, anything can be a portion of it, whether it is unintelligent objects or sensor nodes; thus extremely different kinds of services can be developed. In this regard, data storage, resource management, service creation and discovery, and resource and power management would facilitate advanced mechanism and much better infrastructure. Cloud computing and fog computing play an important role when the quantity of data and information IoT are critical. Thus, it would not be potential for standalone strength forced IoT to handle. Cloud of things is an integration of IoT with cloud computing or fog computing which can aid to realize the objectives of evolving IoT and future Internet. Fog computing is an expansion to the notion of cloud computing to the network brim, making it suitable for IoT and other implementations that need real-time and fundamental interactions. Regardless of many virtually and services unlimited resources presented by cloud-like intelligent building monitoring and others, it yet countenances various difficulties when interfering many smart things in human's life. Mobility, response time, and location consciousness are the most prominent problems. Fog and mobile edge computing have been established, to get rid of these difficulties of cloud computing. In this article, we suggest a novel framework based on computer propped diagnosis and IoT to detect and observe type-2 diabetes patients. The recommended healthcare system aims to obtain a better accuracy of diagnosis with mysterious data. The overall experimental results indicate the validity and robustness of our proposed algorithms.

With the development of the industrial Internet of Things (IIoT), the existing IIoT platform has gradually formed a large-scale, heterogeneous distributed environment. How to ensure information security and achieve efficient product traceability is an urgent problem to be solved in the development of the steel IoT platform. In view of the low transparency of information traceability of current steel products and the defects of information islands, in this article the blockchain-based steel IoT quality traceability system is developed and adopted the alliance chain mode and the Hyperledger blockchain platform. Experimental tests prove that production companies, logistics, and consumers can participate in the information certification of steel products via the system. Consumers can understand the real product manufacturing process, effectively avoiding the incomplete information and low transparency in the traditional information traceability process, and effectively trace the quality of steel products. The system provides an effective scheme for promoting the transformation and upgrading of the steel industry.

Summary The role of education, which propagates knowledge, becomes increasingly significant in the past little years due to the fulminatory expansion in knowledge. Meantime, the model of education process is going via a conversion in which the learning of different students need to be completed in various ways. Therefore, the smart education environment is encouraged. It incorporates different information and communication technologies to activate learning process and adjust to the requirements of different students. The quality of learning process for students can be enhanced through continually monitoring and analyzing the state and activities of different students via information sensing devices and information processing platforms for offering feedback about learning process of different students. The Internet of Things pledges to achieve a great variation in life, goodness of individual's life, and organizations' productivity. Via a vastly dispensed locally smart network of intelligent objects, the IoT has the chance to allow expansions and improvements to essential utilities in various fields, while introducing a novel ecosystem for developing application. Applying the concept of Internet of Things in any education environment will increase the quality of education process because students will learn rapidly, and teachers will fulfill their job efficiently. This paper is designed to illustrate the basic concepts, definitions, characteristics, technology, and challenges of Internet of Things. We also illustrated the role of Internet of Things in building a smart educational process, and also, in making efficient and effective decisions, which is vital in our daily life.

The study objective is to develop a big spatial data model to predict the epidemiological impact of influenza in Vellore, India. Large repositories of geospatial and health data provide vital statistics on surveillance and epidemiological metrics, and valuable insight into the spatiotemporal determinants of disease and health. The integration of these big data sources and analytics to assess risk factors and geospatial vulnerability can assist to develop effective prevention and control strategies for influenza epidemics and optimize allocation of limited public health resources. We used the spatial epidemiology data of the HIN1 epidemic collected at the National Informatics Center during 2009-2010 in Vellore. We developed an ecological niche model based on geographically weighted regression for predicting influenza epidemics in Vellore, India during 2013-2014. Data on rainfall, temperature, wind speed, humidity and population are included in the geographically weighted regression analysis. We inferred positive correlations for H1N1 influenza prevalence with rainfall and wind speed, and negative correlations for H1N1 influenza prevalence with temperature and humidity. We evaluated the results of the geographically weighted regression model in predicting the spatial distribution of the influenza epidemic during 2013-2014.