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[Display omitted] • Deep learning enabled predictive nucleic acid amplification tests analysis. • On-site fluorescence signal processing powered by cloud computing. • Accurate prediction can be obtained using the early 22.5% data. • Approach can be universally extended to other areas of biomedical research. Pandemics such as COVID-19 have exposed global inequalities in essential health care. Here, we proposed a novel analytics of nucleic acid amplification tests (NAATs) by combining paper microfluidics with deep learning and cloud computing. Real-time amplifications of synthesized SARS-CoV-2 RNA templates were performed in paper devices. Information pertained to on-chip reactions in time-series format were transmitted to cloud server on which deep learning (DL) models were preloaded for data analysis. DL models enable prediction of NAAT results using partly gathered real-time fluorescence data. Using information provided by the G-channel, accurate prediction can be made as early as 9 min, a 78% reduction from the conventional 40 min mark. Reaction dynamics hidden in amplification curves were effectively leveraged. Positive and negative samples can be unbiasedly and automatically distinguished. Practical utility of the approach was validated by cross-platform study using clinical datasets. Predicted clinical accuracy, sensitivity and specificity were 98.6%, 97.6% and 99.1%. Not only the approach reduced the need for the use of bulky apparatus, but also provided intelligent, distributable and robotic insights for NAAT analysis. It set a novel paradigm for analyzing NAATs, and can be combined with the most cutting-edge technologies in fields of biosensor, artificial intelligence and cloud computing to facilitate fundamental and clinical research. [ABSTRACT FROM AUTHOR]

The recent pandemic events in Thailand, Covid-19 in 2018, demonstrated the need for an event-based smart monitoring system. While a distributed multi-level architecture has emerged as an architecture of choice for a larger-scale smart event-based system that requires better latency, security, scalability, and reliability, a recently introduced data mesh paradigm can add a few additional benefits. The paradigm enables each district to become an event-based smart monitoring mesh and handle its analytics and monitoring workload. Districts can form a set of domains in a network of event-based smart community monitoring systems and provide data products for others during a crisis. This paper presents a distributed data mesh paradigm for an event-based smart monitoring product in a given community with predefined domains. The paper presents smart monitoring as a data product between domains. Key considerations for designing an event-based smart monitoring data product are given. The author introduces three possible domains necessary for creating a smart monitoring system in each community. Each domain creates a data product for a given domain and shares data between domains. Finally, a three-layer analytics architecture for a smart monitoring product in each domain and a use case is presented. [ABSTRACT FROM AUTHOR]

Abstract: Linked Open Science is an approach to solve challenges of an executable paper. It is a combination of four “silver bullets”: 1) publication of scientific data, metadata, results, and provenance information using Linked Data principles, 2) open source and web-based environments for executing, validating and exploring research, 3) Cloud Computing for effcient and distributed computing, and 4) Creative Commons for the legal infrastructure. We will use a realistic scientific research setting related to research on deforestation of the Brazilian Amazon rainforest to provide scenarios to illustrate the application of Linked Open Science. [Copyright &y& Elsevier]

At present, universities are facing a critical period of transition from digital campus to smart campus. Because cloud service data has the characteristics of diversity, in addition to structured data, there are also unstructured data, which play an important role in the study and life of the majority of students. Therefore, it is very important to improve the construction level of smart campus cloud service platform. At this stage, there are great differences between the service platform under the traditional model and the smart campus platform under the big data computer system. Under the traditional mode, the construction method of smart campus cloud service platform in the city has some shortcomings. Therefore, it is urgent to build a smart campus cloud service platform. Based on this, this paper compares the smart campus cloud service platform of the traditional service platform and the big data computer system. The results show that the latter has a timely response speed and can bring high-quality and efficient data services to users. At the same time, it also analyzes the ideas and goals of building an intelligent cloud service platform, and puts forward corresponding construction methods. [ABSTRACT FROM AUTHOR]

In most circumstances, accessing measurements from entire smart metres set in less-voltage grid area for distribution monitoring is not practicable because of less bandwidth as well as excessive delays in accessing Smart Meter monitoring. Estimating the condition of a distribution system can be done using voltage, active and reactive power measurements from smart metres subset. On one hand, increasing number of selected smart metres improves distribution state assessment accuracy, it degrades monitoring data time. It improves the electrical system's dependability and efficiency while also providing security to the system. Smart grid infrastructure is a modified form of smart grid. Smart grid infrastructure is a modified form of the smart grid. It has high-capacity converters, sensing and metering technologies, and automated control to improve efficiency and dependability. The goal of this article is to use smart metering technology to improve the observer of low-voltage distribution grids. The use of a distribution network-based state estimation technique employing smart metre readings for near-real-time monitoring of low-voltage distribution grids is the focus of this research. This paper propose smart metering based on cloud architecture for low voltage distribution networks with distribution network based state estimation algorithm in application of monitoring. The article will discuss the critical role of cloud solution in achieving interoperability, scalability and flexibility as well as enabling integration of various services for distribution system automation. As an example of coordinated operation of diverse distribution grid services by cloud, distributed state estimation technique as well as autonomous network reconfiguration will be demonstrated. • The goal of this article is to use smart metering technology to improve the observability of low-voltage distribution grids. • To deal with estimating grid voltages with reasonable accuracy using a state estimate technique that employs smart meter data. • Here proposed architecture enhanced issue of enhancing low-voltage distribution grids observability using smart metering infrastructure. • The experimental results show comparative analysis for various distributed network types and technique based comparison between existing and proposed technique. [ABSTRACT FROM AUTHOR]

As Global Navigation Satellite Systems (GNSS) infrastructure expands and more satellites are launched, the number of signals and measurements available to users increases, resulting in a corresponding rise in data generation rates. GNSS receivers with the ability to process triple frequency measurements and multi-constellation reception, have been complemented with the massive deployment of connected, GNSS-enabled devices such as smartphones, wearables, and IoT sensors, triggering the growth of a highly dispersed cloud of GNSS data. This trend observed in other domains presents both challenges and opportunities for applications of GNSS technology. The scientific exploitation of GNSS data involves developing new concepts through the analysis of the aforementioned sources of information. Traditionally, the extraction of insights from GNSS data has relied on searching and downloading datasets from multiple repositories. These datasets provided by numerous organisations are scattered worldwide, containing gems of information difficult to track and discover. This approach hinders research productivity, as scientists are forced to shift their focus from data exploration, analysis, modelling, visualization, and realisation of conclusions to a long list of repetitive and manual tasks of little added value. The GNSS Science Support Centre (GSSC) hosts ESA's innovative Digital Platform for GNSS, GSSC Now, which integrates a large GNSS repository with cutting-edge features for data exploration and analysis. The paper presents key functionalities of GSSC Now and highlights its value through case studies of real-world GNSS applications. This platform expands established computational limits, placing the data source side-by-side with cloud computing capabilities. The change of paradigm provides scientists with seamless execution of Machine Learning models, interactive algorithms and visualisation tools on a vast repository of GNSS data, removing barriers that hinder research productivity. [ABSTRACT FROM AUTHOR]

Cloud Computing services are widely expanded all over the world because of it comfort and reliable services offered to the user on demand. The majority of the users who are using different pervasive devices are directly or indirectly dependent on the cloud services. The consumer data stored at vendor end has lot of security concerns. However, cloud vendors offer many cloud-computing mechanisms explore the data leakages are challenging issues to be addressed in various levels. This paper presents the systematic investigation of data leakages, which occur in cloud services. In our study we present systematic analysis of (i) data leakages in cloud environment, (ii) security issues related to cloud environment, (iii) cloud security mechanisms and finally we present the challenges in cloud security that need proper solutions [ABSTRACT FROM AUTHOR]

Every data packet must pass through a few intermediate nodes to reach its destination. Among other reasons, tremendous growth in internet devices encourages those intermediate nodes to drop the data packets. Optimizing the data packet route is an effective solution to deal with packet loss. Advanced machine learning approaches have been identified as a powerful support tool for routing optimization in node networks. Cloud computing has kept pace with the continuously developing hardware infrastructure. Improved connection, processing power, and memory units enable real-time machine learning. This paper proposes and evaluates an approach for optimizing the packet path, by one hop, for intermediate nodes as a backup called Cloud Acknowledgement Scheme (CAS). It offers information on the transmission trend and the tendencies of certain adjacent nodes or groups of neighboring nodes in a network. The proposed CAS has been validated via a series of machine learning experiments using real-world node data. [ABSTRACT FROM AUTHOR]

The association between technology and social development is particularly important in the health field, pointing to research in e-Health to optimize health services. This paper aimed to carry out a bibliometric mapping of software architecture for e-Health systems, so an exploratory and descriptive study was performed, with a quantitative approach and based on secondary data from ISI Web of Science. The bibliometric indicators showed 127 different papers in 97 sources, with 16.92 citations per paper and a collaboration index of 4.18 with higher scientific productivity from Europe and North America. Scattered networks of co-citation of the sources and co-occurrence of the keywords were identified, pointing to three main approaches in the research theme, including (i) a managerial view of e-Health systems based on the development of frameworks; (ii) data privacy in health services; (iii) and application of digital technologies, especially Big Data, IoT, Cloud Computing, and Blockchain as corroborated by the word cloud. In addition, for the evaluation of the countries that have the largest number of scientific production and to assess the most used keywords, a descriptive statistical analysis was performed to organize the dataset and to obtain the measures of centrality, dispersion, and frequency. This paper contributes to direct the discussion about software architecture and its multifaceted aspects in the health field, guiding architects and practitioners in their e-Health projects. [ABSTRACT FROM AUTHOR]

Over the past two decades, the way computing resources are been developed, deployed, upgraded, and applied changed dramatically, with more and more software and hardware solutions being transferred to cloud technologies. Data Warehouses (DW), defined as a way of organizing corporate data in an integrated manner over (sequential) time periods, "structured & disposed" in order to generate a "single data source", were also affected by the evolution, thus giving rise to the concept of Cloud Data Warehouse (CDW). This technology allows users to be more technologically free, as they do not need to spend time investing in software and hardware, they only pay for the resources they used and the infrastructure itself has greater flexibility and scalability. However, selecting the most suitable platform or technology for a CDW can be a complex task due to the large number of factors that can influence the decision and due to the existing offer in the market. The objective of this paper is to describe the process of benchmarking a set of CDW platforms, with the goal of analyzing and exposing each one's performance results. These platforms are Snowflake, Google BigQuery, Amazon Redshift, and Azure Synapse. The metrics to be measured are data loading and query running time, and alias running times. For this benchmark, the dataset used was Star Schema Benchmark (SSB), a dataset based on the well-known TPC Benchmark™ H (TPC-H). [ABSTRACT FROM AUTHOR]

To address the limitations of 5 G, 6 G wireless networks are envisaged to provide sub-millisecond latency, ultra-high connection density, extremely high data rates, better coverage, reliability, availability, etc., for cloud computing, Internet-of-Everything, and cyber-physical systems. However, avoiding several trust-related issues in the design of the wireless networks may delay the goal. In recent years, Blockchain has become a promising technology that can transform various fields by providing innovative and profitable solutions. Blockchain's intrinsic features, such as immutability, decentralization, anonymity, and transparency, can corroborate trust among isolated networks to secure access control, authentication, better integrity, secrecy, and effectual resource sharing. This paper paints a holistic picture of 6 G wireless networks and Blockchain, including architecture, Blockchain-assisted 6 G services, deployment, 6 G-assisted Blockchain service models, consensus mechanisms for Blockchain-as-a-Service, 6 G-assisted mining and consensus, and others. It also sheds light on the integration possibilities of Blockchain and 6 G, its potential benefits, and the effects of consensus mechanisms on 6 G concerning security, scalability, and energy consumption, to name a few. The existing 6 G projects and applications have also been summarized, along with the discussion on research and projects to standardize 6 G. Various research directions for Blockchain, 6 G, and their integration have also been explored and reported. [ABSTRACT FROM AUTHOR]

Smart farming is a new concept that can be described as applying different kinds of modern technology to make farming more efficient. One such technology is the Internet of Things, an extension of the Internet and other connections into mundane devices. This paper uses dedicated sensors and cloud computing technologies to model a smart farming system that helps farmers keep track of state of the crops and livestock. It also covers implementing and testing an Android tracker app that is supposed to become the first component of the mentioned system, a client application that communicates with the cloud computing services yet to be implemented. The end product provides its users with an easy way of keeping track of the state of their farms, simplifying the farmers' work and enhancing productivity. [ABSTRACT FROM AUTHOR]

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a Distributed Software Architecture (DSA) for Smart Building (SB) based on the Reactive Manifesto (RM) principles. To follow the RM principles, we analyze the usage of different deployment approaches, particularly the impact of using a container orchestrator on the application layer. After running performance tests on the different configurations, the container orchestrator usage led to enhanced distributed processing, lowering the latency, increasing flexibility, enhancing security, and providing cost-effectiveness and scalability. We introduce the implementation of a modern DSA, developed following state-of-the-art cloud patterns and compliant with the RM for the SB context. Furthermore, we have ensured the reproducibility of this implementation by making the initial tests and overall architecture code available in public repositories. The research follows the Design Science Research (DSR) methodology for elaborating each phase until we get the artifact (DSA) and, with this, contribute to the Knowledge Base. The architecture was properly tested, considering the performance as the principal test layer. This solution is tailored for application in domains of the Internet of Things (IoT), focusing on the SB and a case study involving the Laboratory of Distributed Systems and Concurrent Programming (LaSDPC) at São Paulo University. Moreover, its applicability extends to IoT domains like smart home, smart campus, smart city, and health-related applications. [ABSTRACT FROM AUTHOR]

With the increasing volume, velocity, and variety of data generated by various sources, big data has become a critical challenge for many organizations. Cloud computing provides an efficient and cost-effective solution to address the challenges of big data analysis. In this paper, we review the state-of-the-art cloud computing technologies for big data analysis, including cloud storage, cloud computing platforms, and cloud-based big data analytics tools. We also identify the major challenges and solutions for cloud-based big data analysis. [ABSTRACT FROM AUTHOR]

Cloud computing technology provides access on demand to virtualized resources, services, and applications via a distributed network. In cloud data centers effective energy utilization is a critical concern in today's technology-driven world. Cloud data centers (CDC) are massive facilities that host and manage an enormous amount of data and computing resources. This article addresses the growing significance and energy-intensive nature of data centers. Due to the rapid growth of cloud computing, it offers on-demand access to resources globally and leads to substantial power consumption and carbon impact on the environment. They consume substantial amounts of energy, and optimizing their energy utilization is essential for reducing operational costs, minimizing environmental impact, and ensuring sustainable growth. To combat this, efficient energy-saving approaches using machine learning methods have been researched. ML methods hold great potential for enhancing energy efficiency in CDCs by analysing data, detecting patterns, and optimizing resource usage. The focus areas include CPU usage prediction, overload finding, underload estimation, selection, migration, and relocation of VMs to attain resources and improve energy utilization. The paper compares energy-saving results achieved by different machine learning techniques in data centers to minimize energy usage and meet service level agreements (SLA). The machine learning approaches reduce energy consumption from 1.6% to 88.5% compared to the benchmark approach mentioned, considering various settings and parameters. [ABSTRACT FROM AUTHOR]

Machine Learning as a Service, MLaaS refers to cloud-based platforms that provide machine learning tools and infrastructure to users, allowing them to access and utilize machine learning capabilities without managing the underlying hardware and software. Cloud service providers offer scalable and flexible resources, enabling businesses to train and deploy machine learning models efficiently and cost-effectively. Multi-Criteria Decision making, MCDM helps individuals or organizations make choices when multiple criteria or objectives are involved. It aims to find the best alternative among a set of available options by considering various criteria or factors that are relevant to the decision. Machine learning as a service (MLaaS) and MCDM are two distinct concepts that can be combined to create powerful decision-making solutions. The present paper proposed an integrated approach based on the Analytic Hierarchy Process and TOPSIS method for choosing the best MLaaS cloud. Four alternatives for MLaaS cloud were analyzed based on seventeen shortlisted criteria. Based on the calculations, conclusions have been reached and potential study areas have been identified. [ABSTRACT FROM AUTHOR]

• FDD methods and process discussed in detail. • IoT and cloud technology for FDD is discussed. • Presents the detailed discussion on fault modeling. • Challenges and future prospects of FDD systems are discussed. This review study examines the latest research and developments in the fault detection and diagnostics of Heating Ventilation and Air Conditioning (HVAC) systems. This review describes the basics of Fault detection and diagnostics in the HVAC systems, and the methods developed for the FDD have been discussed in detail. Machine learning methods have become prevalent in the FDD. Supervised and unsupervised machine learning methods have been discussed. Data preprocessing and feature selection are the two essential steps of the FDD process using machine learning. Fault prognosis has also been discussed in brief. Further, fault modeling and its applications in the FDD have been covered. Various approaches have been used to model the different faults in HVAC systems. This paper reviews FDD systems based on four aspects, i.e., detection, diagnostics, prognostics, and modeling of faults. Then this review provides a comparative study of different FDD methods. Finally, the paper discusses future challenges for the more efficient FDD systems to reduce the energy consumption of the HVAC systems. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a parallel distributed version of Viterbi algorithm that combines the advantages of Spark, the big data framework, and hidden Markov models to solve the decoding problem for large scale multidimensional data. The scope of the paper includes a review of hidden Markov models, a study of decoding problem, a presentation of related work, and a discussion of previously proposed implementations. The main part of the paper consists of a description of development and implementation of a parallel distributed Viterbi algorithm in a cloud computing environment, followed by a description of evaluation experiments of the presented algorithm. The results showed that the proposed algorithm is faster, with high scalability and no deterioration in forecast accuracy is observed. [ABSTRACT FROM AUTHOR]

Gateways in the Internet of Things (IoT) play a decisive role in routing the preprocessed filtered data to cloud platforms. While investigating gateways, the need for a comprehensive systematic literature review that can elaborate the working and functionalities of IoT gateways was felt. This paper presents a systematic literature review of IoT gateways in general and smart gateways in particular. It considered papers published over the last 10 years, i.e., from 2011 till July 2021. A methodical literature analysis technique is used in this review; out of 2347 papers, 67 articles are selected for complete analysis based on well-defined criteria. The survey starts with the broad categorization of gateways in IoT as basic and smart gateways. Further, smart gateways are sub-divided into three categories as passive gateways, semi-automated and fully-automated gateways. The survey is performed based on well-defined criteria, which include: the type of gateways, their requirements, tools/platforms, the approach adopted, evaluation and their application domain. This paper demonstrates further bifurcation of functional requirements of IoT gateways. Research gaps and open issues have been identified in the area. Future prospects have accordingly being suggested to help academicians embark on advanced research. [ABSTRACT FROM AUTHOR]

Service Level Agreement (SLA) is an important basis to ensure the availability of cloud service. Cloud service providers seek to not only fulfill the SLA, but also simultaneously minimize the risk uncertainty. Under such a mechanism, service providers look forward to determine the optimal number of backup virtual machines (VMs) to minimize operational costs. In this paper, we propose a data-driven risk measurement method under multi-stage SLA and empirically portray the likelihood and risk values of compensation events at different stages under several service periods. Furthermore, a multi-objective optimization function is established to balance the provisioning cost and risk volatility for cloud service providers with different risk preferences. This paper provides a practical method to cloud service providers by addressing the risk uncertainty in multi-stage SLA from cloud computing resource management perspective. [ABSTRACT FROM AUTHOR]

Cloud Computing (CC) and virtualization concepts are two advanced technologies introduced to empower distance and blended learning. Besides, they play a crucial role in equipping learners with practical skills and fostering hands-on experience to defend against cyber-attacks. Many Higher Education Institutions (HEIs) in developed countries have already embraced the promise of CC to raise educational standards. However, the pace of its adoption in developing countries has stagnated. Moreover, existing solutions in the literature are not sustainable. They either rely on on-premise infrastructure or are bound by a single cloud service provider. Consequently, they are likely prone to failures and a sudden outage. To fill this gap, this paper is a first that comprehensively addresses the above issues and introduces a federated hybrid CC system based on an extension of Apache Virtual Computing Lab (VCL). The proposed system provides an independent open-source implementation, greater configuration flexibility, and methodological improvements as compared to existing studies in the literature. In addition, it promotes the sustainability of the CC services, extensible cloud architecture, and fault tolerance. VCL is primarily focused on provisioning Virtual Laboratories (VL) for remote cybersecurity and computer networks education, with potential expansion to other domains of engineering education. In addition, this paper introduces GPT-TerminalPro, a terminal-based tool driven by OpenAI's Generative Pretrained Transformer (GPT-3.5) that provides intelligent assistance to users while performing lab tasks. To experimentally evaluate the VCL's performance, the standard Linux tools as well as the Apache benchmark and httperf HTTP load generators are utilized. VCL has been tested with 30 users and 61 virtual user computing environments provisioning to validate the overall performance. The results are fascinating: the provisioning time including all VCL background tasks is always less than a minute and utilizes fewer computing resources while providing a better user experience. This paper will encourage the adoption of CC in low-income countries. • Breaking barriers for cloud adoption in developing countries education institutions. • Proposing a sustainable and open federated hybrid cloud computing system. • Effective provisioning of remote cybersecurity virtual laboratories. • Proposing GPT3-TerminalPro, an innovative chat-enabled AI tool based on GPT-3.5. • The VM ready time including all background tasks is less than one minute. [ABSTRACT FROM AUTHOR]

A graph G is two-disjoint-cycle-cover [ r 1 , r 2 ] -pancyclic if for any integer ℓ satisfying r 1 ≤ ℓ ≤ r 2 , there exist two vertex-disjoint cycles C 1 and C 2 in G such that | V (C 1) | = ℓ and | V (C 2) | = | V (G) | − ℓ. The k -dimensional data center network DCell with n port switches and order t k , n , denoted by D k , n , plays an important role in big data cloud computing. In this paper, we study the structural properties of D k , n and show that D k , n is two-disjoint-cycle-cover [ 3 , ⌊ t k , n 2 ⌋ ] -pancyclic for n ≥ 7 and n + k ≥ 11 , that is, for any integer ℓ satisfying 3 ≤ ℓ ≤ ⌊ t k , n 2 ⌋ , there exist two vertex-disjoint cycles C 1 and C 2 such that | V (C 1) | = ℓ and | V (C 2) | = t k , n − ℓ in D k , n. This paper generalizes the results about the Hamiltonicity in (Wang et al., 2015 [25]) and the pancyclicity in (Hao and Tian, 2021 [11]). • It is the first study to consider two-disjoint-cycle-cover pancyclicity in data center networks. • The two-disjoint-cycle-cover pancyclicity of data center network DCell is shown. • This result extends and generalizes the Hamiltonicity and bipancyclicity of DCell. [ABSTRACT FROM AUTHOR]

Software-defined Networking (SDN) is a transformative approach for addressing the limitations of legacy networks due to decoupling of control planes from data planes. It offers increased programmability and flexibility for designing of cloud-based data centers. SDN-Enabled cloud data centers help in managing the huge traffic very effectively and efficiently. However, the security of SDN-Enabled Cloud data centers against different attacks is a key concern for cloud security professionals. Distributed Denial of Service Attacks have emerged as one of the most devastating attacks that constantly worried the entire cloud security research community. To prelude this, it is pertinent to fundamentally focus on classification of these attacks and their defence strategies in an effective way which has been the basis of this research paper. The aim of this paper is to formulate and conceptualize the taxonomies of DDoS attacks and its Défense mechanisms. Improved taxonomy of DDoS attacks highlights the various vulnerable points of vulnerability in SDN-enabled cloud architecture. Additionally, a taxonomy of defence mechanisms offers an extensive survey of recent techniques for detecting and mitigating DDoS attacks in the SDN-enabled cloud environment. Finally, we discuss the open research issues and challenges for the cloud security research community for carrying out future research and investigation. [ABSTRACT FROM AUTHOR]

In this paper, the effects of load and air-flow change on a serpentine multi-channel proton-exchange membrane fuel cell (PEMFC) were studied through numerical simulation and cloud computing technology. For this purpose, load change, air-flow change pattern and initial value, as well as its lag time to the load change, were considered. The transient response of local oxygen distribution to load change and flow change was investigated, and the local oxygen starvation (LOS) behavior observed. Detailed results are presented to show the transient response of the cell in terms of average oxygen concentration and water content distribution. This work shows that the local oxygen starvation process depends on factors such as load changes and air-flow rate of change, which can be alleviated or eliminated by adjusting air flow and pattern. It is also shown that simulation efficiency can be improved by cloud computing technology. Finally, the validity of the numerical model proposed in this paper is verified by the comparison between the model results and experimental data. • Using cloud computing for numerical simulation can reduce the solution time by 99%. • The effect of control-oriented operating conditions on local oxygen starvation is analyzed. • A mitigation strategy that can reduce the area ratio of LOS by 18% is summarized. [ABSTRACT FROM AUTHOR]

With the development of software, massive information systems generate large-scale data resources involving a large amount of log information, which is of lots of meaningful contents. Accurate analysis and anomaly prediction based on logs are particularly important for building safe and reliable systems. It is evident that current anomaly prediction is not accurate enough, and there are few applications to the study of real-time reliability evaluation. Therefore, this paper uses ensemble learning model to analyze and predict anomaly of the massive system logs based on the complete procedures of log processing, including log analysis, feature extraction, anomaly detection, prediction evaluation, and real-time reliability evaluation. Compared with the traditional machine learning methods, the proposed model is able to improve the accuracy, recall rate and F1 value of anomaly prediction. The evaluation results are used to correct the real-time reliability in view of the low predicted recall rate, which greatly improves the accuracy of real-time reliability and thus provides accurate data basis and anomaly location basis for Artificial Intelligence for IT Operations. In this paper we have made the following innovations and contributions in anomaly detection and reliability measurement. The whole process was established from original log to real-time reliability measurement. New methods were adopted to improve the accuracy, recall and F1 value of anomaly detection. A real-time reliability measurement method is proposed. [ABSTRACT FROM AUTHOR]

Task scheduling problem in a cloud computing environment is one of the most important issues in this field. Because task schedulers should be aware of underlying platform heterogeneity, task interdependencies, and virtual machine (VM) variable configurations. An efficient task scheduling algorithm can potentially increase the efficiency of cloud computing, by choosing the appropriate virtual machine to do a specific task-scheduling problem, for dependent tasks on heterogeneous resources which is a well-known NP-Hard problem. To reduce minimum total execution time, makespan , in cloud computing many heuristics were presented in literature but lots of them suffer from low efficiency. In this article, a new task priority strategy and applying task duplication methods are proposed, for solving the task scheduling problem of the dependent tasks in heterogeneous cloud computing systems. The novelty of the current paper is to present a new list scheduling algorithm with a new task priority strategy and applying pertinent task duplication techniques. This paper uses optimistic cost table downward (OCTd) and optimistic cost table upward (OCTu) procedures to prioritize tasks in an efficient ordered list; then, it utilizes Heterogeneous Earliest Finish Time (HEFT)-duplication method for performing task duplication technique which significantly reduces makespan. To validate the proposal, we experimentally analyzed the proposed scheduling algorithm with different scientific workflows such as Molecular , LU -Like, FFT , and Montage datasets. The performance comparison of the novel heuristic scheduling algorithm against other existing approaches proved the superiority of proposed algorithm in terms of makespan , speedup , SLR , and efficiency which are prominent scheduling evaluation metrics. [ABSTRACT FROM AUTHOR]

To achieve the objective of smart manufacturing, the adoption of cloud computing is inevitable. However, the implications of fully or partially moving the manufacturing-related workloads to the cloud is a strategic decision that is rarely investigated thoroughly enough in the literature, especially for standard off-the-shelf enterprise IT applications, such as various SAP products. Specifically, in the related research, it is kept on a high level of conceptual representation without analysis of the specific cloud products required for such infrastructure. In this work, we discuss cloud setup for smart manufacturing while taking into account four main pillars of cloud infrastructure required for standard IT solutions, namely: compute, storage, networking, and backups. We also consider different degrees of high availability requirements since these fundamentally impact how cloud architecture should be designed. After discussing these elements, we demonstrate how these elements are mapped to the real-world cloud offerings on the example use case of Microsoft Azure. We close the paper with a discussion on further consideration of operational costs while pointing out the complexity of the pricing model. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) nowadays represents a fundamental technology for the Industry 4.0 initiatives for connecting devices, operators and processes so as to eventually fuel digital transformation. However, if based exclusively on Cloud Computing, the IIoT faces inevitable limitations and barriers. Therefore, in this paper we deal with the Edge Intelligence (EI), a novel paradigm at the confluence of Edge Computing and artificial Intelligence, and, particularly, we analyze its impact on the processing of the IIoT data in the proximity of the events of interest rather than on Cloud servers. Therefore, a general discussion on the most relevant benefits, limitations and open challenges of the IIoT-EI duo is proposed hereinafter and exemplified through an emblematic IIoT use case: this is related to safety-critical tasks based on video analytics and provides a full-fledged comparison between Cloud-and EI-based IIoT deployments (in terms of reliability, responsiveness, bandwidth usage, energy footprint and usability), finally outlining a general strategy for large-scale EI-aided IIoT systems engineering. [ABSTRACT FROM AUTHOR]

The modern development approaches are establishing microservices and cloud computing as major trends to benefit the technological community. However, these technologies are often prone to multiple issues regarding parallel development by numerous parties, delivery strategies and resource allocation. This paper proposes a novel architecture for developing self-adaptive microservices, using Kubernetes through the Azure Container Apps, including a strategy that will complement the architecture to enhance the development of microservices and aiming to achieve a solution that allows the readers to deliver software faster, with more resilience, more scalable, and more cost-effective, depending as low as possible from human intervention to maintain and scale. The author will apply the acquired knowledge to propose and test an architecture for a real use case scenario, building a notifications service integrated with a complex cloud-based web application system. [ABSTRACT FROM AUTHOR]

A significant challenge in high-performance computing is to ensure the even distribution of applications across computational resources, preventing issues such as resource fragmentation and network congestion. While cloud computing offers advantages, it introduces scheduling delays caused by data transmission. To address this issue, edge computing has emerged as an alternative to traditional cloud systems, aiming to minimize latency. While various methods have been proposed to address this challenge, they often prioritize one aspect at the expense of overall system performance. In this paper, we present a novel algorithm utilizing ant colony optimization to compute a fitness function and prioritize multiple objectives in scheduling. The algorithm effectively determines how to distribute applications between edge and cloud servers to enhance computational efficiency. This entails a delicate balance between scheduling delays and energy consumption in two distinct phases. Initially, the algorithm identifies applications sensitive to delays and ensures their execution on local edge servers. Subsequently, it identifies applications that require intensive computation and migrates them to the cloud layer, where cloud servers can process them. The results demonstrate that this approach reduces delay costs by 21.19% and decreases energy consumption by 13.76%. [ABSTRACT FROM AUTHOR]

This paper introduces an implementation of different GIS tools into Streamlit application: FCNN Terrain Classification, Earth Engine Dataset Parsing, and GIS Timelapse Animations. This toolkit is integrated with terrain multi-classification models using Fully Convolutional Neural Networks (FCNNs) for imagery data into Streamlit microservices. The proposed methodology involves labeled and unlabeled data collection from ESA WorldCover and Sentinel-2 MSI on the Google Earth Engine, compressing datasets into TFRecords format with 9 diverse terrain types, and handling Google Cloud training computations. The experimental results demonstrate the effectiveness of the CNN-based approach, achieving a tolerable from 60% up to 80% accuracy of the model and robust classification performance. The simplicity and efficiency of the proposed method make it suitable for real-world tasks requiring reliable and fast GIS analytics. [ABSTRACT FROM AUTHOR]

• A taxonomy discussing application execution strategies in fog computing. • A comprehensive discussion about components of application execution in fog computing. • Critical analysis providing in detail information about key aspects of application execution strategies. • Identifying research gaps and highlight new research directions which needs to be addressed in the future. With tremendous advancements in smart phone industry and IoT devices, edge computing has emerged to provide computational services at edge of the network. As a result, applications can be executed in a distributive manner to meet the latency requirements of applications. Despite these advancements, these devices are still considered to have limited energy, memory and computational resources compared to stationary standalone devices. Mobile edge computing extends mobile cloud computing close to end-users, decreasing latency and bandwidth utilisation by offloading tasks to edge servers. Fog computing places nodes between cloud and edge servers. A challenging approach in these paradigms is establishing offloading decision to edge/fog server, and distributed application execution at run time for heterogeneous devices. This paper reviews execution of resource-intensive tasks on mobile devices in distributed frameworks. The objective is to identify research gaps and highlight new research directions that can be pursued in future. Application execution involves application partitioning, task allocation, task execution and task resumption. In this paper, we propose a separate taxonomy for each step in application execution, reviews state-of-the-art techniques, and analyses critical aspects of application execution in a distributed manner. It further investigates similarities and differences among these techniques based on each step of application execution. [ABSTRACT FROM AUTHOR]

Software-as-a-service (SaaS) is a license for access to a specific cloud application through the Internet. However, these services are delayed and sometimes totally disrupted because of the Internet's unavailability that provides opportunities for a lot of attacks. Research into the security of the cloud focuses mainly on prohibiting malicious users from using the cloud to launch attacks, such as those currently done by botnets, including launching a DDoS attack, sending spam, etc. Because SaaS uses computational power from both servers of cloud computing providers and machines of customers, we argue that SaaS may be elaborately exploited in an unprecedented way as an attack vector for botnets. The previous research work has developed a novel framework for detecting the attacks with the aid of a deep learning approach (Doriguzzi-Corin et al., 2020). Although the attack node in the network has been identified, they were not diminished. An enhanced and powerful adversary model is to be provided as a solution to this problem. Hence, this paper intends to develop a novel framework for attack node mitigation using a secure SaaS Framework. The main contribution of this paper provides an attack detection process that takes place in Deep Belief Network (DBN), in which the weight, as well as activation function, are fine-tuned with Median Fitness oriented Sea Lion Optimization algorithm (MFSLnO). If DBN detects an attack node, the control is transferred to a lightweight bait approach that reliably mitigates the most common attack nodes without disrupting regular connections will be deployed. The performance of the proposed work yielded the best results over the traditional models with a packet loss ratio of 16% and throughput of 89%. [ABSTRACT FROM AUTHOR]

• This paper aims to explain the role of GEE in seismic anomalies detection. • Two recent powerful earthquakes in Japan (13 February and 20 March 2021) were discussed. • Time series of AOT, Chlorophyll and Ozone precursors deduced from GEE platform. • They were investigated using Median and LSTM methods. • Our satisfactory results show that we will see a significant leap forward in studies of earthquake precursors. Nowadays, satellite data is an appropriate and undeniable source for studying earthquake precursors due to their diversity, wide coverage, being up to date and low cost. Time series analysis of satellite data plays an important role in the process of detecting seismic anomalies in earthquake warning systems. But in order to reduce uncertainty during the seismic anomalies detection, it is necessary the use a variety of satellite data, although it leads to increase of data size and processing time. This paper aims to explain the role of Google Earth Engine (GEE) cloud platform in considerable progress of seismo-Lithospheric Atmospheric Ionospheric (LAI) anomalies detection in earthquake early warning systems. Among the different studied earthquakes, for example two recent powerful earthquakes in Japan (13 February and 20 March 2021) have been discussed. Deduced time series of three precursors (i.e. Aerosol Optical Thickness (AOT), Chlorophyll and Ozone) from GEE platform were investigated using Median method and a Long Short-Term Memory (LSTM) neural network to detect potentially seismo-LAI anomalies. Our satisfactory results show that with the addition of other various satellite data and also known predictors intelligent algorithms such as deep learning to GEE platform, we will see a significant leap forward in studies of earthquake precursors. [ABSTRACT FROM AUTHOR]

An important increase in the number of smart things, connected objects, smartphones, and sensors are founded every day, creating a huge amount of data called Big Data. Because of the dynamism of things and the limited capability of their resources and services to process and store this huge data, Cloud computing became an efficient, accessible, and reasonable solution. IoT in combination with Cloud functionalities provides a new phenomenon called the Cloud of Things (CoT) where many new possibilities are enabled. With the heterogeneity of different objects, the dynamism of their context, their distribution and the infinite cloud services, a middleware is a key solution. The main aim of this paper is to study middleware for self-adaptation of the Cloud of things environment into the context of objects. Toward this end, we first present some related features. Next, we compare some middlewares according to their characteristics. Then, we present some middlewares for static and dynamic adaptation and finally, we conclude this paper with a list of current challenges and issues in the design of a new middleware able to adapt dynamically and in execution time any cloud service into the context of the current object. [ABSTRACT FROM AUTHOR]

Many companies are now moving their business to the cloud because the cloud provides user with unlimited resources and uninterrupted service. The aim of the paper is to rank services provided by a set of cloud-providers based on multi-criteria decision-making (MCDM) methods. The ranking of the cloud-providers set is based on the Interval neutrosophic set theory (INS). The paper proposes INS and TOPSIS techniques to help users and experts express their evaluations and rank cloud-providers based on various criteria. INS is used to deal with the imprecise knowledge, generated by the ambiguity from the experts' evaluations. To demonstrate the effectiveness of the framework, the closeness index (CI) values of CSPs were ranked in decreasing order. A numerical example for a case study for a set 6 CSP and 10 criteria is presented. The numerical results show that SoftLayer's is the best cloud-provider with a CI value equal to 0.69. The worst cloud-provider is Microsoft Azure with a CI value equal to 0.20. A sensitivity analysis was performed to check the consistency and robustness of the model. [ABSTRACT FROM AUTHOR]

The Architecture, Engineering, Construction and Owner-operated (AECO) industry faces an array of productivity challenges, in part stemming from limited interoperability and suboptimal management of building information across project lifecycles. This paper investigates the application of rule-based systems from the manufacturing industry, especially as used in the semiconductor manufacturing sector, to improve productivity in the AECO industry. A comparative methodology is employed, examining processes in both the manufacturing and AECO industries. The results indicate that methodologies from semiconductor mask manufacturing can be effectively adapted for use in the AECO industry, particularly in design for buildability and standardized rule application. This paper includes the implementation of a Proof-of-Concept (POC) for Manufacturing Rule Check (MRC) in the AECO industry, focusing on Prefabricated Bathroom Units (PBU) production. The MRC developed for the POC could be integrated into the Model Checker platform of Singapore's CORENET X regulatory approval system, as well as deployed as a specialized application in the cloud that facilitates ease of collaboration among the design to fabrication players in the construction process. • Conducted a comparative analysis between semiconductor and AECO processes • Implemented Manufacturing Rule Check system focused on prefabricated bathrooms • Enhanced BIM and IDD interoperability with rule-based checking on regulatory platforms • Demonstrated the significance of adopting IDD to streamline AECO workflows [ABSTRACT FROM AUTHOR]

The animation industry is currently undergoing significant transformations fueled by technological advancements. Emerging technologies such as Artificial Intelligence, Big Data Analytics, Cloud Computing, the Internet of Things, Virtual and Augmented Reality, and Blockchain are reshaping the animation industry's value chain. This paper explores the impact of Industry 4.0 technologies on various stages of the animation industry's value chain, including conceptualization, pre-production, production, post-production, distribution, and consumption. Through a combination of literature review and semi-structured interviews, the paper identifies key capabilities of Industry 4.0 technologies in the animation industry. Subsequently, experts from different countries were engaged to validate and evaluate these capabilities, scoring them based on their importance and effectiveness in production and marketing activities. Analysis of data from 60 experts resulted in the identification of the most critical capabilities, which were categorized into four groups based on their relation to either the production or marketing of animation products. Furthermore, the analysis of questionnaires enabled the development of four scenarios depicting the potential future trajectories of animation companies based on their adoption of Industry 4.0 capabilities. The paper delves deeper into each scenario, elucidating their underlying factors and characteristics. Ultimately, it is predicted that companies leveraging Industry 4.0 technologies for both production and marketing activities will emerge as the leaders of the animation industry. This paper offers valuable insights into the evolving landscape of the animation sector and provides a roadmap for companies seeking to harness the potential of Industry 4.0 advancements for sustained leadership and success. • Industry 4.0 can profoundly transform the animation industry through its production-oriented and marketing-oriented capabilities. • Artificial intelligence can generate facial expressions and lip-sync animations based on audio input or text transcripts. • Big Data Analytics provides the opportunity for personalizing animation products. • Cloud Computing provides new ways of delivering products such as streaming animations. • Digital twins enable animators to visualize and iterate on their designs before production begins. [ABSTRACT FROM AUTHOR]

Outsourcing data to the cloud offers various advantages, such as improved reliability, enhanced flexibility, accelerated deployment, and so on. However, data security concerns arise due to potential threats such as malicious attacks and internal misuse of privileges, resulting in data leakage. Data encryption is a recognized solution to address these issues and ensure data confidentiality even in the event of a breach. However, encrypted data presents challenges for common operations like access control and range queries. To address these challenges, this paper proposes Secure Multi-dimensional Data Retrieval with Access Control and Range Search in the Cloud (SMDR). In this paper, we propose SMDR policy, which supports both access control and range queries. The design of the SMDR policy cleverly utilizes the minimum and maximum points of buckets, enabling the SMDR policy is highly appropriate for supporting range queries on multi-dimensional data. Additionally, we have made modifications to Ciphertext Policy-Attribute Based Encryption (CP-ABE) to enable effective integration with the SMDR policy, and then constructed a secure index using the SMDR policy and CP-ABE. By utilizing the secure index, access control and range queries can be effectively supported over the encrypted multi-dimensional data. To evaluate the efficiency of SMDR, extensive experiments have been conducted. The experimental results demonstrate the effectiveness and suitability of SMDR in handling encrypted multi-dimensional data. Additionally, we provide a detailed security analysis of SMDR. [ABSTRACT FROM AUTHOR]

Cloud Computing is an alternative to conventional IT Outsourcing. As a result, cloud computing migration between organizations is rapidly growing. The adoption of this technology brings many positive aspects but prescribes various risks and concerns. An organization that officially provides its cloud computing services to external providers and implies that its IT functions and process are outsourced to third-party providers of BPO services. For privacy-making public audit processes in dynamic cloud data storage, a modified Boneh-Lynn-Shachame Dynamic Auditing (MBLSDA) algorithm is suggested in this paper. The proposed algorithm executes an audit process for multiple users based on a batch audit simultaneously and effectively to enable Third Party Auditing (TPA). This paper integrates the homomorphic authenticator in an algorithm of dynamic signing audit by random marking in terms of the privacy conserving public auditing process. When the user of cloud service, store or update the data, it encrypts using Rijndal algorithm, and Boneh-Lynn-Shacham signature generation is used for key generation. Finally, a hybrid of these a proposed Modified Boneh-Lynn-Shacham Signing Dynamic audit the data to provide the security. [ABSTRACT FROM AUTHOR]

To the superiority of low cost and convenience, large-scale Internet of things has been deployed for various monitoring pervasively. Typically cloud platform is utilized as a remote data and control center. However, it is a challenge to the bandwidth and real-time feedback to the server because of mass data with uploading and processing. In this paper, to cope with the above problems, we design a gateway based on edge computing. The gateway not only retains the function of connecting multiple sensors and the interaction between devices and cloud, but also can provide real-time local services for terminal devices. It also can drop abnormal data through sensors' correlation analysis and reduce the amount of data upload by diverting the computing tasks of a server. In this paper, an anomaly detection method based on the bag of words model is introduced. At first, the gateway makes a bag of words representation by the data collected from each sensor, including normal and abnormal data. Then it aggregates these class predictions using the bag of words to calculate the final prediction. Through the simulation analysis based on the data such as humidity, temperature, and lighting monitored by sensors in agricultural greenhouses, the results show that the bag of words model can detect abnormal sensor data. [ABSTRACT FROM AUTHOR]

This paper summarizes research on the adoption of cloud enterprise resource planning (ERP) systems in small and medium-size organizations (SMEs) and large enterprises (LEs) that have employed the diffusion of innovation (DOI), and the theory and technology, organization, and environment (TOE) framework. Based on a systematic literature review, this paper concludes on 13 primary factors that are the most common influencers when adopting cloud ERP at the organizational level. These factors are categorized and discussed through the DOI, and the TOE framework. The perceived relative advantage, compatibility, complexity, trialability, and observability are identified as innovation characteristics influencing the decision to adopt cloud ERP. The technological factors are identified as system quality, security, vendor lock-in, and data accessibility, while the organizational factors are identified as a financial advantage and top management support. Finally, competitive and regulatory pressures and support are environmental factors affecting the adoption decisions. Out of the 13 factors identified, vendor lock-in, relative advantage, top management support, and competitive and regulatory environment were the most common factors affecting the decision to adopt cloud ERP. Finally, this review identifies a gap related to the lack of studies in the context of LEs, and a lack of differentiation between small and medium-size organizations, which may be fulfilled by future research. [ABSTRACT FROM AUTHOR]

Today, with the non-stop expansion of urbanization, mapping urban areas and monitoring their dynamic changes have become challenges for governments and also a hot topic for researchers. Remote sensing imageries play a key role in urban studies, the extraction of urban built-up areas, and monitoring their changes. A variety of studies have proposed methods for the extraction of regional, national, and global built-up areas. However, the majority of them used limited features and applied a manual sample selection strategy for classification, leading to time-consuming and low-efficient algorithms. This paper proposes a fully automatic procedure to real-time extract built-up areas by integrating the Luojia 1–01 nighttime lights (NTL) images, Sentinel-2 multispectral data, Sentinel-1 Radar images, and SRTM elevation data in cloud-computing Google Earth Engine. Firstly, potential built-up areas (PBA) and non-built-up areas (NBA) are obtained by applying Otsu and multi-level thresholding to some of the extracted spectral-textural-spatial (STS) features and by applying logical rules. Secondly, built-up and non-built-up samples are automatically selected and are used to train a Support Vector Machine (SVM) supervised classifier and to classify the hybrid feature set so that a preliminary classified map (PCM) can be obtained. Thirdly, the PCMs are automatically corrected using the non-built-up area, and morphological operations in the so-called post-classification to provide a refined classified map (RCM) and final built-up map. Four study areas in Northern America, Europe (Scandinavia), the Middle East, and Eastern Asia were selected to test the proposed method. Also, five state-of-the-art built-up products, accompanied by Google Earth images, were used as the reference data. The results indicate that the proposed method can accurately and automatically select samples and map built-up areas with a spatial resolution of 10 m. Its performance is validated with an average overall accuracy of 94.4% and an average Kappa coefficient of 0.89 and by visual comparison of our method results with other reference data. The proposed method has significant potential to be used in real-time extracting built-up areas and in monitoring their dynamic changes on national and global scales. [ABSTRACT FROM AUTHOR]

Cloud computing can be considered as a disruptive technology that is making life easier for Cloud users. Determining a focus of research in a specific subject area is sometimes challenging. A systematic map enables a synthesis of a scheme for categorizing data in a field of interest. The goal of this research paper is to carry out a systematic mapping study of policy language and programming models on the cloud. The mapping involved contribution category such as method, research category such as evaluation and major topics extracted from the abstracts of primary studies. The result indicated there are more publications on evaluation research in term of security with 8.9%. There were more papers published on validation research, solution proposal and experience research on the topic of paradigms with 7.53%, 6.85% and 4.11% respectively. Also, there were more publications on philosophical research in terms of privacy with 4.11%. In addition, there were more articles published on opinion research in terms of the survey with 4.11%. On the other hand, there were no articles on metric in terms of framework, paradigms and accountability, and reliability to the best of the researchers' knowledge. The outcome of this systematic study will be of benefit to cloud users, researchers, practitioners and providers. [ABSTRACT FROM AUTHOR]

With the rapid growth of internet connected devices and the tremendous amount of data that could be generated and exchanged in IoT environment, we need to reconsider in the current IoT architecture that based on Cloud computing system, to avoid the issues related to performance and scalability. Message Queue Telemetry Transport (MQTT) is one of promising protocol for data exchange in IoT that could encounter such issues because it relies on central broker located in Cloud, and this may lead to increase network congestion, performance overhead or bottleneck. Therefore, we need to leverage Fog computing by developing a distributed architecture for MQTT that contain multiple brokers. In this case, IoT services can be coordinated and managed between Fog computing and Cloud computing. However, this will open new security challenges for several reasons. Firstly, security procedures need to be modified because MQTT that based on distributed architecture require additional multiple brokers and different communication standards that may increase security threats and increase security management complexity. Secondly, MQTT is inherently lacking efficient security features because it performs username/password-based authentication in a plain text, that protected by cryptographic protocol SSL/TSL which is not consider as lightweight protocol for resources constrained devices. This paper will present taxonomy and realization process of IoT authentication scheme. In addition, the paper will discuss challenges of applying authentication mechanisms for IoT systems that based on distributed MQTT brokers. [ABSTRACT FROM AUTHOR]