Integration Of Cloud Computing Research Articles

Integration and Applications of Fog Computing and Cloud Computing Based on the Internet of Things for Provision of Healthcare Services at Home

Due to the COVID-19 pandemic, the world has faced a significant challenge in the increase of the rate of morbidity and mortality among people, particularly the elderly aged patients. The risk of acquiring infections may increase during the visit of patients to the hospitals. The utilisation of technology such as the “Internet of Things (IoT)” based on Fog Computing and Cloud Computing turned out to be efficient in enhancing the healthcare quality services for the patients. The present paper aims at gaining a better understanding and insights into the most effective and novel IoT-based applications such as Cloud Computing and Fog Computing and their implementations in the healthcare field. The research methodology employed the collection of the information from the databases such as PubMed, Google Scholar, MEDLINE, and Science Direct. There are five research articles selected after 2015 based on the inclusion and exclusion criteria set for the study. The findings of the studies included in this paper indicate that IoT-based Fog Computing and Cloud Computing increase the delivery of healthcare quality services to patients. The technology showed high efficiency in terms of convenience, reliability, safety, and cost-effectiveness. Future studies are required to incorporate the models that provided the best quality services using the Fog and Cloud Computation techniques for the different user requirements. Moreover, edge computing could be used to significantly enhance the provision of health services at home.

A secure road condition monitoring scheme in cloud based VANET

Connected vehicular cloud computing (CVCC) is a promising technology that integrates cloud computing and vehicular ad-hoc network (VANET) for road condition monitoring tasks. However, maintaining security and privacy is crucial for wide deployment of such framework. Existing researches fail to address critical security flaws in CVCC scenario. This paper performs cryptanalysis on the CVCC based road condition monitoring scheme recently proposed by Wang et al. (2019). The scheme fails to achieve anonymity, unlinkability and nonrepudiation. Moreover, honest but curious RSUs may collude with each other to learn sensitive information, such as vehicle user’s trajectory. Also, an attacker can induce forged emergency case into the system through replay attack. Furthermore, in the scheme, the cloud server cannot segregate a threshold number of current reports, from a threshold number of combination of old and current reports. Along with this cryptanalysis, this paper proposes a secure road condition monitoring scheme in cloud based VANET, addressing the critical issues identified in the existing works. The security of the proposed scheme has been thoroughly evaluated using an adversary model, BAN Logic and AVISPA tool. Moreover, the performance of the proposed scheme is analyzed and compared with the existing schemes in terms of security and computation overhead.

Lifecycle Design of Disruptive SCADA Systems for Waste-Water Treatment Installations

Capacity to remotely monitor and control systems for waste-water treatment and to provide real time and trustworthy data of system’s behavior to various stakeholders is of high relevance. SCADA systems are used to undertake this job. SCADA solutions are usually conceptualized and designed with a major focus on technological integrability and functionality. Very little contributions are brought to optimize these systems with respect to a mix of target functions, especially considering a lifecycle perspective. In this paper, we propose a structured methodology for optimizing SCADA systems from a lifecycle perspective for the specific case of waste-water treatment units. The methodology embeds techniques for handling entropy in the design process and to assist engineers in designing effective solutions in a space with multiple constrains and conflicts. Evolutionary multiple optimization algorithms are used to handle this challenge. After the foundation of the theoretical model calibrated for the specific case of waste-water treatment units, a practical example illustrates its applicability. It is shown how the model can lead to a disruptive solution, which integrates cloud computing, IoT, and data analytics in the SCADA system, with some competitive advantages in terms of flexibility, cost effectiveness, and increased value added for both integrators and beneficiaries.

Information Management Strategy of Bridge Construction Technology Based on Big Data

With the rapid integration of big data, cloud computing, BIM and other new technologies into construction enterprises, the degree of information of bridge construction is gradually deepened. This process is usually very complex, there are many influencing factors, and the construction progress is difficult to control. This paper summarizes the fiber composite materials which have potential application in Bridge Engineering in recent years. This paper first discusses the development prospect of carbon fiber, and then briefly introduces the mechanical properties of carbon fiber composite laminates. Referring to relevant literature, this paper introduces BIM Technology in detail.Based on the current situation and application advantages of BIM technology,it fully understands the practical application of BIM Technology in bridge engineering design, construction stage and later operation and maintenance. Their tensile strength, elastic modulus, longitudinal and transverse thermal expansion are introduced in detail.The results show that BIM Technology has been widely used in bridge engineering projects. In 2015, the Growth rate of BIM technology usage in construction industry country a was 1.536.

Research on failure rate of self-service payment terminal of power supply company based on cloud computing

With the continuous development of power companies, the load on the user side continues to diversify, the traditional payment methods in the power system are constantly updated, and the scale of self-service payment terminals has been fully expanded. However, as the scale of self-service payment terminals becomes larger and terminal equipment functions become more abundant, the failure rate of terminal equipment is also on the rise. Cloud computing can provide a large number of low-cost computing and storage resources, and has the characteristics of easy deployment and strong scalability, which can meet the software and hardware needs of big data analysis. Therefore, the effective integration of cloud computing and big data technology and the use of their respective advantages have important practical significance in the big data analysis of equipment failure. Completing the prediction of the failure rate of the terminal equipment according to the designed big data analysis framework will help to arrange the maintenance plan of the terminal equipment reasonably and improve the reliability of the power self-service payment terminal.

Big Data with Cloud Computing: Discussions and Challenges

Big Data and cloud computing integration has become a formidable strategy for businesses to unlock the potential of enormous and complicated data sets. With the scalability, flexibility, and cost-effectiveness that this combination provides, businesses are able to handle and analyse massive amounts of data in a distributed, as-needed way. But there are also issues and restrictions that need to be resolved with this integration. This overview of the literature focuses on the issues, difficulties, and potential applications of big data and cloud computing. It offers information on the advantages of this integration, such as improved data processing capabilities, increased scalability, and cost reduction. The difficulties with data migration, security, privacy, data governance, talent needs, vendor lock-in, and compliance are all discussed. Future research areas are also highlighted, such as enhanced analytics methods, edge computing integration, privacy-preserving data analysis, hybrid cloud architectures, data governance, real-time decision-making, and applications specialised to particular industries. Organisations can maximise the potential of Big Data with cloud computing and generate priceless insights to fuel innovation and informed decision-making by comprehending, tackling, and investigating these difficulties.

Privacy-preserving location data stream clustering on mobile edge computing and cloud

The advancements in positioning technologies have led to the emergence of various location-based services, resulting in a drastic increase in location-based data generation, producing big-data. Location data are often linked with user privacy, as they can reveal sensitive information such as the places visited by a person. Moreover, most location-based services involve resource-constrained devices, needing lightweight data processing approaches. Due to these reasons, privacy and efficiency have been two of the primary components of location-based data processing. The existing approaches do not study both issues in the same setting. Consequently, current methods fail to provide efficient privacy preservation solutions towards location-based data stream processing. To address these issues, we investigate the effective integration of edge computing, cloud computing and differential privacy for location-based data clustering, which is an essential area in service recommendations (e.g. recommending the closest hotels to a particular location). In the proposed setup, we use local differential privacy to ensure user privacy. Next, we apply edge-based clustering on the differentially private input data using mobile edge devices. Next, the centroids of the clusters are collected at a cloud server to generate final clustering in a privacy-preserving manner. Our experiments show that the proposed approach provides maximum accuracy of 90% on lower privacy budgets (e.g. ɛ = 0.45-0.5).

RETRACTED: Research on the construction of university education information platform supported by cloud computing

The level of information construction in Colleges and universities is an important indicator to reflect the level of education in Colleges and universities. With the development of cloud computing technology, how to solve the above problems and how to use cloud computing to improve the university education information platform is a problem that the builders of university education information platform should think deeply. This paper first analyzes the current situation of university education information cloud platform, and then puts forward the education information scheme based on cloud computing, and finally realizes the education information system based on cloud computing. The system promotes the integration of cloud computing, big data and other cutting-edge technologies with the education industry, and improves the work efficiency of relevant personnel in Colleges and universities.

Discussion on the Integration and Application of Artificial Intelligence, Big Data and Cloud Computing

With the development of the Internet and technology, a revolution in information technology is taking place. Artificial intelligence, big data and cloud computing have been applied in various fields, promoted social progress and brought new growth points for economic development. Artificial intelligence, big data, and cloud computing are independent, mutually reinforcing, and complementary to each other. They are also gradually merging from a single development direction to a three-dimensional integration, forming the "ABC Golden Triangle." In the future, the integrated development of the three is an inevitable development trend, and the integrated application of the three will also be reflected in all walks of life. Based on it, this article discusses the integration, application fields and future development trends of artificial intelligence, big data, and cloud computing.

Integration of Cloud Computing, Big Data, Artificial Intelligence, and Internet of Things

Cloud computing, internet of things (IoT), artificial intelligence, and big data are four very different technologies that are already discussed separately. The use of the four technologies is required to be more and more necessary in the present day in order to make them important components in today's world technology. In this paper, the authors center their attention on the integration of cloud, IoT, big data, and artificial intelligence. Several kinds of research papers have surveyed artificial intelligence, cloud, IoT, and big data separately and, more precisely, their main properties, characteristics, underlying technologies, and open issues. However, to the greatest of the authors' knowledge, these works require a detailed analysis of the new paradigm that combines the four technologies, which suggests completely new challenges and research issues. To bridge this gap, this paper presents a survey on the integration of cloud, IoT, artificial intelligence, and big data.

A Surveillance Video Real-Time Analysis System Based on Edge-Cloud and FL-YOLO Cooperation in Coal Mine

Video monitoring is an important means to ensure production safety in coal mine. However, the currently intelligent video surveillance is difficult to respond in real-time due to the latency of cloud computing. In this paper, a cloud-edge cooperation framework is proposed, which integrates cloud computing and edge computing in a coordinated manner. The cloud computing is used to process non-real-time and global tasks, while the edge computing is responsible for handling local monitoring video in real-time. In order to realize cloud-edge data interaction and online optimization for edge models, the heterogeneous converged network is built. In addition, an object detection model FL-YOLO composed of depthwise separable convolution and down-sampling inverted residual block is proposed, which realizes real-time video analysis at the edge. Finally, this paper discusses the complexity of FL-YOLO by its computational cost and model size. The experiment results show that the model size of FL-YOLO is 16.1MB, which is very light, and it achieves 36.7 FPS on NVIDIA Jetson TX1 and an AP of 76.7% on Multi-scene pedestrian dataset. Comparing with mainstream object detection models, FL-YOLO completes faster detection speed and higher accuracy, and it has lower calculation complexity and smaller model scale. Furthermore, the AP on Single-scene pedestrian dataset of FL-YOLO is improved to 80.7% by cloud-edge cooperation. K-Fold method is also used to further compared the performance of FL-YOLO and other models. Moreover, system test is implemented on coal mine, which validates the actual engineering effect of the proposed cloud-edge cooperation framework.

Solving Coupling Security Problem for Sustainable Sensor-Cloud Systems Based on Fog Computing

Modern societies are becoming increasingly reliance on inter-connected digital systems. Despite numerous benefits, it is important to overcome existing security problems in a highly inter-connected system, like Sensor-Cloud systems. Sensor-Cloud is the product of the integration of wireless sensor networks and cloud computing. However, when a physical sensor node receives multiple service commands simultaneously, there will be some service collisions, namely, coupling security problem. This coupling security problem may lead to the failure of sustainable services and the system security threat. In order to solve the problem, sustainable resource management and maximum resource utilization are important. In this paper, we extend the Kuhn-Munkres algorithm based on fog computing to achieve sustainability. To begin with, we design a buffer queue in fog computing layer which will return the result to the cloud layer directly to increase the resource utilization. Then, we extend the Kuhn-Munkres algorithm to get the initial assignments of resources. The last step is to determine whether the initial assigned resources can be further scheduled, which means that we further improve the resource utilization to realize sustainable resource management. The results demonstrate that our method outperforms the traditional scheduling methods, which decreases both of the rounds and computational costs of scheduling by 24.04-57.78 percent and 9.88-31.51 percent, respectively. The experimental evaluations proved that the performance of the proposed fog-based scheme can effectively solve coupling security problem for sustainable Sensor-Cloud systems.

A Decentralized Blockchain-based Architecture for a Secure Cloud-Enabled IoT

The integration of cloud computing and Internet of Things (IoT) offers a promising, rich platform for data collection and analysis in smart healthcare. In such a model, IoT devices collect data about patient health status through multiple intelligent sensors, whereas cloud offers scalable resources to quickly meet workload demands. Despite these remarkable improvements, the current architectures do not sufficiently address the security needs for patient medical records. In this perspective, and bearing in mind the specific characteristics of each technology, we propose a distributed security mechanism in a way that fits with IoT and cloud constraints. Our contribution to secure cloud-enabled IoT is twofold. First, we rely on OM-AM (Objective, Model, Architecture and Mechanism) for modeling and analysing the security and privacy requirements of smart healthcare. Second, we use blockchain architecture along with Attribute-Based Access Control (ABAC) model as a decentralized flexible system to support access control decisions. In particular, we rely on XACML (eXtensible Access Control Markup Language) to easily build and implement robust policies required for maintaining a secure IoT-based environment. The novelty of the proposed framework lies at smartly leveraging the recent technologies to keep health information confidential. In fact, putting blockchain and IoT together would undoubtedly create a totally new solution for remote patient monitoring. The simulation results show that the proposal is an efficient way of implementing ubiquitous and cognitive tools for smart healthcare systems.

Optimizing Energy Consumption for Cloud Internet of Things

Internet of Things (IoT) and Cloud Computing (CC) are considered as two of the greatest technology revolutions in the last few years. New technology raised in recent years is known as CloudIoT or Cloud of Things (CoT), which integrates cloud computing and internet of things. Dynamic and heterogeneous environment, energy efficiency, and delay-sensitive are the major issues in CoT. Energy efficiency is one of the basic requirement of IoT applications and resources in cloud, which is really challenging issue in CoT. This paper proposes optimization of energy scheme for CoT by applying genetic algorithm (GA). An extensive numerical simulation is carried out to verify the effectiveness of the proposed method compared to the ETCORA algorithm. The analytical results present better performances of the proposed optimizing energy consumption technique. Results also show that the proposed method indeed outperforms the ETCORA in reducing energy consumption of task requests.

Research on Sharing Logistics Information Platform Model based on Computer Big data Technology

A few years ago, computer big data was still in the stage of concept speculation, but since the 13th five year plan outline pointed out that the national computer big data strategy should be implemented, the situation has changed greatly. The computer big data logistics information platform takes computer big data and Internet of things as the core support, integrates cloud computing, mobile Internet, artificial intelligence and other technologies to provide process visualization, dynamic tracking and integrated intelligent logistics services for relevant users. On this shared information platform, the relevant entities can realize real-time data exchange and information sharing, which greatly improves the operational efficiency of logistics integration of upstream and downstream enterprises in the supply chain, and promotes the development of local economy [1].

Task Offloading Strategy of 6G Heterogeneous Edge-Cloud Computing Model considering Mass Customization Mode Collaborative Manufacturing Environment

With the continuous integration of cloud computing, edge computing, and Internet of things (IoT), various mobile applications will emerge in future 6G network. Driven by real-time response and low energy consumption requirements, mobile edge-cloud computing (MECC) will play an important role to improve user experience and reduce costs. However, due to the complexity of applications, the computing capacity of devices cannot meet the low-latency and low energy consumption requirement. Meanwhile, subject to the limited supplement of power and energy system, the heterogeneous multilayer mobile edge-cloud computing (HetMECC) is proposed to join cloud server, edge server, and terminal devices for data calculation and transmission. By dividing computing tasks, terminal applications can receive reliable and efficient computing services. The simulation results show that the proposed model can achieve the low-latency requirement of data calculation and transmission and improve the robustness of architecture.

An Edge-Driven Security Framework for Intelligent Internet of Things

The rise of the intelligent Internet of Things (IoT) promotes the development of emerging technologies and industries with the integration of edge computing and cloud computing. This integrated paradigm becomes a promising prospect to continuously expand the service capabilities of IoT. However, edge-driven intelligent IoT systems raises serious security issues as the systems are prone to be compromised by malicious attacks. In this article, an edge-driven security framework is investigated for intelligent IoT systems. First, we introduce the architecture of edge-driven intelligent IoT, and present typical edge-driven intelligent IoT applications. Second, we point out the security threats in edge-driven intelligent IoT in terms of attack behaviors of adversaries. Third, we develop a case study of edge-driven intelligent IoT from the security perspective. Finally, we discuss future research directions in this emerging area.

Network perception task migration in cloud-edge fusion computing

With the development of cloud computing, edge computing has been proposed to provide real-time and low-delay services to users. Current research usually integrates cloud computing and edge computing as cloud-edge fusion computing for more personalized services. However, both cloud computing and edge computing suffer from high network consumption, which remains a key problem yet to be solved in cloud-edge fusion computing environments. The cost of network consumption can be divided into two parts: migration costs and communication costs. To solve the high network consumption problem, some virtual machines can be migrated from overloaded physical machines to others with the help of virtualization technology. Current network perception migration strategies focus more on the communication cost by optimizing the communication topology. Considering both communication and migration costs, this paper addresses the high network consumption problem in terms of the communication correlations of virtual machines and the network traffic of the migration process. It proposes three heuristic virtual machine migration algorithms, LM, mCaM and mCaM2, to balance communication costs and migration costs. The performance of these algorithms is compared with those of existing virtual machine migration algorithms through experiments. The experimental results show that our virtual machine migration algorithms clearly optimize the communication cost and migration cost. These three algorithms have a lower network cost than AppAware, an existing algorithm, by 20% on average. This means that these three algorithms improve the network performance and reduce the network consumption in cloud-edge fusion computing environments. They also outperform existing algorithms in terms of operation time by 70% on average.

Research on big data storage and compression method for dual core intelligent meter

With the rapid integration and development of cloud computing, big data and smart grid, not only the type and accuracy requirements of the collected data are increasing, but also the storage depth is getting higher and higher. In hardware, the dual port RAM design is used to improve the real-time performance of reading and writing, digital filter is used for preprocessing in software, and “Equivalent Normalization (EN)” is used to improve storage depth. These schemes save hardware storage space and improve data transmission efficiency in practical application. So the advantages of dual-core intelligent power meter will be further exploited and brought into play.

Expected impact of industry 4.0 technologies on sustainable development: A study in the context of Brazil's plastic industry

Abstract The impact of Industry 4.0 technologies on sustainability based on economic, environmental, and social aspects is gaining increasing attention from researchers. In this study, we investigated these impacts using the Triple Bottom Line perspective for sustainable development. A sustainability-oriented model for evaluating the influence of Industry 4.0 technologies on sustainable metrics is proposed. This model analyses the impact of Industry 4.0 technologies on several key performance indicators related to sustainable development. The model was tested in the plastics industry which has a high potential of Industry 4.0 technological aggregation in emerging economies. A fuzzy TOPSIS multi-criteria method was used to classify Industry 4.0 technologies, identifying those with the strongest and weakest impacts on sustainable development. As a result, we show that the internet of things, cyber-physical systems, sensors, and big data implementation are drivers for sustainable development. We also evidenced that these technologies are associated with substantial positive impacts on economic metrics. However, there was a much smaller positive influence on environmental and social metrics, suggesting an imbalance on the Triple Bottom Line perspective for the plastics industry. In addition, we found negative impacts from robots on job creation and low influence from cloud computing and system integration technologies for sustainable development. Based on these findings, our work contributes to the decision-making process by aiding managers, process engineers, and stakeholders in understanding and estimating the expected impacts from Industry 4.0 technologies on economic, environmental, and social aspects for sustainable development.