Combination Of Cloud Computing Research Articles

Risks and Challenges of Using Cloud Computing in the Internet of Things

The term Internet of Things (IoT) did not differ from other terms in its origins, as its first signs appeared in the industrial and commercial sectors. Therefore, the benefits of this technology must be generalized to all sectors to achieve intelligent communication resulting from advanced Internet protocols, cloud services, and smart devices such as smartphones. The combination of cloud computing and IoT is associated with more security vulnerabilities. For various reasons, security is seen as the most critical concern when integrating an IoT system with the cloud. This study aimed to identify the risks and challenges of using cloud computing in the Internet of Things. The study followed the descriptive approach to explain the risks and challenges of using cloud computing. The concept of the Internet of Things and cloud computing was explained, and the challenges and risks associated with them. In addition to, the risks resulting from integrating cloud computing into the Internet of Things.

RPKI Defense Capability Simulation Method Based on Container Virtualization

As the main inter-domain routing protocol in today’s internet, the Border Gateway Protocol (BGP) faces serious security risks during actual usage. Research on BGP malicious attack methods requires a realistic network environment, and evaluation methods based on physical networks often suffer from high costs and insufficient flexibility. Thus, we propose an efficient BGP simulated network deployment system based on a virtualization technology called the SOD–BGP. This system, combining cloud computing and virtualization technologies, creates a scalable, highly flexible basic network environment that allows for the automated simulation and evaluation of actual BGP prefix hijacking attack scenarios. A Resource Public Key Infrastructure (RPKI) simulation suite is introduced into the system, emulating a certificate issuance system, certificate storage, and a certificate synchronization verification mechanism, thus aligning the simulation environment with real-world usage scenarios. Finally, we propose a data collection and performance evaluation technique to evaluate BGP networks deploying RPKI under different attack scenarios and to explore the effectiveness of RPKI defense mechanisms at various deployment rates. A comparative analysis with other simulation techniques demonstrates that our approach achieves a balanced performance in terms of deployment speed, complexity, and RPKI integrity, providing a solid simulation technology foundation for large-scale BGP security defense strategies.

Design of emergency evacuation route for large buildings based on cloud computing and GIS big data

In order to ensure the safety of life and property in large buildings, the design of emergency evacuation routes for large buildings based on cloud computing and GIS big data is studied. Combining cloud computing and GIS big data, a command model for emergency evacuation of large buildings is built. Emergency evacuation functions are realized through the access layer, business logic layer, cloud computing layer and data layer. GIS big data of large buildings is stored in the model data layer. GIS geographic data is clustered through the MapReduce based parallel K-means clustering algorithm in the cloud computing layer. After clustering, the emergency evacuation road network of large buildings is constructed through GIS in the business logic layer. On the road network, the emergency evacuation route selection method combining Dikstra algorithm and ant colony algorithm is used to realize the optimal route selection of emergency evacuation of large buildings. Experiments show that this method can effectively select the best evacuation path in large buildings, and the evacuation speed of the selected path is fast, which can ensure the safety of people in buildings.

SSKHOA: Hybrid Metaheuristic Algorithm for Resource Aware Task Scheduling in Cloud-fog Computing

Cloud fog computing is a new paradigm that combines cloud computing and fog computing to boost resource efficiency and distributed system performance. Task scheduling is crucial in cloud fog computing because it decides the way computer resources are divided up across tasks. Our study suggests that the Shark Search Krill Herd Optimization (SSKHOA) method be incorporated into cloud fog computing's task scheduling. To enhance both the global and local search capabilities of the optimization process, the SSKHOA algorithm combines the shark search algorithm and the krill herd algorithm. It quickly explores the solution space and finds near-optimal work schedules by modelling the swarm intelligence of krill herds and the predator-prey behavior of sharks. In order to test the efficacy of the SSKHOA algorithm, we created a synthetic cloud fog environment and performed some tests. Traditional task scheduling techniques like LTRA, DRL, and DAPSO were used to evaluate the findings. The experimental results demonstrate that the SSKHOA outperformed the baseline algorithms in terms of task success rate increased 34%, reduced the execution time by 36%, and reduced makespan time by 54% respectively.

Workflow-Based Fast Data-Driven Predictive Control With Disturbance Observer in Cloud-Edge Collaborative Architecture

Data-driven predictive control (DPC) has been studied and applied in various scenarios. However, the challenge of computational efficiency remains. With the development of cloud computing, it provides potential solutions to the computational problem. Hence, this paper proposes a workflow-based fast DPC method in cloud-edge collaborative architecture. First, a workflow construction method of DPC is designed to make full use of the distributed computing ability of cloud computing. Next, to tackle the uncertainty in the cloud workflow processing, we design a cloud-edge collaborative scheme. In this scheme, a edge data-driven disturbance observer is proposed to estimate and compensate the uncertain event with guaranteed UUB stability. Then, An autonomous cloud control experimental system based on container technology is designed and implemented to execute the workflow-based DPC controller. Evaluations demonstrate that computation times are reduced by 45.19 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> and 74.35 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> for two real-time control examples, and by a maximum of 85.10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> for a high-dimensional control example. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by the challenge of the further combination of cloud computing and control system such as DPC. In the existing cloud-based control system, the computation mission of native control algorithm is deployed in a single cloud server directly. However, the structure of cloud environment is distributed, and the existing computation mode could not make full use of the parallel computing of cloud computing. Thus, the computation time could not be reduced significantly, and would still have serious effects on the control quality. In this work, a novel workflow-based DPC system in cloud-edge collaborative architecture is proposed, which decompose the native control mission into distributed cloud workflow with multiple smaller computation tasks. Then, an edge disturbance observer is designed to compensate the uncertainty occurring in the cloud workflow processing. As the evaluations show, the proposed workflow-based control system in cloud-edge collaborative architecture could be applied in the control mission with real-time requirement and the high-dimension control mission with intensive data.

Combining Cloud Computing Technology to Enhance Information Technology Application in High Quality Employment for College Students

Combining Cloud Computing Technology to Enhance Information Technology Application in High Quality Employment for College Students

Joint Caching and Computing Offloading Strategies for Power Supply Chain Services Under Cloud Edge Collaborative Computing Environment

Aiming at the problem of long task calculation delay of traditional edge computing offloading strategy (COS), a joint caching and COS for power supply chain services under cloud edge collaborative computing environment is proposed by introducing the master-slave game model. First, for the network of information interaction between power grid system and electrical equipment suppliers, the corresponding overall model of cloud edge collaborative network is constructed by combining cloud computing, edge computing, supplier terminal communication and remote cloud. Then, the cloud-side collaborative network model is analyzed and solved by building the service function cache model, task unloading and data transmission model and power business data calculation model, respectively. Finally, by introducing the master-slave game model, the whole system is divided into three different interests of power service providers, communication providers and communication network terminal providers and the joint caching and COS for power supply chain services is given. Through simulation experiments, the proposed computing unloading strategy and the other three methods are compared and analyzed under the same experimental conditions. The results show that the service cache hit rate of the proposed method is the highest, reaching 96.35%. Compared with the other three comparison methods, it increased by 14.96%, 11.24% and 4.85%, respectively. In addition, its task computing delay is the least affected by agent computing power and agent computing resources, and its performance is better than the other three comparison methods.

Provably Secure Lightweight Mutual Authentication and Key Agreement Scheme for Cloud-Based IoT Environments.

A paradigm that combines cloud computing and the Internet of Things (IoT) allows for more impressive services to be provided to users while addressing storage and computational resource issues in the IoT environments. This cloud-based IoT environment has been used in various industries, including public services, for quite some time, and has been researched in academia. However, various security issues can arise during the communication between IoT devices and cloud servers, because communication between devices occurs in open channels. Moreover, issues such as theft of a user's IoT device or extraction of key parameters from the user's device in a remote location can arise. Researchers interested in these issues have proposed lightweight mutual authentication key agreement protocols that are safe and suitable for IoT environments. Recently, a lightweight authentication scheme between IoT devices and cloud servers has been presented. However, we found out their scheme had various security vulnerabilities, vulnerable to insider, impersonation, verification table leakage, and privileged insider attacks, and did not provide users with untraceability. To address these flaws, we propose a provably secure lightweight authentication scheme. The proposed scheme uses the user's biometric information and the cloud server's secret key to prevent the exposure of key parameters. Additionally, it ensures low computational costs for providing users with real-time and fast services using only exclusive OR operations and hash functions in the IoT environments. To analyze the safety of the proposed scheme, we use informal security analysis, Burrows-Abadi-Needham (BAN) logic and a Real-or-Random (RoR) model. The analysis results confirm that our scheme is secure against insider attacks, impersonation attacks, stolen verifier attacks, and so on; furthermore, it provides additional security elements. Simultaneously, it has been verified to possess enhanced communication costs, and total bit size has been shortened to 3776 bits, which is improved by almost 6% compared to Wu et al.'s scheme. Therefore, we demonstrate that the proposed scheme is suitable for cloud-based IoT environments.

An improved k-NN anomaly detection framework based on locality sensitive hashing for edge computing environment

Large deployment of wireless sensor networks in various fields bring great benefits. With the increasing volume of sensor data, traditional data collection and processing schemes gradually become unable to meet the requirements in actual scenarios. As data quality is vital to data mining and value extraction, this paper presents a distributed anomaly detection framework which combines cloud computing and edge computing. The framework consists of three major components: k-nearest neighbors, locality sensitive hashing, and cosine similarity. The traditional k-nearest neighbors algorithm is improved by locality sensitive hashing in terms of computation cost and processing time. An initial anomaly detection result is given by the combination of k-nearest neighbors and locality sensitive hashing. To further improve the accuracy of anomaly detection, a second test for anomaly is provided based on cosine similarity. Extensive experiments are conducted to evaluate the performance of our proposal. Six popular methods are used for comparison. Experimental results show that our model has advantages in the aspects of accuracy, delay, and energy consumption.

Holistic Cloud-AI Fusion for Autonomous Conversational Commerce in High-Velocity E-Commerce Channels

Given the pace at which e-commerce is growing, high velocity channels require synchronous, individual, and highly engaging methods of dealing with the customers. In this research, the combination of cloud computing and AI is proposed and an organization conversational commerce framework will be created to enhance digital economies flexibility and autonomy. Taking advantage of cloud-AI synergy, the presented system improves efficiency, computing, and customer interaction through constructive architectural structures and sophisticated algorithms. This research assesses the potential of the proposed framework in improving the overall performance and the level of customer interaction of e-commerce transactions through comparing its efficiency in improving response time and enhancing user satisfaction metrics using a case study approach. The study concludes that moving towards the integration of cloud and AI both facilitates scalability while at the same time improves resource utilization and withstands fluctuating e-commerce conversational systems. Finally, this paper provides suggestions for e-commerce firms interested in implementing such cloud-AI solutions, with a focus on the areas of concern and future research directions.

Two-stage hybrid genetic algorithm for robot cloud service selection

Robot cloud service platform is a combination of cloud computing and robotics, providing intelligent cloud services for many robots. However, to select a cloud service that satisfys the robot’s requirements from the massive services with different QoS indicator in the cloud platform is an NP hard problem. In this paper, based on the cost model between the cloud platform, cloud services and cloud service robotics, we propose a two-stage service selection strategy, namely, candidate services selection stage according to the specific QoS requirements of service robots and final cost optimization stage. Additionally, with respect to optimizing the final cost for the model, we propose a Dynamic Vector Hybrid Genetic Algorithm (DVHGA) that is integrated with local and global search process as well as a three-phase parameter updating policy. Specifically, inspired by momentum optimization in deep learning, dynamic vector is integrated with DVHGA to modify the weights of QoS and ensure the reasonable allocation of resources. Moreover, we suggest a linear evaluation method for the service robots and the cloud platform concerning time and final cost at the same time, which could be expected to be used in the real application environment. Finally, the empirical results demonstrate that the proposed DVHGA outperforms other benchmark algorithms, i.e., DABC, ESWOA, GA, PGA and GA-PSO, in convergence rate, total final cost and evaluation score.

Evaluation of problems and countermeasures in marketing teaching based on cloud computing data aggregation algorithm

In this study, we present a highly practical marketing course. Through learning marketing, we can cultivate students’ ability to analyze, solve and practice problems. Therefore, this paper would study the problems encountered in the marketing course through cloud computing technology and propose corresponding countermeasures, so as to better improve students’ ability to analyze and solve problems and enhance the effectiveness of the classroom. This paper also analyzed the data aggregation algorithm based on Particle Swarm Optimization (PSO) algorithm. The results showed that when other conditions were the same, only 6 students approve of the traditional marketing teaching mode, accounting for 12%; there were 44 people who did not approve, accounting for 88%. This number was more than half; 49 students recognized the optimized marketing teaching mode, accounting for 98%; there was 1 person who did not approve, accounting for 2%. The proportion of people who did not approve after optimization was far less than that of the traditional model, which showed that cloud computing data aggregation algorithm could find out the problems in marketing teaching and provide correct countermeasures to solve them, so that the teaching model could be optimized. (1) The data aggregation algorithm based on cloud computing has a good optimization effect on the evaluation of marketing teaching problems and countermeasures, indicating a new direction for future research. (2) The combination of cloud computing and data aggregation algorithm is not only a novel research perspective, but also a perfect idea. (3) Marketing course is a very important course in today’s society. The study of its teaching problems and countermeasures evaluation will help students improve their learning efficiency and enhance their ability to adapt to the society.

SMART CANTEEN MANAGEMENT SYSTEM

The introduction of the Internet enables financial transactions through digital payments, supporting the vision of a cashless society. The appearance of the corona virus (COVID-19) had a significant impact on how we handled currency in the past and marked a turning point in the shift from cash to contactless digital payments. By enabling devices to provide goods and services without requiring human involvement, Internet of Things (IoT) technology also advances digital payments. This study presented an Internet of Things (IoT)-enabled cashless vending machine that combines cloud computing and payment gateway for placing orders and making purchases via mobile applications. The system enables a pre-installed mobile application to scan a Quick Response (QR) code attached to a vending machine's body, opens a web-based virtual machine through the code, enables users to select and order items from the virtual vending, initiates and authorizes a digital payment through an IoT gateway installed inside the physical vending machine by connecting users' and vendors' financial entities, and then disburses the purchased goods. It uses an ATmega 2560 Microcontroller, a Esp8266 Wi-Fi module, mobile application software, and payment gateway API to run on the Arduino platform. After 150 consecutive API test calls, the system selected a product with an average response time of 14500 milliseconds. This outcome demonstrates a successful time for enhancing customer purchasing experiences with digital payment systems and an adaptable and reasonably priced intelligent vending machine based on the Internet of Things to be introduced for mass production. Key Words: smart canteen, ordering system, blynk, android phone, vending machine,

Integrated cloud computing and blockchain systems: A review

Blockchain technology is one of the crypto-currency technologies that has received a lot of attention. It has also found use in various applications, including the Internet of Things (IoT) and Cloud computing. Nonetheless, Blockchain has a significant scalability issue, restricting its ability to support services with various transactions. On the other hand, cloud computing is the on-demand availability of shared computer system resources, although issues now beset it in automation, processes, management, policies, and human aspects. Combining cloud computing and blockchain technology into a single system can improve network control, task scheduling, data integrity, resource management, pricing, fair payment, and resource allocation. In this article, we offered a comprehensive and up-to-date survey of cloud computing and Blockchain integration, a critical service for business applications due to the benefits of privacy, security, and service support. The lack of a comprehensive assessment examining the significance of BaaS platforms used in cloud computing prompted this review. We focus on the various BaaS tools that are currently in use. This report also examines the most common BaaS platforms incorporating Blockchain as a cloud service, such as Alibaba, Oracle, Azure, Amazon, and IBM. Furthermore, this research highlighted some major technological issues associated with merging Blockchain with cloud computing.

The future of cross-border wholesale payments: Mobilising central bank money across borders

Large-value cross-border payments constitute the life-blood of international trade and commerce and are thus crucial to national economies. This paper explores this high-value payment ecosystem, which has become more complex and expensive following the prudential regulatory reform rolled out in response to the global financial crisis. Recent technology innovations, in particular the emergence of blockchain and distributed ledger technology, have led to the creation of a parallel system of value transfers based on crypto currencies. The combination of cloud computing, software innovations and the increased use of application programming interfaces has enabled the creation of new business and technology models that are innovating this space. On top of this, the arrival of central bank digital currencies may have the potential to support cross-border payments, both retail and wholesale. This paper explores how far these developments can support a better system for large-value cross-border payments, providing a number of examples of real-world business approaches. A critical lens is applied to gaps outside of the technology field, such as central bank policies and liquidity management, which will need to be closed before true innovation can occur at scale.

Efficient CP-ABE Scheme Resistant to Key Leakage for Secure Cloud-Fog Computing

&lt;p&gt;With the evolution of the Internet, people&amp;rsquo;s lives are growing increasingly intelligent, and massive amounts of data are also generated. The combination of cloud computing and fog computing can store and process it efficiently. However, a new and urgent challenge has emerged about how to ensure that data in cloud and fog nodes are accessed securely. In order to address this issue, we propose an efficient anti-key leakage CP-ABE (EAL-CP-ABE) scheme based on cloud-fog computing. In our scheme, we achieve the identity tracing of the authorized user who leaked the private key by means of white-box tracing and the maximum number of user accesses. Meanwhile, we design a reward and punishment mechanism which rewards those with a good reputation and revokes authorized users who have exceeded the access limit or sold private keys to further reduce the possibility of key leakage. Due to limited local resources, we use fog nodes to achieve outsourced decryption of ciphertext. In addition, the detailed security analysis is provided to prove that the EAL-CP-ABE scheme is IND-CPA secure and traceable based on the hardness assumptions. Finally, we demonstrate that the EAL-CP-ABE scheme is efficient through the performance analysis.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Automatic Recognition and Repair System of Mural Image Cracks Based on Cloud Edge Computing and Digitization

Mural painting is the art on the wall, it is the painting that people draw on the wall, it is one of the earliest forms of painting in human history, and it is also an accessory part of the building. The decorative and beautifying functions of murals make them an important aspect of environmental art. Cloud edge computing is a combination of cloud computing and edge computing, that fully absorbs the advantages of both cloud computing and edge computing and maximizes their advantages. In this study, based on cloud edge computing and digital technology, the automatic identification and repair system of fresco image cracks is studied. Image segmentation techniques have been proposed in this study, using 60 murals in three regions as experimental objects. Through experimental analysis, it is found that the traditional pine poise treatment method takes the shortest repair time. However, for a specific image, it is difficult to guarantee the quality of its restoration. The mural image in area A was repaired with the conventional pine pitch repair method, which took 113.01 seconds, and the subjective evaluation was 69 points. Using the repair method described in this study to repair, it takes 127.38 seconds, and its subjective evaluation score is the highest, which is 87 points. The experimental results have shown that the cloud edge computing method and digital technology have had a certain positive effect on the identification and repair system of fresco image cracks.

Virtual Industrial Process Optimization Models with Cloud Management

The increasing development in ICT has driven and accelerated the advent of IoT technologies, creating the opportunity to empower and innovate data management and production management within factories with data collection, integration, storage and processing as a key challenge. By using the technologies offered by the fourth industrial revolution, it has been possible to test models through simulation before implementing them in real physical systems and with greater complexity. Specifically, by using a combination of cloud computing, IoT platforms, programming software and calculation software, three industrial processes have been virtualized with protocols based on the TCP/IP standard. By considering relevant parameters and variables, an optimization model is developed to improve the operation sequences reducing costs within the factory and a second model to minimize the total cost of operation subject to power balance and production limits. An indispensable element to consider a 4.0 architecture is the database. Mongodb Atlas NoSQL has been able to provide the research with both effective data ingestion and retrieval, which are challenging tasks in case of opting for a SQL database. This paper focuses on the concept of optimization models and their data management from the cloud. The results show that Mongodb is a highly reliable database with enormous scalability and reliability to store data coming from mathematical models or sensors of virtualized production processes, with or without a predefined structure. Moreover, with IoT platforms, it is possible to visualize the retrieved data and interpret them in a graphical way to improve a decision making within the processes.

Intelligent Traffic Flow Prediction and Analysis Based on Internet of Things and Big Data

Nowadays, the problem of road traffic safety cannot be ignored. Almost all major cities have problems such as poor traffic environment and low road efficiency. Large-scale and long-term traffic congestion occurs almost every day. Transportation has developed rapidly, and more and more advanced means of transportation have emerged. However, automobile is one of the main means of transportation for people to travel. In the world, there are serious traffic jams in almost all cities. The excessive traffic flow every day leads to the paralysis of the urban transportation system, which brings great inconvenience and impact to people's travel. Various countries have also actively taken corresponding measures, i.e., traffic diversion, number restriction, or expanding the scale of the road network, but these measures can bring little effect. Traditional intelligent traffic flow forecasting has some problems, such as low accuracy and delay. Aiming at this problem, this paper uses the model of the combination of Internet of Things and big data to apply and analyze its social benefits in intelligent traffic flow forecasting and analyzes its three-tier network architecture model, namely, perception layer, network layer, and application layer. Research and analyze the mode of combining cloud computing and edge computing. From the multiperspective linear discriminant analysis algorithm of the combination method of combining the same points and differences between data and data into multiple atomic services, intelligent traffic flow prediction based on the combination of Internet of Things and big data is performed. Through the monitoring and extraction of relevant traffic flow data, data analysis, processing and storage, and visual display, improve the accuracy and effectiveness and make it easier to improve the prediction accuracy of overall traffic flow. The traffic flow prediction of the system of Internet of Things and big data is given through the case experiment. The method proposed in this paper can be applied in intelligent transportation services and can predict the stability of transportation and traffic flow in real time so as to optimize traffic congestion, reduce manual intervention, and achieve the goal of intelligent traffic management.

Research on Electrical Equipment Monitoring and Early Warning System Based on Internet of Things Technology

With the rapid increase of global population, the power load increases rapidly, and the electrical equipment such as generator, transformer, and power line in the power system is an important basis for production and life. Its safe operation is of great significance because it is related to people’s economic development and life stability. In recent years, with the rapid development of Internet of Things technology, the Internet of Things and electrical equipment safety early warning are combined to use their respective advantages to provide a new way for electrical equipment monitoring and early warning system. Through a variety of key technologies of the Internet of Things, such as sensor technology, network communication technology, and cloud computing technology, data, information exchange, and communication between electrical equipment and Internet technology are carried out according to the agreed protocol, and the safety of the operation status of electrical equipment is monitored in real time, so as to prevent power equipment failure and other problems. Based on this background, this paper studies the Internet of Things technology and electrical equipment monitoring and early warning system and analyzes its three-tier network architecture mode from the Internet of Things technology, namely, perception layer, network layer, and application layer. The combination of cloud computing and edge computing is studied and analyzed to provide theoretical support for the research of electrical equipment monitoring and safety early warning system. The wireless sensor network equipment is also installed on the electrical equipment through the Internet of Things technology to transmit data to the base station, so as to monitor whether the equipment operates safely. The monitoring and early warning system of wireless sensor system based on Internet of Things is given through case experiment. This system realizes relevant intelligent application services, which can not only ensure the stability of information transportation but also real-time monitoring of electrical equipment, early warning, shorten troubleshooting time, reduce the workload of power station staff, and achieve the functions of safety early warning, emergency command, and control. It is of great significance to the monitoring and early warning system of electrical equipment.