Cloud Computing Networks Research Articles

An environment safety monitoring system for agricultural production based on artificial intelligence, cloud computing and big data networks

Monitoring the agricultural production environment is crucial for optimal crop growth and resource efficiency. Cloud Computing, Artificial Intelligence (AI), and Big Data have revolutionized traditional agriculture, promising improved output and product quality. The popularity of these technologies drives their application in safety monitoring. This system facilitates data collection and transmission among equipment, overcoming challenges of traditional systems like investment, costs, and maintenance. In this paper, cloud computing-based AI optimization technology and big data network were proposed to monitor the safety of the agricultural production environment, and the shortcomings of traditional distance vector hop (DV hop) positioning algorithms were analyzed in depth. RSSI (Received Signal Strength Indication) technology improved the traditional DV Hop location method. The paper analyses direct and indirect transmission for data transmission between WSN and cloud nodes and favors indirect transmission because it consumes less invalid energy. Finally, the article compares several evaluations of alternative algorithms for monitoring system performance, including data transmission reliability, data reception rate, and data delay. The experimental results in this paper showed that in the data reception rate test, the data reception rate of System 2 was 97% at the lowest and 99% at the highest, both exceeding 95%.

Optimal urban competitiveness assessment using cloud computing and neural network

In the network economy domain, urban competitiveness refers to the comparison between cities in terms of competition and development. It is the ability to gain competitive advantage under different factors. The evaluation of urban competitiveness will help cities to learn from each other, and provides reference for the government to enhance urban competitiveness. Unlike various studies in the literature exploiting only the non-linear characteristics of urban competitiveness, this paper selects BP (Back Propagation) network as the main framework for evaluation. A Genetic Algorithm BP (GABP) network based on genetic optimization is utilized. The weights are optimized besides the crossover mutation of GA algorithm. To compensate the slow prediction in the stand-alone mode, this work proposes a MapReduce (MP) based method; MR-GABP via cloud computing. The model ensures effective urban competitiveness evaluation with improved convergence speed and threshold generation speed. The systematic experiments conducted verify effectiveness of the method while the results obtained reveal that performance of the method is better than the other methods in terms of accuracy and recall yielded as 95.1% and 92.6% respectively.

Enhanced Security for Large-Scale 6G Cloud Computing: A Novel Approach to Identity based Encryption Key Generation

Cloud computing and 6G networks are in high demand at present due to their appealing features as well as the security of data stored in the cloud. There are various challenging methods that are computationally complicated that can be used in cloud security. Identity-based encryption (IBE) is the most widely used techniques for protecting data transmitted over the cloud. To prevent a malicious attack, it is an access policy that restricts access to legible data to only authorized users. The four stages of IBE are setup, key extraction or generation, decryption and encryption. Key generation is a necessary and time-consuming phase in the creation of a security key. The creation of uncrackable and non-derivable secure keys is a difficult computational and decisional task. In order to prevent user identities from being leaked, even if an opponent or attacker manages to encrypted material or to decode the key this study presents an advanced identity-based encryption technique with an equality test. The results of the experiments demonstrate that the proposed algorithm encrypts and decrypts data faster than the efficient selective-ID secure IBE strategy, a competitive approach. The proposed method's ability to conceal the identity of the user by utilizing the Lagrange coefficient, which is constituted of a polynomial interpolation function, is one of its most significant aspects.

Generic Home Automation System Using IoT Gateway Based on Wi-Fi and ant+ Sensor Network

This research article explores the use of internet of things (IoT) technology in home automation, including cloud computing and sensor networks to improve quality of life, and the increasing affordability through mobile connectivity. In this proposed smart home system, our main objective is to build a home automation system for the common consumer, which can help him to use home appliances with confidence and control at a low cost. The paper describes the building of an IoT gateway using the ANT multi-hop wireless network protocol and the Wi-Fi protocol, specifically utilizing the nRF24L01 and Esp8266 chips. Various sensor nodes, such as a water tank level sensor, human presence sensor, smart LED door sensor, and smart switch, will be integrated into the system. The main goal of the research is to develop an affordable solution for smart home technology for everyday consumers.

Cloud Computing Network Empowered by Modern Topological Invariants

The cloud computing networks used in the IoT, and other themes of network architectures, can be investigated and improved by cheminformatics, which is a combination of chemistry, computer science, and mathematics. Cheminformatics involves graph theory and its tools. Any number that can be uniquely calculated by a graph is known as a graph invariant. In graph theory, networks are converted into graphs with workstations or routers or nodes as vertex and paths, or connections as edges. Many topological indices have been developed for the determination of the physical properties of networks involved in cloud computing. The study computed newly prepared topological invariants, K-Banhatti Sombor invariants (KBSO), Dharwad invariants, Quadratic-Contraharmonic invariants (QCI), and their reduced forms with other forms of cloud computing networks. These are used to explore and enhance their characteristics, such as scalability, efficiency, higher throughput, reduced latency, and best-fit topology. These attributes depend on the topology of the cloud, where different nodes, paths, and clouds are to be attached to achieve the best of the attributes mentioned before. The study only deals with a single parameter, which is a topology of the cloud network. The improvement of the topology improves the other characteristics as well, which is the main objective of this study. Its prime objective is to develop formulas so that it can check the topology and performance of certain cloud networks without doing or performing experiments, and also before developing them. The calculated results are valuable and helpful in understanding the deep physical behavior of the cloud’s networks. These results will also be useful for researchers to understand how these networks can be constructed and improved with different physical characteristics for enhanced versions.

Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks

CC’s (Cloud Computing) networks are distributed and dynamic as signals appear/disappear or lose significance. MLTs (Machine learning Techniques) train datasets which sometime are inadequate in terms of sample for inferring information. A dynamic strategy, DevMLOps (Development Machine Learning Operations) used in automatic selections and tunings of MLTs result in significant performance differences. But, the scheme has many disadvantages including continuity in training, more samples and training time in feature selections and increased classification execution times. RFEs (Recursive Feature Eliminations) are computationally very expensive in its operations as it traverses through each feature without considering correlations between them. This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets. The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers. The proposed AKFA (Adaptive Kernel Firefly Algorithm) is for selecting features for CNM (Cloud Network Monitoring) operations. AKFA methodology is demonstrated using CNSD (Cloud Network Security Dataset) with satisfactory results in the performance metrics like precision, recall, F-measure and accuracy used.

Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

CC’s (Cloud Computing) networks are distributed and dynamic as signals appear/disappear or lose significance. MLTs (Machine learning Techniques) train datasets which sometime are inadequate in terms of sample for inferring information. A dynamic strategy, DevMLOps (Development Machine Learning Operations) used in automatic selections and tunings of MLTs result in significant performance differences. But, the scheme has many disadvantages including continuity in training, more samples and training time in feature selections and increased classification execution times. RFEs (Recursive Feature Eliminations) are computationally very expensive in its operations as it traverses through each feature without considering correlations between them. This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets. The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers. The proposed AKFA (Adaptive Kernel Firefly Algorithm) is for selecting features for CNM (Cloud Network Monitoring) operations. AKFA methodology is demonstrated using CNSD (Cloud Network Security Dataset) with satisfactory results in the performance metrics like precision, recall, F-measure and accuracy used.

AN ANALYSIS OF THE LITERATURE ON SOCIETY'S CONCERNS ON INDIA'S CYBERSECURITY IN THE TWENTY-FIRST CENTURY

Cyber security is concerned with matters of cyber-ethics and cyber-safety. The idea of cyber security should be introduced to and ingrained from a young age. Security countermeasures aid in preserving the confidentiality, availability, and integrity of information systems by preventing or lessening asset losses brought on by cyber security threats. An intrusion detection system (IDS) application analyses what happens during an execution and searches for indications that the computer has been mistreated. Numerous metaphors, including those relating to biological processes, healthcare, markets, three-dimensional space, and the preservation of tangible goods, were taken into account. These in turn sparked the investigation of a wide range of prospective tactics for advancing cyber security in the future. The concepts of "Heterogeneity," "Motivating Secure Behaviour," and "Cyber Wellness" were employed to define these tactics. Cyber security plays an important role in the development of information technology and Internet services. Our attention tends to gravitate toward "Cyber Security" when we hear about "Cyber Crimes." Therefore, how well our system handles "Cyber Crimes" is the first factor we take into account when talking about "National Cyber Security". This newsletter focuses on expanding trends in cyber security in light of the widespread adoption of cutting-edge technologies including mobile computing, cloud computing, e-commerce, and social networking. In the research, it is also covered how challenges are exacerbated by a lack of coordination between security organizations and crucial IT infrastructures.

Retracted: Real-Time Detection of Body Nutrition in Sports Training Based on Cloud Computing and Somatosensory Network.

[This retracts the article DOI: 10.1155/2022/9911905.].

THE METAVERSE: A VIRTUAL WORLD IN THE PALM OF YOUR HAND

This paper explores the actual and future impact of the Metaverse as a virtual space. Thus, it focuses the probe on the technical challenges that face this everlasting emerging technology. Today, the Metaverse presents a digital environment to build collective architecture and historical heritage in a virtual space. In this digital world, the modeling and design methodology is based on individual archetypes that can puzzle new elements. Currently, traditional methods require change and adaptation in both the education and work market, especially due to the remote-work integration in the last few years. For example, many components are required to build a Virtual Reality (VR) laboratory or a VR museum. Virtual environments present us with novel opportunities to bring together the real world with a virtual extension or duplication. This technology will remove physical boundaries and design constraints and consequently will open a gate to a metaphysical world. Imagine a world with limitless space where gravity doesn’t exist, and water can float upward. There is no limit for art and architecture but even this magic has its limitation related to computer technology. Therefore, this paper surveys the state-of-the-art computational technologies and the ecosystems of the Metaverse. The paper covers the fields of Computer Vision, Human-Computer Interaction, Artificial Intelligence, Robotics, Internet of Things (IoT), Cloud Computing, and future mobile networks. In application, the Metaverse will allow users to have a fantastic experience being part of worldwide entertainment and socio-economic network.

Active/Active data center strategies for financial services: Balancing high availability with security

The financial services sector demands high availability and security due to its critical role in global economies and the sensitive nature of its data. Active/active data center strategies have emerged as a robust solution, enabling continuous operations even during outages or maintenance by ensuring that all data centers are fully operational and synchronized. This paper explores the benefits and challenges of implementing active/active data centers in financial services, focusing on the intricate balance between achieving high availability and maintaining rigorous security standards. Active/active configurations provide unparalleled redundancy and uptime, crucial for minimizing service disruptions that could lead to significant financial losses or reputational damage. By distributing workloads across multiple active data centers, financial institutions can enhance resilience against localized failures and ensure seamless failover capabilities. However, this high availability comes with complex challenges, particularly in ensuring that security protocols are uniformly applied across all active sites. The risk of data breaches and cyberattacks increases as more endpoints are exposed, making it essential to implement advanced security measures, such as end-to-end encryption, real-time threat detection, and stringent access controls. The review also discusses the importance of regulatory compliance, as financial institutions must navigate a landscape of diverse and often stringent regulations across different jurisdictions. Achieving a balance between high availability and security requires not only technological innovation but also strategic planning and ongoing risk assessment. Emerging trends in cloud computing and software-defined networking (SDN) are also considered, highlighting how these technologies can support the dynamic and secure operation of active/active data centers. Ultimately, this review underscores the need for financial institutions to adopt a holistic approach that integrates high availability with robust security frameworks, ensuring that their data centers can meet the demands of a rapidly evolving digital environment while safeguarding against potential threats. Keywords: Security, Financial Services, Data Center, Balancing High Availability, Active.

Privacy-Preserved Image Protection Supporting Different Access Rights

The boom in cloud computing and social networking has led to a large number of online users in the networks. It is necessary to use appropriate privacy protection mechanisms to prevent personal privacy leakage. In general, image privacy protection techniques proceed with the whole image. However, the image may contain multiple users’ information, and different people’s data may require different privacy protection. In addition, cloud servers are semi-trusted instead of trusted service providers due to unknown security vulnerabilities and the possibility of grabbing sensitive data from the cloud server. In order to provide image protection with different privacy-preserved access rights and to prevent the cloud server from retrieving sensitive information from the image, we propose a privacy-preserved scheme that supports different privacy-preserved access rights in a single image based on the difficulty of solving the factorization problem and the discrete logarithm problem. The cloud server performed the image management method without knowing the privacy information contained in the protected images. By working out the proposed scheme in detail, we experimentally validate the scheme and discuss various options of access rights in practice.

Efficient Unbounded Fully Attribute Hiding Inner Product Encryption in Cloud-Aided WBANs

By integrating cloud computing and traditional wireless body area networks (WBANs), the emerging cloud-aided WBANs for smart health care have been significantly developed in recent years. However, this new technique that is now in its infancy days also suffers from some challenging issues resulted from untrustworthy cloud servers, such as data security. While attribute-based encryption seems to be suitable for ensuring data security in such complex scenarios, the flaw of existing ABE works in policy hiding inevitably leads to extra privacy issues. For efficiently guaranteeing data security without revealing anything privacy information about the data owner, we adopt the conception named inner-product encryption (IPE) in this article and propose a full attribute hiding IPE scheme that concurrently achieves unbounded attribute vectors and acceptable communication as well as computation efficiency. Compared with the only existing IPE scheme presented by Okamoto and Takashima that simultaneously achieves full attribute hiding and unbounded attributes, our scheme not only covers all the merits in their scheme but also has the high practicality due to tinier size of public parameters and more desirable computation efficiency. Eventually, performance comparisons and implementation results reveal that our article is highly feasible and effective than other works.

Multi-Objective Optimization for Resource Allocation in Vehicular Cloud Computing Networks

Modern transportation is associated with considerable challenges related to safety, mobility, the environment and space limitations. Vehicular networks are widely considered to be a promising approach for improving satisfaction and convenience in transportation. However, with the exploding popularity among vehicle users and the growing diverse demands of different services, ensuring the efficient use of resources and meeting the emerging needs remain challenging. In this paper, we focus on resource allocation in vehicular cloud computing (VCC) and fill the gaps in the previous research by optimizing resource allocation from both the provider’s and users’ perspectives. We model this problem as a multi-objective optimization with constraints that aims to maximize the acceptance rate and minimize the provider’s cloud cost. To solve such an NP-hard problem, we improve the nondominated sorting genetic algorithm II (NSGA-II) by modifying the initial population according to the matching factor, dynamic crossover probability and mutation probability to promote excellent individuals and increase population diversity. The simulation results show that our proposed method achieves enhanced performance compared to the previous methods.

A market study of early adopters of fault detection and diagnosis tools for rooftop HVAC systems

Retrofit fault detection and diagnosis (FDD) products are undergoing major advances in their ability to optimize the operation and maintenance of building heating, ventilation, and air conditioning (HVAC) systems as a result of advances in artificial intelligence, cloud computing, and low-cost sensor networks. However, many market barriers still exist to their widespread adoption. This paper is the first to study the market and barriers for FDD products from the perspective of early adopters of HVAC rooftop retrofit FDD products as part of a field study of early adopters. Researchers engaged multiple sites and installed different FDD products at these sites for the purposes of (1) evaluating market readiness, market barriers, and user experience, (2) evaluating energy and demand savings, (3) evaluating the purchase, installation, training, and use process, and (4) determining overall FDD product effectiveness. This paper presents and focuses on the market study goals, methods, results, and findings determined based on survey data collected from key stakeholders participating in the study. These stakeholders included FDD providers, energy efficiency program managers and engineers, HVAC contractors, building owners, facility managers, energy managers, and consultants. The study provides results and analyses concerning (1) current knowledge and awareness levels for FDD technologies, (2) attitudes towards FDD products, (3) market barriers and resources available for FDD, and (4) insights on how stakeholders perceive and determine the value and cost for purchasing, using, and adopting FDD technologies and products. The study produced twelve (12) key findings, which provide valuable input for energy efficiency programs in their development of more effective FDD marketing programs that can increase adoption of FDD technologies and reduce barriers to widespread adoption.

Member Tampering Attack on Burmester-Desmedt Group Key Exchange Protocol and Its Countermeasure

With the rapid development of cloud computing and mobile networks, more and more application scenarios require a secret group key for secure communication. Group Key Exchange (GKE) protocol provides a secret group key for three or more members. Burmester and Desmedt presented an influential GKE protocol, which has a broadcast version and a cyclic version. In this paper, we investigate the security weaknesses of the Burmester-Desmedt protocol. We report that both the broadcast version and the cyclic version of the Burmester-Desmedt protocol suffer member tampering attacks if the two members that belong to both group A and group B are corrupted. That is, two corrupted members can add some unknowing members of group A to group B and trick the legal members of group B to believe that these unknowing members share the secret group key with them after a protocol run. Furthermore, to defeat the member tampering attack, we propose digital signature-based improvements on the broadcast version and the cyclic version of the Burmester-Desmedt protocol. We hope our research results will encourage the development of more robust and effective GKE protocols that stand rigorous security analysis.

Security of Zero Trust Networks in Cloud Computing: A Comparative Review

Recently, networks have shifted from traditional in-house servers to third-party-managed cloud platforms due to its cost-effectiveness and increased accessibility toward its management. However, the network remains reactive, with less accountability and oversight of its overall security. Several emerging technologies have restructured our approach to the security of cloud networks; one such approach is the zero-trust network architecture (ZTNA), where no entity is implicitly trusted in the network, regardless of its origin or scope of access. The network rewards trusted behaviour and proactively predicts threats based on its users’ behaviour. The zero-trust network architecture is still at a nascent stage, and there are many frameworks and models to follow. The primary focus of this survey is to compare the novel requirement-specific features used by state-of-the-art research models for zero-trust cloud networks. In this manner, the features are categorized across nine parameters into three main types: zero-trust-based cloud network models, frameworks and proofs-of-concept. ZTNA, when wholly realized, enables network administrators to tackle critical issues such as how to inhibit internal and external cyber threats, enhance the visibility of the network, automate the calculation of trust for network entities and orchestrate security for users. The paper further focuses on domain-specific issues plaguing modern cloud computing networks, which leverage choosing and implementing features necessary for future networks and incorporate intelligent security orchestration, automation and response. The paper also discusses challenges associated with cloud platforms and requirements for migrating to zero-trust architecture. Finally, possible future research directions are discussed, wherein new technologies can be incorporated into the ZTA to build robust trust-based enterprise networks deployed in the cloud.

An energy-efficient and secure identity based RFID authentication scheme for vehicular cloud computing

Vehicular Cloud Computing (VCC) is a contemporary paradigm that includes the Internet of Things (IoT), cloud computing, and vehicular networking technologies. VCC offers vehicle-to-device, vehicle-to-infrastructure, and vehicle-to-vehicle communication in which the vehicles can communicate using sensing abilities. VCC is exploiting the IoT environment, cloud architecture, and vehicle resources. However, the energy-efficient privacy of communicators and security of communication are assertive problems in VCC. To accomplish this goal, we present an identity-based authentication scheme for VCC which also uses radio frequency identification (RFID). The security and robustness of the devised scheme are evaluated using informal and formal analysis. The informal analysis shows that our scheme is vigorous to resist various attacks. The formal analysis is done through Random Oracle Model (ROM) which shows that the scheme is secure and efficient. The performance of our scheme is also determined and compared with various related schemes which clearly illustrate the efficiency of the proposed scheme. Thus, our scheme is very efficient for employment in the VCC environment.

Design and Implementation of Cloud Computing Network Security Virtual Computing and Defense Technology

In order to improve the defense effect in network security, a research and implementation method of cloud computing network security virtual computing and defense technology is proposed. This architecture makes full use of the structural advantages of the virtualized environment, which can realize the trusted measurement of the user’s virtual machine in a more reliable way and can support the user’s diverse authentication requests. This paper introduces the concept of cloud computing, the classification of cloud computing, and the characteristics of cloud computing network security. In the case of fully considering the coupling relationship between the physical network and the logical network, the topology of the cloud computing network is established, and based on the network topology, the relevant network theory is used to analyze the cloud computing network. The avalanche failure under the computing network is studied. The research results show that the relative performance under different trusted measurement periods can reach more than 97%, which can flexibly meet the needs of user trusted authentication and can effectively provide trusted protection for user virtual machines. Adding additional protection measures to some special nodes in the cloud computing network topology to ensure that they are not damaged when attacked can greatly improve the robustness of the entire cloud computing network topology, therby ensuring that the network can avoid the attack. A large area will not be paralyzed due to the avalanche effect, and at the same time, the function and topology of the network itself have not changed. This method can effectively improve the security protection effect in network security.

Design and Implementation of City-level Smart Mathematics Education Platform Based on Big Data

<p style="text-align: justify;"><span style="font-family: "times new roman";">In the context of big data, with the rapid development of cloud computing and mobile networks, smart education has opened up the era of digitization, networking and smart cloud. Smart learning, smart education, interactive online classrooms and personalized learning have become new trends in future education development. The era of big data has had a great impact on my country's education field. A variety of network technologies and smart classrooms have been widely used in the teaching process, realizing large-scale reforms of traditional education models. The realization of the smart education platform can provide students with a learning platform with a personalized learning experience and a good smart learning atmosphere, which greatly improves the traditional teaching and learning mode. This article first analyzes the key points of smart education and provides the design principles of smart math education platform. In order to promote smart education and smart learning, secondly, it designs and implements an open and shared interactive smart math education platform. Provide specific reference materials for researchers in smart service, smart management, smart evaluation, and smart education.</span>