Convergence Of Cloud Computing Research Articles

Enhancing Data Analytics in Environmental Sensing Through Cloud IoT Integration

Transformational advances in environmental sensing have been made possible by the convergence of Cloud Computing and the Internet of Things (IoT). The potential for these technologies is to work together and improve environmental monitoring data analytics. Integrating IoT in the cloud creates a robust environment for advanced analytics by overcoming previous hurdles in data granularity, real-time monitoring, and geographical coverage. These developments aren’t without their own set of difficulties, however, including data security, interoperability, and ethical concerns. This paper explores integrating Cloud IoT with environmental sensing. The paper addresses these issues and emphasises the need to establish ethical data-gathering methods, implement standardised communication protocols, and address privacy concerns. The study concludes by examining the challenges, concerns, and potential of integrating Cloud IoT. The integration of Cloud Computing and IoT not only transforms environmental sensing but also provides a solid foundation for collaborative research, data-driven decision-making, and environmentally conscious management.

Innovative Cloud Computing Techniques for Big Data Administration Getting Past Roadblocks and Developing New Paths

The digital era's introduction of big data has fundamentally altered the landscape of information management, providing organizations across a wide range of industries with unprecedented opportunities and difficulties. This scientific inquiry digs into the convergence of cloud computing and big data, presenting a thorough history of their evolution and underlining their critical roles in modern data governance. Through a meticulous examination of the critical role of effective big data management across diverse industries, the paper emphasizes the critical importance of cloud computing solutions in addressing the multifaceted complexities arising from data volume, velocity, variety, veracity, and security. Conventional big data handling approaches inside cloud computing frameworks, such as batch processing, stream processing, data warehousing, and virtualization, are thoroughly evaluated in light of their inherent constraints. The paper then examines modern cloud computing solutions that have been precisely tuned to handle the issues of scalability, real-time processing, and security inherent in large data management. The investigation is complemented by the inclusion of informative case studies and real-world examples that demonstrate the practical applicability of these pioneering ideas across a wide range of sectors, from entertainment to manufacturing. Furthermore, the paper defines emerging technologies and trends in cloud computing, highlights persistent obstacles, and outlines prospects for innovation and future research in the field. Ethical and regulatory aspects such as data privacy, fairness, openness, and compliance are extensively reviewed and emphasized. Finally, the article recommends for ongoing innovation, academic investigation, and ethical discernment to fully realize the promise of big data while limiting related hazards, emphasizing the dynamic nature of big data governance within the context of cloud computing. Key Words: Cloud computing, Big data, Hadoop ,Spark, AI and machine learning integration Blockchain

Advancing Cloud Technology Security: Leveraging High-Level Coding Languages like Python and SQL for Strengthening Security Systems and Automating Top Control Processes

In today's dynamic business environment, staying ahead of competitors requires the integration of cutting-edge technologies into organizational processes. Cloud Computing, a transformative technological advancement, offers a promising avenue for achieving operational efficiency and innovation. This paper explores the integration of Cloud Computing with two powerful coding languages, Python and SQL, to enhance cloud security and automate control processes. Cloud Computing's adoption has revolutionized resource management through virtualization and diverse computing models. However, it also introduces security challenges like data breaches and unauthorized access. Python and SQL emerge as essential tools for addressing these challenges and automating various control processes. Python's versatility empowers organizations to establish sophisticated security protocols and automate tasks such as intrusion detection, anomaly detection, real-time monitoring, and computer vision. On the other hand, SQL's role involves automating control processes like resource provisioning, scaling, backup, recovery, access control, and database management.
 Integrating Python and SQL offers a holistic approach to cloud security enhancement. However, challenges such as skill set requirements, code quality, integration, maintenance, scalability, monitoring, and data privacy must be addressed. Fortunately, solutions like Snowpark, dbt, Hex, and Dataiku provide platforms that unify various programming languages, fostering collaboration and streamlining tasks. This convergence of Cloud Computing with Python and SQL presents numerous benefits. Automation enhances efficiency, reduces human error, and ensures consistent control process execution. This synergy allows organizations to achieve scalability, cost savings, improved security, and comprehensive monitoring and reporting.
 As institutions increasingly d on Cloud Computing to drive innovation and competitiveness, the importance of fortifying these systems against evolving threats cannot be overstated. Integrating Python and SQL represents a pivotal juncture in achieving this goal. By harnessing their combined power, organizations can create robust security mechanisms, streamline operations, and promote cross-functional collaboration. As the digital landscape evolves, embracing this approach is crucial for sustaining success in a rapidly changing environment.

Software architectures of the convergence of cloud computing and the Internet of Things: A systematic literature review

Abstract Context Over the last few years, there has been an increasing interest in the convergence of cloud computing and the Internet of Things (IoT). Although software systems in this domain have attracted researchers to develop a large body of knowledge on software architecture designs, there is no systematic analysis of this knowledge. Objective This study aims to identify and synthesise state-of-the-art architectural elements including the design patterns, styles, views, quality attributes, and evaluation methodologies in the convergence of cloud computing and IoT. Method We used systematic literature review (SLR) methodology for a detailed analysis of 82 primary studies of a total of 1618 studies. Results We extracted six architectural design patterns in this domain; among them, edge connectivity patterns stand out as the most popular choice. The service-oriented architecture is the most frequently applied style in this context. Among all applicable quality attributes, scalability, timeliness, and security were the most investigated quality attributes. In addition, we included nine cross analyses to address the relationship between architectural patterns, styles, views, and evaluation methodologies with respect to different quality attributes and application areas. Conclusions Our findings indicate that research on software architectures in this domain is increasing. Although few studies were found in which industrial evaluations were presented, industry requires more scientific and empirically validated design frameworks to guide software engineering in this domain. This work provides an overview of the field while identifying areas for future research.

QoS provisioning framework for service-oriented internet of things (IoT)

The proliferation of ubiquitous sensing technology is bringing a rising number of the innovative models that have unique characteristics of the utility computing. These models have offered great opportunities to improve IT industries and business processes through the convergence of cloud computing and internet of things (IoT). Although this convergence establishes seamless intelligent interaction among physical and virtual entities, it has difficulty not only to meet the required level of quality of service(QoS) but also to satisfy the user’s complex demands. As a result, the predisposition to create a dynamic service-oriented environment has become a fundamental design issue. The main objective of this study is to introduce a dynamic QoS provisioning framework (QoPF) for service-oriented IoT using backtracking search optimization algorithm (BSOA). The QoPF framework is proposed to maximize the composite service quality in IoT application layer by making a balance between service reliability and acceptable cost of the computational time. The effectiveness of the QoPF framework is evaluated using a number of performance metrics such as throughput, delay time, and jitter. The experimental results demonstrate that worthiness of the QoPF to meet QoS requirements more than other state-of-the-art techniques in the literature review.

Pre-Migration of Vehicle to Network Services Based on Priority in Mobile Edge Computing

In 5G mobile networks, the convergence of cloud computing and communication leads to mobile edge computing, benefiting vehicular networks. However, the advent of a wide variety of new services and devices has changed the vehicular network landscape, challenging vehicle-to-network services’ migrations. In this paper, we focus on optimizing long-term average latency of multiple services with a different quality of services (QoS). We first introduce an offline algorithm, which can be used to find the optimal migration strategy of services. Then, we analyze the negative effect of trajectory prediction and suggest an optimizing method to reduce this effect by partial updating. Finally, based on this method, we propose a partial dynamic optimization algorithm to approximate the optimal solution, by integrating the priority queue, which utilizes QoS information. We simulate the average service latency, confirming that the proposed partial dynamic optimization algorithm keeps a stable service latency and performs better than other existing algorithms, considering the negative effect of trajectory prediction. We also verify that the proposed algorithm can meet the low latency requirement of the vehicles and the different demands of different services. Besides, the partial dynamic optimization algorithm has a lower time complexity.

Analytical foundations for development of real-time supply chain capabilities

The emergence of real-time supply chain visibility technologies has raised challenges for organisations in developing the required management capabilities required to exploit enhanced visibility. The convergence of cloud computing, mobile technology, distributed computing, and data integration technologies has enabled managers for the first time to have real-time visibility of material flows in end to end supply chains, enhancing their ability to identify bottlenecks and disruptions of material flows anywhere in their network. To effectively harness these technologies, a new set of managerial decision-making capabilities as well as enhanced data governance disciplines will be required. In this research, we employ organisational information processing theory to explore the relationship of analytical capabilities, data quality, reporting quality, and real-time data capabilities on supply chain performance. Our research model suggests that the benefits of real-time information technologies are dependent on quality reporting and managerial analytical strengths to derive supply chain benefits. The implications for managerial applications and research are further described based on these findings.

Authenticated key agreement scheme for fog-driven IoT healthcare system

The convergence of cloud computing and Internet of Things (IoT) is partially due to the pragmatic need for delivering extended services to a broader user base in diverse situations. However, cloud computing has its limitation for applications requiring low-latency and high mobility, particularly in adversarial settings (e.g. battlefields). To some extent, such limitations can be mitigated in a fog computing paradigm since the latter bridges the gap between remote cloud data center and the end devices (via some fog nodes). However, fog nodes are often deployed in remote and unprotected places. This necessitates the design of security solutions for a fog-based environment. In this paper, we investigate the fog-driven IoT healthcare system, focusing only on authentication and key agreement. Specifically, we propose a three-party authenticated key agreement protocol from bilinear pairings. We introduce the security model and present the formal security proof, as well as security analysis against common attacks. We then evaluate its performance, in terms of communication and computation costs.

Pull vs. Push: How OmniEarth Delivers Better Earth Observation Information to Subscribers

Abstract. Until very recently, the commercialization of Earth observation systems has largely occurred in two ways: either through the detuning of government satellites or the repurposing of NASA (or other science) data for commercial use. However, the convergence of cloud computing and low-cost satellites is enabling Earth observation companies to tailor observation data to specific markets. Now, underserved constituencies, such as agriculture and energy, can tap into Earth observation data that is provided at a cadence, resolution and cost that can have a real impact to their bottom line. To connect with these markets, OmniEarth fuses data from a variety of sources, synthesizes it into useful and valuable business information, and delivers it to customers via web or mobile interfaces. The “secret sauce” is no longer about having the highest resolution imagery, but rather it is about using that imagery – in conjunction with a number of other sources – to solve complex problems that require timely and contextual information about our dynamic and changing planet. OmniEarth improves subscribers’ ability to visualize the world around them by enhancing their ability to see, analyze, and react to change in real time through a solutions-as-a-service platform.

Design principles for utility-driven services and cloud-based computing modelling for the Internet of Things

By following an analysis of the state of the art in the convergence of cloud computing and the Internet of Things (IoT), this paper presents design principles for the IoT in cloud environments. A framework for on-demand establishment of IoT services based on the automated formulation of societies of internet-connected objects is described and the interactions between architecture modules are explained in detail to validate this approach. The main building blocks of the functional framework and its operational components follow the utility-driven cloud-based computing model. The framework leverages well-known technologies (i.e. linked sensor data) and standards (notably the W3C semantic sensor networks ontology). Finally, an example for service formulation and delivery of services for a smart campus scenario is provided and discussed. This paper also introduces some experiment results using the utility-driven service formulation model for mobile applications.

Utilization of Digital Contents through the Convergence of Cloud Computing and Hierarchical Mobile Networks

As digital contents are increasingly available over the Internet, the user’s mobility also is scaling up as wireless networks are widely adopted. Although the Internet offers a convenient medium for the transmission of digital contents, some issues such as network conditions, computing resources and receiver heterogeneity may affect the QoS of such transmissions. Thus, the convergence of cloud computing for managing resources, computing allocations and provisioning compounded with the emergence of HMIPv6 will lead to a more robust solution with this QoS issues. The advantages of Cloud Computing and HMIPv6 are utilized to address the shortage in computation, memory, and energy resources to support advanced graphic processing functions in real-time, especially for high-resolution digital multimedia contents, as well as security vulnerabilities for wireless networks connecting mobile terminals. This paper presents a model for the accessing digital contents through cloud computing infrastructure and hierarchical mobile networks.