Whether it’s cellphones, personal computers, or gaming consoles, technology is a part of everyone’s everyday lives, and storage is frequently a problem. One of the solutions we have for this difficulty is the on-demand accessibility of computer framework assets, which enables cloud storage and makes it accessible in any format and from any device. It is a major benefit of cloud computing. Amazon Web Administrations (or basically AWS) may be a secure cloud services stage advertising about everything businesses ought to construct advanced applications with adaptability, versatility, and unwavering quality. Another advantage is that it is significantly less expensive than purchasing items with comparable functionality, such as an SSD storage device. For many enterprises, it is also preferable to host their servers in the cloud using services like Google Cloud and Oracle Cloud. Our main topic of the paper is to compare three different major cloud computing services—AWS, Google, and Azure. Since there are different types of cloud computing services available, we would compare them to determine which is best for usage by individuals or organizations. We can also look at the services’ shared features and unique aspects that they provide to consumers.

Quality of Service (QoS) has a significant role in the provision of resources within service oriented distributed systems. In Quality of Service, cloud computing creates new challenges for improvements using the concept of virtualization. Currently, Cloud Computing is very emerging technology in every field of data storage and resource distribution over the network. Considering this new emerging technology, for the ease of data accessibility, price, resource use, restoration, response time and number of constraints the quality performance measures need to be upgraded. The paper highlights the research gap in providing a solution to achieve a Quality of Services in Cloud Computing. We also review the issues and challenges arising in cloud computing to guarantee quality.

Big Data warehouses are a new class of databases that largely use unstructured and volatile data for analytical purpose. Examples of this kind of data sources are those coming from the Web, such as social networks and blogs, or from sensor networks, where huge amounts of data may be available only for short intervals of time. In order to manage massive data sources, a strategy must be adopted to define multidimensional schemas in presence of fast-changing situations or even undefined business requirements. In the paper, we propose a design methodology that adopts agile and automatic approaches, in order to reduce the time necessary to integrate new data sources and to include new business requirements on the fly. The data are immediately available for analyses, since the underlying architecture is based on a virtual data warehouse that does not require the importing phase. Examples of application of the methodology are presented along the paper in order to show the validity of this approach compared to a traditional one.

This paper presents the design and implementation of a remote supervision and control system for a gunpowder drum filling plant. The use of technologies such as SCADA (Ignition), IoT (Node-RED), ERP (Odoo) and digital twins (Factory I/O) to achieve remote monitoring and operation of the plant is described. The solution allows full control of the production process from any location through customized graphical interfaces. The challenges encountered in integrating multiple systems and platforms are discussed, along with the security measures implemented within the Azure cloud environment. The results demonstrate the effectiveness of 4.0 technologies in automating and digitizing critical industrial processes. This research will lay the groundwork for the adoption of these approaches in companies seeking greater efficiency, traceability and safety in their operations. The experience gained allows us to successfully face the digital transformation in modern manufacturing.

Network Function Virtualization (NFV) allows for the dynamic provisioning of Virtualized Network Functions, adapting services to the complex and real-time network environment for enhanced network performance. Despite the benefits of NFV, virtual network functions (VNFs) migration and the energy consumption pose significant challenges due to the dynamic nature of the physical network, especially in the edge-cloud continuum network. Currently, people tackle this challenge by predicting the network traffic and then proactively migrating the VNFs using the predicted values. There are two challenges here. First, when SFCs belong to different network service providers, the historical network traffic data is held by the service providers, and they are reluctant to share the historical data due to privacy concerns. Without the historical network traffic data of each SFC, the resource provider that owns the underlying network cannot effectively predict the network traffic; in addition, without the feedback of the migration results of the underlying physical network, the network traffic prediction will be difficult to match the needs of the physical network. In this manuscript, we solve the problem of network traffic prediction in the NFV-based edge-cloud continuum network for multiple network service providers. We aim to improve migration performance and reduce energy consumption while ensuring resource and latency requirements. To protect the privacy of each network service provider, we apply an FL algorithm. After that, to improve the migration performance, we tailor the loss function. In the loss function, factors such as the number of migrations, the number of migration failures, and the energy consumption of the NFV-based edge-cloud continuum network are added. Additionally, to obtain the results of the SFC migration, we develop a mathematical model for the problem of multiple network service providers in the NFV-based network. The effectiveness of our algorithm is evaluated through extensive simulations, and the results show a significant reduction in the number of migrated nodes and energy consumption while improving the SFC acceptance ratio compared to state-of-the-art schemes which only use the difference between predicted and actual flows to define the loss function.

In this work, we propose a Fog-enabled UAV-as-a-Service (FU-Serve) architecture to address the issues of data transmission delay for serving time-critical Internet of Things (IoT) applications. Traditionally, in a UAV-as-a-Service (UaaS) platform, different UAVs host heterogeneous sensors, which sense the physical phenomenon and transmit the sensed data to a centralized entity. Transmission of data from the sensors to the centralized entity and making any decision for an application consumes a significant amount of time. Consequently, the traditional UaaS architecture is unsuitable for serving time-critical IoT applications such as transportation, healthcare, and industries. To address these issues of service latency for time-critical IoT applications, we present the FU-Serve architecture by introducing the concept of fog computing in the UaaS platform. We discuss all the components of FU-Serve elaborately in this paper. Additionally, we architect optimal and dynamic fog node selection mechanisms for FU-Serve, which reduce the transmission delay in the networks. The simulation results show that the FU-Serve outperforms by $75\%$ compared to the traditional UaaS platform.

The next generation of wireless and mobile networks will have to handle a significant increase in traffic load compared to the actual one. This situation calls for novel ways to increase spectral efficiency. Therefore in this paper, we propose a wireless spectrum hypervisor architecture that abstracts a radio frequency (RF) front-end into a configurable number of virtual RF front-ends. The proposed architecture has the ability to enable flexible spectrum access in existing wireless and mobile networks, which is a challenging task due to the limited spectrum programmability, $i.e.$, the capability a system has to change the spectral properties of a given signal to fit an arbitrary frequency allocation. The main goal of the proposed approach is to improve spectral efficiency by efficiently using vacant gaps in congested spectrum-bandwidths or adopting network densification through infrastructure sharing. We demonstrate mathematically how our proposed approach works and present several simulation results proving its functionality and efficiency. Additionally, we designed and implemented an open-source and free proof of concept prototype of the proposed architecture, which can be used by researchers and developers to run experiments or extend the concept to other applications. We present several experimental results used to validate the proposed prototype. We demonstrate that the prototype can easily handle up to 12 concurrent physical layers.

Recently, cloud technology has become popular for smart societies. The Cloud technology has made dynamical network changes by enabling the construction of a logical network without building a physical network. Despite recent research on the cloud, it is necessary to study security functions for the identification of fake VNFs and the encryption of communication between entities. In this paper, we proposed an LW_PKI mechanism that detects a fake VNF and guarantees data security through mutual authentication between VNFs. To evaluate the LW_PKI, we built a MANO environment to test the performance of authentication and key generation for data security. In addition, we applied the artificial intelligence algorithm to detect abnormal behavior by using real attack data in the MANO environment. The LW_PKI guaranteed the reliability of a smart service by enhancing the security of the cloud environment.

Cyber-physical Systems involve the creation, continuous updating and monitoring of virtual replicas that closely mirror their physical counterparts. These virtual representations are fed by real-time data from sensors, Internet of Things (IoT) devices and other sources, enabling a dynamic and accurate reflection of the state of the physical system. This emphasizes the importance of data synchronization, visualization and interaction within virtual environments as a means to improve decision-making, training, maintenance and overall operational efficiency. This paper presents a novel approach to a cyber-physical system to harness the power of Industry 4.0, Digital Twin technology, Virtual Reality (VR) and 3D SCADA in the context of monitoring and optimizing an olive mill. In conclusion, this paper presents a methodology that leverages digital twins and virtual reality to create an immersive, data-driven simulation and monitoring system for an olive mill. The proposed CPS takes data from the physical environment through the existing sensors and measurement elements in the olive mill, concentrates them and exposes them to the virtual environment through the Open Platform Communication United Architecture (OPC-UA) protocol, thus establishing a bidirectional and real-time communication. Furthermore, in the proposed virtual environment, the digital twin is interfaced with the 3D SCADA system, allowing it to create virtual models of the process. This innovative approach has the potential to revolutionize the olive oil industry by improving operational efficiency, product quality and sustainability, while optimizing maintenance practices.

Market turbulence with fiscal investment influences has altered IT infrastructure performance as business pursue extravagant new technology adoption. Yet, few studies have examined how shareware solution goes beyond Medium Size Enterprise that pushes efficiency and sustainability. This PLS-SEM integrated with dual primary compilation approach lessens shallow perceptions coupled with outlooks that streamline each phenomenal activity that is worthy of the necessity for competitive innovation. This unified model was applied to sampling respondents and analyzed using an ordinal regression relationship that generates a robust association that triggers the hypothesis acceptance. The adopting of BOINC shareware mesh network towards unified processing designs that were employed to build the yield by promising financial possibility using coordinated interworks hence improved group accomplishment and establishing greater esteem. This paper showcases a flexible inner IT infrastructure alongside the economic uncertainty with the framework advancement for Exostructure as a Service. Associated theoretical and practical implications were discussed.

The rapid technological progress has ushered in a new era of human-computer interaction, where the distinction between the physical and virtual realms is becoming increasingly blurred. This research paper explores the profound and multifaceted intersection of Human-Data Interaction (HDI) and Data Virtualization (DV), examining how emerging technologies can significantly enhance the exploration, comprehension, and utilization of complex, multidimensional data sets. Informed by the insights gleaned from prior research in this domain , the present study delves into the potential of DV techniques to improve HDI, with a particular focus on three experimental investigations conducted within the realms of education, healthcare, and retail. The findings reveal the benefits and potential challenges associated with the implementation of DV in these diverse contexts, offering valuable guidance for the design and development of future HDI systems. Drawing upon a diverse array of authoritative sources, this paper presents a holistic, forward-looking perspective on the future of HDI, underscoring the critical role that DV will play in shaping the next generation of human-computer interfaces and facilitating a deeper, more intuitive understanding of the digital world. Furthermore, the paper presents a preliminary framework for integrating HDI principles into standard design practices. This framework outlines key considerations and guidelines to help designers and developers incorporate HDI techniques more effectively into the development of data-driven applications and interfaces.The proposed framework outlines key considerations for enhancing data accessibility and comprehension, empowering users to exercise greater control over their data, and cultivating transparent dialogues between data providers and end-users. By establishing this conceptual foundation, the paper aims to facilitate the seamless integration of HDI principles into standard design practices, ultimately leading to more intuitive, user-centric, and ethically-grounded approaches to data interaction and utilization.

Fundamental science and applied research and technology development (RTD) are facing significant challenges that particularly compound to the notorious credibility, reproducibility, funding and sustainability crises. The underlying, serious shortcomings are substantially amplified by a metrics-obsessed publication culture, and a growing cohort of academics fishing for fairly stagnant (public) funding budgets. This work presents, for the first time, a groundbreaking strategy to successfully address these severe issues; the novel strategy proposed here leverages the distributed ledger technology (DLT) “blockchain” to capitalize on cryptoeconomic mechanisms, such as tokenization, consensus, crowdsourcing, smart contracts, reputation systems as well as staking, reward and slashing mechanisms. This powerful toolbox, which is so far widely unfamiliar to traditional scientific and RTD communities (“TradSci”), is synergistically combined with the exponentially growing computing capabilities for virtualizing experiments through digital twin methods in a future scientific “metaverse”. Project contributions, such as hypotheses, methods, experimental data, modelling, simulation, assessment, predictions and directions are crowdsourced using blockchain, and captured by so-called non-fungible tokens (“NFTs”). The so enabled, highly integrative approach, termed decentralized science (“DeSci”), is destined to move research out of its present silos, and to markedly enhance quality, credibility, efficiency, transparency, inclusiveness, sustainability, impact, and sustainability of a wide spectrum of academic and commercial research initiatives.