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Tool design is very important in the modern manufacturing industry. However, the traditional tool design process often has a heavy workload, which reduces the production efficiency of tool manufacturing enterprises. To deal with these problems, this paper proposes an integrated tool design platform to solve the problems in tool design. The tool-integrated design platform uses parametric modeling technology to allow design engineers to quickly create a geometric model of the tool by setting key parameters. The platform also introduces cutting simulation technology. Design engineers can perform simulation analysis on the platform and evaluate its performance and feasibility by simulating different tool design schemes. At the same time, this paper also uses cloud database technology to realize the function of multi-person remote collaborative design. In addition, the platform also uses genetic algorithm to optimize the cutting depth, cutting width, feed rate, and spindle speed of the tool with the minimum three-way milling force as the goal to guide the actual processing and production. In summary, by introducing technologies such as parametric modeling, parametric simulation, and cloud databases, enterprises can effectively solve the problems in the tool design process. The design time cycle is greatly shortened, and multi-person remote collaborative design becomes more convenient. This will significantly improve the enterprise's production efficiency and help the tool's precise design. [ABSTRACT FROM AUTHOR]

Recently, various emerging technologies have been introduced to host IoT applications. Edge computing, utilizing volunteer devices, could be a feasible solution due to the significant and underutilized resources at the edge. However, cloud providers are still reluctant to offer it as an edge infrastructure service because of the unpredictable nature of volunteer resources. Volunteer edge computing introduces challenges such as reliability, trust, and availability. Testing this infrastructure is prohibitively expensive and not feasible in real-world scenarios. This emerging technology will not be fully realized until dedicated research and development efforts have substantiated its potential for running reliable services. Therefore, this paper proposes VonEdgeSim, a simulation of volunteer edge computing. To the best of our knowledge, it is the first and only simulation capable of mimicking volunteer behavior at the edge. Researchers and developers can utilize this simulation to test and develop resource management models. We conduct experiments with various IoT applications, including Augmented Reality, Infotainment, and Health Monitoring. Our results show that incorporating volunteer devices at the edge can significantly enhance system performance by reducing total task delay, and improving task execution time. This emphasizes the potential of volunteers to provide reliable services in an edge computing environment. The simulation code is publicly available for further development and testing. [ABSTRACT FROM AUTHOR]

This paper proposes an embedded Internet of Things (IoT) system for bioreactor sensor integration, aimed at optimizing temperature and turbidity control during cell cultivation. Utilizing an ESP32 development board, the system makes advances on previous iterations by incorporating superior analog-to-digital conversion capabilities, dual-core processing, and integrated Wi-Fi and Bluetooth connectivity. The key components include a DS18B20 digital temperature sensor, a TS-300B turbidity sensor, and a Peltier module for temperature regulation. Through real-time monitoring and data transmission to cloud platforms, the system facilitates advanced process control and optimization. The experimental results on yeast cultures demonstrate the system's effectiveness at maintaining optimal growth, highlighting its potential to enhance bioprocessing techniques. The proposed solution underscores the practical applications of the IoT in bioreactor environments, offering insights into the improved efficiency and reliability of culture cultivation processes. [ABSTRACT FROM AUTHOR]

This paper focuses on the portability of Cloud Computing (CC) services, specifically on the problems with the portability of Infrastructure as a Service (IaaS). We analyze the current state of CC with the intention of standardizing the portability of CC solutions. CC IaaS providers often use proprietary solutions, which leads to a problem known as "vendor lock-in". Another problem might appear during migration between two providers if huge scripts are written in a proprietary language. To solve the portability problem, we applied the Model-Driven Architecture (MDA) approach to propose the general IaaS reference architecture. Using a generic IaaS model, we are able to describe entities of the IaaS environment and then design necessary transformation rules for specific IaaS environments in a simplified but flexible way. Using this model, we continue designing transformation rules that define the transcript of IaaS services. The CC-portability problem is thus solved by transforming a specific IaaS service description from one description to another through the generic model. This approach is extensible and can be adopted for the evolution of CC services. Therefore, it can be used as a generic solution to IaaS-portability issues. Using this flexible approach, the introduction of a new CC environment requires only the design of a single transformation rule that prevents proprietary peer-to-peer full-mesh mappings. Thanks to the proposed model and the transformation rules described, we were able to experimentally confirm the functionality of the transfer of the environment description between three cloud providers. [ABSTRACT FROM AUTHOR]

The assessment and adaptation of remote symptom analysis remains a significant challenge for researchers. Qualitative evaluation in real-time clinical settings is crucial for the formative adaptation of remote symptom analysis. Remote patient monitoring and cloud integration unlock opportunities for affordability, personalization, and transparency. Despite facing barriers, the authors skilfully showcase how the integration of Natural language processing (NLP) and Deep learning (DL) can lead to impactful interventions. The study identifies Bi-directional long short term memory (Bi-LSTM) and contextual embedding techniques as pivotal in improving model accuracy. By combining patient symptom sequences with specialist recommendations, the research aims to facilitate the smooth transition of patients from the hospital to home, ensuring they receive the necessary attention. The 96% accuracy rate discovered by the researchers is a compelling indicator of the potential to drive the development of future remote palliative care units. This paper emphasizes the importance of capturing patient symptom notes and highlights the extensive pilot studies conducted by the researcher before to developing this innovative adaptation. [ABSTRACT FROM AUTHOR]

Machine learning is regarded as an effective approach in network intrusion detection, and has gained significant attention in recent studies. However, few intrusion detection methods have been successfully applied to detect anomalies in large-scale network traffic data, and low explainability of the complex algorithms has caused concerns about fairness and accountability. A further problem is that many intrusion detection systems need to work with distributed data sources in the cloud. In this paper, we propose an intrusion detection method based on distributed computing to learn the latent representations from large-scale network data with lower computation time while improving the intrusion detection accuracy. Our proposed classifier, based on a novel hierarchical algorithm combining adaptability and visualization ability from a self-structured unsupervised learning algorithm and achieving explainability from self-explainable supervised algorithms, is able to enhance the understanding of the model and data. The experimental results show that our proposed method is effective, efficient, and scalable in capturing the network traffic patterns and detecting detailed network intrusion information such as type of attack with high detection performance, and is an ideal method to be applied in cloud-computing environments. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) networks comes with many challenges, especially in network architecture designs. IoT is populated by several kinds of devices with different characteristics that are autonomously managed. These devices do not have enough resources and they require to process data in real-time. Hence, there is a need to design suitable architectures for IoT networks that are as efficient as possible. Previously, Cloud Computing (CC) seemed to provide a good solution of processing data from IoT networks. Recently, Mobile Edge Computing (MEC) seems to be offering a better solution than CC by ensuring a better Quality of Services (QoS) provisioning. As a result, many MEC solutions have emerged for QoS improvement in IoT networks. These solutions mainly focus on device resource management without considering data routing from an end-user device to another, especially when the latter are mobile and need to communicate with each other. In this paper, we propose to design an adaptive routing protocol for a MEC-based network to manage efficiently, the end-user devices' energy consumption during data routing. The proposed adaptive Mobile Edge Computing-based protocol consists of two main phases: firstly, we subdivide the network's objects into clusters by exploiting a link quality prediction algorithm. Secondly, we route the data to their destination adaptively by considering the object's movement during the routing process. As presented in the simulation results, our protocol outperforms other existing routing protocols for IoT networks in terms of energy consumption. We then propose the use of our solution for data routing in IoT networks that require huge data processing and forwarding. [ABSTRACT FROM AUTHOR]

The utilization of cloud computing systems has increased recently in many organizations and applications due to on-demand service as well as the quick response capabilities of cloud-based services. However, the dynamic load conditions on cloud infrastructure cause system failures. Therefore, it is essential to balance the load on the cloud server to efficiently process the tasks. Proper balancing of workloads on the cloud computing system optimizes response time, resource utilization, and system stability. To accomplish this goal, this paper proposes a novel energy-efficient load balancing technique named 'improved k-means levy flight white shark optimization based random forest (IK-LWS-RF) algorithm'. This proposed algorithm performs two distinct operations namely clustering and scheduling to balance the workloads among the virtual servers. At first, the set of tasks needed to be executed is clustered according to their task lengths into distinct groups using an Improved K-means (IK) algorithm. The deviations that occurred on executing clustering workloads are reduced by utilizing the levy flight white shark (LWS) algorithm. The clustered tasks are then scheduled or assigned to virtual servers based on their processing capacities utilizing a random forest (RF) algorithm. Thus, the proposed IK-LWS-RF algorithm efficiently balances the workloads and as a result, resource utilization maximizes and task response time minimizes. The analytic results inherit the IK-LWS-RF algorithm's supremacy over other compared techniques. The IK-LWS-RF algorithm takes very less response time of about 11.23 ms and, achieves greater memory utilization of about 94.7% and CPU utilization of about 98.2%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a powerful technology adopted in various industries. Applications of the IoT aim to enhance automation, productivity, and user comfort in a cloud and distributive computing environment. Cloud computing automatically stores and analyzes the large amounts of data generated by IoT-based applications. Cloud computing has become a crucial component of the information age through easier data administration and storage. Currently, government agencies, commercial enterprises, and end users are rapidly migrating their data to cloud environments. This may require end-user authentication, greater safety, and data recovery in the event of an attack. A few issues were discovered by authors after analysis and assessments of various aspects of the published research papers. The research analysis reveals that the existing methods need to be further improved to address the contemporary dangers related to data security and privacy. Based on the research reports, it can be stated that safe end-to-end data transmission in a cloud-IoT environment requires modifications and advancements in the design of reliable protocols. Upcoming technologies like blockchain, machine learning, fog, and edge computing mitigate data over the cloud to some level. The study provides a thorough analysis of security threats including their categorization, and potential countermeasures to safeguard our cloud-IoT data. Additionally, the authors have summarized cutting-edge approaches like machine learning and blockchain technologies being used in the data security privacy areas. Further, this paper discusses the existing problems related to data privacy and security in the cloud-IoT environment in today’s world and their possible future directions. [ABSTRACT FROM AUTHOR]

Rapid growth in the popularity of cloud computing has been largely caused by increasing demand for scalable IT solutions, which could provide a cost-effective way to manage the software development process and meet business objectives. Optimization of cloud resource usage remains a key issue given its potential to significantly increase efficiency and flexibility, minimize costs, ensure security, and maintain high availability of services. This paper presents a novel concept of a Cloud Computing Resource Prediction and Optimization System, which is based on exploratory data analysis that acknowledges, among others, the information value of outliers and dynamic feature selection. The optimization of cloud resource usage relies on long-term forecasting, which is considered a dynamic and proactive optimization category. The analysis presented here focuses on the applicability of classical statistical models, XGBoost, neural networks and Transformer. Experimental results reveal that machine learning methods are highly effective in long-term forecasting. Particularly promising results – in the context of potential prediction-based dynamic resource reservations – have been yielded by prediction methods based on the BiGRU neural network and the Temporal Fusion Transformer. [ABSTRACT FROM AUTHOR]

Nowadays, the use of Avatars that are unique digital depictions has increased by users to access Metaverse—a virtual reality environment—through multiple devices and for various purposes. Therefore, the Avatar and Metaverse are being developed with a new theory, application, and design, necessitating the association of more personal data and devices of targeted users every day. This Avatar and Metaverse technology explosion raises privacy and security concerns, leading to cyber attacks. MV-Honeypot, or Metaverse-Honeypot, as a commercial off-the-shelf solution that can counter these cyber attack-causing vulnerabilities, should be developed. To fill this gap, we study user's engagements with Avatars in Metaverse, analyze possible security vulnerabilities, and create a model named Simplified Avatar Relationship Association with Non-linear Gradient (SARANG) that draws the full diagram of infrastructure components and data flow through accessing Metaverse in this paper. We also determine the most significant threat for each component's cyberattacks that will affect user data and Avatars. As a result, the commercial off-the-shelf (COTS) of the MV-Honeypot must be established. Graphic Abstract [ABSTRACT FROM AUTHOR]

Data trading enables data owners and data requesters to sell and purchase data. With the emergence of blockchain technology, research on blockchain-based data trading systems is receiving a lot of attention. Particularly, to reduce the on-chain storage cost, a novel paradigm of blockchain and cloud fusion has been widely considered as a promising data trading platform. Moreover, the fact that data can be used for commercial purposes will encourage users and organizations from various fields to participate in the data marketplace. In the data marketplace, it is a challenge how to trade the data securely outsourced to the external cloud in a way that restricts access to the data only to authorized users across multiple domains. In this paper, we propose a cross-domain bilateral access control protocol for blockchain-cloud based data trading systems. We consider a system model that consists of domain authorities, data senders, data receivers, a blockchain layer, and a cloud provider. The proposed protocol enables access control and source identification of the outsourced data by leveraging identity-based cryptographic techniques. In the proposed protocol, the outsourced data of the sender is encrypted under the target receiver's identity, and the cloud provider performs policy-match verification on the authorization tags of the sender and receiver generated by the identity-based signature scheme. Therefore, data trading can be achieved only if the identities of the data sender and receiver simultaneously meet the policies specified by each other. To demonstrate efficiency, we evaluate the performance of the proposed protocol and compare it with existing studies. [ABSTRACT FROM AUTHOR]

The expansion of the cloud computing market has provided a breakthrough in efficiently storing and managing data for individuals and companies. As personal and corporate data move to the cloud, diverse attacks targeting the cloud have also increased for heist beneficial information. Therefore, cloud service providers offer protective environments through diverse security solutions. However, security solutions are limited in preventing advanced attacks because it is challenging to reflect the environment of each user. This paper proposes a Cloud Intrusion Detection System (C-IDS) that adapts to each user's cloud environment and performs real-time attack detection using Natural Language Processing (NLP). Notably, the C-IDS learns the deployed client environment logs and detects anomalies using the Seq2Seq model with BI-LSTM and Bahdanau attention. We used multiple domain datasets, Linux, Windows, Hadoop, OpenStack, Apache, OpenSSH, and CICIDS2018 to verify the performance of the C-IDS. C-IDS consists of a 'recognition' that identifies logs in the deployed environment and a 'detection' that discovers anomalies. The recognition results showed an average accuracy of 98.2% for multiple domain datasets. Moreover, the detection results based on the trained model exhibited an average accuracy of 94.2% for the Hadoop, OpenStack, Apache, and CICIDS2018 datasets. [ABSTRACT FROM AUTHOR]

This paper evaluated deployment efficiency by comparing manual deployment with automated deployment through a CI/CD pipeline using Jenkins. This study involved moving from a manual deployment process to an automated system using Jenkins and experimenting with both deployment methods in a real-world environment. The results showed that the automated deployment system significantly reduced the deployment time compared to manual deployment and significantly reduced the error rate. Manual deployment required human intervention at each step, making it time-consuming and prone to mistakes, while automated deployment using Jenkins automated each step to ensure consistency and maximized time efficiency through parallel processing. Automated testing verified the stability of the code before deployment, minimizing errors. This study demonstrates the effectiveness of adopting a CI/CD pipeline and shows that automated systems can provide high efficiency in real-world production environments. It also highlights the importance of security measures to prevent sensitive information leakage during CI/CD, suggesting the use of secrecy management tools and environment variables and limiting access rights. This research will contribute to exploring the applicability of CI/CD pipelines in different environments and, in doing so, validate the universality of automated systems. [ABSTRACT FROM AUTHOR]

Serverless computing is one of the recent compelling paradigms in cloud computing. Serverless computing can quickly run user applications and services regardless of the underlying server architecture. Despite the availability of several commercial and open-source serverless platforms, there are still some open issues and challenges to address. One of the key concerns in serverless computing platforms is security. Therefore, in this paper, we present a multi-layer abstract model of serverless computing for an security investigation. We conduct a quantitative analysis of security risks for each layer. We observe that the Attack Tree and Attack-Defense Tree methodologies are viable approaches in this regard. Consequently, we make use of the Attack Tree and the Attack-Defense Tree to quantify the security risks and countermeasures of serverless computing. We also propose a novel measure called the Relative Risk Matrix (RRM) to quantify the probability of attack success. Stakeholders including application developers, researchers, and cloud providers can potentially apply these findings and implications to better understand and further enhance the security of serverless computing. [ABSTRACT FROM AUTHOR]

The Olympic Games are a sporting event and a catalyst for urban development in their host city. In this study, we utilized remote sensing and GIS techniques to examine the impact of the Olympic infrastructure on the surface temperature of urban areas. Using Landsat Series Collection 2 Tier 1 Level 2 data and cloud computing provided by Google Earth Engine (GEE), this study examines the effects of various forms of Olympic Games facility urban planning in different historical moments and location typologies, as follows: monocentric, polycentric, peripheric and clustered Olympic ring. The GEE code applies to the Olympic Games that occurred from Paris 2024 to Montreal 1976. However, this paper focuses specifically on the representative cases of Paris 2024, Tokyo 2020, Rio 2016, Beijing 2008, Sydney 2000, Barcelona 1992, Seoul 1988, and Montreal 1976. The study is not only concerned with obtaining absolute land surface temperatures (LST), but rather the relative influence of mega-event infrastructures on mitigating or increasing the urban heat. As such, the locally normalized land surface temperature (NLST) was utilized for this purpose. In some cities (Paris, Tokyo, Beijing, and Barcelona), it has been determined that Olympic planning has resulted in the development of green spaces, creating "green spots" that contribute to lower-than-average temperatures. However, it should be noted that there is a significant variation in temperature within intensely built-up areas, such as Olympic villages and the surrounding areas of the Olympic stadium, which can become "hotspots." Therefore, it is important to acknowledge that different planning typologies of Olympic infrastructure can have varying impacts on city heat islands, with the polycentric and clustered Olympic ring typologies displaying a mitigating effect. This research contributes to a cloud computing method that can be updated for future Olympic Games or adapted for other mega-events and utilizes a widely available remote sensing data source to study a specific urban planning context. [ABSTRACT FROM AUTHOR]

The processing of personal data gives a rise to many privacy concerns, and one of them is to ensure the transparency of data processing to end users. Usually this information is communicated to them using privacy policies. In this paper, the problem of user notification in case of data breaches and policy changes is addressed, besides an ontology-based approach to model them is proposed. To specify the ontology concepts and properties, the requirements and recommendations for the legislative regulations as well as existing privacy policies are evaluated. A set of SPARQL queries to validate the correctness and completeness of the proposed ontology are developed. The proposed approach is applied to evaluate the privacy policies designed by cloud computing providers and IoT device manufacturers. The results of the analysis show that the transparency of user notification scenarios presented in the privacy policies is still very low, and the companies should reconsider the notification mechanisms and provide more detailed information in privacy policies. [ABSTRACT FROM AUTHOR]

Internet of Vehicles applications are known to be critical and time-sensitive. The value proposition of edge computing comprises its lower latency, advantageous bandwidth consumption, privacy, management, efficiency of treatments, and mobility, which aim to improve vehicular and traffic services. Successful stories have been observed between IoV and edge computing to support smooth mobility and the use of local resources. However, vehicle travel, especially due to high-speed movement and intersections, can result in IoV devices losing connection and/or processing with high latency. This paper proposes a Cluster Collaboration Vehicular Edge Computing (CCVEC) framework that aims to guarantee and enhance the connectivity between vehicle sensors and the cloud by utilizing the edge computing paradigm in the middle. The objectives are achieved by utilizing the cluster management strategies deployed between cloud and edge computing servers. The framework is implemented in OpenStack cloud servers and evaluated by measuring the throughput, latency, and memory parameters in two different scenarios. The results obtained show promising indications in terms of latency (approximately 390 ms of the ideal status) and throughput (30 kB/s) values, and thus appears acceptable in terms of performance as well as memory. [ABSTRACT FROM AUTHOR]

This paper presents a web-based software tool designed to process, analyze, and visualize satellite laser altimetry data, specifically from the Ice, Cloud, and land Elevation Satellite (ICESat) mission, which collected data from 2003 to 2009, and ICESat-2, which was launched in 2018 and is currently operational. These data are crucial for studying and understanding changes in Earth's surface and cryosphere, offering unprecedented accuracy in quantifying such changes. The software tool ICEComb provides the capability to access the available data from both missions, interactively visualize it on a geographic map, locally store the data records, and process, analyze, and explore the data in a detailed, meaningful, and efficient manner. This creates a user-friendly online platform for the analysis, exploration, and interpretation of satellite laser altimetry data. ICEComb was developed using well-known and well-documented technologies, simplifying the addition of new functionalities and extending its applicability to support data from different satellite laser altimetry missions. The tool's use is illustrated throughout the text by its application to ICESat and ICESat-2 laser altimetry measurements over the Mirim Lagoon region in southern Brazil and Uruguay, which is part of the world's largest complex of shallow-water coastal lagoons. [ABSTRACT FROM AUTHOR]

This paper presents a UAV-based road illumination measurement system and evaluates its performance through experiments. The system employs a HUBSAN Zino 2+ UAV, STM32F103RCT6 microcontroller, BH1750 illuminance sensor, and GPS and integrates flight, processing, measurement, cloud platform, obstacle avoidance, communication, and power supply units via the OneNET cloud platform. Both hardware and software designs were implemented, using the Z-score algorithm to handle outliers in illumination data. The system showed a single-point measurement error rate of 1.14% and a MAPE of 5.08% for multi-point measurements. In experiments, the system's horizontal and vertical illuminance RMSE were 1.92 lx and 1.75 lx, respectively. The real-time visualization interface improved operational efficiency, cutting labor costs by half and time costs by nearly four-fifths. UAV control and monitoring from the roadside ensured safety during measurements. The system's efficiency and wide measurement range enabled extended experiments, collecting illuminance data across multiple horizontal and vertical planes. This resulted in the creation of both horizontal and innovative vertical-plane illuminance distribution maps. These findings provide valuable data for evaluating road lighting quality, enhancing road traffic safety, and improving road illumination design. [ABSTRACT FROM AUTHOR]

Cloud computing and green cloud computing are related concepts, yet they have distinct focuses and objectives. Cloud computing involves the provision of diverse computing services over the internet, such as servers, storage, databases, software, and more. It allows users to access and utilize these services as needed, often from shared and remote computing resources instead of relying on dedicated on-site infrastructure. Cloud computing has become a foundational technology in modern IT, providing businesses and individuals with flexibility, scalability, and cost-effectiveness. Green computing, also known as sustainable or eco-friendly computing, is a technological and operational domain that concentrates on enhancing the environmental friendliness of computing systems and practices. It encompasses various aspects of the IT lifecycle. This paper offers a comparative analysis between cloud computing and green computing, aiming to elucidate their distinctions and similarities, particularly within the context of environmental sustainability and resource efficiency. [ABSTRACT FROM AUTHOR]

Today, with the Industrial Revolution 4.0, digital transformation is taking place strongly in many different fields. Digital transformation can bring a competitive advantage to enterprises. However, enterprises also spend a lot of money to implement digital transformation. Therefore, whether or not digital transformation increases company performance is always of concern to company leaders. This paper aims to analyse the impact of Digital Transformation on Financial Performance, including Revenue, Profit and ROA in Vietnam. Digital Transformation is represented by the variables Cloud Computing, Big Data Analytics, Artificial Intelligence, and the Internet of Things. We used Google Forms to survey 314 leaders of enterprises in Vietnam. Then, we used descriptive statistics, correlation and regression analysis to analyse the data. Research results show that Cloud Computing, Big Data Analytics, and Artificial Intelligence positively impact financial performance. Enterprises that apply digital transformation have better financial performance than other enterprises. In contrast, there is no connection between the Internet of Things and financial performance. In addition, Firm type and Firm size also affect financial performance. FDI enterprises have higher performance than others, and more giant-sized enterprises have higher performance than others. [ABSTRACT FROM AUTHOR]

With applications in computer networks, robotics, genetics, data center network optimization, cryptocurrency exchange, transportation and logistics, cloud computing, and social network analysis, the problem of sorting permutations on transposition trees under various operations is highly relevant. The goal of the problem is to sort or rearrange the markers in a predetermined order by swapping them out at the vertices of a tree in the fewest possible swaps. Only certain classes of transposition trees, like path, star, and broom, have computationally efficient algorithms for sorting permutations. In this paper, we examine the so-called n − b r o o m transposition trees. A single broom or simply a b r o o m is a spanning tree formed by joining the center of the star graph with one end of the path graph. A generalized version of a broom known as an n − b r o o m is created by joining the ends of n brooms to one vertex, known as the n − b r o o m center. By using the idea of clear path markers, we present a novel algorithm for sorting permutations on an n − b r o o m for n > 2 that reduces to a novel 2 − b r o o m algorithm and that further reduces to two instances of a 1 − b r o o m algorithm. Our single-broom algorithm is similar to that of Kawahara et al.; however, our proof of optimality for the same is simpler. [ABSTRACT FROM AUTHOR]

Cloud-based computing networks have taken over the digital landscape. From small non-profits to large multinational corporations, more and more entities have been offloading computing effort to the cloud in order to take advantage of the increased cost-efficiency and scalability of cloud computing. One of the new types of cloud that have emerged is P2P cloud, which disengages from a traditional datacenter setup by allowing users to instead share their own computing hardware into a cloud to reap the benefits of cloud computing's advantages at an even lower cost. However, this new paradigm comes with a slew of challenges: (i) security, when operating with the devices of strangers and (ii) fairness when not operating in a market-based system. This paper aims to address these two issues by proposing an algorithm based on social credits for a P2P cloud system that uses a social network to establish its security measures. We implement our new Social Credit algorithm along with two other task-migration-based load-balancing algorithms adapted for a P2P social cloud in "Cloudsim Plus", and the relative gains are shown in terms of fairness and other related metrics. [ABSTRACT FROM AUTHOR]

In today's competitive and globalized world, innovation is essential for organizational survival, offering a means for companies to address environmental impacts and social challenges. As innovation processes accelerate, managers need to rethink the entire value-creation chain, with digital transformation emerging as a continuous process of organizational adaptation to the evolving societal landscape. The research question focuses on how digital technologies—such as artificial intelligence, Big Data, cloud computing, industrial and service robots, and the Internet of Things—influence innovation-driven revenues among enterprises within the European Union (EU). The paper examines, using neural network analysis, the specific impact of each digital technology on innovation revenues while exploring how these technologies affect various types of social innovation within organizations. Through cluster analysis, the study identifies patterns among EU countries based on their digital technology adoption, innovation expenditures, and revenues and the proportion of enterprises engaged in innovation activities. The findings highlight the central role of digital technologies in enhancing innovation and competitiveness, with significant implications for managers and policymakers. These results underscore the necessity for companies to strategically integrate digital technologies to sustain long-term competitiveness in the rapidly evolving digital landscape of the EU. [ABSTRACT FROM AUTHOR]

In the cloud-edge collaborative environment, the edge server manager will divide the physical resources based on virtualization technology, so as to deploy multiple applications on the same server. However, due to the imperfect virtualization technology and the complexity and dynamics of the applications deployed on virtual machines (VMs), it is difficult for cloud service providers to evaluate the performance of VMs and thus cannot implement dynamic resource management effectively. To address this problem, this paper proposes an adaptive resource allocation approach. Firstly, we use the profiling tools to collect hardware counters and corresponding performance that reflect the resource usage in real time. Then, we select the data instances that contribute more to the performance prediction based on Gradient-based One Side Sampling (GOSS) to build a VM performance prediction model. When the prediction results indicate the performance cannot meet users' requirements, we further apply one of the reinforcement learning framework-Deep Deterministic Policy Gradient (DDPG) to optimize the allocation of fine-grained resources. Our proposed method enables adaptive allocation of fine-grained resources in cloud environment, and the extensive experiments demonstrate that the average accuracy of performance prediction by our proposed method surpasses 95%, whereas the metrics derived from the others ranges only between 75 and 97.5%. Furthermore, the average accuracy by our proposed method on the several benchmark applications is 88.4%, gaining a performance improvement of 9.1% compared to the suboptimal baseline. [ABSTRACT FROM AUTHOR]

Cloud architecture and its operations interest both general consumers and researchers. Google, as a technology giant, offers cloud services globally. This paper analyzes the Google cluster usage trace, focusing on three key aspects: task execution times, rescheduling frequency, and the relationship between task priority and rescheduling. Firstly, we examine how memory and processor performance impact task execution times across different machines. Next, we investigate how the number of task constraints influences rescheduling frequency and overall environmental efficiency. Furthermore, we analyze how task priority affects rescheduling and explore its correlation with task constraints. The results reveal that doubling the memory size can accelerate tasks by a factor of nine and that 90% of rescheduling is associated with tasks having less than seven constraints. We aim to enhance data center performance by identifying bottlenecks in the Google Cluster Dataset and providing recommendations for all cloud service providers. Our key findings indicate that memory plays a more significant role than the processor, and tasks with higher constraints have a less pronounced impact on rescheduling than anticipated. [ABSTRACT FROM AUTHOR]

The cloud service provider market has recently expanded its offerings by providing edge as a service. This involves offering resources equivalent to those already available in the cloud, but through data centers located closer to the end user, with the goal of improving service latencies. Application providers face the challenge of selecting appropriate resources, both from the edge and cloud, to deploy their applications in a way that minimizes deployment costs while satisfying latency requirements. This paper presents Edarop (EDge ARchitecture OPtimization), an innovative orchestration mechanism for the optimal allocation of virtual machines in geographically distributed edge and cloud infrastructures. Edarop is capable of handling different edge and cloud vendors, each offering various types of VMs in different regions, with different prices, and network latencies. It also supports multiple simultaneous applications with different latency requirements and load profiles. Edarop employs Integer Linear Programming (ILP) to ensure the globally optimal solution within a reasonable time frame for the considered use cases. Several variants of the mechanism are provided, depending on whether the objective is to minimize cost, response times, or both. These variants are compared to each other and to alternative approaches, with the results showing that, unlike other methods, Edarop consistently respects latency constraints while minimizing the proposed objectives. [ABSTRACT FROM AUTHOR]

Cloud is the new enabler of data processing, archiving and analyzing, wherein offered services are built with flexible and low-coupling schemes following a microservice architecture, which is commonly managed by service mesh managers. Microservice architecture allows designers to build microservice systems based on design patterns. However, current service mesh managers are based only on pipeline patterns and delegate the construction of other patterns to virtual container managers. This limitation prevents designers from defining new patterns that can provide microservice systems with different features. This paper presents a new approach for constructing microservices systems that integrate processing patterns following a service mesh strategy. This approach will enable designers to create processing patterns not considered in current service meshes and to build designs based on the combination of patterns. The approach proposes the integration of components for implicit handling of processing patterns; this means that service mesh core tasks such as discovery process, microservice coupling and workload management become transparent, eliminating manager or end-user intervention. Encouraging results were obtained, in terms of performance and execution flexibility, in a case study, where a set of existing applications collaborating in a traditional workflow was converted into a microservice application integrating processing patterns (parallel and distributed) generated following this new approach. [ABSTRACT FROM AUTHOR]

Fog computing and the Internet of Things (IoT) have revolutionized healthcare monitoring systems, enabling real-time health data collection and transmission while overcoming cloud computing limitations. However, efficiently selecting fog nodes for application modules with varying deadline requirements and ensuring adherence to quality of service (QoS) criteria pose significant challenges due to resource constraints and device limitations. In this paper, we present a novel two-layered hierarchical design for fog devices, leveraging cluster aggregation to optimize the selection of fog nodes for healthcare applications. We introduce three efficient algorithms to minimize system latency and reduce energy consumption in fog computing environments. Our proposed model is rigorously evaluated using the iFogSim toolkit and compared with cloud-based and latency-aware model [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology.19, 2018, 10.1145/3186592]. In four distinct network topologies, our model exhibits an average latency reduction of at least 87% and energy consumption reduction of at least 76% when compared to the Cloud-based model. Similarly, when compared to the Latency-aware model proposed in [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology. 19, 2018, 10.1145/3186592], our model showcases a minimum reduction of 43% in average latency and 27% in energy consumption. Our contribution lies in addressing the complexity of selecting fog nodes for application modules with diverse deadline requirements, while ensuring QoS. This work advances the field of real-time healthcare monitoring systems, promising substantial improvements in efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

Modeling IoT applications in distributed computing systems as workflows enables automating their procedure. There are different types of workflow-based applications in the literature. Executing IoT applications using device-to-device (D2D) communications in distributed computing systems especially edge paradigms requiring direct communication between devices in a network with a graph topology. This paper introduces a toolkit for simulating resource management of scientific workflows with different structures in distributed environments with graph topology called WIDESim. The proposed simulator enables dynamic resource management and scheduling. We have validated the performance of WIDESim in comparison to standard simulators, also evaluated its performance in real-world scenarios of distributed computing. The results indicate that WIDESim’s performance is close to existing standard simulators besides its improvements. Additionally, the findings demonstrate the satisfactory performance of the extended features incorporated within WIDESim. [ABSTRACT FROM AUTHOR]

Modern technologies like quantum and cloud computing have reshaped computing, offering immense power and scalability. While beneficial, they also challenge the security of traditional cryptographic systems. Quantum computing threatens RSA and ECC with algorithms like Shor's algorithm, which can accelerate computations exponentially. This risks exposing these systems to attacks, necessitating quantum-resistant cryptography. Cloud computing poses data security concerns, requiring robust cryptographic mechanisms and access controls. Lattice-based cryptography, leveraging problems like the Short Integer Solution (SIS), emerges as a solution. This paper presents a novel quantum-resistant public key encryption scheme based on ElGamal and SIS, ensuring security against quantum and classical threats in modern cryptographic environments. [ABSTRACT FROM AUTHOR]

At present, there are some problems in strength quality training methods, such as the sports training effect can not reach the expected goal, the feasibility is not good, and the lack of pertinence leads to the unsatisfactory training effect of some athletes. In order to improve the quality training effect of competitive Taekwondo special strength, this paper studies the quality training method of competitive Taekwondo special strength based on multi-scale Retinex algorithm under the background of "Internet+". In this method, the strength quality training video image acquisition module is used to collect the strength quality training video image and transmit it to the strength quality training video image processing module, and the multi-scale Retinex algorithm is used to enhance the strength quality training video image and correct the measurement error of special strength quality training; Analyze the data related to the special strength quality training of competitive Taekwondo athletes in the intelligent evaluation module of strength quality training results, construct an evaluation index system, and evaluate the strength quality training results of competitive Taekwondo athletes; According to the evaluation results of the evaluation module, provide targeted special strength quality training programs for competitive taekwondo athletes; All sports training data are stored in the database management module. The experimental results show that this method can significantly improve the special strength quality training effect of competitive Taekwondo athletes, and can better grasp the training content and movement control accuracy. [ABSTRACT FROM AUTHOR]

As the number of cars increases annually, driving behavior analysis has become a direction worthy of in-depth research in the field of public transportation. Traditional vehicle brakes generate torque during non-braking high-speed rotation, which has a negative impact on transmission efficiency. In this paper, the sparrow search algorithm was combined with the backpropagation neural network algorithm in the context of collecting and analysing multi-source data during vehicle driving, and the backpropagation neural network algorithm was improved by using the sparrow search algorithm’s global search ability. A driving behavior recognition model was built in order to accurately distinguish various driving behaviors and evaluate their impact on fuel consumption and driving safety. The experimental outcomes showed that the vehicle driving behavior recognition method based on cloud computing and the backpropagation neural network algorithm combined with the sparrow search algorithm featured a high driving behavior recognition efficiency, and the research method featured a small error in the identification of vehicle driving behavior. The prior accuracy of the driving behavior-based fuel consumption evaluation model was as high as 89.216%, and the regression fit for the employed model after training reached around 98%. To sum up, the obtained results not only allow the implementation of new ideas and methods for the intelligent vehicle driving and traffic safety management, but they also provide an important theoretical support and practical guidance for the design and optimization of intelligent transportation systems. [ABSTRACT FROM AUTHOR]

Traditional vehicular edge computing research usually ignores the mobility of vehicles, the dynamic variability of the vehicular edge environment, the large amount of real‐time data required for vehicular edge computing, the limited resources of edge servers, and collaboration issues. In response to these challenges, this article proposes a vehicular edge computing optimization scheme based on the Lyapunov function and Deep Reinforcement Learning. In this solution, this article uses Digital Twin technology (DT) to simulate the vehicular edge environment. The edge server DT is used to simulate the vehicular edge environment under the edge server, and the base station DT is used to simulate the entire vehicular edge system environment. Based on the real‐time data obtained from DT simulation, this paper defines the Lyapunov function to simplify the migration cost of vehicle tasks between servers into a multi‐objective dynamic optimization problem. It solves the problem by applying the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Experimental results show that compared with other algorithms, this scheme can effectively optimize the allocation and collaboration of vehicular edge computing resources and reduce the delay and energy consumption caused by vehicle task processing. [ABSTRACT FROM AUTHOR]

Innovations in cloud technology over the recent years have shown tremendous growth. Apart from the need to possess a basic internet connection, another major hiccup for researchers in cloud computing is load balancing. It refers to the way resources are distributed and tasks are performed to achieve the most optimal utilization. Effective load balancing provides more user satisfaction. There are many algorithms developed to tackle the challenge of Load Balancing. An attempt is made in this paper to provide solution to this issue by developing an optimization technique that efficiently regulates the scheduler in assigning tasks to cloud resources such that optimal results are obtained. A hybridized Sparrow Search Algorithm - Grey Wolf Optimizer (HSSAGWO) had been proposed to optimize the task scheduling activity in cloud. The exploration and exploitation activities that are a part of original algorithms have been refined to achieve better performance in the proposed HSSAGWO algorithm. The performance efficiency of HSSAGWO algorithm had been ascertained by comparing it with Sparrow Search Algorithm (SSA), Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA), and Particle Swarm Optimization (PSO). Simulated experiments had been conducted using Cloudsim 3.0 tool for obtaining the results. The performance comparison had been carried out by considering the makespan, cost and response time parameters. The proposed HSSAGWO technique had produced an improvement of 9.31%,12.23%,15.55% and 17.95% for makespan when compared with SSA, GWO, GSA and PSO algorithms respectively when arrival rate is 10. [ABSTRACT FROM AUTHOR]

This article seeks to conduct a bibliometric analysis focusing on exponential technologies such as big data, internet of thing (IoT), artificial intelligence (AI), blockchain and cloud computing. It aims to outline research trends in this domain and explore their correlation with resilience. The study aims to track the evolution of research trends in this field over time and identify less explored dimensions of exponential technologies. Leveraging performance analysis and science mapping techniques, the paper highlights the significant growth and potential in these areas, considering them as pivotal agendas of the twenty-first century. By examining scientific productivity metrics such as publications, authors, institutions, countries and keywords, the article offers insights into emerging areas within exponential technologies. As the first comprehensive study of its kind, it provides a broad overview of the main trends and patterns in resilience research encompassing big data, IoT, AI, blockchain and cloud computing, consolidating them into a single cohesive narrative. [ABSTRACT FROM AUTHOR]

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Three-dimensional point clouds, as an advanced imaging technique, enable researchers to capture plant traits more precisely and comprehensively. The task of plant segmentation is crucial in plant phenotyping, yet current methods face limitations in computational cost, accuracy, and high-throughput capabilities. Consequently, many researchers have adopted 3D point cloud technology for organ-level segmentation, extending beyond manual and 2D visual measurement methods. However, analyzing plant phenotypic traits using 3D point cloud technology is influenced by various factors such as data acquisition environment, sensors, research subjects, and model selection. Although the existing literature has summarized the application of this technology in plant phenotyping, there has been a lack of in-depth comparison and analysis at the algorithm model level. This paper evaluates the segmentation performance of various deep learning models on point clouds collected or generated under different scenarios. These methods include outdoor real planting scenarios and indoor controlled environments, employing both active and passive acquisition methods. Nine classical point cloud segmentation models were comprehensively evaluated: PointNet, PointNet++, PointMLP, DGCNN, PointCNN, PAConv, CurveNet, Point Transformer (PT), and Stratified Transformer (ST). The results indicate that ST achieved optimal performance across almost all environments and sensors, albeit at a significant computational cost. The transformer architecture for points has demonstrated considerable advantages over traditional feature extractors by accommodating features over longer ranges. Additionally, PAConv constructs weight matrices in a data-driven manner, enabling better adaptation to various scales of plant organs. Finally, a thorough analysis and discussion of the models were conducted from multiple perspectives, including model construction, data collection environments, and platforms. [ABSTRACT FROM AUTHOR]

With the proliferation of cloud services driven by accessibility, enhanced performance, and cost-effectiveness, cloud service providers continuously seek methods to expedite job completion, thereby augmenting profits and diminishing energy consumption expenditures. In pursuit of this objective, numerous scheduling algorithms have been devised. Nevertheless, many of these techniques address only one specific objective within the scheduling process. This paper introduces a novel approach founded on the Reptile Search Algorithm (RSA). The RSA, albeit effective, predominantly navigates by tracing the path of optimal individuals, often overlooking valuable insights from others. Modified RSA (MRSA) is proposed to enhance RSA by incorporating a distribution estimation methodology to harness the maximum potential of the positional knowledge embedded within the majority population. MRSA is simulated using the Cloudsim tool and evaluated under diverse test conditions. The efficacy of MRSA is assessed by employing various parameters and comparing them with existing algorithms. The findings indicate that MRSA is superior to other algorithms regarding resource utilization, energy consumption, and execution cost. [ABSTRACT FROM AUTHOR]