Cloud Computing Research Articles

TrusO- A Secured Approach Data Transmission from IOT to Cloud

IoT is the intercommunication between electronic devices that acts as an intelligent sensing system. The IoT is connected to the internet to share data among various electronic devices to work intelligently and efficiently. Massive amounts of data are shared among the devices transferred through the insecure internet for storage. Cloud computing is a tremendous emerging technology to store large amounts of data related to the IoT. Data privacy is a significant issue in cloud computing. The data, which should be highly confidential, are transferred by the IoT devices, and will be stored in the cloud. The data in the cloud are highly vulnerable to various attacks namely Man-in-The-Middle attack, Impersonation attack, spoofing attack, Password Guessing attack etc. To avoid particular situations, this article proposes an access control Attribute-Based Encryption (ABE) method to store the data in the cloud using a Trusted Organizer (TrusO) in NTRU encryption. With this approach, the data are proved to be confidential during the data transmission and while stored in the cloud infrastructure. The data stored in the cloud shall be accessed by trusted parties, and TrusO will rectify unwanted data leakage. So it ensures data privacy, confidentiality, and access control to the data in the cloud that are transferred by the IoT devices.

Prioritising elements of digitalisation for lean and green SME operations: an ISM-MICMAC study in the Indian context

PurposeThe purpose of this paper is to identify the key elements of digitalization for lean and green operations and develop a conceptual framework for their implementation. The paper focuses on small and medium-sized enterprises (SMEs) and aims to explore the role of digitalisation in enhancing their operational efficiency and sustainability. By identifying key factors and metrics related to digitalisation, the paper seeks to provide insights for strategic management to improve lean and green practices in SMEs.Design/methodology/approachInterpretive structural modeling (ISM) and Matrix of Cross-Impact Multiplication Applied to a Classification (MICMAC) approaches were deployed to classify the major dimensions of digitalisation. These methods were used to analyse the direct and indirect relationships among the identified elements of digitalisation. A comprehensive literature review and expert consultations were conducted to identify 13 key elements relevant to lean and green operations. The experts also assisted in determining the contextual relationships between the variables for the ISM model.FindingsThe analysis classified the 13 identified elements of digitalisation into different levels according to their driving power and dependence. The results from the ISM model indicated three levels of classifications. At level-1, Internet of things (IoT) and smart sensors (IoT & SS), automation and robotics directly influence lean and green operations. At level-2, real-time monitoring and control system and at level-3 fundamental elements of digitalisation such as big data analytics, predictive maintenance, cloud computing, energy management systems (EMSs), additive manufacturing, blockchain, digital workflow automation and digital collaboration platform.Originality/valueAll elements are interrelated and essential for making strategic decisions. This study emphasis the significance of prioritising these attributes to attain long-term excellence through digitalisation. For the industries that seek the reward of lean and green operations for their growth, this paper has great practical utility. Identifying the key factors of digitalisation would help strategic managers in handling lean and green environment of SMEs through these aspects.

An Evolutionary Review on Resource Scheduling Algorithms Used for Cloud Computing with IoT Network

The goal of the distributed computing paradigm known as "cloud computing," which necessitates a large number of resources and demands, is to share the resources as services delivered over the internet. Task scheduling is a very significant stage in today's cloud computing. While lowering the makespan and cost, the task scheduling method must schedule the tasks to the virtual machines. Various academics have proposed many scheduling methods for organizing work in cloud computing environments. Scheduling has been considered the most important for cloud computing since it might directly impact a system's performance, including the efficiency of resource utilization and running costs. This paper has compared all the already used algorithms that work on different parameters. We have tried to give better solutions for resource allocation and resource scheduling. In this study, various swarm optimization, evolutionary, physical, evolving, and fusion meta-heuristic scheduling methods are categorized according to the environment of the scheduling problem, the main scheduling goal, the task-resource mapping pattern, and the scheduling constraint. More specifically, the fundamental concepts of cloud task scheduling are addressed without difficulty.

Predictive modeling for healthcare worker well-being with cloud computing and machine learning for stress management

This paper provides a new method for stress management-focused predictive modeling of healthcare workers' well-being via cloud computing and machine learning. The need for proactive measures to track and assist healthcare workers' mental health is highlighted by the rising expectations placed on them. Using various data sources, our system compiles information from surveys, social media, electronic health records, and wearable devices into a single location for analysis. Predictive models that predict healthcare workers' stress levels and well-being are developed using gradient boosting, a strong machine learning (ML) technique. This work is suitable for gradient boosting due to its resilience to overfitting and capacity to process many kinds of data. Healthcare organizations may improve the health of their employees by using our technology to detect stress patterns and identify the causes of that stress. It can use specific treatments and support systems to alleviate that stress. Widespread adoption and real-time monitoring are made possible by the scalability, flexibility, and accessibility of cloud computing infrastructure. This method shows promise in the direction of proactive solutions driven by data for controlling the stress of healthcare workers and improving their general well-being.

Context-aware self-powered intelligent soil monitoring system for precise agriculture

The agricultural sector is transforming with advanced technologies such as internet of things (IoT), cloud computing, and machine learning, for increased productivity and sustainability. However, fixed sensor deployments struggle to capture the dynamic and heterogeneous soil properties with irregularities in farming operations, and negatively impacting crop performance and resource utilization. This paper presents a novel context-aware, self-powered intelligent soil monitoring system (ISMS) applied in precision agriculture. By integrating advanced sensors, energy harvesting, real-time data analytics, and context-aware decision support, ISMS provides real-time context insights into soil, energy, and weather conditions. The informed decisions are enabled and tailored to their specific agricultural environment. The system utilizes a multi-parameter soil sensor, photovoltaic (PV) panel, and intelligent context-aware analytics for a sustainable, cost-effective solution powered by solar energy and OpenWeather application program interface (API) for weather data. Field tests over two months demonstrated the system's effectiveness, together with continuous operation without grid power. This research highlights ISMS's potential in enhancing soil nutrient management and decision-making and offering significant economic and environmental benefits for modern agriculture, especially in remote areas.

Optimizing precision farming: enhancing machine learning efficiency with robust regression techniques in high-dimensional data

Smart precision farming leverages IoT, cloud computing, and big data to optimize agricultural productivity, lower costs, and promote sustainability through digitalization and intelligent methodologies. However, it faces challenges such as managing complex variables, addressing multicollinearity, handling outliers, ensuring model robustness, and enhancing accuracy, particularly with small to medium-sized datasets. To overcome these obstacles, reducing retraining time and resolving the complexity issue is essential for improving the machine learning algorithm’s performance, scalability, and efficiency, especially when dealing with large or high-dimensional datasets. In a recent study involving 435 drying parameters and 1,914 observations, two machine learning algorithms - Ridge and Lasso - were employed to analyze and compare the impact of two variable selection techniques, specifically the regularization methods Ridge and Lasso, before and after addressing heterogeneity in highly ranked variables (50, 100, 150, 200, 250, 300). Additionally, robust regression methods such as S, M, MM, M-Hampel, M-Huber, M-Tukey, MM-bisquare, MM-Hampel, and MM-Huber were applied. The results demonstrated that the robust methods, when applied to Ridge and Lasso, achieved the highest efficiency, with the smallest values for MAPE, MSE, SSE, and the highest R2 values, both before and after accounting for heterogeneity. As a result of the study, the best models are the Ridge model with the MM bisquares before heterogeneity, the Ridge model with the MM method after heterogeneity, and the Lasso model with the MM method before heterogeneity and the Lasso model with MM Hampel after heterogeneity.

An Approach of Privacy Preservation and Data Security in Cloud Computing for Secured Data Sharing

Introduction: Cloud computing has revolutionized how individuals and businesses engage with data and software, turning the internet into a powerful computing platform by centralizing resources. Despite its benefits, there's a challenge in safeguarding sensitive information stored externally. Cryptography faces threats, particularly chosen-ciphertext attacks aiming for secret keys or system information. While more common in public-key encryption, these attacks are less frequent in symmetrically coded systems. Security efforts include validating system resilience and continuous improvement, which are vital in countering evolving threats like adaptive chosen ciphertext attacks. Method: In the evaluation model, stringent measures emphasize robust encryption for system security. Despite the planning, no ciphertext attack guarantees success, necessitating adaptive security protocols. Adaptive attacks like CCA2 expose vulnerabilities, enabling attackers to manipulate ciphertexts persistently. Result: We observe an average gain of 65% for the decryption algorithm. Efforts focus on strengthening security. The flawed 32-bit key-based encryption in the modified Cramer-Shoup structure undergoes remediation. Conclusion: Conventional uncertainties validate resilience, emphasizing continuous evaluation and enhancement to counter evolving threats.

Implementation of predictive maintenance in various Industry: A Review

Predictive maintenance has become a crucial approach in the manufacturing industry, offering solutions to minimize downtime, optimize maintenance costs, and enhance operational efficiency. This paper reviews the definition, objectives, benefits, and implementation of predictive maintenance in the manufacturing sector. By leveraging advanced technologies such as the Internet of Things (IoT), machine learning, big data analytics, and cloud computing, predictive maintenance enables real-time monitoring of equipment conditions and failure prediction before they occur. The objectives are to reduce operational costs, increase equipment reliability, and optimize production performance. The benefits include reduced frequency and duration of downtime, lower repair costs, extended equipment lifespan, and improved workplace safety. Case studies discussed show that companies adopting this technology experience increased production efficiency and reduced maintenance costs. The conclusion of this paper suggests that companies invest in technology and infrastructure, develop employee skills, integrate systems, collaborate with technology providers, and conduct continuous monitoring and evaluation. With these steps, companies can more effectively implement predictive maintenance and achieve competitive advantages in an increasingly dynamic market

Applications of AI in Design, Manufacturing and Production

Innovation, Adaptation and Improvement are the major thrust for the manufacturing Industries. AI, an emerging technology which is bringing a boon in the market industries. With its increasing features and technology now, it is making its way for manufacturing and product designing. When it comes to AI the base starts with machine learning which has supervised and unsupervised learning in it. Artificial Intelligence models to some extent has a conceptual adaptation of neural networks ,which works similar to that of human brain .AI analyses all the data and draws conclusions for machine and detect all the irregularities and make perfect out of it.AI in relation to CAD is a virtual assistant which gives a broad way into the areas of creativity and ideas which helps in creating multiple variants after recognising the pattern and which in turns helps in making the products in less time. The application of AI which are based upon Machine learning, Internet of things ,block-chain technology, cloud computing and cyber physical system have paved the path for significant improvement in the product designing, quality of product ,improving the manufacturing process and has reduced the time between the development of the new products and its launching while focusing on sustainability. This scientific paper describes how Artificial intelligence is applied in designing of products, interfaces, relation to CAD packages and improving the way of manufacturing which is the need of the hour and demand of society to nurture the satisfaction of customers.

Software-Defined Networking Challenges and Research Opportunities for Future Interest

Software Defined Network (SDN) has become one of the most preferred solutions for the management of large-scale complex networks. The network policies in the large-scale network are difficult to embed on entire network devices simultaneously, whereas in SDN these policies can be embedded on the top of the network. The SDN network is divided into two parts which are vertically integrated to form the entire network. Currently many aspects of the classical architecture of the Internet are etched in stone – a so-called ossification of the Internet – which has led to major obstacles in IPv6 deployment and difficulty in using IP multicast services. Yet, there exist many reasons to extend the Internet, e.g., for improving intra-domain and inter- domain routing for high availability of the network, providing end-to-end connectivity for users, and allowing dynamic management of network resources for new applications, such as data center, cloud computing, and network virtualization. To address these requirements, the next- generation architecture for the Future Internet has introduced the concept of Software-Defined Networking (SDN). At the core of this emerging paradigm is the separation and centralization of the control plane from the forwarding elements in the network as opposed to the distributed control plane of existing networks. With the advent of cloud computing, many ecosystem and business paradigms are encountering potential changes and may be able to eliminate their IT infrastructure maintenance processes. Real-time performance and high availability requirements have induced telecom networks to adopt the new concepts of the cloud model: software-defined networking (SDN) and network function virtualization (NFV). NFV introduces and deploys new network functions in an open and standardized IT environment, while SDN aims to transform the way network’s function. SDN and NFV are complementary technologies; they do not depend on each other. However, both concepts can be merged and have the potential to mitigate the challenges of legacy networks. In this paper, our aim is to describe the benefits of using SDN in a multitude of environments such as in data centers, data center networks, and Network as service offerings.

DDOS Protection System for Cloud: Architecture and Tool

Distributed Denial of Service (DDoS) attacks, in the realm of cloud computing, have become the most serious threats to the availability and reliability of services. However, these attacks become the direct cause of the target system slowdown or shutdown by saturating the servers with a massive amount of traffic. This harms the cloud-based applications' performance. As cloud infrastructure has become the backbone of every company, the development of effective and scalable DDoS protection mechanisms to ensure the continuity of services is a must. This paper gives a clear overview of the architecture and equipment involved in the cloud-based DDoS protection system. We look at the different layers of protection such as traffic filtering, rate-limiting, anomaly detection, and the application of security services native to the cloud. Examples of such Web Application Firewalls (WAFs), Content Delivery Networks (CDNs), and Cloud Security Posture Management (CSPM) systems. The architecture employs distributed and multi-layered security solutions for detecting and mitigating the attack in real-time whilst keeping the legitimate users safe from any effects

Challenges in Cloud Computing Adoption for SMEs in the Middle East

Challenges in Cloud Computing Adoption for SMEs in the Middle East

Analyzing Programming Language Trends Across Industries: Adoption Patterns and Future Directions

This study examines the adoption of programming languages across industries such as finance, healthcare, game development, data science, and embedded systems. It analyzes factors like performance, developer productivity, and ecosystem support influencing language choice. The research shows that while Java, C++, and Python remain dominant due to their maturity, versatility, and widespread usage, newer languages like Rust, Go, and Kotlin are gaining popularity in specific fields that require improved safety, scalability, and developer-centric features. The paper also explores the challenges of balancing modern language adoption with legacy systems, including compatibility, resource allocation, and organizational inertia. Additionally, it investigates the role of community support, tooling, and frameworks in driving language adoption. The study predicts future trends driven by advancements in AI, cloud computing, and cybersecurity, highlighting how these technological shifts shape language preferences. Furthermore, it delves into the influence of programming paradigms, emerging technologies, and organizational priorities in shaping industry-specific language trends. The research underscores the need for a strategic approach to language adoption, balancing innovation with the practical challenges posed by legacy systems and workforce adaptability. As industries evolve, they must navigate the trade-offs between adopting innovative languages and maintaining legacy systems, which remain critical for many operations. This research provides valuable insights into how programming languages are evolving to meet the demands of a rapidly changing technological landscape, emphasizing the importance of security, efficiency, and developer productivity in shaping the future of software development.

Cloud Access Security Brokers: An easy-implement Method for Accessing Cloud Services Securely

In this changing digital age, cloud services have become very common. But the main challenge is to provide secure access to cloud services for retailers and users and also for providers. Read here The Important Role of Cloud Access Security Brokers (CASBs). Either on-premise or cloud, CASBs take the place of hardened enforcement points of security in policy that are capable of bringing corporate security policy together, layering it on top of cloud resource access. These essential functions include authentication, single sign-on, authorization, credential mapping, device profiling, encryption, tokenization, many more. But in general, the most common challenges for CASBS provide these in the cloud: threat detection, access control policy enforcement, risk assessment, data protection and compliance. The literature survey presented in this paper focuses on these threats and vulnerabilities, and clearly highlight the requirement of improved protective processes in Cloud computing. Complementing CASBs with machine learning (ML) is at the heart of our proposed solution. If you are only looking at how well ML algorithms worked in detecting real-time threats, or in automating access control policies, comprehensive risk assessments, classifying sensitive data and monitoring compliance Machine learning techniques like Decision Tree and Random Forest algorithms have been applied with the initiative taken by us on CSE-CICIDS database, a real-world dataset that is described with the characteristics for cloud utilization behaviors along with limited numbers of security violation incidents occurred. The Random Forest Algorithm is performing considerably better than others, it has perfect precision, recall and F1-scores, it is able to predict all the records in the data set correctly.

TinyHLS: a novel open source high level synthesis tool targeting hardware accelerators for artificial neural network inference

Objective.In recent years, wearable devices such as smartwatches and smart patches have revolutionized biosignal acquisition and analysis, particularly for monitoring electrocardiography (ECG). However, the limited power supply of these devices often precludes real-time data analysis on the patch itself.Approach.This paper introduces a novel Python package, tinyHLS (High Level Synthesis), designed to address these challenges by converting Python-based AI models into platform-independent hardware description language code accelerators. Specifically designed for convolutional neural networks, tinyHLS seamlessly integrates into the AI developer's workflow in Python TensorFlow Keras. Our methodology leverages a template-based hardware compiler that ensures flexibility, efficiency, and ease of use. In this work, tinyHLS is first-published featuring templates for several layers of neural networks, such as dense, convolution, max and global average pooling. In the first version, rectified linear unit is supported as activation. It targets one-dimensional data, with a particular focus on time series data.Main results.The generated accelerators are validated in detecting atrial fibrillation on ECG data, demonstrating significant improvements in processing speed (62-fold) and energy efficiency (4.5-fold). Quality of code and synthesizability are ensured by validating the outputs with commercial ASIC design tools.Significance.Importantly, tinyHLS is open-source and does not rely on commercial tools, making it a versatile solution for both academic and commercial applications. The paper also discusses the integration with an open-source RISC-V and potential for future enhancements of tinyHLS, including its application in edge servers and cloud computing. The source code is available on GitHub:https://github.com/Fraunhofer-IMS/tinyHLS.

A Comparative Implementation of Iris Flower Classification Across Platform

This paper presents a comparative analysis of the Iris Flower Classification problem implemented across three distinct computational platforms: classical computing using Jupyter Notebook, cloud computing via Google Colab, and quantum computing leveraging IBM Qiskit. The study evaluates the platforms based on execution efficiency, classification accuracy, and scalability. While classical and cloud-based implementations yielded similar performance metrics, the quantum approach demonstrated unique computational characteristics and challenges, such as noise and resource limitations. This research highlights the growing potential of quantum computing for machine learning applications and provides a foundation for future advancements in integrating quantum techniques with traditional methodologies. Key Words: Iris Flower Classification, Machine Learning, Classical Computing, Cloud Computing, Quantum Computing, Qiskit, Google Colab, Quantum Machine Learning, Comparative Analysis

IoT Integration With CMPA‐PINN for Islanding Detection Through Microgrid Hierarchical Control

ABSTRACTInternet of Things (IoT) and cloud computing are becoming increasingly important in the solution of many industrial problems. Effective management of microgrid (MG) requires a strong and scalable information and communication technology (ICT) infrastructure. IoT devices with effective measurement and control capabilities have the potential to be very important in the MG environment. MG was run in both grid‐connected and island mode. This paper proposes to improve the MG hierarchical control with IoT using CMPA‐PINN techniques for islanding detection. The proposed hybrid method is the joint execution of both the Coronavirus Mask Protection Algorithm (CMPA) and physics‐informed neural networks (PINNs). Hence, it is named as CMPA‐PINN approach. The major goal of this proposed method is to reduce the deviation of voltage, frequency, and total harmonic distortion (THD). The proposed CMPA is used to optimize the traffic flow over a communication network, and the PINNs are used to predict the optimized traffic flow. By then, the MATLAB platform has adopted the proposed method, and the current process is used to compute its execution. The proposed technique outperforms all current systems, including maximum power point tracking (MPPT), multi‐agent reinforcement learning (MARL), and deep reinforcement learning (DRL). The proposed approach shows the THD is 2%, which is lower than other existing systems.

A Dynamic Energy‐Efficient Scheduling Method for Periodic Workflows Based on Collaboration of Edge‐Cloud Computing Resources

ABSTRACTEdge‐cloud computing offers an efficient method to flexibly allocate various computing resources for periodic workflow applications commonly employed in industrial production, commercial operations, and scientific research. Rationalized allocation of computational resources for scheduling in the edge‐cloud environment is the key to reducing energy consumption of the periodic workflows scheduling process. To this end, this paper proposes an optimization method for dynamic energy‐efficient scheduling of periodic workflows based on the collaboration of edge‐cloud computational resources while satisfying the constraints of workflow deadlines. In our method, periodic workflow scheduling is defined to be performed on a three‐tier integrated scheduling architecture of user terminals, edge computing platform, and cloud computing platform. Task groups are generated based on workflow critical paths, and appropriate edge‐cloud computing resources are selected for workflow tasks using corresponding scheduling policies at each scheduling stage. It reduces energy consumption during task scheduling while satisfying workflow deadline constraints. Comparative experiments in a simulated edge‐cloud environment show that our method reduces energy consumption of the scheduling process by 19.38%, 22.7%, and 37.34% compared to GA, PSO, and cloud computing, respectively. That is, the method effectively reduces the scheduling energy consumption during periodic workflow processing and significantly improves computational resource utilization.

Leveraging Big Data for Effective Educational Management

This paper examines the transformative role of big data in educational management, emphasizing its potential to enhance decision-making, streamline administrative operations, and improve student outcomes. With the proliferation of digital tools in education, big data provides actionable insights for personalized learning, resource optimization, and institutional performance. The study outlines the benefits, challenges, and limitations of implementing big data analytics in educational settings, including ethical considerations and infrastructural demands. Key technologies and tools such as predictive analytics, machine learning, and cloud computing are discussed, along with case studies highlighting best practices and lessons learned. This paper emphasizes the importance of evidence-based strategies in building innovative and effective educational systems. Keywords: Big Data, Educational Management, Predictive Analytics, Personalized Learning, Data-Driven Decision Making, Learning Analytics.

Performance Portrait Method: Robust Design of Predictive Integral Controller

The performance portrait method (PPM) can be characterized as a systematized digitalized version of the trial and error method—probably the most popular and very often used method of engineering work. Its digitization required the expansion of performance measures used to evaluate the step responses of dynamic systems. Based on process modeling, PPM also contributed to the classification of models describing linear and non-linear dynamic processes so that they approximate their dynamics using the smallest possible number of numerical parameters. From most bio-inspired procedures of artificial intelligence and optimization used for the design of automatic controllers, PPM is distinguished by the possibility of repeated application of once generated performance portraits (PPs). These represent information about the process obtained by evaluating the performance of setpoint and disturbance step responses for all relevant values of the determining loop parameters organized into a grid. It can be supported by the implementation of parallel calculations with optimized decomposition in the high-performance computing (HPC) cloud. The wide applicability of PPM ranges from verification of analytically calculated optimal settings achieved by various approaches to controller design, to the analysis as well as optimal and robust setting of controllers for processes where other known control design methods fail. One such situation is illustrated by an example of predictive integrating (PrI) controller design for processes with a dominant time-delayed sensor dynamics, representing a counterpart of proportional-integrating (PI) controllers, the most frequently used solutions in practice. PrI controllers can be considered as a generalization of the disturbance–response feedback—the oldest known method for the design of dead-time compensators by Reswick. In applications with dominant dead-time and loop time constants located in the feedback (sensors), as those, e.g., met in magnetoencephalography (MEG), it makes it possible to significantly improve the control performance. PPM shows that, despite the absence of effective analytical control design methods for such situations, it is possible to obtain high-quality optimal solutions for processes that require working with uncertain models specified by interval parameters, while achieving invariance to changes in uncertain parameters.