Cloud Data Storage Research Articles

Cloud Data Integrity Verification Algorithm Based on Data Mining and Accounting Informatization

Data integrity verification means that the data in the cloud are uploaded by the user. In addition to the user’s own update of the data, any external factors including the cloud service provider’s data are destroyed, tampered with, and lost, and the data are not updated in a timely manner. Any inconsistencies in the actual data required can be detected by the user. This article aims to study cloud data integrity verification algorithms based on data mining and accounting informatization. This article proposes data mining technology, accounting information to help business managers assist management work. The article uses Company H as an example to illustrate the accounting information system and proposes a new information management strategy based on it. The CBF algorithm and data integrity verification algorithm are used to study the cloud storage data integrity verification protocol, the cloud data integrity verification model is constructed, the data program flow design is analyzed, and the time-consuming operation of file data insertion is analyzed. The experiment in this paper uses 16 standard mathematical calculation formulas to strengthen the analysis. The results show that the study of cloud data integrity verification algorithms based on data mining and accounting information is beneficial to the integrity and protection of data. When the number of documents added increases from 0 to 400, the document agreement shows an upward trend, and the agreement in this paper basically fluctuates between 10 and 80.

ML Based Authentication Scheme for Data Storage in Cloud Based IoT

Today, organizations are using IoT devices to accurately collect real data and make better business decisions to increase customer satisfaction. The data collected should be stored and stored in a well-designed storage system, which encourages companies to review their data storage infrastructure. The company needs to store data created by the Internet of Things, and that data grows exponentially, forcing IoT to think about cloud storage for data storage. Security issues are a major concern when handling and processing data in DI and cloud environments. Secure integration of IoT and cloud computing, and introduced a model to ensure this integration. The secure database of any IoT operating system was suffers from poorly protected read and write functions, which limits data storage on any IoT platform. In addition, clouds can provide space to store a wide variety of data that plays an important role in the world of cyber security. However, large centralized systems operating in the cloud are also very vulnerable due to their power, so they can be transformed into a kind of double-edged sword. In this paper, we propose a novel secure lightweight authentication scheme for data storage (SLA-DS) in IoT and cloud server. The SLA-DS integrates IoT and cloud technology combination which mainly focuses on security issues.

Reconfigurable battery systems: Challenges and safety solutions using intelligent system framework based on digital twins

Research on Reconfigurable Battery Systems (RBS) is gaining emphasis over the traditional fixed topology of the battery pack due to its advantages of adapting flexible topology (series-parallel) during its operation in the pack for meeting the non-linear time-dependent load requirements. There could emerge serious issues such as those related to safety due to malfunction of the switching circuit, heat generation from switches during frequent switching of circuits, charging temperature rise, increased charging time, sensing issues arising from the use of low-accuracy voltage/current sensors, state of charge/state of health estimation, and cost issues due to the use of increasing number of switches, fuses, contactors, relays, circuit breakers etc. To address these mentioned issues, the problem of optimal switching circuit topology for RBS is formulated as a mathematical multi-objective optimisation problem. An intelligent system framework based on digital twins is proposed. The proposed framework is further extended to a life cycle management approach that includes the interactions among pack design, pack assembly and operational and recycling levels. This could provide greater access of real-time big data cloud storage to the battery designers, manufacturers and recycling industries, who can make use of it to optimise their designs, systems and operations.

Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment

Collaborative modelling of the Internet of Things (IoT) with Artificial Intelligence (AI) has merged into the Intelligence of Things concept. This recent trend enables sensors to track required parameters and store accumulated data in cloud storage, which can be further utilized by AI based predictive models for automatic decision making. In a smart and sustainable environment, effective waste management is a concern. Poor regulation of waste in surrounding areas leads to rapid spread of contagious disease risks. Traditional waste object management requires more working staff, increases effort, consumes time and is relatively ineffective. In this research, an Intelligence of Things Enabled Smart Waste Management (IoT-SWM) model with predictive capabilities is developed. Here, local sinks (LS) are deployed in specified locations. At every instant, the current status of smart bins in each LS is notified to users to determine the priority level of LS to be emptied. Based on aggregated sensor values for the three smart bins, LS weight and poison gas value, the priority order of emptying LS is computed, and decision is made whether to notify the users with an alert message or not. It also helps in predicting the LS, which is likely to be filled up at a faster rate based on assigned timestamp. This model is implemented in real time with many LS and it was observed that bins, which were close to more crowded sites filled up faster compared to sparse populated areas. Random forest algorithm was used to predict whether an alert notification is to be sent or not. An average mean of 95.8% accuracy was noted while using 60 decision trees in random forest algorithm. The average mean execution latency recorded for training and testing sets is 13.06 sec and 14.39 sec respectively. Observed accuracy rate, precision, recall and f1-score parameters were 95.8%, 96.5%, 98.5% and 97.2% respectively. Model buildup and the validation time computed were 3.26 sec and 4.25 sec respectively. It is also noted that at a threshold value of 0.93 in LS level, the maximum accuracy rate reached was 95.8%. Thus, based on the prediction of random forest approach, a decision to notify the users is taken. Obtained outcome indicates that the waste level can be efficiently determined, and the overflow of dustbins can be easily checked in time.

SHUBHCHINTAK: An efficient remote health monitoring approach for elderly people.

With the proliferation of IoT technology, it is anticipated that healthcare services, particularly for the elderly persons, will become a major thrust area of research in the coming days. Aim of this work is to design a fit-band containing multiple sensors to provide remote healthcare services for the elderly persons. An application has been designed to capture health data from the fit-band, pre-process the data and then send them to cloud for further analysis. A wireless Bluetooth enabled connection is proposed to establish communications between sensors and the application for data transmission. In the proposed application, there are three different front-end interfaces for three different users: system administrator, patient and doctor. The data collected from the patient’s fit-band are sent to a cloud data storage, where the data will be analyzed to detect anomaly (e.g., heart attack, sleep apnea, etc.). A Convolution Neural Network (CNN) model is proposed for anomaly detection. For the classification of anomaly, a Long Short Term Memory (LSTM) model is proposed. In the presence of anomaly, the system immediately connects a doctor through a phone call. A prototype system termed as Shubhchintak has been developed in Android/IOS environment and tested with a number of users. The fit-band provides data tracking with an overall accuracy of 99%; the system provides a response with 3000 requests in less than 100 ms. Also, Shubhchintak provides a real-time feedback with an accuracy of 97%. Shubhchintak is also tested by patients and doctors of a nearby hospital. Shubhchintak is shown to be a simple to use, cost effective, comfortable, and efficient system compared to the existing state of the art solutions.

Home-Based Audiometry With a Smartphone App: Reliable Results?

The aim of this study was to assess the test-retest reliability of a smartphone-based hearing test, performed without supervision of a hearing professional in an uncontrolled environment. The hearing application is based on an automated hearing test (DuoTone) and relies on verification procedures (ambient noise monitoring algorithm, graphical user interface) to ensure appropriate measurement conditions. Thresholds obtained with DuoTone were compared to those obtained with standard clinical audiometry for 0.5, 1, 2, and 4 kHz in 13 subjects. Subsequently, test-retest reliability was analyzed using anonymized cloud-stored data from a large group of app users (1,641 subjects) who performed multiple hearing tests. Thresholds at minimum or maximum presentation level of the hearing test (10 dB HL, 85 dB HL) were excluded to avoid floor/ceiling effects. A subset (500 subjects) was created to exclude potentially unreliable data. Test-retest thresholds were compared at 12 test frequencies, from 125 Hz to 12 kHz. Thresholds determined by DuoTone and clinical audiometry did not differ significantly for each test frequency. Regarding test-retest analysis, the percentage of test-retest results within 5 dB ranged from 60% to 77% per test frequency. Results from the subset were not substantially different. Test-retest reliability for app users was comparable to results published in the literature regarding test-retest reliability of audiometry, performed in the clinic. Initial validation results suggest that thresholds obtained with DuoTone are comparable to clinical audiometry (four frequencies tested). The hearing app provides reliable hearing thresholds between 15 and 80 dB HL (12 frequencies tested) with a test-retest reliability comparable to clinical audiometry.

Secured Cloud Data Storage Encryption Using Post-Quantum Cryptography

Secured Cloud Data Storage Encryption Using Post-Quantum Cryptography

A novel image cryptosystem using Gray code, quantum walks, and Henon map for cloud applications

Cloud computing plays a vital task in our daily lives, in which an enormous amount of data is stored daily on cloud storage. The highest priority for cloud data storage is guaranteeing the security of confidential data. The security of confidential data can be realised through utilising one of the cryptographic mechanisms including encryption and data hiding. With the rapid development for the realization of quantum computers, modern cryptosystems may be cracked including cloud systems. Accordingly, it is a crucial task for achieving confidentiality of data stored on cloud storage before the availability of quantum computers. Therefore, this study aims to utilise one of the quantum computational models, as a quantum-inspired system, to layout a new data confidentiality technique that can be applied in digital devices to have the capability for resisting the potential attacks from quantum and digital computers. In this paper, a new image security algorithm for real-time cloud applications using Gray code, quantum walks (QW), and Henon map is proposed. In the proposed image cryptosystem, the generated key streams from QW and Henon map is related to the plain image with high sensitivity of slight bit changes on the plain image. The outcomes based on deep analysis proves that the presented algorithm is efficient with high security for real-time application.

Symptom-Based COVID-19 Prognosis through AI-Based IoT: A Bioinformatics Approach.

Objective Internet of Things (IoT) integrates several technologies where devices learn from the experience of each other thereby reducing human-intervened likely errors. Modern technologies like IoT and machine learning enable the conventional to patient-specific approach transition in healthcare. In conventional approach, the biggest challenge faced by healthcare professionals is to predict a disease by observing the symptoms, monitoring the remote area patient, and also attending to the patient all the time after being hospitalised. IoT provides real-time data, makes decision-making smarter, and provides far superior analytics, and all these to help improve the quality of healthcare. The main objective of the work was to create an IoT-based automated system using machine learning models for symptom-based COVID-19 prognosis. Methods Comparative analysis of predictive microbiology of COVID-19 from case symptoms using various machine learning classifiers like logistics regression, k-nearest neighbor, support vector machine, random forest, decision trees, Naïve Bayes, and gradient booster is reported here. For the sake of the validation and verification of the models, performance of each model based on the retrieved cloud-stored data was measured for accuracy. Results From the accuracy plot, it was concluded that k-NN was more accurate (97.97%) followed by decision tree (97.79), support vector machine (97.42), logistics regression (96.50), random forest (90.66), gradient boosting classifier (87.77), and Naïve Bayes (73.50) in COVID-19 prognosis. Conclusion The paper presents a health monitoring IoT framework having high clinical significance in real-time and remote healthcare monitoring. The findings reported here and the lessons learnt shall enable the healthcare system worldwide to counter not only this ongoing COVID but many other such global pandemics the humanity may suffer from time to come.

Dynamic Deduplication Algorithm for Cross-User Duplicate Data in Hybrid Cloud Storage

The escalating growth of distributed big data in hybrid cloud storage architecture introduces a new set of challenges. Constantly, content enrichment puts pressure on capacity. Nonetheless, the explosion of user data places a significant strain on broadband and storage capacity. Consequently, many cloud storage providers will implement deduplication to compress data, reduce transfer bandwidth, and reduce cloud storage space. In cloud storage systems, it is a data compression and storage optimization method. By locating and removing redundant data, it can save storage space and bandwidth. An MTHDedup deduplication strategy based on the Merkle hash tree is presented in a hybrid cloud environment to address the issue of convergent encryption algorithms being susceptible to brute-force attacks and ciphertext computation time overhead. Merkle hash trees are constructed using additional encryption algorithms to generate encryption keys during file- and block-level deduplication, ensuring that generated ciphertexts are unpredictable. The method is effective against both internal and external brute-force attacks, thereby increasing data security. Our method reduces the computational burden of ciphertext generation and the key storage space, and the performance advantage increases with the number of privilege sets.

Intelligent Data Processing and Optimization of University Logistics Combined with Block Chain Storage Algorithm

The traditional Hadoop-based logistic data processing platform of universities cannot fully collect and control remote data in the process of data processing and has defects such as poor efficiency, high error, and difficult query. In existing designs, devices often need to store complete block data. When retrieving or verifying specific data on the chain, a large number of blocks need to be traversed to find the corresponding data, which reduces the response speed on the user side. In addition, the traditional consensus algorithm is not suitable for resource-constrained terminal devices. Therefore, this paper proposes an intelligent data processing and optimization method of university logistics combined with block chain storage algorithm. For unstructured data on the cloud storage network, the storage information can be obtained through domain analysis, and the storage frequency can be dynamically adjusted based on the estimated data flapping. Moreover, a data storage audit scheme is designed on the cloud storage network to improve data storage efficiency. In order to ensure the integrity verification efficiency of unstructured big data cloud storage, a multilayer block chain network model suitable for university logistics data is designed by introducing block chain technology. The throughput and average occupancy under the same conditions in different environments are evaluated and analyzed, and it is verified that the proposed method is not affected by the initial priority distribution and has good stability and optimization performance. The results show that the proposed method can significantly improve the cloud storage efficiency of unstructured data, can effectively respond to a large number of requests to access data, and has good big data processing capability.

Secure Data Deduplication System with Efficient and Reliable Multi-Key Management in Cloud Storage

<p>The revolutionary growth in the processing and storage mechanisms over the Internet has given the enhancement to inexpensive and strong computing properties. Cloud computing is a rising technology, which offers the data storage facility also application accessing facility in online environment. This system stands countless opportunities also challenges. In that, security of data and the increasing similar data in cloud (duplication) are very important issues to be addressed. So, Deduplication method is developed to reduce the similar data that is present in the storage system. In this paper, a novel technique is proposed to remove the duplicate data from cloud also help to save the bandwidth access and storage space. The experimental results demonstrate that the proposed system provide the more security for data in cloud storage and also overcomes the main drawbacks of the existing systems. In one-server storage and distributed storage systems, we have created a solution which provides data security and space efficacy. The chunk data generates encryption keys consistently; the same chunk is therefore always encrypted with the same chip text. In addition, the keys cannot be derived from the chunk data encrypted. Because the information to be accessed and decrypted by each user is encrypted by using a key known to the user alone, even a complete system breach cannot expose which chunks are utilised by which users.</p> <p> </p>

Enhancing Security of Data in Cloud Storage Using Decentralized Infrastructure

Abstract: Nowadays, a considerable amount of data is stored in the cloud, which must be kept safe against unauthorised access. Various algorithms are employed to ensure data privacy and security. Every system's goal is to achieve confidentiality, integrity, and availability (CIA). The existing centralised cloud storage, on the other hand, does not have these CIA qualities. Decentralized cloud storage is used in conjunction with hash algorithm used to improve data security and storage approaches. It efficiently aids in the protection of data against manipulation or the deletion of a portion of data. A data connects the blocks that hold data in a block chain. Each block contains a hash value that is stored in the following block. As a result, the possibilities of data loss are reduced. Keywords: Decentralized Cloud Storage, SHA-512, Advance Encryption Standard, Encrypt, Decryp,CIA

CRITERIA FOR SELECTING A CLOUD-BASED LEARNING MANAGEMENT SYSTEM FOR A HIGHER EDUCATION INSTITUTION

The paper explores the essence of the criteria and indicators which can be used to select a cloud-oriented learning support system for a higher education institution. The following criteria with corresponding indicators are identified: design criterion (reliability, accessibility, multilinguality, security, adaptability, ease of use and administration, free use); technological criterion (user access rights differentiation, cloud storage of data, integration with other cloud-based services, ability to download different types of files); communication criterion (user registration, communication between registered users, creating groups, creating forums and chats); information-didactic criterion (structuredness, calendar, assessment of student achievement, file sharing, testing and surveys, group and individual modes of work; analytics for a particular course). The most downloaded LMS are shown based on the results published by LMS Market Share. The paper offers an analysis of a number of cloud-based learning management systems (Google Classroom, Moodle, Edmodo, Studyboard, Oracle, Learner Nation, iSpring, Canvas, Schoology, Blackboard, NeoLms) in terms of the above-mentioned criteria and indicators. The systems were selected based on the method of expert evaluation. The expert evaluation showed that the most convenient and high-quality cloud-based learning management system for building a cloud-oriented learning environment of a higher education institution which best meets all the criteria are NeoLMS, Canvas and Google Classroom. These LMS offer all the functionalities which are essential in the educational process. We see the development of methodological recommendations for higher education regarding the high-quality and successful implementation of such learning management systems in the educational process as prospects for further research.

Automising the CAN Communication System Using Raspberry-Pi

Abstract: This Project is set an objective of developing and improvement of communication system of Electrical parameters control in EV system withaccuracy the use of latest controller Raspberry-pi and CAN module, today’straction control unit TCU has CAN communication platform, which communicates with all accessories e.g., Cluster, Vehicle control unit, Battery management system BMS, CAN plays vital role in vehicle network management for program flashing. The focus of this project to check effectiveness CAN protocol in TCU withrespect to e-motor functions to have commandcontrol and atomize its functions andacknowledge the status and error from the TCU, an addition to that managing the all data received from the system in a cloud management is to be performed here with example of electric motor and illuminating devices. Cloud data storage will be help full forindustries and owner of object in securities. Adding CAN bus with Raspberry-pi for many applications is the aim of this project by addingnecessary futures.

An optimized and efficient multiuser data sharing using the selection scheme design secure approach and federated learning in cloud environment

An optimized and efficient multiuser data sharing using the selection scheme design secure approach and federated learning in cloud environment

Development of Joint Activity Angle Measurement and Cloud Data Storage System.

In this study, we developed a range of motion sensing system (ROMSS) to simulate the function of the elbow joint, with errors less than 0.76 degrees and 0.87 degrees in static and dynamic verification by the swinging and angle recognition modules, respectively. In the simulation process, the ɣ correlation coefficient of the Pearson difference between the ROMSS and the universal goniometer was 0.90, the standard deviations of the general goniometer measurements were between ±2 degrees and ±2.6 degrees, and the standard deviations between the ROMSS measurements were between ±0.5 degrees and ±1.6 degrees. With the ROMSS, a cloud database was also established; the data measured by the sensor could be uploaded to the cloud database in real-time to provide timely patient information for healthcare professionals. We also developed a mobile app for smartphones to enable patients and healthcare providers to easily trace the data in real-time. Historical data sets with joint activity angles could be retrieved to observe the progress or effectiveness of disease recovery so the quality of care could be properly assessed and maintained.

Towards Data Storage Security in Cloud Computing Using Hybridized Advanced Encryption Standard & Authentication Scheme

Cloud computing is at the heart of modern day computing and thus the rise in malicious attacks are increasing every day towards it. In recent times hackers have been engaging in different string concept on login fields and phishing in order to intrude cloud accounts. This research is proposed to carry out effective security mechanisms on loopholes in cloud data storage. We intend to engage the use of the Advanced Encryption Standard (AES), security Algorithms like Secure Hash Algorithm 1 & 3 (SHA1, SHA 3) and Message-Digest Algorithm (MDS) and the Standard Authentication Scheme (AS) as sure guide against phishing which when implemented can provide various login stages and codes like OTP (One Time Password) to mitigate attacks. With the application of these methods towards securing cloud computing data there will be less intrusion in accounts and loss of files. Keywords: Brute-force, Cracking, Hacking, Advanced Encryption Standard, Authentication Scheme, AES, ES, Security

Analysing the Cloud Data Storage Technology and Its Architecture Applications

The idea of using the internet to store data in the cloud is becoming more popular these days. Cloud computing data storage is the most crucial and significant research topic. The article begins with an introduction to cloud computing, storage in the cloud, and architecture for storage in the cloud. GFS (Google towards genuine corporate examples) and HDFS (Hadoop Distributed File System) are the cloud data storage technologies that we examine next (Hadoop Distributed File System) With that, we come to the end of our series. We show how to enhance the conventional method of storing files using the eyeOS Web operating system. Distributed storage and fault-tolerant control are provided by HDFS, a component of Hadoop. Keywords: “Cloud Computing; Cloud Storage; Web Operating System; Distributed File System”

Retraction Note to: Secured storage and disease prediction of E-health data in cloud

Retraction Note to: Secured storage and disease prediction of E-health data in cloud