Cloud Storage Facilities Research Articles

IoT-Driven Infusion Monitoring & Management System

This research investigates the monitoring and management of an intravenous drip infusion system using the Internet of Things (IoT) and non- contact liquid level sensors, an Arduino circuit board, a GSM interface, and a cloud storage facility. The three sensors in the system are able to detect the presence of fluid without coming into contact with it. They can also send the data they collect to the cloud facility, where it can be used to access drip conditions and monitor fluid levels as well as control infusion flow when needed. This system can be considered an improvement over several systems on the market that are similarly intended to use optical sensors, TDR (Time domain reflectometry), and other types of sensors to monitor the IV drip flow. While it is true that each type of sensor has a combination of benefits and drawbacks, none of the other systems on the market can further regulate the drip flow like this one does. Since the sensors we've employed don't respond to fluids, the patients don't suffer any damage at all. This is now a crucial necessity in the biomedical industry since human reliance on IV drip infusions has led to a number of incidents, the most serious of which involved the patient suffering severe injuries from back blood flow in the nozzle. This system is made to guarantee that all safety precautions are taken and that the patient and attendant are in the best possible circumstances in order to minimize such incidents, accurately monitor and control fluid flow from drip to patients' veins, and prevent back blood flow. Keywords: Internet of Things, Intravenous drip infusion, Non‐contact Liquid Level Sensor, Thing speak, Esp-32, GSM.

Distribution-free Phase-II monitoring of high-dimensional industrial processes via origin and modified interpoint distance based algorithms

Improvements in measuring devices, the development of sensing technologies, automated record-keeping and cloud storage facilities have offered us a large volume of data streams involving many variables for data-driven decision-making. Consequently, monitoring high-dimensional (HD) processes has become an essential and exciting topic in statistical process monitoring. HD monitoring poses a significant challenge because considerable uncertainty is involved in estimating process parameters when an adequately large reference sample is unavailable before monitoring. Thus, designing an effective, conveniently implementable monitoring scheme for the HD processes remains a challenging task that has yet to be effectively addressed. This paper develops two distribution-free exponentially weighted moving average (EWMA) schemes to monitor multivariate and HD processes based on origin and interpoint distances. Previous works on interpoint distance-based charts are generally not distribution-free unless only one reference point is used as a benchmark to compute the interpoint distances. We offer an improved and general way to achieve conditionally distribution-free properties closely. The proposal also combines the advantage of the nonparametric approaches for jointly monitoring the process location and scale parameters with the better shift detection performance of an EWMA scheme. The new EWMA schemes can be easily implemented even when the data dimension is large with a small number of Phase-I samples. Control limits using the median run length as a metric are evaluated and tabulated for some cases to implement the proposed chart quickly. Extensive comparative studies have been conducted to verify the efficacy of the proposed monitoring schemes. Finally, the proposed schemes are illustrated with a real problem of monitoring high-dimensional quality characteristics from semiconductor manufacturing processes.

Implementation of Data Mining on a Secure Cloud Computing over a Web API using Supervised Machine Learning Algorithm

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random Forest over a restful Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model.

A Novel Blockchain and Bi-Linear Polynomial-Based QCP-ABE Framework for Privacy and Security over the Complex Cloud Data.

As a result of the limited resources available in IoT local devices, the large scale cloud consumer’s data that are produced by IoT related machines are contracted out to the cloud. Cloud computing is unreliable, using it can compromise user privacy, and data may be leaked. Because cloud-data and grid infrastructure are both growing exponentially, there is an urgent need to explore computational sources and cloud large-data protection. Numerous cloud service categories are assimilated into numerous fields, such as defense systems and pharmaceutical databases, to compute information space and allocation of resources. Attribute Based Encryption (ABE) is a sophisticated approach which can permit employees to specify a higher level of security for data stored in cloud storage facilities. Numerous obsolete ABE techniques are practical when applied to small data sets to generate cryptograms with restricted computational properties; their properties are used to generate the key, encrypt it, and decrypt it. To address the current concerns, a dynamic non-linear polynomial chaotic quantum hash technique on top of secure block chain model can be used for enhancing cloud data security while maintaining user privacy. In the proposed method, customer attributes are guaranteed by using a dynamic non- polynomial chaotic map function for the key initialization, encryption, and decryption. In the proposed model, both organized and unorganized massive clinical data are considered to be inputs for reliable corroboration and encoding. Compared to existing models, the real-time simulation results demonstrate that the stated standard is more precise than 90% in terms of bit change and more precise than 95% in terms of dynamic key generation, encipherment, and decipherment time.

Practical Multi-Keyword Ranked Search with Time- Based Access Control over Encrypted Cloud Data

The recent trends suggest that there is an increase in the inclination towards storing data in the cloud due to explosive and massive growth of the volume of the data in the cloud computing environment. It helps them to reduce their computational and storage costs but also undeniably brought in concerns about security and privacy as the owners of the highly sensitive data lose control of it directly. The sensitive data could include electronic-based medical records, confidential fiscal documents, etc. An increased distrust about storage of files in a third-party service provider of cloud resources would contradict the very same reason for which cloud storage facilities were introduced. That’s because we cannot deny the fact that cloud based storage systems offer on- demand and ubiquitous access to flexible storage and computational resources. The keyword ranked search methodologies used in the existing systems mainly focus on enhancing and enriching the efficiency of searching the files and their respective functionalities but a lack of straight forward analysis of security and issues with providing access control have not been addressed. To address these disadvantages, in this paper, we propose an efficient Multi-Keyword Ranked Search scheme with Fine-grained access control (MRSF).MRSF is a methodology which can combine matching of coordinates technique with Term Frequency-Inverse Document Frequency (TF-IDF) to thereby achieve a highly precise retrieval of any cipher text of interest. It also improves the secure k-nearest neighbors (kNN) method. By utilizing an access strategy which is polynomial based, it can effectively refine the search privileges of the users’. Professional security analysis proves that MRSF is secure with respect to safeguarding the secrecy of outsourced data and the privacy of tokens and indices. Along with this enhanced methodology of ranked search scheme, a time limit based access control feature has also been proposed to ensure that the adaptive attackers are stalled from giving prolonged access to the data files. Session expiry will ensure security of data and that is to be achieved by providing a time window for the file retrieval. Extensive experiments also show that MRSF reaches higher search precision and many more functionalities when compared to the existing systems.

IMPLEMENTASI CLOUD STORAGE MENGGUNAKAN OWNCLOUD DAN UBUNTU SERVER STUDI KASUS PADA PT INDONESIA NIPPON SEIKI

The purpose of this study was to produce real work based on the need for cloud storage for ease in the company's business processes which in its benefits are intended for file sharing in the e-mail delivery process and provide security for attached files because infrastructure such as servers and storage media are within the scope of the company. The research method that I use is the prototype
 method, where the author first analyzes the needs and from the results of the analysis the authors design the stages that will be carried out and use UML (Unified Modeling Language) as a software modeling tool developed. The results achieved are that the file sharing facility in cloud storage can be used as a medium for sending large files in the process of sending e-mails by uploading files to be sent into cloud storage to then get the download link to be sent via e-mail to then downloaded by the recipient user, and information security can be better maintained because all infrastructure both from the server and storage media are within the scope of the company, this is done to prevent theft of information by irresponsible parties. The conclusion is that before applying cloud storage, the maximum limit for sending email is only 5 Mb, after cloud storage is implemented at the company, the business process of sending e-mails with large file attachment sizes is not a problem because users can utilize cloud storage facilities, and data security. To be guaranteed because cloud storage infrastructure is within the scope of the company.

CLOUD STORAGE FACILITY AS A FLUID QUEUE CONTROLLED BY MARKOVIAN QUEUE

Cloud storage faces many problems in the storage process which badly affect the system's efficiency. One of the most problems is insufficient buffer space in cloud storage. This means that the packets of data wait to have storage service which may lead to weakness in performance evaluation of the system. The storage process is considered a stochastic process in which we can determine the probability distribution of the buffer occupancy and the buffer content and predict the performance behavior of the system at any time. This paper modulates a cloud storage facility as a fluid queue controlled by Markovian queue. This queue has infinite buffer capacity which determined by the M/M/1/N queue with constant arrival and service rates. We obtain the analytical solution of the distribution of the buffer occupancy. Moreover, several performance measures and numerical results are given which illustrate the effectiveness of the proposed model.

Advanced framework for highly secure and cloud‐based storage of colour images

The evergrowing virtualised information technology infrastructure is powered by cloud-centric technology around the world. Cloud-based multimedia storage has become an essential aspect for users and business behemoths. However, as per a survey of Norton, around 3800 breaches have been publicly disclosed with 4.1 billion numbers of records exposed in 2019, which is a 54% rise when compared to 2018. So the data security is a widely quoted barrier for cloud storage. Ciphering the confidential images before transmission and subsequent storage in cloud database needs critical attention for techno-specific applications. In image encryption, chaos-based keys can do better confusion, but the diffusion process using XOR is vulnerable to chosen plain text attack. The proposed colour image encryption scheme innately uses Deoxyribo Nucleic Acid coding that blends well with chaotic cryptosystem for an efficient statistical shift. The ciphered images are stored in authenticated and authorised cloud storage facilities. The experimentation is carried out with the help of Amazon Web Services storage instances. The proposed image encryption scheme offers a strong resistance towards the brute force, occlusion, statistical and differential attacks and yields near-zero correlation and good entropy.

Embedded Streaming Principal Components Analysis for Network Load Reduction in Structural Health Monitoring

Principal component analysis (PCA) is a well-established approach commonly used for dimensionality reduction. However, its computational cost and memory requirements hamper the adoption of PCA in heavily resource-constrained embedded platforms. Streaming approaches have been proposed that may enable embedded implementations of the PCA. Among them, the history PCA (HPCA) algorithm stands out for its robustness to the variability in parameters and accuracy. This article presents a parallel and memory-efficient implementation of HPCA in a structural health monitoring (SHM) application based on a heterogeneous network with sensor nodes measuring three-axial accelerations and gateways collecting measurements from several nodes and sending them to the cloud storage and analytic facility. In the targeted application, standard PCA reaches $15\times $ compression factor with an average reconstruction signal-to-noise ratio of 16 dB and a negligible impact on the accuracy in the tracking of structural modal frequencies. By embedding HPCA on our SHM network gateways, we achieve the same compression factor as standard PCA, with more than $1000\times $ reduction in data memory footprint for running the algorithm. Furthermore, we parallelize HPCA on the gateway, and we achieve a speedup of $7.1\times $ (on 8 cores). Finally, we explore a fixed-point HPCA implementation on sensors (network end nodes), that maximally distributes compression workload, minimizes required communication bandwidth, and maintains the same quality of reconstruction as HPCA in floating point, with a compression factor of $10\times $ .

Advanced Data Storage Security System for Public Cloud

Cloud is a collective term for a large number of developments and possibilities. Various data can be stored by the large amount of people onto the cloud storage facility without any bound of limitations as it provides tremendous space. Open systems like Android (Google Apps) still face many day- to-day security threats or attacks. With recent demand, cloud computing has raised security concerns for both service providers and consumers. Major issues like data transfer over wireless network across the globe have to be protected from unauthorized usage over the cloud as altered data can lead to great loss. In this regard, data auditing along with integrity, dynamic capabilities, and privacy preserving, and plays as an important role for preventing data from various cloud attacks which is considered in this work. The work also includes efficient auditor which plays a crucial role in securing the cloud environment. This paper presents a review on the cloud computing concepts and security issues inherent within the context of cloud computing and cloud infrastructure.

Usage of cloud storage facilities by medical students in a low-middle income country, Sri Lanka: a cross sectional study

BackgroundCloud storage facilities (CSF) has become popular among the internet users. There is limited data on CSF usage among university students in low middle-income countries including Sri Lanka. In this study we present the CSF usage among medical students at the Faculty of Medicine, University of Kelaniya.MethodsWe undertook a cross sectional study at the Faculty of Medicine, University of Kelaniya, Sri Lanka. Stratified random sampling was used to recruit students representing all the batches. A self-administrated questionnaire was given.ResultsOf 261 (90.9%) respondents, 181 (69.3%) were females. CSF awareness was 56.5% (95%CI: 50.3–62.6%) and CSF usage was 50.8% (95%CI: 44.4–57.2%). Awareness was higher in males (P = 0.003) and was low in senior students. Of CSF aware students, 85% knew about Google Drive and 70.6% used it. 73.6 and 42.1% knew about Dropbox and OneDrive. 50.0 and 22.0% used them respectively. There was no association between CSF awareness and pre-university entrance or undergraduate examination performance. Inadequate knowledge, time, accessibility, security and privacy concerns limited CSF usage. 69.8% indicated that they would like to undergo training on CSF as an effective tool for education.ConclusionCSF awareness and usage among the students were 56.5 and 50.8%. Google drive is the most popular CSF. Lack of knowledge, accessibility, concerns on security and privacy limited CSF usage among students. Majority were interested to undergo training on CSF and undergraduate Information Communication Technology (ICT) curricula should introduce CSF as effective educational tools.

Edge computing for Internet of Things: A survey, e-healthcare case study and future direction

The world has recently witnessed the emergence of huge technological growth in the field of data transmission and smart living through various modes of information and communication technology. For example, edge computing has taken a leading role to embark upon the problems related to the internetwork bandwidth minimization and service latency reduction. Inclusion of small microcontroller chips, smart sensors and actuators in the existing socio-economic sectors have paved the Internet of Things (IoT) to act upon the dissemination of smart services to the end users. Thus, a strong need of understating of the industrial elements of edge computing has become necessary that can share the mutual goal while assimilating with the IoT. This paper advocates the crucial role of industrial standards and elements of the edge computing for the dissemination of overwhelming augmented user experience with conjunction with the IoT. First, we present the taxonomical classification and review the industrial aspects that can benefit from both of the IoT and edge computing scenario, then discuss about each of the taxonomical components in detail. Second, we present two practically implemented use cases that have recently employed the edge-IoT paradigm together to solve urban smart living problems. Third, we propose a novel edge-IoT based architecture for e-healthcare i.e. EH-IoT and developed a demo test-bed. The test results showed promising results towards minimizing dependency over IoT cloud analytics or storage facility. We conclude with discussion on the various parameters such as, architecture, requirement capability, functional issues, and selection criteria, related to the survival of edge-IoT ecosystem incorporation.

The medical science DMZ: a network design pattern for data-intensive medical science.

ObjectiveWe describe a detailed solution for maintaining high-capacity, data-intensive network flows (eg, 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations.Materials and MethodsHigh-end networking, packet-filter firewalls, network intrusion-detection systems.ResultsWe describe a “Medical Science DMZ” concept as an option for secure, high-volume transport of large, sensitive datasets between research institutions over national research networks, and give 3 detailed descriptions of implemented Medical Science DMZs.DiscussionThe exponentially increasing amounts of “omics” data, high-quality imaging, and other rapidly growing clinical datasets have resulted in the rise of biomedical research “Big Data.” The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large datasets. Maintaining data-intensive flows that comply with the Health Insurance Portability and Accountability Act (HIPAA) and other regulations presents a new challenge for biomedical research. We describe a strategy that marries performance and security by borrowing from and redefining the concept of a Science DMZ, a framework that is used in physical sciences and engineering research to manage high-capacity data flows.ConclusionBy implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements.

RESTORE AMERICA'S WIRE ACT: CLOUD KILLER?

RESTORE AMERICA'S WIRE ACT: CLOUD KILLER?

The Medical Science DMZ.

Objective We describe use cases and an institutional reference architecture for maintaining high-capacity, data-intensive network flows (e.g., 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations.Materials and Methods High-end networking, packet filter firewalls, network intrusion detection systems.Results We describe a “Medical Science DMZ” concept as an option for secure, high-volume transport of large, sensitive data sets between research institutions over national research networks.Discussion The exponentially increasing amounts of “omics” data, the rapid increase of high-quality imaging, and other rapidly growing clinical data sets have resulted in the rise of biomedical research “big data.” The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large data sets. Maintaining data-intensive flows that comply with HIPAA and other regulations presents a new challenge for biomedical research. Recognizing this, we describe a strategy that marries performance and security by borrowing from and redefining the concept of a “Science DMZ”—a framework that is used in physical sciences and engineering research to manage high-capacity data flows.Conclusion By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements.

An Efficient Privacy Preserving Search Scheme with Access Control for Cloud Data Centers

The internet and the emergence of social networks produce terabytes of data every day. In this big data scenario, the ability to outsource the data to a cloud storage facility saves the data management and storage facility cost. Some major challenges with this scheme are providing security and ensuring the privacy of the outsourced data. Although data security can be achieved through encryption, searching on encrypted data become a complex task. The proposed work suggests an efficient searching scheme for encrypted cloud data based on hierarchical clustering of documents. The hierarchical clustering method preserves the semantic relationship between the documents in the encrypted domain to speed up the search process. Consequently, the proposed system has linear computational complexity during the search phase in response to an exponential increase in the number of documents. The system also ensures data privacy by providing only limited access of the documents to the different types of users by implementing access control mechanisms resulting in more secured data storage in the cloud.