Centralized Data Storage Research Articles

Development of the Web-based State Border Geoinformation System of the Republic of Croatia (SBGiS)

The development of the State Border Geoinformation System of the Republic of Croatia (SBGiS), based on open-source technologies, has facilitated and accelerated the storage, processing, handling, analysis and visualization of large amounts of spatial data used in open border issues, as well as standard affairs on maintaining the regulated state border of the Republic of Croatia. Centralized data storage and their spatial visualization through an interactive map allows citizens to see the situation at the state border in real time, which significantly increases the safety of movement in the border zone, but also aids in the prevention of state border breaches. The SBGiS is the only system that has been established not only for maintaining a regulated state border but also for resolving border disputes and delimitation procedures. At the end of the paper, a proposal for future system upgrades is given.

Lifespan and Failures of SSDs and HDDs: Similarities, Differences, and Prediction Models

Data center downtime typically centers around IT equipment failure. Storage devices are the most frequently failing components in data centers. We present a comparative study of hard disk drives (HDDs) and solid state drives (SSDs) that constitute the typical storage in data centers. Using six-year field data of 100,000 HDDs of different models from the same manufacturer from the Backblaze dataset and six-year field data of 30,000 SSDs of three models from a Google data center, we characterize the workload conditions that lead to failures. We illustrate that their root failure causes differ from common expectations and that they remain difficult to discern. For the case of HDDs we observe that young and old drives do not present many differences in their failures. Instead, failures may be distinguished by discriminating drives based on the time spent for head positioning. For SSDs, we observe high levels of infant mortality and characterize the differences between infant and non-infant failures. We develop several machine learning failure prediction models that are shown to be surprisingly accurate, achieving high recall and low false positive rates. These models are used beyond simple prediction as they aid us to untangle the complex interaction of workload characteristics that lead to failures and identify failure root causes from monitored symptoms.

Efficient, Traceable and Privacy-Aware Data Access Control in Distributed Cloud-Based IoD Systems

The emerging combination of Internet of Things (IoT) and aerospace integration aided by satellite and 6G communication techniques has stimulated the Internet of Unmanned Aerial Vehicles (UAVs), i.e., Internet of Drones (IoD). To accommodate and share the enormous real-time UAV data, cloud-based IoD is an inevitable choice to lower the heavy burden of mobile UAVs. Nevertheless, how to protect highly sensitive UAV data in such a honest-but-curious, open and distributed environment with resource-limited UAVs is a significant challenge. Although our previous work (PATLDAC) in SPNCE’21 devises a cloud-based UAV data access control scheme with policy privacy protection, limited access time and user traceability, it incurs inflexible and centralized cloud data storage and access as well as untrustworthy metadata in untrusted cloud environment for data access and user tracing. To this end, we further propose a blockchain-based privacy-aware data access control (BPADAC) scheme for distributed and secure UAV data sharing in cloud-based IoD. Based on fine-grained, traceable and privacy-preserving UAV data access characteristic of our previous work, we extend it by leveraging blockchain and Distributed Hash Table (DHT) for distributed and trustful UAV data access and storage, together with reliable and limited access mechanism to guarantee cloud UAV data sharing service provision. We also design public and undeniable user tracing mechanism to prevent user key abuse with traitor denial. Finally, we present formal security analysis and prototype the system leveraging the smart contracts of Ethereum blockchain for performance evaluation to show the feasibility of BPADAC.

Credible pigeon permissioned blockchain traceability platform integrated with IoT based on HACCP

The pigeon food production industry from breeding to processing into food for market circulation involves many stages and people, which is prone to food safety issues and difficult to regulate. To address these problems, one possible solution is to establish a traceability system. However, in traditional traceability systems, a number of stages involved and each of them provides their own data accumulated in the database. Therefore, complex traceability data are compose of too many stages easily result in confusing information for customers. Besides, centralized data storage makes data vulnerable to be tampered with. To solve these problems, hazard analysis and critical control points (HACCP) principles have been utilized in our work which is a comprehensive traceability system. In this work, we analyze the pigeon food production industry through HACCP principles and determine some critical control points (CCPs), including incubation, breeding, transportation, slaughtering, processing, and logistics. With the help of these CCPs, we are able to build a traceability system with critical and abundant data but not too complicated for users. To further improve the system, there are different kinds of techniques integrated into it. Firstly, a permissioned blockchain, Hyperledger Fabric, is selected as blockchain module to enhance trustworthiness of data. Secondly, the system contains various IoT devices for automatically collecting environmental parameter data with the aim of reducing human interference. Besides, it is worth mentioning that the proposed information management device can decrease the data entry burden. Consequently, the implementation of the traceability system increase consumers’ confidence in pigeon food production. To summarize, it is a new application of modern agricultural information technique in food safety and a bold experiment in the field of poultry, particularly pigeons.

Research on Monitoring and Early Warning of the Mine Backfill System Based on Blockchain Technology

At present, the mine filling system is a mostly automated operation process, and it has many problems, such as centralized data storage and low safety performance. Aiming at solving the above problems, this paper proposes a mine filling overlimit warning system based on blockchain to achieve the functions of an equipment operation overlimit warning, data storage, and data retrieval. Firstly, the original data model is trained based on transfer learning to obtain the overlimit early warning model and predict the overlimit of equipment operation. Then, the interplanetary file system (IPFS) storage device is used to store the running data and overlimit data, and the corresponding file identifier CID is stored on the blockchain. At the same time, alliance chain technology is used to allow administrators and users to retrieve data based on credentials to ensure the privacy of the data retrieval process. System experiments and analysis evaluations show that the combination of blockchain and IPFS to store monitoring data can effectively save storage space and improve the efficiency of storage and retrieval. The application of an overlimit early warning model can optimize the mine filling process and prolong the operational life of system equipment.

Analysis and Design of Information Systems for Lecturer Performance Reports at Jambi Muhammadiyah University

In a tertiary institution, the existence of lecturers is needed to carry out the tridharma activities of higher education and it is the responsibility of the lecturers themselves. Higher Education Tridharma covers Education, Research and Service. There are several problems that researchers see related to the performance of lecturers, especially for the University of Muhammadiyah Jambi Higher Education. So far, the processing of lecturer performance data at the University of Muhammadiyah Jambi is carried out conventionally, namely inputting data into Microsoft Excel by the admin on duty and additional files that have been submitted by each lecturer are simply stored on the computer without a data center or centralized data storage. . The purpose of this study is to analyze and design a lecturer performance report information system that is able to simplify business processes, use lecturer performance reports according to needs and process lecturer performance data which includes identity data, lecturer tridharma data, and other supporting data more effectively and efficiently by system prototyping method that produces a web-based lecturer performance report information system at the Muhammadiyah University of Jambi to overcome existing problems

Differential Evolution Algorithm With Asymmetric Coding for Solving the Reliability Problem of 3D-TLC CT Flash-Memory Storage Systems

In recent years, NAND flash memory has been widely used in mobile devices, laptops, desktops, and data center storage systems due to its low-power consumption, high performance, high density, lightweight, shock resistance, and high-reliability natures. However, as the stacked layers and the storage density increase, flash memory also suffers from a higher raw bit error rate (RBER) and shorter lifetime. Observing that reliable cell states suffer from less data retention errors and program disturbance, we propose a differential evolution coding scheme to increase the probability of storing data in more reliable cell states, thereby reducing the RBER. We conducted the experiments over a development platform of SSD storage device with 3D-TLC charge trap (CT) NAND flash memory. The experimental results showed that the proposed differential evolution algorithm with asymmetric coding scheme could averagely reduce RBER by 48.88%, 65.45%, 52.61%, 61.99%, 80.19%, and 33.18% compared with baseline, asymmetric coding algorithm, asymmetric coding scheme with stripe-pattern elimination algorithm, UAC- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> LC, word-line batch score modulation programming, and SCB schemes.

Federated Social Recommendation with Graph Neural Network

Recommender systems have become prosperous nowadays, designed to predict users’ potential interests in items by learning embeddings. Recent developments of the Graph Neural Networks (GNNs) also provide recommender systems (RSs) with powerful backbones to learn embeddings from a user-item graph. However, only leveraging the user-item interactions suffers from the cold-start issue due to the difficulty in data collection. Hence, current endeavors propose fusing social information with user-item interactions to alleviate it, which is the social recommendation problem. Existing work employs GNNs to aggregate both social links and user-item interactions simultaneously. However, they all require centralized storage of the social links and item interactions of users, which leads to privacy concerns. Additionally, according to strict privacy protection under General Data Protection Regulation, centralized data storage may not be feasible in the future, urging a decentralized framework of social recommendation.As a result, we design a federated learning recommender system for the social recommendation task, which is rather challenging because of its heterogeneity, personalization, and privacy protection requirements. To this end, we devise a novel frameworkFedratedSocial recommendation withGraph neural network (FeSoG). Firstly,FeSoGadopts relational attention and aggregation to handle heterogeneity. Secondly,FeSoGinfers user embeddings using local data to retain personalization. Last but not least, the proposed model employs pseudo-labeling techniques with item sampling to protect the privacy and enhance training. Extensive experiments on three real-world datasets justify the effectiveness ofFeSoGin completing social recommendation and privacy protection. We are the first work proposing a federated learning framework for social recommendation to the best of our knowledge.

BBNP: A Blockchain-Based Novel Paradigm for Fair and Secure Smart Grid Communications

As the future energy infrastructure, smart grid aims to overcome the disadvantages of traditional power grid, e.g., low efficiency and unstable service. However, the frequent collection and analysis of the user’s electricity data may bring various security and privacy threats. Besides, the traditional centralized data storage model in the smart grid is prone to the single point of failure. To address these challenges, in this article, for fair and secure smart grid communication, a blockchain-based novel paradigm, named BBNP, is proposed. Specifically, based on the pseudorandom function and auxiliary information generation and sharing technology, a lightweight data aggregation protocol is designed first to protect the user’s data privacy and ensure communication confidentiality. Then, a novel efficient authentication mechanism is proposed to generate and share session keys in a noninteractive way, which is leveraged for MAC authentication to achieve data integrity of the transmitted data. After that, based on the subjective logic reputation model, a blockchain node consensus mechanism is studied to efficiently store smart grid big data and effectively solve the single point failure problem. By constructing the long-term reputation model for consensus nodes (CNs) and integrating batch verification technology, the problems of CN fair selection and scalability of large-scale nodes are solved simultaneously. Finally, the performance evaluation indicates that BBNP outperforms the state-of-the-art similar schemes in computing complexity, communication cost, system availability, and fairness of block generation.

Preventing the Cloud Networks through Semi-Supervised Clustering from Both Sides Attacks

Cloud computing is a centralized data storage system providing various services worldwide. Different organizations are using the cloud for other purposes. As the number of users on the cloud server increases, so does the rate of attacks on the cloud. Various researchers have devised different solutions to solve these problems, the most widely used being the Intrusion Detection System (IDS). In this paper, a network architecture has been designed in which an efficient technique, semi-supervised clustering, has been used. In this technique, users’ responses inside and outside the cloud server have been observed, and various rules and mechanisms have been enforced based on these responses. The network is divided into three different scenarios. In the first scenario, attacks outside the cloud server have been detected, and then ways to prevent these attacks are discussed. The second scenario uses Cloud Shell, allowing authentic users to access the cloud server through authentic queries. In the third scenario, this tool’s performance and detection rate have been measured by applying different results to the confusion matrix. A comparative analysis has been done with other papers at the end of the paper, and conclusions have been drawn based on different results.

ThingSpeak Sunucusunu Kullanarak Sıcaklık ve Nem Bilgisini İzleme

The aim of this study is to measure the temperature and humidity of the environment using a convenient sensor, to process the measured values in a data storage center with a Wi-Fi module internet communication protocol, and to observe &amp; monitor the processed data. In the study, Atmel Atmega328 processor is used as a microcontroller, DHT22 temperature and humidity sensor is used to measure temperature and humidity, and ESP8266 serial Wi-Fi module is used for internet connection. By using the connection between the processor and the computer, the data is transferred to ThingSpeak, which is a data storage server. Parameters and desired parameter units have been determined for the measured values whether locked or open channel which is created according to preferences in ThingSpeak server. The temperature and humidity values are gathered at a specified time interval, and they are graphed over ThingSpeak, along with the time when the measurement take place, and observations are made.

Big Data Processing in Geo-Distributed Data Centers Using Cost Minimization Methods

Abstract: The explosive growth of demands on huge process imposes a crucial burden on computation, storage, and communication in data centers, that so incurs wide operational expenditure to data center suppliers. Therefore, Price decrease has become Associate in Nursing rising issue for the longer term huge data era. All totally different from customary cloud services, one in each of the foremost choices of huge data services is that the tight coupling between data and computation as computation tasks is also conducted provided that the corresponding data is obtainable. As a result, 3 factors, i.e., task assignment, data placement and data movement, deeply the operational expenditure of data centers. Throughout this paper, we tend to tend to unit of measurement meant to see the worth decrease downside via a joint improvement of these three factors for big data services in geo-distributed data centers. To elucidate the task completion time with the thought of every data transmission and computation, we tend to tend to propose a two-dimensional stochastic process and derive the standard task completion time in closed-form. Moreover, we tend to tend to model the matter as a mixed-integer non-linear programming (MINLP) and propose economical resolution to correct it. The high efficiency of our proposal is valid by thorough simulation primarily based studies Keywords— Nonlinear Programming, Big Data, Computer Centres, Integer Programming, Minimisation, Mixed Integer Nonlinear Programming, Cost Minimization, Data Centers, Big Data Services, Operational Expenditure, Task Assignment, Big Data

Development of a Personalized Recommendation System for E-Commerce Products for Distributed Storage Systems.

Because the distributed storage system is based on network technology, it can store data in multiple independent low-cost physical storage devices, and it is also suitable for large-capacity storage, so it has become more and more popular. Today, common applications of distributed storage systems include cloud storage services, data center storage services, and P2P storage services. Typical ones are GFS, HDFS, OceanStore, and Dynamo. Due to regional and economic differences, the development level of global e-commerce (b2c) is very inconsistent. b2c contains the following key tags: buying and selling, which is the core of the website platform. E-commerce provides business users with transparent information and high-quality cheap products. Logistics is the basic guarantee for customers to execute transactions, and it is also a strict indicator of the website platform. There will be many visits during the operation of the e-commerce system, and the number of users in the early stage will increase exponentially. A safe and efficient e-commerce system can provide users with one-stop transaction support and convenient transaction processes. The personalized recommendation system has formulated some rules for certain fields, based on these rules, and defined certain types of knowledge for certain items to meet the needs of certain users and use the defined reasoning rules to generate recommendation results.

5G-BSS: 5G-Based Universal Blockchain Smart Sensors.

A smart sensor is a sensor with information processing functions. It is the product of the combination of sensor integration and a microprocessor. It has the characteristics of intelligence, networking and high precision. It has been widely used in aerospace, aviation, intelligent transportation, industrial control and medical and health care. However, in some specific application scenarios with high data security requirements and low transmission delay, such as environmental detection, transportation, etc., smart sensors have three obvious shortcomings. First, the data transmission delay is high. Second, the confidentiality and integrity of the data transmission process cannot be effectively guaranteed. Third, centralized data storage is easily leaked and tampered with by malicious users and semi-trusted administrators. Therefore, a 5G-based blockchain smart sensor 5G-BSS was designed. 5G-BSS has three innovation points. First, the 5G communication module enables the smart sensor 5G-BSS. The 5G communication module is integrated into the smart sensor 5G-BSS to reduce the delay of data transmission and improve the speed and reliability of data transmission. Second, cryptographic algorithms enable the smart sensor 5G-BSS. The data encryption module of the smart sensor 5G-BSS improves the confidentiality and integrity of the data transmission process. Third, blockchain empowers the smart sensor 5G-BSS. The blockchain client is integrated into the smart sensor 5G-BSS to ensure the centralized storage of data and prevent data leakage and tampering by semi-trusted administrators. The operation process of the hardware and software architecture is described in detail and tested on the Fisco-Bcos. The experimental results show that 5G-BSS not only has fast data transmission speed but also can effectively guarantee the integrity, confidentiality and availability of data. 5G-BSS is suitable for application scenarios with high requirements for data security and data transmission, such as environmental monitoring, intelligent transportation, autonomous driving, etc.

FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction.

With the SARS-CoV-2's exponential growth, intelligent and constructive practice is required to diagnose the COVID-19. The rapid spread of the virus and the shortage of reliable testing models are considered major issues in detecting COVID-19. This problem remains the peak burden for clinicians. With the advent of artificial intelligence (AI) in image processing, the burden of diagnosing the COVID-19 cases has been reduced to acceptable thresholds. But traditional AI techniques often require centralized data storage and training for the predictive model development which increases the computational complexity. The real-world challenge is to exchange data globally across hospitals while also taking into account of the organizations' privacy concerns. Collaborative model development and privacy protection are critical considerations while training a global deep learning model. To address these challenges, this paper proposes a novel framework based on blockchain and the federated learning model. The federated learning model takes care of reduced complexity, and blockchain helps in distributed data with privacy maintained. More precisely, the proposed federated learning ensembled deep five learning blockchain model (FLED-Block) framework collects the data from the different medical healthcare centers, develops the model with the hybrid capsule learning network, and performs the prediction accurately, while preserving the privacy and shares among authorized persons. Extensive experimentation has been carried out using the lung CT images and compared the performance of the proposed model with the existing VGG-16 and 19, Alexnets, Resnets-50 and 100, Inception V3, Densenets-121, 119, and 150, Mobilenets, SegCaps in terms of accuracy (98.2%), precision (97.3%), recall (96.5%), specificity (33.5%), and F1-score (97%) in predicting the COVID-19 with effectively preserving the privacy of the data among the heterogeneous users.

Research on information sharing incentive mechanism of China's port cold chain logistics enterprises based on blockchain

Blockchain technology resolves the bottleneck of traditional centralized data storage technology. It can better facilitate the seamless operation and supervision of port cold chain logistics and can meet the massive business demand for cold chain imports and the surging demand for pandemic prevention and control. However, it is a challenging task to incentivize cold chain enterprises in the maritime industry to adopt blockchain technology and actively participate in information sharing platform. Using evolutionary game theory, through an analysis of evolutionary stability strategies, this paper explores the evolutionary path of port cold chain logistics enterprises that participate in information sharing. An incentive mechanism based on blockchain smart contract is designed to encourage cold chain logistics enterprises to actively participate in information sharing within chains. Smart contracts can continuously encourage enterprises to participate in information sharing by timely adjusting the incentive participation costs of participating enterprises.

Effectiveness of containment strategies in preventing SARS-CoV-2 transmission

BackgroundDespite substantial resources deployed to curb SARS-CoV-2 transmission, controlling the COVID-19 pandemic has been a major challenge. New variants of the virus are frequently emerging leading to new waves of infection and re-introduction of control measures. In this study, we assessed the effectiveness of containment strategies implemented in the early phase of the pandemic. MethodsReal-world data for COVID-19 cases was retrieved for the period Jan 1 to May 1, 2020 from a number of different sources, including PubMed, MEDLINE, Facebook, Epidemic Forecasting and Google Mobility Reports. We analyzed data for 18 countries/regions that deployed containment strategies such as travel restrictions, lockdowns, stay-at-home requests, school/public events closure, social distancing, and exposure history information management (digital contact tracing, DCT). Primary outcome measure was the change in the number of new cases over 30 days before and after deployment of a control measure. We also compared the effectiveness of centralized versus decentralized DCT. Time series data for COVID-19 were analyzed using Mann-Kendall (M-K) trend tests to investigate the impact of these measures on changes in the number of new cases. The rate of change in the number of new cases was compared using M-K z-values and Sen’s slope. ResultsIn spite of the widespread implementation of conventional strategies such as lockdowns, travel restrictions, social distancing, school closures, and stay-at-home requests, analysis revealed that these measures could not prevent the spread of the virus. However, countries which adopted DCT with centralized data storage were more likely to contain the spread. ConclusionsCentralized DCT was more effective in containing the spread of COVID-19. Early implementation of centralized DCT should be considered in future outbreaks. However, challenges such as public acceptance, data security and privacy concerns will need to be addressed.

MULTIAGENT METHODS OF MANAGEMENT OF DISTRIBUTED COMPUTING IN HYBRID CLUSTERS

Modern information technologies include the use of server systems, virtualization technologies, communication tools for distributed computing and development of software and hardware solutions of data processing and storage centers, the most effective of such complexes for managing heterogeneous computing resources are hybrid GRID- distributed computing infrastructure combines resources of different types with collective access to these resources for and sharing shared resources. The article considers a multi-agent system that provides integration of the computational management approach for a cluster Grid system of computational type, the nodes of which have a complex hybrid structure. The hybrid cluster includes computing modules that support different parallel programming technologies and differ in their computational characteristics. The novelty and practical significance of the methods and tools presented in the article are a significant increase in the functionality of the Grid cluster computing management system for the distribution and division of Grid resources at different levels of tasks, the ability to embed intelligent computing management tools in problem-oriented applications. The use of multi-agent systems for task planning in Grid systems will solve two main problems - scalability and adaptability. The methods and techniques used today do not sufficiently provide solutions to these complex problems. Thus, the scientific task of improving the effectiveness of methods and tools for managing problem-oriented distributed computing in a cluster Grid system, integrated with traditional meta-planners and local resource managers of Grid nodes, corresponding to trends in the concept of scalability and adaptability.

Implementation of Database Distributed Sharding Horizontal Partition in MySQL. Case Study of Application of Food Serving On Kemkes

In today's digital era, database systems are becoming a mandatory thing that is used in every industry field to improve the performance of the industry, over time with the increasing need for databases, single databases and single servers are considered less capable in handling data that continues to increase in number, so it often leads to a decline in performance. Therefore came the idea of centralized data storage that can be modified to accommodate the availability, scalability, reliability, and management of data. One way that can be done is to apply Distribute Database using Sharding Horizontal Partition technique and experimental methods using mariadb and spider engine. The test was carried out on the database of food processing sites (TPP) in the TPP application at the ministry of health. The test results prove that although in some journals about Sharding, more sharding is implemented on NoSQL DBMS such as MongoDB, influx dB. In this study it can be proven that sharding can also be done in relational DBMS such as MariaDB and MySQL, data can be distribute and load process too, test result successful although the performance of time query response is still better single database than distribute database.

A Conceptual Model for Blockchain-Based Agriculture Food Supply Chain System

In agriculture supply chain management, traceability is a crucial aspect to ensure food safety for increasing customer loyalty and satisfaction. Lack of quality assurance in centralized data storage makes us move towards a new approach based on a decentralized system in which transparency and quality assurance is guaranteed throughout the supply chain from producer to consumer. The current supply chain model has some disadvantages like a communication gap between the entities of the supply chain and no information about the travel history and origin of the product. The use of technology improves the communication and relation between various farmers and stakeholders. Blockchain technology acquires transparency and traceability in the supply chain, provides transaction records traceability, and enhances security for the whole supply chain. In this paper, we present a blockchain-based, fully decentralized traceability model that ensures the integrity and transparency of the system. This new model eliminated most of the disadvantages of the traditional supply chain. For the coordination of all transactions in the supply chain, we proposed a decentralized supply chain model along with a smart contract.