Remote Data Storage Research Articles

Small business, big footprint: the digital carbon footprint dilemma in small and medium-sized enterprises

A major contributor to climate change is the emissions from Information and Communication Technology (ICT) devices and digitalisation. Energy use, heat production, and the operation of assets all contribute to the production of harmful emissions. However, indirect emissions, such as production and disposal, also play a role. We rely mainly on the output of small and medium-sized enterprises (SMEs). This paper focuses on the emissions of SMEs. Is it certain that cloud services (remote data storage and management) leave a much smaller carbon footprint than ICT devices for own use? These two solutions lead to a paradox: using more modern devices to produce less emissions requires more energy and generates more heat. This article analyses how to resolve this paradox for SMEs.

Multiple channels, low-cost, and dual data storage data logger for building a soil temperature network

Temperature measurement is critical in many areas of research, particularly in agriculture, where it can have a significant impact on crop health and yield. Experiments such as seed germination often require numerous temperature sensors to collect extensive data. Typically, data loggers are used to store information, but market options are expensive and offer limited, non-customizable inputs (channels), creating challenges for comprehensive soil temperature monitoring. This study aims to develop a network of open-source, low-cost data loggers with multiple customizable channels for local and remote temperature data storage. The hardware includes Arduino, temperature sensors, a Real Time Clock, and a LoRa module to transmit data to a LILYGO TTGO board, which sends it to a remote MongoDB database while also storing it locally on a microSD card. In addition, a digital tool was developed to retrieve and display both current and historical readings from the MongoDB database. The total cost of this hardware is approximately US$ 72 (based on current prices) for the simplest network, which is approximately 18 % of the commercial cost. The system achieved a root mean square error (RMSE) of 1.6 °C compared to a manual sampling probe thermometer, proving it to be a reliable measurement source. The hardware developed in this study surpasses commercial options by allowing the integration of multiple sensors and emitters, creating a network of data loggers at a lower cost. In addition to the hardware, an open-source digital tool was developed to visualize historical data at no additional cost.

The AGILEScience mobile application for the AGILE space mission

AGILE is a space mission launched in 2007 to study X-ray and gamma-ray phenomena through data acquired by different payload instruments. The AGILE Team developed an application called AGILEScience that allows to visualize information about the AGILE space mission from mobile devices, such as smartphones and tablets. The AGILEScience application can be downloaded freely for iOS and Android devices.Beside sharing information about the AGILE space mission with the public for outreach purposes, similarly to what other applications do, the AGILEScience app offers some new and unique features in gamma-ray astrophysics: (i) it gives public access in nearly real-time to the sky view of a gamma-ray satellite for the first time, (ii) it interacts with the AGILE remote gamma-ray data storage and analysis system, allowing data analysis to be sent and results to be visualized, and (iii) it allows the AGILE Team to access a password-protected section of the app to view detailed AGILE pipeline results and submit advanced analyses. The last two features are critical to allow remote and easy access to the results of the AGILE automated pipelines.In particular, the ability to visualize results and execute manual data analysis from mobile devices is key during the follow-up of transient events and to easily monitor the satellite status via smartphone.

Storage and Analysis of Remote Sensing Data

Storage and Analysis of Remote Sensing Data

A Novel Design of a Low-Cost SCADA System for Monitoring Standalone Photovoltaic Systems

Standalone photovoltaic (PV) systems are pivotal in the global transition towards sustainable energy, offering reductions in fossil fuel dependence and helping homes and businesses lower electricity costs. Key to optimizing the performance and efficiency of standalone systems are supervisory control and data logging (SCADA) systems. They monitor and record operational data such as power output, facilitating early detection of potential issues. This paper introduced a novel design for both the Human-Machine Interface (HMI) and data storage in a SCADA system for standalone PV systems, addressing two crucial aspects: real-time monitoring and efficient data retrieval, both at very low cost. The proposed design utilized Bluetooth Low Energy technology to transmit voltage and current data from the PV panel to a mobile application, marking a departure from traditional HMI approaches. This method enabled historical data analysis for trend identification. Additionally, the system intermittently transferred collected data to a cost-effective cloud storage service via Wi-Fi, allowing for substantial data storage at no cost. Remote data storage, another key feature of this design, simplifies data retrieval, which is particularly beneficial for systems in rural areas. Emphasizing open-source development, this design ensured flexibility and customization options. To demonstrate its practical effectiveness of the design, a one-day power curve of the PV system and the battery voltage data are presented, showcasing the design's capability in handling extensive and remote data storage.

Portable dual-channel blood enzyme analyzer for point-of-care liver function detection.

Because the liver is an important metabolic center in the human body, the reliability and timeliness of chronic liver disease diagnosis are particularly important. Alanine aminotransferase and aspartate transaminase are the two most important liver function indicators, and their test results are crucial in the diagnosis of liver diseases. However, the simultaneous detection of these two indicators is currently restricted by the need for expensive equipment and complicated detection processes. This study proposes a portable dual-channel blood enzyme analyzer (BEA) for point-of-care-testing. The device uses photometric reflectance to quantify the enzyme concentration by evaluating the reflected light intensity. The BEA also precisely controls and maintains the temperature at 37 °C ± 0.1 °C in the dual-channel assay. We assessed the responses of this system within a clinically relevant range by testing blood samples from a local hospital. The test verified that BEA for ALT and AST achieved a detection limit of 3.5 U L-1 and 4 U L-1, detection range of 4-350 U L-1 and 4-250 U L-1, coefficients of variation (CV) that were both less than 10%, and a linear correlation coefficient of 0.9827 and 0.9714 compared with a high-precision clinical biochemistry analyzer (Roche Cobas C702), respectively. We realized remote data analysis and storage through connection with smartphones, which can be applied to remote diagnostics and preventative personal disease management. Therefore, BEA has broad application prospects in the future internet of medical things.

MIX-RS: A Multi-Indexing System Based on HDFS for Remote Sensing Data Storage

A large volume of remote sensing (RS) data has been generated with the deployment of satellite technologies. The data facilitates research in ecological monitoring, land management and desertification, etc. The characteristics of RS data (e.g., enormous volume, large single-file size and demanding requirement of fault tolerance) make the Hadoop Distributed File System (HDFS) an ideal choice for RS data storage as it is efficient, scalable and equipped with a data replication mechanism for failure resilience. To use RS data, one of the most important techniques is geospatial indexing. However, the large data volume makes it time-consuming to efficiently construct and leverage. Considering that most modern geospatial data centres are equipped with HDFS-based big data processing infrastructures, deploying multiple geospatial indices becomes natural to optimise the efficacy. Moreover, because of the reliability introduced by high-quality hardware and the infrequently modified property of the RS data, the use of multi-indexing will not cause large overhead. Therefore, we design a framework called Multi-IndeXing-RS (MIX-RS) that unifies the multi-indexing mechanism on top of the HDFS with data replication enabled for both fault tolerance and geospatial indexing efficiency. Given the fault tolerance provided by the HDFS, RS data is structurally stored inside for faster geospatial indexing. Additionally, multi-indexing enhances efficiency. The proposed technique naturally sits on top of the HDFS to form a holistic framework without incurring severe overhead or sophisticated system implementation efforts. The MIX-RS framework is implemented and evaluated using real remote sensing data provided by the Chinese Academy of Sciences, demonstrating excellent geospatial indexing performance.

Research on high-speed data acquisition system of negative ion source breakdown

During the long pulse discharge experiment of negative ion source, the risk of breakdown increases significantly. Current data acquisition system applied on the negative ion source based neutral beam injection (NNBI) is low-speed and lacks of the ability to accurately collect and characterize the electrical signals at the moment of breakdown, which makes it difficult for researchers to analyze the cause of breakdown. To solve this issue, a high-speed data acquisition (DAQ) system using random trigger method, pre-trigger method, multithreading and MDSplus database is researched and designed to collect electrical signals during breakdown specifically. Random trigger instructs the system to start data acquisition when the breakdown signal arrives, pre-trigger method helps acquire samples for a period of time before the breakdown signal arrives, multithreading enables multi-channel data acquisition, MDSplus database realizes remote data storage and timeline synchronization with other data acquisition systems. With this design, it is possible to obtain accurate electrical signals of breakdown and make significant savings in storage costs at the same time.

How innovations in methodology offer new prospects for volume electron microscopy

Detailed knowledge of biological structure has been key in understanding biology at several levels of organisation, from organs to cells and proteins. Volume electron microscopy (volume EM) provides high resolution 3D structural information about tissues on the nanometre scale. However, the throughput rate of conventional electron microscopes has limited the volume size and number of samples that can be imaged. Recent improvements in methodology are currently driving a revolution in volume EM, making possible the structural imaging of whole organs and small organisms. In turn, these recent developments in image acquisition have created or stressed bottlenecks in other parts of the pipeline, like sample preparation, image analysis and data management. While the progress in image analysis is stunning due to the advent of automatic segmentation and server‐based annotation tools, several challenges remain. Here we discuss recent trends in volume EM, emerging methods for increasing throughput and implications for sample preparation, image analysis and data management.

Study on applicability of data collection frequency for heavy-duty vehicles based on remote monitoring

The remote online system of heavy-duty vehicles is composed of vehicle terminal and monitoring platform based on the specified communication protocol, data format and data transmission frequency, which is an important embodiment of heavy-duty vehicle pollution control in the application for Internet of Vehicles. The influence of different collection frequencies and compute cycles on vehicle energy consumption and emission calculation is studied in this paper. The calculation errors of mileage, vehicle fuel consumption per 100 kilometers and emission factors of 14 collection frequencies and 7 compute cycles were calculated by continuously monitoring the emission and energy consumption data for 10 heavy-duty vehicles uploaded in 1 month. The result shows that decreasing the data collection frequency will lead to the increase of error and the decrease of correlation, while increasing the computing cycle can reduce the error. When the calculation error is 1%, 5% and 10%, the collection frequency shall be at least 0.5Hz, 0.2Hz and 0.1Hz, and the computing cycle shall be greater than 1800 seconds, 3600 seconds and 7200 seconds. The study content of this paper provides the theoretical foundation for the application and storage of remote monitoring data of heavy-duty vehicles, and provides a solution to the waste of data storage space caused by the problem of remote monitoring big data set.

Implementation of Efficient Biometric-Based Access for Secured Cloud Services

In our data-driven culture, the need for remote data storage and computing services has grown exponentially, requiring secure access to such data and services. This study proposes a new biometric-based authentication system for secure access to remote (cloud) servers. The proposed approach treats the user's biometrics as confidential credentials. It then uses the user’s biometric data to generate a unique ID and uses it to generate the user's private key. In addition, we present an efficient way to generate session keys for secure messaging between two interlocutors using two biometric templates. That is, you don't have to store your private and session keys somewhere. The proposed approach follows formal security analysis using detailed RealorRandom (ROR) model-based formal security analysis, informal (unmathematical) security analysis, and widely accepted automated Internet security verification., Can withstand multiple known attacks against (passive / active) attackers. Protocol and application (AVISPA) tools. Finally, numerous and comparative studies have shown the effectiveness and usefulness of the proposed approach.

Research on Information Security Technology of Intelligent Home Remote Control Terminal

The development of remote-control technology of smart home promotes the rapid development of remote-control information. The use of remote-control information is closely related to the transmission efficiency and application of information data. At the same time, the research of remote-control technology of smart home is also the basis of realizing information sharing. To this end, this paper is the development of remote-control information in the new period and the challenge for the smart home remote control technology is expounded, and then on the basis of in-depth study in the new period in remote control information large data storage and management of application analysis, and put forward for the remote-control information data storage strategy, hoping to bring positive influence.

Data Transfers Using Bandwidth Reservation Through Multiple Disjoint Paths of Dynamic HPNs

Bandwidth reservation was initially designed to provide QoS for real-time multimedia applications, and has been extended to many other applications and widely used because of its proved high efficiency and effectiveness. Particularly, it has now become an indispensable service to support big data transfers in dynamic high-performance networks (HPNs) for remote data storage and processing. A number of problems regarding data transfers using bandwidth reservation have been investigated in the past few years, and most of them focused on the data transfers on one path or two disjoint paths of the dynamic HPNs. In this paper, three important problems regarding data transfers using bandwidth reservation on multiple node- and edge-disjoint paths in dynamic HPNs to achieve are studied: (i) the earliest completion time for a data transfer request through multiple disjoint variable paths with variable bandwidths, (ii) the earliest completion time for a data transfer request through multiple disjoint fixed paths with fixed bandwidths, and (iii) the minimum number of disjoint fixed paths with fixed bandwidths for a deadline-constrained data transfer request. We prove all of these problems to be NP-complete and propose one heuristic algorithm for each. We compare the proposed algorithms with two existing scheduling algorithms and one algorithm derived from an existing algorithm, and conduct extensive simulations. The simulation results show that our proposed heuristic algorithms have much better overall scheduling performance.

Design of Distributed Remote Sensing Data Storage System based on Hadoop Framework

Design of Distributed Remote Sensing Data Storage System based on Hadoop Framework

Research and Design of Distributed IoT Water Environment Monitoring System Based on LoRa

In order to solve the problems of high labor cost, long detection period, and low degree of information in current water environment monitoring, this paper proposes a lake water environment monitoring system based on LoRa and Internet of Things technology. The system realizes remote collection, data storage, dynamic monitoring, and pollution alarm for the distributed deployment of multisensor node information (water temperature, pH, turbidity, conductivity, and other water quality parameters). Moreover, the system uses STM32L151C8T6 microprocessor and multiple types of water quality sensors to collect water quality parameters in real time, and the data is packaged and sent to the LoRa gateway remotely by LoRa technology. Then, the gateway completes the bridging of LoRa link to IP link and forwards the water quality information to the Alibaba Cloud server. Finally, end users can realize the water quality control of monitored water area by monitoring management platform. The experimental results show that the system has a good performance in terms of real‐time data acquisition accuracy, data transmission reliability, and pollution alarm success rate. The average relative errors of water temperature, pH, turbidity, and conductivity are 0.31%, 0.28%, 3.96%, and 0.71%, respectively. In addition, the signal reception strength of the system within 2 km is better than ‐81 dBm, and the average packet loss rate is only 94%. In short, the system’s high accuracy, high reliability, and long distance characteristics meet the needs of large area water quality monitoring.

Machine Learning-based Whitefly Feature Identification and Counting

This article uses LabVIEW, a software program to develop a whitefly feature identification and counting technology, and machine learning algorithms for whitefly monitoring, identification, and counting applications. In addition, a high-magnification CCD camera is used for on-demand image photography, and then the functional programs of the VI library of LabVIEW NI-DAQ and LabVIEW NI Vision Development Module are used to develop image recognition functions. The grayscale-value pyramid-matching algorithm is used for image conversion and recognition in the machine learning mode. The built graphical user interface and device hardware provide convenient and effective whitefly feature identification and sample counting. This monitoring technology exhibits features such as remote monitoring, counting, data storage, and statistical analysis.

Software Defined Network Function Virtualization Framework for Securing Cloud with Data Fusion and Machine Learning Techniques

Computing in the cloud is one of the platforms that may be used to provide distributed computing resources. Supplying and managing cloud resources most effectively is referred to as resource management. A recent development in technology known as fog computing is an example of an expanded and dispersed infrastructure. This architecture maintains application processes between end devices and the network edge to provide more dependable and efficient services. These services include remote data storage, allowing customers to access their data from a distant location. Providing remote storage service is an advantageous function offered by cloud suppliers. On the other hand, the data stored in the cloud is geographically dispersed and kept in various data centers, significantly increasing the risk to users' privacy and security. One of the problems that might arise with privacy is when many data centers store the same information. Many cloud service providers check their customers' data using a Third-Party Auditor (TPA) to address concerns about client privacy and data integrity. Currently, most trusted TPAs only have one validator, making it impossible to expand the data integrity across several data centers. The various verifiers used by TPAs have been reduced in number in response to Man in the Cloud (MiTC) attacks. As a result, they cannot check and authenticate the integrity of data stored in several data centers. A unique Peer to Peer (P2P) authentication protocol with Certificate Authority (CA) and Data Storage Protocol is presented as a solution to the problem that has been outlined above to check for and go around any issues that may arise (DSP). The efficiency of the proposed protocol is demonstrated by the incorporation of TPAs and Certificate Authorities. The proposed protocol has been tested with a single user and a single storage server, as well as multiple storage servers in ownCloud with one backup server, two storage servers, three clients, and two TPAs. The NoSQL server in an organization's cloud is set up to save data to storage servers in the appropriate format. The Amanda backup server is used to back up the mirror copy of the stored data on the storage servers. Automated Validation of Internet Security Protocols with Data Fusion and Applications, or AVISPA for short, is a technology that may be used to verify data stored in the cloud. The findings make it abundantly evident that the suggested protocol is strong enough to guarantee the authenticity of data kept in several data centers.

DATABASE SECURITY AND STUDY OF DATA ENCRYPTION METHODS IN CLOUD STORAGE

Purpose of the article is discusses the importance of database encryption and makes an in depth review of various encryption techniques. Security of Data is the most important task in today’s world. Governments, companies and other organizations have lost a lot of money and many others have closed down due to the activities of dubious hackers and attackers. Over the years various encryption schemes have been developed in order to protect the database from various attacks by the intruders. As data is the life wire of every organization, there is the need to remotely and securely store the data generated daily by these organizations in order to enable them recover quickly in the event of attach and hack. Cloud storage is needed here for the remote data storage. For many establishments, data security is one of their major concern when sending their files into the cloud. They worry about their files being seen or even compromised by malicious and dubious people because that’s what happened in the past. Data encryption techniques are required to protect the integrity of the stored data. In the past, many businesses felt comfortable allowing the cloud providers to manage all their data, believing that security risks could be managed through contracts, controls and audits. Over time it has become apparent, however, that cloud providers cannot honor such commitments when responding to government requests for information. This paper discuses, the importance of database encryption and makes an in depth review of various encryption techniques

Improved Elman neural network in turbine blade fault diagnosis

To remotely monitor and maintain large-scale complex equipment in real-time, it is required to create a comprehensive framework integrating remote data collection, transmission, storage, analysis and prediction. The framework is designed to provide manufacturers with proactive, systematic, integrated operation and maintenance service, where the data analysis and health forecasting are the most important part. This paper conducts health management for the turbine blades. An output-hidden feedback (OHF) Elman neural network is developed by adding a self-feedback factor in the context nodes. Thus, this improved method can increase the accuracy of the fault diagnosis for guide vane damage. Through the results, the applicability of this improved Elman neural network has been verified.

Digital technologies and development of new services based on the housing services company

The object of the study are the processes of incorporation of digital technologies in the activities of subjects of the housing and communal services market. The information and telecommunication technologies have been analysed in the paper, the use of which allows us to expand the range of services provided to residents of apartment buildings by management organizations in the field of housing and communal services. Factors and conditions of diversification of services of management companies due to introduction of “Uber-technologies” have been considered. One of the tools of digitalization of housing and communal services is the so-called “uberization”. “Uberization” of housing services in large cities has significant economic potential. As of the end of 2019, commercial projects of “uberization” of housing services in Russia are at the stage of pilot testing. At the same time, Russia has a rather unique situation – there is a “goal – setting” on the part of the state, institutionalized by regulations, state national and Federal programs, and most importantly-there is a request from the society, since such services are technologically little different from buying food on the Internet or ordering a taxi. The development of “Uber-technologies” is impossible without a comprehensive solution to the problems of modern digital society, which can really have an impact on the work of a mass digital platform. These issues include: ensuring human rights in the digital world, including the identification and preservation of the user’s digital data, as well as the problem of ensuring the trust of citizens in the digital environment; threats to the individual, business and the state associated with the trends of building complex hierarchical information and telecommunications systems that widely use virtualization, remote (cloud) data storage, as well as heterogeneous communication technologies and terminal devices.; expansion of opportunities for external information and technical impact on the information infrastructure, including critical information infrastructure; growth of computer crime scales.