Enable Data Storage Research Articles

An improved approach to organising mobile edge computing in a 5G network

Mobile edge computing is an important element in ensuring the efficiency of the 5G network as a whole, as it enables data storage and computing at the network edge. Existing solutions do not fully address the issues of load distribution between computing nodes, and most solutions do not offer methods for verifying computations and controlling errors. Accordingly, this paper aims to develop an approach to the organization of mobile edge computing in a 5G mobile network that would authenticate distribution servers and computing nodes, manage the process of distributing computing nodes, have a procedure for verifying the correctness of calculations, and take into account the parameters of computing nodes during distribution. To achieve this goal, we propose to use the developed method. The method of load balancing and selection of computing nodes for edge computing via 5G allows for identifying available nodes and distributing computing blocks among them. It also provides mutual authentication of elements and includes a method of data verification and error detection for the MEC system. The provided solution allows for controlling errors during calculations and protecting the server from incorrect data. These methods are optimized according to minimum network resources and computing time criteria. These improvements increase the efficiency of mobile edge computing in a 5G network.

Smart Autonomous Nurse Assistant Robot (SANAR)

Abstract: In the evolving landscape of healthcare, the integration of innovative technologies has become paramount to enhance efficiency, accessibility, and usability across all sectors, particularly in the medical industry. This project proposes the development of a Smart Autonomous Nurse Assistant Robot (SANAR) aimed at revolutionizing medical care delivery, especially during emergencies and pandemics like Covid-19. SANAR utilizes cutting-edge technology to streamline disease diagnosis, monitor patient health in real-time, and facilitate drug delivery. The core of SANAR's functionality lies in its utilization of a health tracker system-based IoT platform, which continuously analyzes vital patient data, including oxygen levels, pulse rate, and temperature. Through simple text commands and a timer-based medicine scheduler, SANAR ensures timely medication administration, leveraging a cloud server for seamless data access and management with utmost security. Equipped with advanced sensors such as oximeter, heartbeat, ultrasonic sensors, and a voice module, SANAR leverages Internet of Things (IoT) technology to enable data storage and retrieval over HTTP and MQTT protocols, facilitating connectivity and scalability. The primary goal of this project is to democratize smart automation in healthcare, making it accessible and affordable to all segments of society, ultimately empowering medical staff to deliver efficient and effective care.

Data Lake Conceptualized Web Platform for Food Research Data Collection

Food research is uniquely intertwined with everyday life and necessitates the utilization of big data. Within this domain, the research data consist of various forms and formats, encompassing biological experiment results, chemical analysis data, nutritional information, microbiological data, sensor data, images, and videos. This diversity stems from the integration of data from various subdomains within the larger field. With recent advancements in deep learning technology, the importance of data has grown significantly, resulting in increased reliance on data-driven research. Although specialized platforms for sharing and utilizing data have been established at the national level, particularly in the bioscience field, food research lacks a dedicated infrastructure and specialized data-sharing platforms. In this study, we develop a platform that leverages Hadoop-based distributed file systems to create a data lake. This platform enables data storage and sharing through a web-based interface. The distributed file system supports scalability by adding data nodes, making it an effective solution for capacity expansion. In addition, the web-based platform ensures high accessibility, allowing users access from anywhere, at any time, using any device. Finally, we introduce the establishment of a 1.8 PB Hadoop-based physical storage system and present an approach for building a highly accessible web platform with substantial utility.

DYNAMIC WIRELESS POWER ALLOCATION FOR SELF SUSTAINABLE SUBSTATIONS

Wireless charging is considered one of the best and easiest methods to charge EV’s anywhere, even during the driving of cars. Employing resonant inductive coupling and smart charging pads, this system facilitates wireless charging without physical connections, ensuring seamless, efficient, and convenient charging experiences for EV users. The cloud connectivity feature ensures seamless data exchange with cloud platforms, enabling data storage, analysis, and remote monitoring. A user-friendly mobile app empowers users to monitor and control the charging process. Keywords: Electric Vehicle, wireless, Charging pads.

STUDYING COMPARATIVE CLOUD COMPUTING TECHNOLOGY

In order to provide quicker innovation, adaptable resources, and scale economies, cloud computing is the supply of computing services, including servers, storage, databases, networking, software, analytics, and intelligence, over the Internet. As technology advanced in recent years, cloud technologies were increasingly utilized. Cloud technologies are appealing because data may be stored, transmitted to numerous locations at once, and accessed at any time. It is a system that enables data storage on the Internet, releasing users from the constraints of physical settings and storage devices and enabling access from any location at any time.

FAIR research data management as community approach in bioengineering.

Research data management (RDM) requires standards, policies, and guidelines. Findable, accessible, interoperable, and reusable (FAIR) data management is critical for sustainable research. Therefore, collaborative approaches for managing FAIR-structured data are becoming increasingly important for long-term, sustainable RDM. However, they are rather hesitantly applied in bioengineering. One of the reasons may be found in the interdisciplinary character of the research field. In addition, bioengineering as application of principles of biology and tools of process engineering, often have to meet different criteria. In consequence, RDM is complicated by the fact that researchers from different scientific institutions must meet the criteria of their home institution, which can lead to additional conflicts. Therefore, centrally provided general repositories implementing a collaborative approach that enables data storage from the outset In a biotechnology research network with over 20 tandem projects, it was demonstrated how FAIR-RDM can be implemented through a collaborative approach and the use of a data structure. In addition, the importance of a structure within a repository was demonstrated to keep biotechnology research data available throughout the entire data lifecycle. Furthermore, the biotechnology research network highlighted the importance of a structure within a repository to keep research data available throughout the entire data lifecycle.

Design of Abiological Digital Poly(phosphodiester)s.

In biological systems, the storage and transfer of genetic information rely on sequence-controlled nucleic acids such as DNA and RNA. It has been realized for quite some time that this property is not only crucial for life but could also be very useful in human applications. For instance, DNA has been actively investigated as a digital storage medium over the past decade. Indeed, the "hard-disk of life" is an obvious choice and a highly optimized material for storing data. Through decades of nucleic acids research, technological tools for parallel synthesis and sequencing of DNA have been readily available. Consequently, it has already been demonstrated that different types of documents (e.g., texts, images, videos, and industrial data) can be stored in chemically synthesized DNA libraries. However, DNA is subject to biological constraints, and its molecular structure cannot be easily varied to match technological needs. In fact, DNA is not the only macromolecule that enables data storage. In recent years, it has been demonstrated that a wide variety of synthetic polymers can also be used for such a purpose. Indeed, modern polymer synthesis allows the preparation of synthetic macromolecules with precisely controlled monomer sequences. Altogether, about a dozens of synthetic digital polymers have already been described, and many more can be foreseen. Among them, sequence-defined poly(phosphodiester)s are one of the most promising options. These polymers are prepared by stepwise phosphoramidite chemistry like chemically synthesized oligonucleotides. However, they are constructed with non-natural building blocks and therefore share almost no structural characteristics with nucleic acids, except phosphate repeat units. Still, they contain readable digital messages that can be deciphered by nanopore sequencing or mass spectrometry sequencing. In this Account, we describe our recent research efforts in synthesizing and sequencing optimal abiological digital poly(phosphodiester)s. A major advantage of these polymers over DNA is that their molecular structure can easily be varied to tune their properties. During the last 5 years, we have engineered the molecular structure of these polymers to adjust crucial parameters such as the storage density, storage capacity, erasability, and readability. Consequently, high-capacity PPDE chains, containing hundreds of bits per chains, can now be synthesized and efficiently sequenced using a routine mass spectrometer. Furthermore, sequencing data can be automatically decrypted with the help of decoding software. This new type of coded matter can also be edited using practical physical triggers such as light and organized in space by programmed self-assembly. All of these recent improvements are summarized and discussed herein.

Menoci: lightweight extensible web portal enhancing data management for biomedical research projects

BackgroundBiomedical research projects deal with data management requirements from multiple sources like funding agencies’ guidelines, publisher policies, discipline best practices, and their own users’ needs. We describe functional and quality requirements based on many years of experience implementing data management for the CRC 1002 and CRC 1190. A fully equipped data management software should improve documentation of experiments and materials, enable data storage and sharing according to the FAIR Guiding Principles while maximizing usability, information security, as well as software sustainability and reusability.ResultsWe introduce the modular web portal software menoci for data collection, experiment documentation, data publication, sharing, and preservation in biomedical research projects. Menoci modules are based on the Drupal content management system which enables lightweight deployment and setup, and creates the possibility to combine research data management with a customisable project home page or collaboration platform.ConclusionsManagement of research data and digital research artefacts is transforming from individual researcher or groups best practices towards project- or organisation-wide service infrastructures. To enable and support this structural transformation process, a vital ecosystem of open source software tools is needed. Menoci is a contribution to this ecosystem of research data management tools that is specifically designed to support biomedical research projects.

Applying Model-Driven Engineering to Distributed Ledger Deployment

Distributed Ledger Technology (DLT) enables data storage in a decentralized manner among collaborating parties. The software architecture of such solutions encompasses models placed in the relevant architectural views. A lot of research is devoted to smart contracts and consensus algorithms, which are realized by distributed applications and can be positioned within the Logical view. However, we see the need to provide modeling support for the Deployment view of distributed ledger solutions. Especially since the chosen DLT framework has a significant impact on implementation and deployment. Besides, consistency between models and configuration deployment scripts should be ensured. So, we have applied Model-Driven Engineering (MDE) that allows on the transformation of models into more detailed models, source code, or tests. We have proposed Unified Modeling Language (UML) stereotypes and tagged values for distributed ledger deployment modeling and placed them in the UML Profile for Distributed Ledger Deployment. We have also designed the UML2Deployment model-to-code transformation for the R3 Corda DLT framework. A UML Deployment model is the source whereas a Gradle Groovy deployment script is the target of the transformation. We have provided the complete solution by incorporating the transformation into the Visual Paradigm modeling tool. Furthermore, we have designed a dedicated plug-in to validate generated deployment scripts. In the paper, we have shown how to design transformation for generating deployment scripts for the R3 Corda DLT framework with the ability to switch to another one.

Integration of distributed ledger technology into software electronic signature exchange service

Abstract Object of the research: information storage, transmission and processing systems. Subject of the research: approaches to implementing the distributed ledger technology, in particular, the blockchain technology, as well as implementation issues and feasibility. Purpose of the work: enhancing the safety of data storage, transmission and processing by embedding distributed ledgers in the existing information system. Research methods: the theoretical and analytical research is supposed to underlie the study of the experience building upon the current application and possible options for boosting the potential of the distributed ledger technology, in particular, the blockchain technology. Scientific novelty. The results of the research are supposed to be used to issue proposals and recommendations on how to improve the security of data storage and processing through the use of distributed ledger technology. Practical importance. Practical perspectives consist in the fact that the discussed process of developing and implementing a distributed system simplifies further integration of the technology under consideration with the resulting blockchain service enabling data storage inside a non-modifiable registry.

A Machine Learning Tool to Transform Raw Movement Data into Cleaned, Annotated, and Usable Information

The term bio-logging is given to ‘the use of miniaturized animal-attached tags for logging and/or relaying data about an animal’s movements, behaviour, physiology, and/or environment’ (Rutz and Hays 2009). It has been spreading over the scientific world through the last ten years and will become even more popular in the next decade thanks to engineering progresses. Indeed, tags tend to miniaturize, and geolocalization technology, as well as network coverage, is getting improved and cheaper. Today, electronic tags are widely used to study species’ movements and migration, resulting in an enormous amount of data ( Li et al. 2015 , Thums et al. 2018). Currently, platforms like Movebank enable data storage, visualization and management. However, no easy-to-use tool is currently available for conducting a various array of pre-treatment procedures on those data, such as outliers identification, elevation or time management. In that context, we are developing an open source platform based on algorithms and machine learning to automatically return cleaned and annotated data.

Magnetic Nanofiber Mats for Data Storage and Transfer.

Electrospun nanofiber mats may serve as new hardware for neuromorphic computing. To enable data storage and transfer in them, they should be magnetic, possibly electrically conductive and able to respond to further external impulses. Here we report on creating magnetic nanofiber mats, consisting of magnetically doped polymer nanofibers for data transfer and polymer beads containing larger amounts of magnetic nanoparticles for storage purposes. Using magnetite and iron nickel oxide nanoparticles, a broad range of doping ratios could be electrospun with a needleless technique, resulting in magnetic nanofiber mats with varying morphologies and different amounts of magnetically doped beads.

Norwegian e-Infrastructure for Life Sciences (NeLS)

The Norwegian e-Infrastructure for Life Sciences (NeLS) has been developed by ELIXIR Norway to provide its users with a system enabling data storage, sharing, and analysis in a project-oriented fashion. The system is available through easy-to-use web interfaces, including the Galaxy workbench for data analysis and workflow execution. Users confident with a command-line interface and programming may also access it through Secure Shell (SSH) and application programming interfaces (APIs). NeLS has been in production since 2015, with training and support provided by the help desk of ELIXIR Norway. Through collaboration with NorSeq, the national consortium for high-throughput sequencing, an integrated service is offered so that sequencing data generated in a research project is provided to the involved researchers through NeLS. Sensitive data, such as individual genomic sequencing data, are handled using the TSD (Services for Sensitive Data) platform provided by Sigma2 and the University of Oslo. NeLS integrates national e-infrastructure storage and computing resources, and is also integrated with the SEEK platform in order to store large data files produced by experiments described in SEEK. In this article, we outline the architecture of NeLS and discuss possible directions for further development.

Norwegian e-Infrastructure for Life Sciences (NeLS).

The Norwegian e-Infrastructure for Life Sciences (NeLS) has been developed by ELIXIR Norway to provide its users with a system enabling data storage, sharing, and analysis in a project-oriented fashion. The system is available through easy-to-use web interfaces, including the Galaxy workbench for data analysis and workflow execution. Users confident with a command-line interface and programming may also access it through Secure Shell (SSH) and application programming interfaces (APIs). NeLS has been in production since 2015, with training and support provided by the help desk of ELIXIR Norway. Through collaboration with NorSeq, the national consortium for high-throughput sequencing, an integrated service is offered so that sequencing data generated in a research project is provided to the involved researchers through NeLS. Sensitive data, such as individual genomic sequencing data, are handled using the TSD (Services for Sensitive Data) platform provided by Sigma2 and the University of Oslo. NeLS integrates national e-infrastructure storage and computing resources, and is also integrated with the SEEK platform in order to store large data files produced by experiments described in SEEK. In this article, we outline the architecture of NeLS and discuss possible directions for further development.

New trends in precision agriculture: a novel cloud-based system for enabling data storage and agricultural task planning and automation

It is well-known that information and communication technologies enable many tasks in the context of precision agriculture. In fact, more and more farmers and food and agriculture companies are using precision agriculture-based systems to enhance not only their products themselves, but also their means of production. Consequently, problems arising from large amounts of data management and processing are arising. It would be very useful to have an infrastructure that allows information and agricultural tasks to be efficiently shared and handled. The cloud computing paradigm offers a solution. In this study, a cloud-based software architecture is proposed with the aim of enabling a complete crop management system to be deployed and validated. Such architecture includes modules developed by using Google App Engine, which allows the information to be easily retrieved and processed and agricultural tasks to be properly defined and planned. Additionally, Google’s Datastore (which ensures a high scalability degree), hosts both information that describes such agricultural tasks and agronomic data. The architecture has been validated in a system that comprises a wireless sensor network with fixed nodes and a mobile node on an unmanned aerial vehicle (UAV), deployed in an agricultural farm in the Region of Murcia (Spain). Such a network allows soil water and plant status to be monitored. The UAV (capable of executing missions defined by an administrator) is useful for acquiring visual information in an autonomous manner (under operator supervision, if needed). The system performance has been analysed and results that demonstrate the benefits of using the proposed architecture are detailed.

A Robust System for Longitudinal Knee Joint Edema and Blood Flow Assessment Based on Vector Bioimpedance Measurements.

We present a robust vector bioimpedance measurement system for longitudinal knee joint health assessment, capable of acquiring high resolution static (slowly varying over the course of hours to days) and dynamic (rapidly varying on the order of milli-seconds) bioresistance and bioreactance signals. Occupying an area of 78×90 mm(2) and consuming 0.25 W when supplied with ±5 V, the front-end achieves a dynamic range of 345 Ω and noise floor of 0.018 mΩrms (resistive) and 0.055 mΩrms (reactive) within a bandwidth of 0.1-20 Hz. A microcontroller allows real-time calibration to minimize errors due to environmental variability (e.g., temperature) that can be experienced outside of lab environments, and enables data storage on a micro secure digital card. The acquired signals are then processed using customized physiology-driven algorithms to extract musculoskeletal (edema) and cardiovascular (local blood volume pulse) features from the knee joint. In a feasibility study, we found statistically significant differences between the injured and contralateral static knee impedance measures for two subjects with recent unilateral knee injury compared to seven controls. Specifically, the impedance was lower for the injured knees, supporting the physiological expectations for increased edema and damaged cell membranes. In a second feasibility study, we demonstrate the sensitivity of the dynamic impedance measures with a cold-pressor test, with a 20 mΩ decrease in the pulsatile resistance associated with increased downstream peripheral vascular resistance. The proposed system will serve as a foundation for future efforts aimed at quantifying joint health status continuously during normal daily life.

An open Web-based system for the analysis and sharing of animal tracking data

BackgroundImprovements in telemetry technology are allowing us to monitor animal movements with increasing accuracy, precision and frequency. The increased complexity of the data collections, however, demands additional software and programming skills to process, store and disseminate the datasets. Recent focus on data availability has also heightened the need for sustainable data management solutions to ensure data integrity and provide longer term access. In the last ten years, a number of online facilities have been developed for the archiving, processing and sharing of telemetry data. These facilities offer secure storage, multi-user support and analysis tools and are a step along the way to improving data access, long-term data preservation and science communication. While these software platforms promote data sharing, access to the majority of the data and to the software behind these systems remains restricted. In this paper, we present a comprehensive, highly accessible and fully transparent software facility for animal movement data.ResultsThe online system we developed (http://oztrack.org) offers a set of robust, up-to-date and accessible tools for managing, processing, visualising and analysing animal location data and linking these outputs with environmental datasets. As OzTrack uses exclusively free and open-source software, and the source code is available online, the system promotes open access not only to data but also to the tools and software underpinning the system.ConclusionsWe outline the capabilities and limitations of the infrastructure design and discuss the uptake of this platform by the Australasian biotelemetry community. We discuss whether an open approach to analysis tools and software encourages a more open approach to sharing data, information and knowledge. Finally, we discuss why a free and open approach enhances longer term sustainability and enables data storage facilities to evolve in parallel with the telemetry devices themselves.

Phase change materials and phase change memory

Abstract

Evolution of the ATLAS distributed computing system during the LHC long shutdown

The ATLAS Distributed Computing project (ADC) was established in 2007 to develop and operate a framework, following the ATLAS computing model, to enable data storage, processing and bookkeeping on top of the Worldwide LHC Computing Grid (WLCG) distributed infrastructure. ADC development has always been driven by operations and this contributed to its success. The system has fulfilled the demanding requirements of ATLAS, daily consolidating worldwide up to 1 PB of data and running more than 1.5 million payloads distributed globally, supporting almost one thousand concurrent distributed analysis users. Comprehensive automation and monitoring minimized the operational manpower required. The flexibility of the system to adjust to operational needs has been important to the success of the ATLAS physics program. The LHC shutdown in 2013-2015 affords an opportunity to improve the system in light of operational experience and scale it to cope with the demanding requirements of 2015 and beyond, most notably a much higher trigger rate and event pileup. We will describe the evolution of the ADC software foreseen during this period. This includes consolidating the existing Production and Distributed Analysis framework (PanDA) and ATLAS Grid Information System (AGIS), together with the development and commissioning of next generation systems for distributed data management (DDM/Rucio) and production (Prodsys-2). We will explain how new technologies such as Cloud Computing and NoSQL databases, which ATLAS investigated as R&D projects in past years, will be integrated in production. Finally, we will describe more fundamental developments such as breaking job-to-data locality by exploiting storage federations and caches, and event level (rather than file or dataset level) workload engines.

The GMOS cyber(e)-infrastructure: advanced services for supporting science and policy

The need for coordinated, systematized and catalogued databases on mercury in the environment is of paramount importance as improved information can help the assessment of the effectiveness of measures established to phase out and ban mercury. Long-term monitoring sites have been established in a number of regions and countries for the measurement of mercury in ambient air and wet deposition. Long term measurements of mercury concentration in biota also produced a huge amount of information, but such initiatives are far from being within a global, systematic and interoperable approach. To address these weaknesses the on-going Global Mercury Observation System (GMOS) project ( www.gmos.eu ) established a coordinated global observation system for mercury as well it retrieved historical data ( www.gmos.eu/sdi ). To manage such large amount of information a technological infrastructure was planned. This high-performance back-end resource associated with sophisticated client applications enables data storage, computing services, telecommunications networks and all services necessary to support the activity. This paper reports the architecture definition of the GMOS Cyber(e)-Infrastructure and the services developed to support science and policy, including the United Nation Environmental Program. It finally describes new possibilities in data analysis and data management through client applications.