IPFS Pinning Service for Open Climate Research Data

Blockchain technology is receiving increased attention over the past few years. Blockchain is essentially a decentralized, distributed digital ledger in which data is organized into blocks. The data stored in Blockchain is secured using cryptographic properties. Storage is the main concern when the volume of transactions increases. Handling data in huge amount may lead to increased overheads as data can only be appended to it, and the previous data cannot be deleted. So, it led to the development of the InterPlanetary File System (IPFS). IPFS is a distributed database with peer-to-peer file system and uses content-addressable hash value which is used to verify the data. Using IPFS, one can upload the data in the form of text, images as well as videos. While uploading the data in IPFS, the protocol returns a unique hash value for the data via gateway. This hash value is securely stored in Blockchain. Based on the hash value, the data can be retrieved. In IPFS, data is distributed among the network participants locally. The proposed work combines both IPFS and Blockchain technologies. This scheme uses IPFS for storing large volume of data, removes duplication, share files, and hosts them. The proposed work minimizes the central server storage space, improves data security by using unique content-addressable hashes for data verification and also reduces computing power.

This paper introduces a decentralized architecture designed for the sharing and distribution of user-generated video streams. The proposed system employs HTTP Live Streaming (HLS) as the delivery method for these video streams. In the architecture, a creator who captures a video stream using a smartphone camera subsequently transcodes it into a sequence of video chunks called HLS segments. These chunks are then stored in a distributed manner across the worker network, forming the core of the proposed architecture. Despite the presence of a coordinator for bootstrapping within the worker network, the selection of worker nodes for storing generated video chunks and autonomous load balancing among worker nodes are conducted in a decentralized fashion, eliminating the need for central servers. The worker network is implemented using the Golang-based IPFS (InterPlanetary File System) client, called kubo, leveraging essential IPFS functionalities such as node identification through Kademlia-DHT and message exchange using Bitswap. Beyond merely delivering stored video streams, the worker network can also amalgamate multiple streams to create a new composite stream. This bundling of multiple video streams into a unified video stream is executed on the worker nodes, making effective use of the FFmpeg library. To enhance download efficiency, parallel downloading with multiple threads is employed for retrieving the video stream from the worker network to the requester, thereby reducing download time. The result of the experiments conducted on the prototype system indicates that those concerned with the transmission time of the requested video streams compared with a server-based system using AWS exhibit a significant advantage, particularly evident in the case of low-resolution video streams, and this advantage becomes more pronounced as the stream length increases. Furthermore, it demonstrates a clear advantage in scenarios characterized by a substantial volume of viewing requests.

A large number of raw data collected by satellites are processed by the production chain to obtain a large number of product data, of which the secure exchange and storage is of interest to researchers in the field of remote sensing information science. Authentic, secure data provide a critical foundation for data analysis and decision-making. Traditional centralized cloud computing systems are vulnerable to attack and, once the central server is successfully attacked, all data will be lost. Distributed ledger technology (DLT) is an innovative computer technology that can ensure information security and traceability, is tamper-proof, and can be applied to the field of remote sensing. Although there are many advantages to using DLT in remote sensing applications, there are some obstacles and limitations to its application. Remote sensing data have the characteristics of a large data volume, a spatiotemporal nature, global scale, and so on, and it is difficult to store and interconnect remote sensing data in the blockchain. To address these issues, this paper proposes a trustworthy and decentralized system using blockchain technology. The novelty of this paper is the proposal of a multi-level blockchain architecture in which the system collects remote sensing data and stores them in the Interplanetary File System (IPFS) network; after generating the IPFS hash, the network rehashes the value again and uploads it on the Ethereum chain for public query. The distributed data storage improves data security, supports the secure exchange of information, and improves the efficiency of data management.

This paper presents a blockchain-based decentralized disk rental system designed to securely and efficiently utilize unused storage capacity. Unlike traditional cloud storage models reliant on centralized providers, this system integrates blockchain technology with the Inter Planetary File System (IPFS) to establish a decentralized, tamper-resistant storage solution. Smart contracts automate storage agreements, ensuring transparency and immutability, while advanced cryptographic techniques and proof mechanisms guarantee data integrity and confidentiality. Leveraging lightning network technology for fast, low-cost transactions, the system distributes data across a decentralized network of nodes, significantly enhancing data privacy, security, and availability. This architecture addresses key vulnerabilities of centralized storage models, such as single points of failure and data breaches, offering a robust, scalable alternative for distributed storage applications. The proposed solution provides a promising pathway for more secure and resilient storage infrastructure in decentralized environments

BlockShare is a decentralized, web-based platform built to enable secure and transparent file sharing among developers, researchers, and enterprises. Unlike traditional systems, it harnesses blockchain technology to provide an immutable record of every file access and sharing event, ensuring accountability and trust. To strengthen security further, BlockShare incorporates an AI-driven module that detects potential threats and evaluates the trustworthiness of peers in real time. The platform is powered by Ethereum smart contracts for access control, a ReactJS-based interface for usability, and the InterPlanetary File System (IPFS) for distributed storage. Together, these components remove single points of failure, safeguard data integrity, and create a tamper-proof audit log of all file transactions. With its emphasis on trustless collaboration, onchain accountability, and intelligent monitoring, BlockShare offers a modern approach to secure digital collaboration in high-stakes environments.

In recent years, several research and development initiatives have focused on developing secure and trustworthy systems for the healthcare industry via pervasive and mobile healthcare (mHealth) solutions. State-of-the-art mHealth solutions primarily rely on centralized storage, such as cloud computing servers, which may escalate the maintenance costs, require ever-increasing storage infrastructure, and pose privacy and security risks to the health-critical data produced, consumed, and transmitted over ad hoc networks. To overcome these limitations, we conducted this study intending to synergize mobile computing (devices to process health-critical data) and blockchain technology (infrastructure to secure storage and retrieval of health-critical data), specifically addressing data security and privacy using a blockchain mHealth system. The research employs an incremental method by (i) developing a framework that acts as a blueprint to architect blockchain-enabled mHealth systems, (ii) implementing a suite of algorithms as a proof-of-concept to automate the framework, and (iii) experimental evaluations to validate the scalability, computation, and energy efficiency of the proposed solution. The proposed framework has been implemented as a frontend using a mobile application interface that exploits the backend via the InterPlanetary File System (IPFS) system and Ethereum blockchain for secure management of mHealth data. We use a case-study-based approach demonstrating how health units, medics, and patients can securely access and distribute health-critical data. For evaluation, we deployed a smart contract prototype on the Ethereum TESTNET network in a Windows environment to test the proposed framework. Results of the evaluation indicate (a) scalability with query response time (range: 10–41 ms), (b) computational performance (CPU utilization: 1.5% – 2.5%), and (c) energy efficiency (gas consumption: 40000 units for 1000 bytes). The proposed solution – framework, algorithms, and experimental evaluation – aims to advance state-of-the-art architecting and implementing cybersecurity mHealth solutions using blockchain technology.

Controls, service type and perceived supplier performance in interfirm service exchanges

Dramatic alterations to the natural environment due to human activity have produced a permanent rupture in the Earth system; the relative stable epoch of the Holocene has given way to a volatile Anthropocene. Acceptance of these claims means that we now live in this altered physical reality, inviting us to rethink how we conceptualise disasters. Yet, disaster scholars have been hesitant to apply the Anthropocene label and to acknowledge the profound changes that it can bring to the study of disasters. This paper queries whether this label is a necessary adage or unnecessary baggage for disaster studies by examining the possibilities and the challenges associated with engaging with the Anthropocene. An analysis of the concepts, causes, and consequences of disasters reveals how the Anthropocene provides, as the very least, a theoretical heuristic for challenging linear temporal assumptions, the epistemological status of uncertainty, and the location of agency in disaster studies.

The Destination Earth (DestinE) climate adaptation digital twin (climate DT) will be run on the EuroHPC supercomputers LUMI and Marenostrum. The climate DT project brings together leading centers from across Europe specializing in climate science and services, Earth system modelling, and supercomputing to deliver an innovative climate information system through DestinE supporting the European Union’s climate adaptation efforts. Usage of the Earth-system model simulations requires an orchestration of a number of software tools that are developed and implemented as part of the climate DT project. Here, we present the progress of the software infrastructure development and support on LUMI to deploy the next generation Earth system models ICON (Icosahedral Nonhydrostatic) and IFS (Integrated Forecasting System) coupled with the ocean models NEMO (Nucleus for European Modelling of the Ocean) and FESOM2 (Finite-volumE Sea ice-Ocean Model). Our priority is to provide the computing environment needed on LUMI for (i) the development and execution of the climate models, impact models and data analysis utilities, (ii) the infrastructure components needed in terms of data storage, discovery, and quality control both internally and towards a link with the DestinE data services, and (iii) the software development tools and optimization support during the development of the climate models to ensure LUMI is efficiently used to reach the desired throughput. Several software tools have been deployed on LUMI successfully, for instance, MultIO the I/O server to execute the climate models outputs, FDB (Fields Data Base) for the data storage and access, Autosubmit and ecFlow for orchestration of workflows on LUMI which configures, executes, and monitors the climate DT experiments. Further, GPU adaptation of ICON and IFS-NEMO/FESOM2 models on LUMI-G (GPU) is undertaken. At the moment IFS and ICON compile and run successfully on LUMI-C (CPU). Profiling and performance analysis of the Earth system models (IFS-NEMO/FESOM2 on LUMI-C and ICON on LUMI-G) will be presented with throughput and performance numbers for the baseline test case configurations. Issues and challenges will be discussed in detail in the presentation.  

The mission of the Earth System Grid Federation (ESGF) is to provide the worldwide climate-research community with access to the data, information, model codes, analysis tools, and intercomparison capabilities required to make sense of enormous climate data sets. Its specific goals are to (1) provide an easy-to-use and secure web-based data access environment for data sets; (2) add value to individual data sets by presenting them in the context of other data sets and tools for comparative analysis; (3) address the specific requirements of participating organizations with respect to bandwidth, access restrictions, and replication; (4) ensure that the data are readily accessible through the analysis and visualization tools used by the climate research community; and (5) transfer infrastructure advances to other domain areas. For the ESGF, the U.S. Department of Energy's (DOE's) Earth System Grid Center for Enabling Technologies (ESG-CET) team has led international development and delivered a production environment for managing and accessing ultra-scale climate data. This production environment includes multiple national and international climate projects (such as the Community Earth System Model and the Coupled Model Intercomparison Project), ocean model data (such as the Parallel Ocean Program), observation data (Atmospheric Radiation Measurement Best Estimate, Carbon Dioxide Information and Analysis Center, Atmospheric Infrared Sounder, etc.), and analysis and visualization tools, all serving a diverse user community. These data holdings and services are distributed across multiple ESG-CET sites (such as ANL, LANL, LBNL/NERSC, LLNL/PCMDI, NCAR, and ORNL) and at unfunded partner sites, such as the Australian National University National Computational Infrastructure, the British Atmospheric Data Centre, the National Oceanic and Atmospheric Administration Geophysical Fluid Dynamics Laboratory, the Max Planck Institute for Meteorology, the German Climate Computing Centre, the National Aeronautics and Space Administration Jet Propulsion Laboratory, and the National Oceanic and Atmospheric Administration. The ESGF software is distinguished from other collaborative knowledge systems in the climate community by its widespread adoption, federation capabilities, and broad developer base. It is the leading source for present climate data holdings, including the most important and largest data sets in the global-climate community, and - assuming its development continues - we expect it to be the leading source for future climate data holdings as well. Recently, ESG-CET extended its services beyond data-file access and delivery to include more detailed information products (scientific graphics, animations, etc.), secure binary data-access services (based upon the OPeNDAP protocol), and server-side analysis. The latter capabilities allow users to request data subsets transformed through commonly used analysis and intercomparison procedures. As we transition from development activities to production and operations, the ESG-CET team is tasked with making data available to all users seeking to understand, process, extract value from, visualize, and/or communicate it to others. This ongoing effort, though daunting in scope and complexity, will greatly magnify the value of numerical climate model outputs and climate observations for future national and international climate-assessment reports. The ESG-CET team also faces substantial technical challenges due to the rapidly increasing scale of climate simulation and observational data, which will grow, for example, from less than 50 terabytes for the last Intergovernmental Panel on Climate Change (IPCC) assessment to multiple Petabytes for the next IPCC assessment. In a world of exponential technological change and rapidly growing sophistication in climate data analysis, an infrastructure such as ESGF must constantly evolve if it is to remain relevant and useful. Regretfully, we submit our final report at the end of project funding. To continue to serve the climate-science community, we are currently seeking additional funding. Such funding would allow us to maintain and enhance ESGF production and operation of this vital endeavor of cataloging, serving, and analyzing ultra-scale climate science data. At this time, the entire ESG-CET team would like to take this opportunity to sincerely thank our funding agencies in the DOE Scientific Discovery through Advanced Computing (SciDAC) program and the Office of Biological and Environmental Research (OBER) - as well as our national and international collaborators, stakeholders, and partners - for allowing us to work with you and serve the community these past several years.

Video streaming generates a substantial fraction of the traffic on the Internet. The demands of video streaming also increase the workload on the video server, which in turn leads to substantial slowdowns. In order to resolve the slowdown problem, and to provide a scalable and robust infrastructure to support on-demand streaming, helper-assisted video-on-demand (VoD) systems have been introduced. In this architecture, helper nodes, which are micro-servers with limited storage and bandwidth resources, download and store the user-requested videos from a central server to decrease the load on the central server. Multi-layer videos, in which a video is divided into different layers, can also be used to improve the scalability of the system. In this paper, we study the problem of utilizing the helper nodes to minimize the pressure on the central servers. We formulate the problem as a linear programming using joint inter- and intra-layer network coding. Our solution can also be implemented in a distributed manner. We show how our method can be extended to the case of wireless live streaming, in which a set of videos is broadcasted. Moreover, we extend the proposed method to the case of unreliable connections. We carefully study the convergence and the gain of our distributed approach.

Peatlands store large amounts of soil carbon and freshwater, constituting an important component of the global carbon and hydrologic cycles. Accurate information on the global extent and distribution of peatlands is presently lacking but is needed by Earth System Models (ESMs) to simulate the effects of climate change on the global carbon and hydrologic balance. Here, we present Peat-ML, a spatially continuous global map of peatland fractional coverage generated using machine learning techniques suitable for use as a prescribed geophysical field in an ESM. Inputs to our statistical model follow drivers of peatland formation and include spatially distributed climate, geomorphological and soil data, along with remotely-sensed vegetation indices. Available maps of peatland fractional coverage for 14 relatively extensive regions were used along with mapped ecoregions of non-peatland areas to train the statistical model. In addition to qualitative comparisons to other maps in the literature, we estimated model error in two ways. The first estimate used the training data in a blocked leave-one-out cross-validation strategy designed to minimize the influence of spatial autocorrelation. That approach yielded an average r2 of 0.73 with a root mean squared error and mean bias error of 9.11 % and −0.36 %, respectively. Our second error estimate was generated by comparing Peat-ML against a high-quality, extensively ground-truthed map generated by Ducks Unlimited Canada for the Canadian Boreal Plains region. This comparison suggests our map to be of comparable quality to mapping products generated through more traditional approaches, at least for boreal peatlands.

An interdisciplinary Earth System Science (ESS) curriculum, community outreach, and research program

Africa is increasingly being exposed to the negative impacts of climate and environmental change, while having less capacity to respond compared to other continents. The vulnerability partially results from unprecedented demographic growth, urbanization, and industrialization. However, the continent has still largely been underserved by the broader Earth system science (ESS) community, as evidenced by the limited amount of ESS data and research that cover Africa compared to other areas of the world. Here, we present the recent University Corporation for Atmospheric Research (UCAR) Africa Initiative that aims to enhance environmental sustainability in Africa by fostering international collaborative research partnerships coled by African scientists. Specifically, we outline urgent challenges and opportunities identified through an international workshop in six areas of ESS, namely, 1) air quality and health, 2) weather, 3) climate, 4) land and water, 5) social science perspectives, and 6) developing equitable collaboration and sustainable infrastructure. We highlight examples of successful partnerships and conclude with recommendations to advance collaborative, actionable ESS research that addresses Africa’s critical environmental challenges. Significance Statement Africa is experiencing the negative impacts of climate and environmental change. However, relevant research and hence suggested solutions are very limited. As a key outcome of the University Corporation for Atmospheric Research (UCAR) Africa Initiative, we highlight the importance of the international community in supporting and coleading successful collaborative efforts with African scientists that can address Africa’s environmental challenges through equitable partnerships. This paper provides a forward-looking perspective by highlighting collaborative research opportunities in six critical Earth system science (ESS) areas and identifies practical directions to advance environmental sustainability in Africa through more coordinated scientific engagement.

Our research targets the lack of ICT infrastructure in rural areas of Bangladesh. The research has the project to build a solar powered, low cost, fully fledged computer server from off-the-shelf components which will act as a network infrastructure for data collection, sharing and network distribution in rural areas of Bangladesh, even where electricity consistency is an issue. It is designed to create a Wi-Fi (2.4GHz) based network around the deployed area effectively creating a High Speed Local Area Network and provide ICT based services such as File Sharing, Instant Messaging, E-Education, Form Submission and a platform for any other on demand web & database services that are required by the community. The research is dedicated to bridging the Digital Divide. The objectives includes (i) building a low cost server using off-the-shelf, low cost hardware and open source software (ii) Provide a high speed data communications network effectively creating a Local Area Network (iii) Run the entire system on an incredibly low power requirement of 3.5 Watts. (iv) Provide a service layer for web services which supports Ajax, php5, MySQL (v) Deploy open source web services for data exchange and communication (vi) provide a system that has low & quick maintenance capabilities, (vii) and finally allow optional cellular (3G) data connectivity to allow the people to have shared internet access.