Storage Services Research Articles

Application of Big Data in Digital Currency and Blockchain Finance

With the rise of emerging economic models, the cryptocurrency system, centered on the internet and electronization, is gradually returning to its core functions — storage, data processing, and related services. The deep value of massive data lies in rapid mining to uncover its inherent patterns, which is not only crucial for the operation, regulation, and monetary policy implementation of cryptocurrencies but also serves as an essential support for the transition from paper money to digital currencies. Given this, this research plans to conduct a detailed exploration of the application of big data in the field of cryptocurrency and blockchain finance, aiming to contribute to the continuous progress of China’s financial industry.

Cumulus: Blockchain-Enabled Privacy Preserving Data Audit in Cloud

Data owners upload large files to cloud storage servers, but malicious servers may potentially tamper data. To check integrity of remote data, Proof of Retrievability (PoR) schemes were introduced. Existing PoR protocols assume that data owners and third-party auditors are honest and audit only the potentially malicious cloud server to check integrity of stored data. In this paper we consider a system where any party may attempt to cheat others and consider collusion cases. We design a protocol, Cumulus, that is secure under such adversarial assumptions and use blockchain smart contracts to act as mediator in case of dispute and payment settlement. We use state channels to reduce blockchain interactions in order to build a practical audit solution. The security of the protocol has been proven in Universal Composability (UC) framework. Finally, we illustrate several applications of our basic protocol and evaluate practicality of our approach via a prototype implementation for fairly selling large files over the Ethereum platform. We evaluate the prototype and show that our scheme has comparable performance.

Modeling Urban Expansion and Its Impacts on Carbon Storage Through Integrative Scenario Analysis for Sustainable Development in the Changchun-Jilin-Tumen Region

The Changchun-Jilin-Tumen (CJT) region in Northeast China is a key economic hub and a strategic gateway for China's integration into Northeast Asia, playing a crucial role in the Belt and Road Initiative. However, rapid urbanization in this region has precipitated substantial environmental challenges, particularly the degradation of carbon storage services (CSS) due to the expansion of impervious surface areas (ISA). This study employs multi-source remote sensing data, advanced geospatial analysis, and scenario-based decision-making technologies to evaluate the impacts of ISA expansion on CSS from 2000 to 2035 using the PLUS and InVEST models. The results reveal significant losses in CSS, with a net decrease of 8.10 × 10⁶ tons of carbon projected by 2022. Under the Natural Development scenario, these losses could escalate to 15.32 × 10⁶ tons by 2035. Conversely, the Ecological Protection and High-Quality Development scenarios suggest that strategic interventions could mitigate these losses, potentially leading to CSS gains of up to 3.42 × 10⁶ tons in the Ecological Protection scenario. This study highlights the pressing need to integrate ecological considerations into urban planning to balance development with environmental sustainability. The findings provide essential guidance for policymakers aiming to align urban growth with carbon neutrality targets, particularly in the context of global climate commitments and the increasing imperative for sustainable urban development.

Optimal Configuration of Electricity-Heat Integrated Energy Storage Supplier and Multi-Microgrid System Scheduling Strategy Considering Demand Response

Shared energy storage system provides an attractive solution to the high configuration cost and low utilization rate of multi-microgrid energy storage system. In this paper, an electricity-heat integrated energy storage supplier (EHIESS) containing electricity and heat storage devices is proposed to provide shared energy storage services for multi-microgrid system in order to realize mutual profits for different subjects. To this end, electric boiler (EB) is introduced into EHIESS to realize the electricity-heat coupling of EHIESS and improve the energy utilization rate of electricity and heat storage equipment. Secondly, due to the problem of the uncertainty in user-side operation of multi-microgrid system, a price-based demand response (DR) mechanism is proposed to further optimize the resource allocation of shared electricity and heat energy storage devices. On this basis, a bi-level optimization model considering the capacity configuration of EHIESS and the optimal scheduling of multi-microgrid system is proposed, with the objectives of maximizing the profits of energy storage suppliers in upper-level and minimizing the operation costs of the multi-microgrid system in lower-level, and solved based on the Karush-Kuhn-Tucker (KKT) condition and Big-M method. The simulation results show that in case of demand response, the total operation cost of multi-microgrid system and the total operation profit of EHIESS are 51,687.73 and 11,983.88 CNY, respectively; and the corresponding electricity storage unit capacity is 9730.80 kWh. The proposed model realizes the mutual profits of EHIESS and multi-microgrid system.

Evaluating ecosystem services in urban salt marshes: Assessing vulnerability to sea-level rise and implications for coastal management

This study presents a spatio-temporal framework that integrates ecosystem services into ecological risk assessment to evaluate the ecosystem service vulnerability of urban salt marshes to sea-level rise. The model was tested at Belle Isle Marsh to quantify and qualify the evolving capacity of urban marshes to continue supplying ecosystem services to an increasing urban populace to the end of the century with focus on carbon storage, nitrogen storage, fish nursery, and Saltmarsh Sparrow viewing. We project that sea-level rise will drive dynamic trade-offs between habitats and ecosystem services over space and time. Ultimately, habitat fragmentation and coversion to open ocean will severely impair carbon storage and wildlife viewing services, while also enhancing short-term fish nursery and nitrogen storage services. This approach offers nuanced understanding of where, when, and how services may interact under future conditions, and enables proactive planning and adaptation to emerging challenges.

Federated learning-based wavelength demodulation system for multi-point distributed multi-peak FBG sensors

Machine learning-based demodulation of multi-peak fiber Bragg grating (FBG) sensors has been extensively studied, demonstrating superior performance compared to conventional algorithms because it can neglect potential physical constraints. As the number of real-world monitoring points increases, the volume of fiber-optic sensing data grows exponentially. This necessitates aggregating data from various regions (e.g., via Wi-Fi), unlike traditional single-point monitoring, which challenges server storage capacity and communication efficiency. To address these issues, this paper proposes a federated learning (FL)-based framework for efficient wavelength demodulation of multi-peak FBGs in multipoint monitoring. Specifically, an arrayed waveguide grating (AWG) with multiplexing capability is employed at different monitoring points to convert spectral features into multi-channel transmission intensities, serving as training data for local models. Subsequently, the local model parameters, trained independently at each point, are uploaded to a central server to derive the optimal global model for demodulation across different monitoring points. The proposed demodulation framework is validated through stress demodulation experiments on multi-peak FBG sensors. Experimental results indicate strong multi-peak extraction performance with a demodulation error of ±0.64 pm. Additionally, the method demonstrates excellent applicability for generating high-precision global demodulation models through multi-node cooperative work under various scenarios.

A Study on the Living Environment and Psychological Stability of Households with Excessive Accumulation through Organizing and Storage Services

A Study on the Living Environment and Psychological Stability of Households with Excessive Accumulation through Organizing and Storage Services

Assessing Trade-Offs and Synergies in Ecosystem Services within the Tianshan Mountainous Region

In managing ecosystem services (ESs), it is vital to understand and effectively regulate the trade-offs and synergies (ToSs) involved. This study investigates the Tianshan Mountains (TSMs), utilizing the InVEST (Integrated Valuation of ESs and Tradeoffs) model to evaluate ecosystem service changes from 2000 to 2020, while employing univariate linear regression to examine their spatiotemporal dynamics. Pearson correlation analysis was also conducted to assess how climatic variables (temperature and precipitation) and vegetation indicators (NDVI, normalized difference vegetation index) influence the overall ecosystem service benefits. The findings reveal notable spatial heterogeneity and dynamic shifts in ESs across the TSMs, with strong synergies observed between carbon storage (CS) and other services (such as habitat quality, HQ; soil conservation, SC; and water yield, WY), especially in areas experiencing increased vegetation. However, the connection between HQ and WY was comparatively weaker and occasionally exhibited negative correlations during specific periods, highlighting the intricate trade-offs among various services. The correlation analysis further showed that climate and vegetation changes significantly impact ecosystem service benefits, with declining precipitation and rising temperatures reducing these benefits, whereas higher NDVI was associated with improved service functions. Ultimately, this study emphasizes the necessity of recognizing and managing ToSs in ESs to promote sustainable regional ecosystem development.

Enhanced Sink Node Method for Protecting Data of Single Owner

Cloud computing services offer computing, storage, networking, and software. Users can access data from any place and any time, under the control of a cloud service provider, by using cloud storage services. CSPs face challenges in protecting their data in the cloud. Public key cryptography can provide security, confidentiality, and integrity. The Schmidt-Samoa cryptosystem (SSC) is a public key cryptosystem that is based on integer factorization hardness. When applying ENHANCED SINK NODE in the cloud, the cryptosystem takes more time to encrypt and decrypt data. In this research, we propose an enhanced Schmidt-Samoa (SSC) public key cryptosystem to address this problem. The ENHANCED SINK NODE cryptosystem uses four prime numbers, which greatly increases the difficulty of breaking the cryptosystem compared to SSC. Further testing showed that the time needed to perform data encryption and decryption in ENHANCED SINK NODE is shorter, but the time needed to do cryptanalysis in ENHANCED SINK NODE is longer than in SSC. The ENHANCED SINK NODE cryptosystem is very secure and makes data more private.

Secure Blockchain Assisted Attribute-Based Keyword Search for Collaborative E-Healthcare

In the age of technological advancement, collaborative E-healthcare emerges as a transformative system eliminating traditional location and accessibility barriers in healthcare services. Here, Searchable Encryption (SE) plays a key role in enabling healthcare providers to outsource encrypted medical data and search services to third parties like cloud servers, thereby reducing storage and management expenses. This intermediary approach poses challenges of single-point failure, privacy breaches, and potentially untrustworthy results. State-of-the-art public key-based SE methods use a cloud-assisted architecture that doesn’t support reliable and practical searches with fine-grained permissions. Also, such systems require additional support to address potential privacy leakages and ensure data availability at the storage server. To address these concerns, we propose a B lockchain-assisted E fficient and S ecure K eyword S earch (BESKS) scheme to enforce fine-grained keyword search privilege control while achieving practical search complexity. Our scheme employs a ciphertext policy attribute-based keyword search mechanism where keywords are encrypted using expressive access policies to build an inverted index structure. The encrypted indexes are stored on the blockchain while encrypted medical documents are stored on InterPlanetary File System (IPFS) nodes to enhance availability and ensure the reliability and scalability of our approach. Our scheme utilizes blockchain-based smart contract for efficient, secure search operations and ensures financial fairness in fine-grained searches. Search tokens are generated based on user attributes and query keywords to facilitate private searches on-chain. To enhance the search process, our secure index enables exact match for a query keyword in constant time to ensure expensive authorization operations are performed only once. Theoretical analysis suggests that our BESKS is more efficient and secure than state-of-the-art schemes. Prototype implementation results on the Ethereum blockchain network further validate its feasibility for real-world applications, demonstrating the scheme's practical applicability in collaborative E-Healthcare systems.

FUZZY MODEL OF ELECTRICITY CONTROL WITH WIRELESS INFORMATION PROCESSED ON GPU

Methods of processing information transmitted through wireless networks with software development are investigated in the work. Innovative methods of data transmission such as optical technologies, quantum data transmission and wireless data transmission technologies are disclosed. It is noted that in the modern understanding, the concept of distributed computing defines the process of convergence (convergence) of distributed processing methods, such as GRID, cloud and fog computing, with the combination of virtual cluster systems (grid clusters, cloud clusters and fog clusters) into a single information communication and computing system . It is emphasized that, unlike cellular modems, ZigBee technology nodes have microcontrollers with a pre-installed operating system and flash memory, which allows solving simple computational tasks in real time before sending data. It is advisable to solve such tasks within the framework of a multi-agent approach, which will increase the efficiency of the use of sensor nodes and the entire sensor network. The advantages of the multi-agent technology of fog computing based on sensor nodes of the wireless network of the ZigBee standard are revealed. The method of multi-agent processing of sensory information and its main components are described. The architecture of the system of distributed sensor data processing is outlined, which includes 4 hardware and software levels: Terminal sensor nodes and controllers of measuring devices and automation devices that implement fuzzy calculations; Coordinators, sensor segment routers and cellular modems that collect, protect and transmit sensor data to the processing center; A data processing center that includes a cluster of servers for GRID calculations and a cloud data storage server; Client devices to access cloud storage, computing cluster servers, and distributed fog computing terminals. It is emphasized that indicators and forecast results can be stored on distributed sensor nodes or transmitted for accumulation in cloud storage for further extraction and intelligent processing in the GRID cluster of the data center.

DoeDat: Enhanced Roundtripping of Crowdsourced Specimen Annotations

The DoeDat platform was launched by Meise Botanic Garden in 2018 to capture label data from imaged herbarium specimens by inviting volunteer contributors (Groom et al. 2018). It has since facilitated data capture from specimens of other natural history collections (Helminger et al. 2020, Mitrache et al. 2023), as well as digitised content from various other disciplines, such as historical photographs, posters and postcards. Volunteers may simply transcribe handwritten and/or typed text, but often also interpret the sparse and scattered information on the image, including trying to georeference its original location. As of April 2024, almost 650.000 tasks have been completed, of which more than 470.000 were herbarium specimens from Meise. DoeDat supports domain standards, including Darwin Core, and follows most of the currently drafted MIDS (Minimum Information about a Digital Specimen) guidelines as to what data is captured for natural history specimens. However, images have to be pre-loaded into the server storage for each project and captured data gets exported as one or more CSV files per project. These data files then still need to be processed before they can be ingested into the local management system (Engledow et al. 2023). Often the data are also subjected to additional quality control before they get openly published. This can result in the pipeline from image to openly published annotations being quite time and labour-consuming. As the biodiversity infrastructure landscape moves more towards FAIR (Findable, Accessible, Interoperable, Reusable) open data, DoeDat will adapt accordingly. This includes digital objects that are easy to annotate. Furthermore, image servers following IIIF (International Image Interoperability Framework) greatly standardise the access and portability of media content, drastically changing the way images are being dealt with. We envision upgrading the DoeDat platform to load images and any required metadata as IIIF manifests, greatly streamlining the process of adding new content and tracking provenance. The transcriptions should be accessible for external systems, loading the updated image manifests and publishing them as annotations such as nanopublications.

Disjoint paths construction algorithm in the data center network DPCell

Abstract With the development of the fourth industrial revolution, the importance of data centers has significantly increased. Data centers are widely used in many fields due to their ability to provide efficient, secure, and reliable data storage and processing services. However, with the increasing amount of data, traditional data center networks (DCNs) are currently facing various challenges, prompting academia and industry to propose new DCN architectures. As a dual-port server-based DCN, DPCell has excellent scalability and bisection width, enabling it to meet the demands of large-scale data storage, processing, and computation in the digital revolution. In order to ensure the secure and reliable data communication in the DPCell, this paper designs a disjoint paths communication scheme based on the actual DCN routing requirements. This scheme constructs the optimal number of disjoint paths in DPCell, with a maximum path length of $2^{k}+3$, where $k$ represents the dimension of the DPCell. Furthermore, experiments have verified that the time complexity of this scheme is sublinear, making it more efficient than the current optimal maximum flow algorithm. To a certain extent, this scheme provides DPCell with the required high bandwidth, fault tolerance, and security for data communication.

Nonlinear Analysis of EDF Scheduling Framework for Resource Distribution in Cloud Computing

In modern times, the field of information technology is significantly shifting towards cloud computing. This innovative technology involves the permanent storage of resources on servers, which clients access via the internet. Typically, a cloud comprises a collection of resources known as virtual machines, capable of managing both computational tasks and storage needs. As the demand for cloud services continues to rise, the scheduling of these resources presents a considerable challenge. Scheduling refers to the workflow management necessary to complete tasks within the system. An efficient scheduler adapts its scheduling policy dynamically in response to changing conditions and task requirements. Cloud computing represents a recently developed technology where resources, whether physical or virtual machines, are permanently stored on servers and accessed by users through the internet. The cloud offers various service models, including Platform as a Service (PaaS), Software as a Service (SaaS), and Infrastructure as a Service (IaaS). In this context, IaaS refers to a set of resources, including virtual machines, that provide computational and storage services. Given the increasing adoption of cloud technology, resource scheduling has become a critical task. Hence, optimizing the scheduling of requests to ensure the best utilization of cloud resources is essential. This paper introduces a Nonlinear Analysis of EDF Scheduling Framework for resource allocation that considers deadlines and processing times when assigning resources to specific jobs. The performance metrics, including Average Turnaround Time, Average Waiting Time, and Average Deadline Violation, show a significant improvement when compared to traditional scheduling models such as First-Come, First-Served (FCFS), the Shortest Job First (SJF), and Simple Earliest Deadline First (EDF) models.

Optimization of Operation Strategy of Multi-Islanding Microgrid Based on Double-Layer Objective

The shared energy storage device acts as an energy hub between multiple microgrids to better play the complementary characteristics of the microgrid power cycle. In this paper, the cooperative operation process of shared energy storage participating in multiple island microgrid systems is researched, and the two-stage research on multi-microgrid operation mode and shared energy storage optimization service cost is focused on. In the first stage, the output of each subject is determined with the goal of profit optimization and optimal energy storage capacity, and the modified grey wolf algorithm is used to solve the problem. In the second stage, the income distribution problem is transformed into a negotiation bargaining process. The island microgrid and the shared energy storage are the two sides of the game. Combined with the non-cooperative game theory, the alternating direction multiplier method is used to reduce the shared energy storage service cost. The simulation results show that shared energy storage can optimize the allocation of multi-party resources by flexibly adjusting the control mode, improving the efficiency of resource utilization while improving the consumption of renewable energy, meeting the power demand of all parties, and realizing the sharing of energy storage resources. Simulation results show that compared with the traditional PSO algorithm, the iterative times of the GWO algorithm proposed in this paper are reduced by 35.62%, and the calculation time is shortened by 34.34%. Compared with the common GWO algorithm, the number of iterations is reduced by 18.97%, and the calculation time is shortened by 22.31%.

452 Data-driven strategic sustainability initiatives of beef and dairy genetics consortiums: A comprehensive landscape analysis for USA, Brazil, and Europe

Abstract The past two decades have witnessed beef and dairy genetic consortiums implementing data-driven germplasm propagation programs in global cattle systems. These programs optimize for multi-dimensional economic, environmental, and animal welfare related sustainability goals and are driven by consumer demands and shifts in supply chains. To summarize these programs and gain insights into the sustainability journey of industry, we did a comprehensive landscape analysis of the strategic initiatives of key global genetics companies, their germplasm offerings, and data platforms offered in three major cattle producing geographies, namely the USA, Brazil, and Europe. Within these locales, they followed selection index-based genomic and reproductive management strategies for both beef, dairy, and beef-on-dairy production, tailored to relevant market and climatic conditions (Figure 1). While individual traits in selection indices ranged between 40 to 90 traits, all traits could be broadly grouped into three: namely production, longevity, health and reproduction. In the USA, companies like ABS Global, Select Sires, Genex Cooperative, STGenetics, Zoetis, Vytelle offer genomic selection programs encompassing germplasm products, and genetic and production data tracking platforms across the beef, dairy, and beef-on-dairy value chains. These companies have implemented reproductive management programs such as in-vitro fertilized embryo transfer, sexed semen, along with genomic selection with a focus on future replacements based on multi-trait sustainability indices. These initiatives are customized for each of the cow-calf, backgrounding, and feedlot sector of beef production. In dairy, these companies have diversified data-driven production programs customizable to conventional and organic production systems. In Europe, companies like CRV, Viking Genetics, and Alta Genetics prioritize traits related to resource efficiency, animal welfare and environmental impact. Semen companies also offer data management tools tailored to European market requirements, alongside services for on-farm semen production and storage to reduce transportation-related emissions. In Brazil, companies such as ABS Pecplan, CRV Brazil, and Semex Brazil, developed herd management solutions focusing on traits like heat tolerance, tropical environmental adaptability, and disease resistance. The landscape analysis revealed a dynamic industry, evolving to meet sustainability goals and market demands. These enterprises collaborate with stakeholders across the value chain, including producers, policymakers, and industry associations, to scale data-driven production systems that balance profitability and environmental footprint of cattle production. As such, they have positioned themselves to capitalize on the growing interest in sustainable livestock production practices worldwide by aligning scientific and technical strategies with evolving regulatory requirements and consumer demands. The global cattle industry is positioned to venture forward by leveraging niche carbon markets and new marketing channels propelled by emerging principles of environmental, social, and corporate governance. Strategic germplasm and data-tracking platform initiatives by these groups thus become an integral part of the livestock sustainability solution palette and will contribute its share to a climate-neutral Earth by 2050.

Design and Evaluation of Real-Time Data Storage and Signal Processing in a Long-Range Distributed Acoustic Sensing (DAS) Using Cloud-Based Services.

In cloud-based Distributed Acoustic Sensing (DAS) sensor data management, we are confronted with two primary challenges. First, the development of efficient storage mechanisms capable of handling the enormous volume of data generated by these sensors poses a challenge. To solve this issue, we propose a method to address the issue of handling the large amount of data involved in DAS by designing and implementing a pipeline system to efficiently send the big data to DynamoDB in order to fully use the low latency of the DynamoDB data storage system for a benchmark DAS scheme for performing continuous monitoring over a 100 km range at a meter-scale spatial resolution. We employ the DynamoDB functionality of Amazon Web Services (AWS), which allows highly expandable storage capacity with latency of access of a few tens of milliseconds. The different stages of DAS data handling are performed in a pipeline, and the scheme is optimized for high overall throughput with reduced latency suitable for concurrent, real-time event extraction as well as the minimal storage of raw and intermediate data. In addition, the scalability of the DynamoDB-based data storage scheme is evaluated for linear and nonlinear variations of number of batches of access and a wide range of data sample sizes corresponding to sensing ranges of 1-110 km. The results show latencies of 40 ms per batch of access with low standard deviations of a few milliseconds, and latency per sample decreases for increasing the sample size, paving the way toward the development of scalable, cloud-based data storage services integrating additional post-processing for more precise feature extraction. The technique greatly simplifies DAS data handling in key application areas requiring continuous, large-scale measurement schemes. In addition, the processing of raw traces in a long-distance DAS for real-time monitoring requires the careful design of computational resources to guarantee requisite dynamic performance. Now, we will focus on the design of a system for the performance evaluation of cloud computing systems for diverse computations on DAS data. This system is aimed at unveiling valuable insights into performance metrics and operational efficiencies of computations on the data in the cloud, which will provide a deeper understanding of the system's performance, identify potential bottlenecks, and suggest areas for improvement. To achieve this, we employ the CloudSim framework. The analysis reveals that the virtual machine (VM) performance decreases significantly the processing time with more capable VMs, influenced by Processing Elements (PEs) and Million Instructions Per Second (MIPS). The results also reflect that, although a larger number of computations is required as the fiber length increases, with the subsequent increase in processing time, the overall speed of computation is still suitable for continuous real-time monitoring. We also see that VMs with lower performance in terms of processing speed and number of CPUs have more inconsistent processing times compared to those with higher performance, while not incurring significantly higher prices. Additionally, the impact of VM parameters on computation time is explored, highlighting the importance of resource optimization in the DAS system design for efficient performance. The study also observes a notable trend in processing time, showing a significant decrease for every additional 50,000 columns processed as the length of the fiber increases. This finding underscores the efficiency gains achieved with larger computational loads, indicating improved system performance and capacity utilization as the DAS system processes more extensive datasets.

Maturity assessment of grid-scale flexibility and energy storage services towards a decarbonized Europe

Background The study evaluates the maturity of grid-scale flexibility services, essential for Europe's transition to a decarbonized economy. Conducted under the Horizon-Europe SINNOGENES project, it focuses on energy storage technologies, regulatory frameworks, and stakeholder perspectives. Methods The methodology comprises three phases. First, significant grid-scale energy storage and flexibility technologies, including Battery Energy Storage Systems (BESS) and Vehicle-to-Grid (V2G) systems, were reviewed. Second, the regulatory framework for flexibility services at the EU transmission systems level was assessed, analyzing key EU directives and national regulations from Spain, Portugal, Germany, Cyprus, and Switzerland. Third, stakeholder perspectives were gathered through a survey of forty-four stakeholders in the European energy sector and bilateral interviews with companies and organizations in the energy sector. Results Technological advancements in grid-scale flexibility services have been identified, along with persistent regulatory challenges. The survey and interviews revealed a varied perception among stakeholders regarding the maturity and implementation of these services. A high demand for flexibility was noted, with 77% of stakeholders expressing a need for or active use of flexibility services. Electrochemical energy storage and demand response were highlighted as the most relevant technologies. However, high implementation costs, perceived insufficient benefits, and regulatory uncertainties were major barriers to widespread adoption. Conclusions The study concludes that despite notable technological progress and some regulatory advancements, several barriers still impede the full maturity and integration of grid-scale flexibility services in Europe. Addressing these regulatory and market challenges is crucial for achieving a fully functional and integrated flexibility service framework across the European grid. Harmonized regulations, financial incentives, and clear long-term pricing signals are essential to support the market.

VeriFog: A Generic Model-based Approach for Verifying Fog Systems at Design Time and Generating Deployment Configurations

Fog Computing is a paradigm decentralizing the Cloud by geographically distributing computation, storage, network resources and related services. It provides benefits such as reducing the number of bottlenecks, limiting unwanted data movements, etc. However, managing the size, complexity and heterogeneity of the Fog systems to be engineered is challenging and can quickly become costly. According to best practices in software engineering, verification tasks could be performed on a system design prior to its implementation and deployment. We propose a generic model-based approach for verifying Fog systems at design time, also enabling the automatic generation of corresponding deployment configuration files. Named VeriFog, this approach is notably based on a customizable Fog Modeling Language (FML). We experimented in practice by modeling three use cases, from three different application domains, and by considering three main types of non-functional properties to be verified. From this modeling and verification effort, we show that we are able to automatically generate usable deployment configuration files for different deployment tools. In direct collaboration with our industrial partner Smile, the approach and underlying language presented in this paper are necessary steps towards a more global model-based support for the complete life cycle of Fog systems.

Factors influencing forest landowner enrollment in preferential property tax programs

One of the most common approaches to conserving forestland in the US are state-run programs offering reduced property taxes on forestland. Although some form of preferential forest property tax program exists in all 50 states of the US, few studies have examined factors associated with landowners’ enrollment in such programs. As interest increases in incentivizing forestland owners to address various environmental or ecological goals, such as enhancing carbon storage or ecosystem services via private forest management, the need for improved knowledge about factors that influence landowners to participate in such programs grows. We examined factors associated with NIPF owner enrollment in preferential property tax programs in Oregon (US). Oregon currently has two primary preferential forest property tax programs. The Forestland Program is the default program for most forestland owners, and the Small Tract Forestland Option (STF), which is a voluntary program wherein landowners pay reduced annual property taxes and an additional timber harvest severance tax. We used a survey to gather information from 1190 Oregon Small Woodlands Association (OSWA) members owning between 10 and 5000 acres of forestland in western Oregon. Respondents were asked about their socio-demographic characteristics and forest management, as well as their enrollment status in Oregon's preferential property tax programs. Our econometric analysis of survey data indicate that preferential property tax programs enable long-term forest ownership, extended rotation ages, and investment in stewardship activities, suggesting that these programs are likely an effective tool for encouraging environmental or ecological goals among NIPF owners in Oregon.