Data Dissemination Research Articles

RDVFF- Reliable Data Dissemination in Vehicular Ad Hoc Networks Based on Validation of Far to Farthest Zone

<p>The rise of connected and autonomous vehicles has resulted in the development of vehicular ad hoc networks (VANETs) as a means of improving road safety, traffic efficiency, and passenger comfort. However, VANETs present challenges to achieving reliability and service quality in networks suffered highly by broadcast storms due to their mobility, scalability, and high node density. By addressing such challenges, this paper seeks to contribute to solving the problem of efficient information dissemination by diluting the broadcast storm and coverage issues by proposing a novel relay selection scheme based on the validation of the far-to-farthest zone technique. A flag control suppression mechanism is presented to overcome repeated information problems. RDVFF includes a separate algorithm for the optimization of participants for relay inclusion to address the latency issue during communication based on a multi-directional multiple-selection scheme. Compared with state-of-art protocols in terms of throughput, delivery ratio, collision, and latency experimental results show improvement of an average of 21.27%, 18.34%, 36.76%, and 38.04% respectively.</p> <p> </p>

Metadata for Discovery. Planning for an Information Network

In advance of building a new high speed rail line aimed at connecting the North and South of England, the UK is undertaking the largest archaeological intervention in its history. While the High Speed 2 (HS2) project has created a wealth of new information, before any of that information can be disseminated to the general public, it must first be properly collected, documented, and linked. To this end, data collection is integral to facilitate effective data dissemination and FAIR - Findable, Accessable, Interoperable and Reusable - data to achieve the greatest public value for an archive. The most exciting search results and maps come from what is often seen as scary technical jargon. This paper will discuss some of the limitations the Archaeology Data Service (ADS) experiences as a digital repository, what is currently being done to maximise the reach of the ADS collections, and what tools have been created to aid both depositors and digital archives alike. Starting at the beginning of the data life cycle, this article will show how large infrastructure projects like HS2 allow the ADS to work with depositors to raise issues about data collection, generation, and description during project development and how collaborative efforts can improve the creation and import of data and metadata into the archive. The ADS has made steps towards keeping our data FAIR but simple, both by streamlining what metadata is essential to foster better discovery and reuse within an archive, and from there, how metadata can be passed to external data catalogues such as ARIADNEplus and The National Archives in the UK.

BOLD v4: A Centralized Bioinformatics Platform for DNA-Based Biodiversity Data.

BOLD, the Barcode of Life Data System, supports the acquisition, storage, validation, analysis, and publication of DNA barcodes, activities requiring the integration of molecular, morphological, and distributional data. Its pivotal role in curating the reference library of DNA barcodes, coupled with its data management and analysis capabilities, makes it a central resource for biodiversity science. It enables rapid, accurate identification of specimens and also reveals patterns of genetic diversity and evolutionary relationships among taxa.Launched in 2005, BOLD has become an increasingly powerful tool for advancing the understanding of planetary biodiversity. It currently hosts 17 million specimen records and 14 million barcodes that provide coverage for more than a million species from every continent and ocean. The platform has the long-term goal of providing a consistent, accurate system for identifying all species of eukaryotes.BOLD's integrated analytical tools, full data lifecycle support, and secure collaboration framework distinguish it from other biodiversity platforms. BOLD v4 brought enhanced data management and analysis capabilities as well as novel functionality for data dissemination and publication. Its next version will include features to strengthen its utility to the research community, governments, industry, and society-at-large.

Merged Path: Distributed Data Dissemination in Mobile Sinks Sensor Networks

Merged Path: Distributed Data Dissemination in Mobile Sinks Sensor Networks

Several Categories of Energy Harvested Routing Protocols, Challenges, and Characteristics in WSN: A Review

The routing protocol is a technique for determining the most efficient channel for data transmission. The route selection procedure, which relies on the kind of network, channel conditions, and measurement systems, presents several challenges. Routing is essential in Wireless Sensor Networks (WSNs) for environmental monitoring, traffic monitoring, and other applications. WSNs are small nodes that can sense, interpret data, and communicate wirelessly. Many routing, power control, and data dissemination techniques have been developed specifically for WSNs, where energy efficiency is a crucial design factor.
 On the other hand, the focus has been on energy harvesting and standard routing methods, which can vary depending on the design and network architecture. In a Wireless Sensor Network (WSN), the data collected by the sensor nodes is typically transferred to the base station, which connects the sensor network to other networks (such as the internet), where it is processed and necessary action is taken. WSN has recently been developed to allow various applications, including traffic enforcement building automation, smart warfare, environmental sensing, and many more.WSN integrates several sensors or nodes deployed around a specific node to perform computational processes.

Фінансові технології в системі стратегічного управління бізнесу

The article aims to study the impact of financial technologies on enterprises' strategic management systems. The study analyses the impact of financial technologies on strategic management in the modern business environment. It has been determined that fintech solutions provide enterprises with competitive advantages through fast and accurate analysis of financial data, which contributes to making informed decisions in real-time. A study of the possibilities of automated decision-making processes and identifying new vectors of development, risks, and trends is carried out, and the directions for optimizing budgeting and forecasting are outlined. It is presented that fintech tools are becoming an integral part of enterprises' financial operations, providing online invoicing, inventory management, cost tracking, etc. The use of fintech tools promotes the application of data analytics at all levels of management, strengthening cooperation and coordination between business units, which affects strategic decision-making. The scientific novelty lies in identifying the positive impact of financial technologies on financial asset management, diversification of investment portfolios, and ensuring competitiveness in the digital economy. The possibilities of increasing efficiency and effectiveness through integrating financial technologies and introducing innovative financial models are considered. The practical value of the study lies in identifying specific business benefits, particularly in optimizing operations, reducing costs, and improving customer experience. The need to invest in fintech tools to ensure effective data collection, analysis, and dissemination at all enterprise strategic management levels is identified. The authors' recommendations are aimed at the continuous adaptation and development of business in a rapidly changing financial environment and ensuring the effective functioning of business in conditions of uncertainty. Keywords: financial technologies, fintech, strategic management, data analytics, innovation, competitive development.

Cluster‐based context‐aware route service management for smart intelligent autonomous vehicles with industrial transport system

SummaryIndustrial transport system refers to the movement of goods, raw materials, and finished products within and between industrial facilities such as factories, warehouses, and distribution centers. It includes a variety of modes of transport such as trucks, forklifts, conveyor belts, and automated guided vehicles (AGVs). Context‐aware route service management refers to system that can dynamically manage and optimize the routes taken by vehicles in transportation system based on contextual information. Our proposed framework for industrial transport systems and smart intelligent autonomous vehicles utilizes cluster‐based context‐aware routing (CCAR), which includes several significant contributions. Firstly, modified elephant herding optimization (MEHO) algorithm is used to efficiently group autonomous vehicles for routing. Secondly, an improved gravitational search (IGS) algorithm is used for cluster head (CH) selection responsible for transferring context information between vehicles (V2V). Thirdly, a deep hybrid multi‐graph neural network (DHMG‐NN) is designed for optimal neighboring roadside unit (RSU) node selection through different design constraints to ensure data dissemination between vehicles and infrastructure (V2I). Finally, we validate the effectiveness of our CCAR framework using various simulation scenarios. Our framework achieves a reduction of up to 78% in computation time and an improvement of up to 25% in customer satisfaction ratio and 15% higher than the overall performance ratio compared to the existing frameworks.

TranStat: an intelligent system for producing road and maritime transport statistics using big data sources

The development of digital technologies, increasing the availability of big data and advanced processing techniques have enabled Statistics Poland to modernise the system for producing road and maritime transport statistics. As a result of the activities undertaken to adopt modern big data technologies and data from sensors, e.g. the Automatic Identification System (AIS) or the e-TOLL electronic toll collection system, new statistics have been obtained and data dissemination has accelerated. In addition, these activities ensure continuity in data production, especially in situations where collecting data from individuals may be difficult (e.g. the COVID-19 epidemic). The primary purpose of this article is to present the innovative TranStat system that enables the production of road and maritime transport statistics based on large volumes of data in order to shape the country’s transport policy. The system was developed under the GOSPOSTRATEG programme and implemented by Statistics Poland, the Statistical Office in Szczecin, the Maritime University of Szczecin, and the Cracow University of Technology. The study presents the most important aspects of the TranStat system, i.e. the characteristics of data sources, the description of functional subsystems, assumptions of the developed models and result data for traffic statistics, transport performance and exhaust emissions calculations for both types of transport. This study also provides information on smart forms implemented by Polish official statistics, reducing the burden on respondents and the costs of surveys.

Seamlessly combined historical and projected daily meteorological datasets for impact studies in Central Europe: The FORESEE v4.0 and the FORESEE-HUN v1.0

The FORESEE is an open access, climatological database for Central Europe containing observed and projected meteorological data for the 1951–2100 period. As a climate service, FORESEE disseminates basic meteorological variables at a daily time step with a 0.1° × 0.1° spatial resolution including maximum/minimum temperature, precipitation, incoming shortwave solar radiation and daylight vapour pressure deficit. The future climate in FORESEE v4.0 and FORESEE-HUN v1.0 is projected by 14 regional climate models from the EURO-CORDEX database using the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. Based on RCP4.5 the country-specific results indicate similar projected mean changes in annual mean temperature (1.5–1.7 °C) but considerable differences in precipitation (from −1.6 to 6.9%) in the region for 2071–2100 relative to 1991–2020. We present two case studies to demonstrate the applicability of FORESEE in climate change impact studies using the ensemble approach. Climate change induced negative weather effect (15.4% and 28.9% mean loss for 2071–2100 according to RCP4.5 and RCP8.5, respectively) might dominate the future winter wheat yields in Hungary that is superimposed to the overall trend determined by other factors. The projections provide consistent results about the mean advance in the start of the growing season for forests in Hungary up to 2100 with ensemble mean of 9.1 days (RCP4.5) and 19.8 days (RCP8.5). We also demonstrate that the representative model selection method might lead to misleading results in impact studies that should be considered. The updated FORESEE is a way forward in the dissemination of policy-relevant essential climate data in Central Europe.

Missed opportunities for integrated testing: An Evaluation of the implementation of TB and HIV EID Testing on the GeneXpert platform in Lesotho

Introduction: Integrated testing of TB and HIV can improve health outcomes and save costs. The GeneXpert (GX) instrument, a multi-disease testing platform, can allow for simultaneous HIV and TB testing. However, utilization of the platform for integrated testing remains suboptimal, and there are operational barriers to implementing integrated TB and HIV testing.
 Methods: We evaluated the implementation of TB and HIV testing on the GX instrument for early infant detection (EID) of HIV and tuberculosis (TB) testing in thirteen health facilities in Lesotho. The data collection process used a survey questionnaire, semi-structured interviews, and a review of records at each health facility. Data was collected for the period 2017 and 2019.
 Results: All thirteen health facilities had at least one GX instrument for TB or EID testing. In 2017, the average utilization rate for the GX instrument for TB and EID testing was 63% and 24%, while in 2019, the average utilization rate was 61% for TB testing and 27% for EID. However, none of the health facilities included testing for TB and HIV on the same GX instrument simultaneously. In general, utilization rates were sufficiently low that all the HIV and TB tests undertaken in 2017 and 2019 could have been performed using only the instruments currently dedicated to TB testing. All except for three sites (Scott, Motebang and Maluti) where the testing rates were high. 
 Conclusion and Policy Implications: Much progress has been made in integrating TB and HIV activities in Africa. However, there is a missed opportunity for the integration of testing for TB and HIV on the GX instrument, which could save costs.
 This evaluation highlights areas that need more focus and more detailed diagnostic network optimization exercises to improve service delivery and allocate stocks, staff, equipment, and training. Better coordination of donor support or increased flexibility in reporting of use will be important to maximize the use of resources, where integration may be more effective than purchasing new equipment. Monitoring and evaluation should be a routine part of the implementation and should be budgeted and demanded by policymakers and donors. Health facilities may require additional resources and technical support to improve data collection, analysis, and dissemination.
 In conclusion, areas highlighted in this evaluation merit further exploration, such as the need to understand integrated testing models, including what works, why, and for which target groups. This will allow for effective targeted interventions to improve testing coverage across several diseases especially taking advantage of the multi-disease testing platforms.
 

Diagnostic Yield vs Diagnostic Accuracy for Peripheral Lung Biopsy Evaluation: Evidence Supporting a Future Pragmatic End Point

Diagnostic yield and accuracy endpoints have been used inconsistently in the evaluation of advanced diagnostic bronchoscopy devices and techniques, limiting between-study comparisons. In addition, diagnostic accuracy can only be adjudicated after prolonged clinical follow-up which delays reporting on the performance of novel devices. Will a conservative diagnostic yield definition would result in few false negative initial results to closely approximate diagnostic accuracy and represent a useful outcome for future studies of diagnostic utility? Commonly utilized definitions of diagnostic yield were applied to a prospective dataset of consecutive peripheral pulmonary lesions sampled by navigational bronchoscopy from 2017 to 2019. All consider malignancy to be diagnostic but differ in their classification of non-malignant biopsy findings, which were subcategorized as specific benign, nonspecific benign, or normal lung. Diagnostic yield calculations were also compared to diagnostic accuracy, defined as the proportion of biopsies deemed diagnostic by each definition that were confirmed accurate through two years of follow-up. A total of 450 biopsied lesions were analyzed. The prevalence of malignancy was 60.9% (274 of 450). On initial bronchoscopy pathology, there were 227 malignant diagnoses (50.4%), with a single false-positive (0.4%). Among 104 biopsies with specific benign findings, only two were false negative for malignancy (1.9%). There were 119 nonspecific benign biopsies with 46 false negatives for malignancy (38.7%). The discrepancy between diagnostic yield and accuracy was 0.7% for the conservative definition which only considered malignant or specific benign findings as diagnostic. A conservative diagnostic yield definition excluding nonspecific benign diagnoses closely approximated diagnostic accuracy through two years follow-up, with a less than 1% discrepancy. Using this conservative yield definition may allow for dissemination of reliable diagnostic utility data without protracted delays needed for follow-up data in this era of rapid technological change in advanced diagnostic bronchoscopy.

Beetle colony optimization algorithm‐based node clustering scheme for efficient data dissemination in vehicular ad hoc networks

SummaryVehicular ad hoc networks (VANETs) are the ultimate solution for preventing road accidents, which result in the loss of precious human life worldwide. In this context, effective communication between the vehicular nodes is essential due to the varying network topology and high vehicular mobility inherent with VANETs. Cluster‐based routing is identified to be a significant approach for achieving efficient routing and improving communication proficiency in VANETs. In this paper, a beetle colony optimization algorithm–based clustering scheme (BCOACS) is proposed for generating optimized clusters for facilitating reliable data dissemination. This BCOACS algorithm includes two vital strategies such as beetle antenna search (BAS) and swarm intelligence for attaining inter‐cluster and intra‐cluster communications. In specific, BAS strategy that includes random search attributed toward gradient direction is used for intra‐cluster communication without using the complete amount of gradient information. On the other hand, a swarm intelligence strategy that encompasses a collective approach of self‐organized and decentralized agents is used for inter‐cluster communication with the view to minimize the load on each cluster head (CH) and to extend the clusters' lifetime. The simulation outcomes of the proposed BCOACS scheme confirmed improved performance in optimizing the number of constructed clusters independent of the increase in the network grid size, transmission range, and number of vehicular nodes in the network compared to the benchmarked approaches. The results also confirmed that the proposed BCOACS scheme achieved a maximized throughput of 13.42%, with reduced delay and protocol overhead of 18.96% and 19.45%, better than the benchmarked schemes used for investigation.

THE HEALTHY BRAIN INITIATIVE ROAD MAP CATALYZES ACTION AMONG STATE, LOCAL, AND NATIONAL PARTNERS

Abstract The Healthy Brain Initiative Road Map Series (Road Map) has achieved measurable success in spurring action in health departments across the country. Evaluation data from the Alzheimer’s Association shows all 50 states have implemented actions from the 2018-2023 Road Map and the majority of states completed at least one action in each of the four domains of the Road Map. The Healthy Brain initiative has also sponsored initiatives to accelerate the implementation of Road Map actions. The projects described are separate from the CDC’s BOLD Program Awards for state and local health departments. The Road Map Strategist Initiative supports local health departments and tribal organizations. It has funded 16 agencies over the past two years to implement Road Map actions in their communities. A coordinated campaign to increase implementation and utilization of the BRFSS caregiving and subjective cognitive decline modules has near nationwide uptake, and, in 2023, a new project, Data for Action, launched in four states to support the use and dissemination of BRFSS and other data. Together, these projects improve assessment, planning and measurement of community health outcomes related to brain health. The 4th edition of the HBI Road Map has an accompanying evaluation tool offering common measures for state and local health departments to track their success as they implement the new actions. This session will explore the past impact of Road Map actions and opportunities for future measurement.

Economic planning at agricultural enterprises: Ukrainian experience of increasing the availability of data in the context of food security

Purpose. The purpose of the research is a comparative assessment of the state of data availability for planning the economic activity of agricultural enterprises, as well as the development of a model for increasing data availability based on the established correlation between socio-economic factors of internal and external influence of agricultural enterprises and readiness to disclose information when receiving consulting services.
 Methodology / approach. The study is based on the results of a survey of agricultural enterprises’ (farms’) managers, collected based on a random sampling, the volume of which satisfies the requirements of representativeness. The results of the research made it possible to conduct a correlation analysis of the dependence model for increasing data availability based on the established correlation between socio-economic factors of internal and external influence of agricultural enterprises and readiness to disclose information when receiving consulting services. The obtained results of the analysis made it possible to confirm the research hypotheses and obtain answers to several research questions.
 Results. The study reveals several dependencies and trends in the formation of openness and availability of data at agricultural enterprises (farms) for the implementation of economic activity planning by third-party consultants/experts. A strong direct connection was established between the unwillingness of enterprises to share management accounting data with third-party experts precisely because of the lack of practice of collecting such data, and not because of privacy concerns or negative past experiences. It was established that at enterprises characterized by a low level of openness to the dissemination of management accounting data, planning work is conducted directly by managers without qualified support of experts or planning is not conducted at all. The results suggest a number of solutions to ensure better access to the data needed for effective planning.
 Originality / scientific novelty. The study concerns the problems of data availability at agricultural enterprises (farms) for the implementation of planning of economic activities by external consultants/experts and uses the method of correlation analysis to establish relationships between variables of the model. The data collection paradigm of agricultural enterprises for planning their activities has been developed. For the first time, the relationship between a number of socio-economic factors and the openness of agricultural enterprises and farms to management consulting was established.
 Practical value / implications. The study formulates a number of proposals for improving the data availability at agricultural enterprises (farms) for the implementation of economic activity planning by third-party consultants/experts, which can be used by regional and state development agencies; state and private scientific institutions; governmental and non-governmental organizations; product manufacturers; legislators, etc., when developing support programs for agricultural producers to motivate them to use more effective planning tools, as well as when determining areas of activity for the regional advisory centers, taking into account the priority tasks in the sphere of planning data collection and analysis at agricultural enterprises and farms. Further studies of the data availability for planning the economic activity of agricultural enterprises are possible on the basis of the current study.

Understanding Stakeholder Dissemination Preferences for an Agriculture, Forestry, and Fishing Injury Surveillance System

ABSTRACT Researchers and epidemiologists are working to improve the capture of agriculture, forestry, and fishing (AgFF) injuries in a variety of ways. A critical component of any surveillance system is the dissemination of information. The purpose of this paper is to report on a survey conducted with AgFF injury surveillance stakeholders to understand preferred dissemination strategies. The survey was distributed using REDCap via web link to organizational stakeholders, which included advisory board members, safety trainers, industry managers and workers, and research collaborators. In total, there were 75 respondents (21% response rate). Occupation and industry influenced preference in update methods. Regarding the length and breadth of updates, 63% of respondents prefer reports (one to five pages), followed by 57% desiring a summary (less than one page), while only 24% wanted a detailed analysis. Social media and news preferences were also different among stakeholders. Surveillance data were desired for 1) trend analysis, 2) tailoring activities and solutions for education, training, outreach and interventions and 3) for research purposes such as grant proposals and evaluation. The dissemination of injury surveillance data should be tailored to the intended audience. Greater attention needs to be paid to the ways in which we share our findings.

Introducing SoNHR-Reporting guidelines for Social Networks In Health Research.

The overall goal of this work is to produce a set of recommendations (SoNHR-Social Networks in Health Research) that will improve the reporting and dissemination of social network concepts, methods, data, and analytic results within health sciences research. This study used a modified-Delphi approach for recommendation development consistent with best practices suggested by the EQUATOR health sciences reporting guidelines network. An initial set of 28 reporting recommendations was developed by the author team. A group of 67 (of 147 surveyed) experienced network and health scientists participated in an online feedback survey. They rated the clarity and importance of the individual recommendations, and provided qualitative feedback on the coverage, usability, and dissemination opportunities of the full set of recommendations. After examining the feedback, a final set of 18 recommendations was produced. The final SoNHR reporting guidelines are comprised of 18 recommendations organized within five domains: conceptualization (how study research questions are linked to network conceptions or theories), operationalization (how network science portions of the study are defined and operationalized), data collection & management (how network data are collected and managed), analyses & results (how network results are analyzed, visualized, and reported), and ethics & equity (how network-specific human subjects, equity, and social justice concerns are reported). We also present a set of exemplar published network studies which can be helpful for seeing how to apply the SoNHR recommendations in research papers. Finally, we discuss how different audiences can use these reporting guidelines. These are the first set of formal reporting recommendations of network methods in the health sciences. Consistent with EQUATOR goals, these network reporting recommendations may in time improve the quality, consistency, and replicability of network science across a wide variety of important health research areas.

Machine learning based centralized dynamic clustering algorithm for energy efficient routing in vehicular ad hoc networks

AbstractVehicular ad hoc network (VANET) is a dynamic and self‐configure wireless network that connects vehicles to provide in‐vehicle infotainment services and comfort to both driver and passengers while on the move. Due to the highly dynamic environment, patchy connectivity, roadside barriers, and a scarcity of road side units (RSUs); existing routing protocols of VANET consume high energy during route construction. As a consequence, the VANET lifespan might be shortened. However, saving energy is a critical factor where VANET is projected to play a key role in the building of sustainable green infrastructure. Clustering is an imperative process where vehicle nodes join to make stable clusters based on common mobility features. In VANET, clustering‐based routing algorithms significantly improve network effectiveness and reduce infrastructure costs. This article proposes fuzzy c‐means (FM) machine learning‐based dynamic clustering algorithm (DCA) to identify trustworthy vehicles for energy‐efficient multi‐hop routing by considering different mobility parameters such as position, direction, speed, and remaining energy of each vehicle node. This article also proposes distance and power‐based variation of FM called fuzzy c‐means using distance and residual power (FMDP) to find stable cluster heads (CHs) for performing better data dissemination to the destination. The proposed DCA outperforms the existing k‐means based clustering algorithms as well as topology‐based DSR and AODV routing protocols in terms of packet delivery ratio, application throughput, end‐to‐end delay, network energy consumption, node energy consumption, and RSU energy consumption. Additionally, clustering methods FM and FMDP of suggested DCA improve end‐to‐end delay by 20%, application throughput by 5%, and reduce network energy consumption by 68% compared with the k‐means based clustering model.

Ethical reflections on groundwater in contaminated areas

Groundwater science has a well-known relationship with social issues when dealing with risk assessment, yet there is a lack of case studies on the topic. Here, we present reflections for dealing with two ethical challenges: (i) the access and use of public domain monitoring data and (ii) the dissemination of scientific research data and its controversial character. The case study is a long-term collaborative research project aiming to bridge the social and natural sciences to tackle ethical questions and their implications for technical reporting and scientific production in a highly contaminated area situated in Portugal. The method included comprehensive interviews with hydrogeologists within the common project, the consulting of documents and the collaborative analysis of situations in online meetings between sociologists and hydrogeologists. The interviews and formal discussions were recorded, transcribed and analysed. In the two cases related to groundwater assessment in contaminated areas we found that researchers refer to emotions and moral dilemmas when they come into regular contact with social actors. Results also show that a different kind of knowledge is produced in these cases and point to three types of researchers’ positionality. The significance of this paper is to encourage reflection and action on ethical issues in the scientific community and specifically in geoethics. Bringing researchers together to share their practice will help to disentangle some of the negative emotions and moral dilemmas of scientific practice and increase the attention researchers pay to other people’s points of view and interests so as to improve the robustness of scientific data.

Measuring the Impact of Data Sharing: From Author-Level Metrics to Quantification of Economic and Non-tangible Benefits.

In early 2023, the National Institutes of Health (NIH) implemented its Data Management and Sharing (DMS) Policy, requiring researchers to share scientific data produced with NIH funding.The policy's objective is to amplify the benefits of public investment in research by promoting the dissemination and reusability of primary data. Given this backdrop, identifying a robust methodology to assess the impact of data sharing across diverse research domains is essential. In this review, we adopted two methodological paradigms, the bottom-up and top-down strategies, and employed content analysis to pinpoint established methodologies and innovative practices within this intricate field. Although numerous author-level metrics are available to gauge the impact of data sharing, their application is still limited. Non-traditional metrics, encompassing economic (e.g., cost savings) and intangible benefits, presently appear to hold more potential for evaluating the impact of primary data sharing. Finally, we address the primary obstacles encountered by open data policies and introduce an innovative "Shared model for shared data" framework to bolster data sharing practices and refine evaluation metrics.

IoT data dissemination scheme for reducing delay in multi-broker environments

With the rapid increase of Internet of Things (IoT) devices, an efficient communication protocol is needed to effectively manage the exchange and generation of data from these devices. Message Queuing Telemetry Transport (MQTT) is widely adopted in IoT systems due to its lightweight design and the support for topic-based publish–subscribe architectures. In MQTT, multiple IoT devices are connected to a server, referred to as a broker, to facilitate efficient data transmission and reduce network overhead. However, the traditional single-broker system is unable to efficiently support the massive number of IoT devices. Although a multi-broker system is proposed as a solution, the use of the flooding method for updating topics has generated a significant amount of inter-broker traffic, causing severe congestion in the brokers. As a result, this will lead to long topic update delay. To resolve this issue, in this paper, we propose a topic update dissemination scheme by considering the congestion in brokers. We formulate the topic update dissemination problem using the Lyapunov function, to minimize the topic update delay in the multi-broker system. To solve the problem, we propose a heuristic algorithm for the update dissemination forwarding decision. The simulation results show that the proposed algorithm can effectively reduce the topic update delay compared to other existing algorithms.